SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition.

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph.

BACKGROUND: Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery. RESULTS: We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs. CONCLUSIONS: GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo's dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

Deficiency in SLC25A15, a hypoxia-responsive gene, promotes hepatocellular carcinoma by reprogramming glutamine metabolism.

BACKGROUND & AIMS: The role of solute carrier family 25 member 15 (SLC25A15), a critical component of the urea cycle, in hepatocellular carcinoma (HCC) progression remains poorly understood. This study investigated the impact of SLC25A15 on HCC progression and its mechanisms. METHODS: We systematically investigated the function of SLC25A15 in HCC progression using large-scale data mining and cell, animal, and organoid models. Furthermore, we analyzed its involvement in reprogramming glutamine metabolism. RESULTS: SLC25A15 expression was significantly decreased in HCC tissues, and patients with low SLC25A15 levels had a poorer prognosis. Hypoxia-exposed HCC cells or tissues had lower SLC25A15 expression. A positive correlation between HNF4A, a transcription factor suppressed by hypoxia, and SLC25A15 was observed in both HCC tissues and cells. Modulating HNF4A levels altered SLC25A15 mRNA levels. SLC25A15 upregulated SLC1A5, increasing glutamine uptake. The reactive metabolic pathway of glutamine was increased in SLC25A15-deficient HCC cells, providing energy for HCC progression through additional lipid synthesis. Ammonia accumulation due to low SLC25A15 levels suppressed the expression of OGDHL (oxoglutarate dehydrogenase L), a switch gene that mediates SLC25A15 deficiency-induced reprogramming of glutamine metabolism. SLC25A15-deficient HCC cells were more susceptible to glutamine deprivation and glutaminase inhibitors. Intervening in glutamine metabolism increased SLC25A15-deficient HCC cells' response to anti-PD-L1 treatment. CONCLUSION: SLC25A15 is hypoxia-responsive in HCC, and low SLC25A15 levels result in glutamine reprogramming through SLC1A5 and OGDHL regulation, promoting HCC progression and regulating cell sensitivity to anti-PD-L1. Interrupting the glutamine-derived energy supply is a potential therapeutic strategy for treating SLC25A15-deficient HCC. IMPACT AND IMPLICATIONS: We first demonstrated the tumor suppressor role of solute carrier family 25 member 15 (SLC25A15) in hepatocellular carcinoma (HCC) and showed that its deficiency leads to reprogramming of glutamine metabolism to promote HCC development. SLC25A15 can serve as a potential biomarker to guide the development of precision therapeutic strategies aimed at targeting glutamine deprivation. Furthermore, we highlight that the use of an inhibitor of glutamine utilization can enhance the sensitivity of low SLC25A15 HCC to anti-PD-L1 therapy.

MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules.

MOTIVATION: Cancer is caused by the accumulation of somatic mutations in multiple pathways, in which driver mutations are typically of the properties of high coverage and high exclusivity in patients. Identifying cancer driver genes has a pivotal role in understanding the mechanisms of oncogenesis and treatment. RESULTS: Here, we introduced MaxCLK, an algorithm for identifying cancer driver genes, which was developed by an integrated analysis of somatic mutation data and protein-protein interaction (PPI) networks and further improved by an information entropy index. Tested on pancancer and single cancers, MaxCLK outperformed other existing methods with higher accuracy. About pancancer, we predicted 154 driver genes and 787 driver modules. The analysis of co-occurrence and exclusivity between modules and pathways reveals the correlation of their combinations. Overall, our study has deepened the understanding of driver mechanism in PPI topology and found novel driver genes. AVAILABILITY AND IMPLEMENTATION: The source codes for MaxCLK are freely available at https://github.com/ShandongUniversityMasterMa/MaxCLK-main.

Antibiofilm Mechanisms of the Helical G3 Peptide against Staphylococcus epidermidis.

Antibacterial peptides (ABPs) have been recognized as promising alternatives to conventional antibiotics due to their broad antibacterial spectrum, high antibacterial activity, and low possibility of inducing bacterial resistance. However, their antibiofilm mechanisms have not yet reached a consensus. In this study, we investigated the antibiofilm activity of a short helical peptide G3 against Staphylococcus epidermidis, one of the most important strains of medical device contamination. Studies show that G3 inhibits S. epidermidis biofilm formation in a variety of ways. In the initial adhesion stage, G3 changes the properties of bacterial surfaces, such as charges, hydrophobicity, and permeability, by rapidly binding to them, thus interfering with their initial adhesion. In the mature stage, G3 prefers to target extracellular polysaccharides, leading to the death of outside bacteria and the disruption of the three-dimensional (3D) architecture of the bacterial biofilm. Such efficient antibiofilm activity of G3 endows it with great potential in the treatment of infections induced by the S. epidermidis biofilm.

FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.

Ultrasound Assessment of Entheseal Sites and Anterior Chest Wall in Ankylosing Spondylitis: A Cross-Sectional Study.

OBJECTIVES: The study was designed to evaluate entheseal sites and anterior chest wall (ACW) of patients with ankylosing spondylitis (AS) using ultrasound (US) and investigate the correlation between disease activity and US score. METHODS: This prospective cross-sectional study included 104 patients with AS and 50 control subjects. Each patient underwent US scanning of 23 entheses and 11 sites of the ACW. The US features, including hypoechogenicity, thickness, erosion, calcification, bursitis, and Doppler signal, were evaluated. Disease activity was assessed based on C reactive protein (CRP), erythrocyte sedimentation rate (ESR), disease activity score-C reactive protein (ASDAS-CRP), and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). RESULTS: The most commonly involved entheses on US were the Achilles tendon (AT) and quadriceps tendon (QT). The most involved site of ACW was the sternoclavicular joint (SCJ). Compared with the control group, significant differences were observed in the AS group in the rates of US enthesitis and ACW in AT (P = .01), SCJ (P = .00), and costochondral joint (CCJ) (P = .01). Patients with high or very high disease activity had a higher erosion score (P = .02). The erosion score was weakly positively associated with CRP, ESR, BASDAI, ASDAS-CRP, and ASDAS-ESR (correlation coefficient: 0.22-0.45). CONCLUSIONS: The most commonly involved entheseal sites on US were AT and QT, while the site of ACW was SCJ. The US assessment of AS should take the ACW into account. High disease activity might indicate erosion in AS.

Ultrasound features and differential diagnosis for superficial nodular fasciitis.

BACKGROUND: Nodular fasciitis (NF) has nonspecific clinical manifestations and is often misdiagnosed as sarcoma. The investigations of imaging methods for NF were limited. OBJECTIVE: To analyze the ultrasound (US) features of NF, and to evaluate the diagnostic value of US for NF. MATERIALS AND METHODS: A total of 61 NF patients were recruited retrospectively, and 551 lesions in the subcutaneous fat layer were included for comparison. We evaluated the ultrasound features of the patients and divided the NF cases into three types. Chi-square test or Fisher exact test were conducted to detect the potential difference in the distributions of three types in the two groups. RESULTS: Among the 61 NF cases, 65.6% were in the upper extremities (n = 40). The proportion of type 1, 2, and 3 were 57.4%, 24.6%, and 18.0%, respectively. NF were significantly more likely locating in the upper extremities than the other soft tissue tumors (p < 0.001). Type 1 and type 2 of sonographic features were significantly more commonly observed in NF than other soft tissue tumors among the three types (p < 0.001). CONCLUSION: The type 1 and type 2 of US features can help to distinguish NF from other lesions. US has great potential to improve the diagnostic accuracy and reduce the unnecessary surgery.

DoMYB5 and DobHLH24, Transcription Factors Involved in Regulating Anthocyanin Accumulation in Dendrobium officinale.

As a kind of orchid plant with both medicinal and ornamental value, Dendrobium officinale has garnered increasing research attention in recent years. The MYB and bHLH transcription factors play important roles in the synthesis and accumulation of anthocyanin. However, how MYB and bHLH transcription factors work in the synthesis and accumulation of anthocyanin in D. officinale is still unclear. In this study, we cloned and characterized one MYB and one bHLH transcription factor, namely, D. officinale MYB5 (DoMYB5) and D. officinaleb bHLH24 (DobHLH24), respectively. Their expression levels were positively correlated with the anthocyanin content in the flowers, stems, and leaves of D. officinale varieties with different colors. The transient expression of DoMYB5 and DobHLH24 in D. officinale leaf and their stable expression in tobacco significantly promoted the accumulation of anthocyanin. Both DoMYB5 and DobHLH24 could directly bind to the promoters of D. officinale CHS (DoCHS) and D. officinale DFR (DoDFR) and regulate DoCHS and DoDFR expression. The co-transformation of the two transcription factors significantly enhanced the expression levels of DoCHS and DoDFR. DoMYB5 and DobHLH24 may enhance the regulatory effect by forming heterodimers. Drawing on the results of our experiments, we propose that DobHLH24 may function as a regulatory partner by interacting directly with DoMYB5 to stimulate anthocyanin accumulation in D. officinale.

DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.

BACKGROUND: Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive. RESULTS: To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. CONCLUSION: DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

Enhanced transverse optical gradient force on Rayleigh particles in two plane waves.

Based on the full wave simulation and the Maxwell stress tensor theory, we demonstrate an enhanced transverse optical gradient force acting on Rayleigh particles immersed in a simple optical field formed by two linearly polarized plane waves. The optical gradient force acting on a conventional dielectric particle can be enhanced by two orders of magnitude via coating an extremely thin silver shell, whose thickness is only about one-tenth of the dielectric core. The analytical results based on the multipole expansion theory reveal that the enhanced optical gradient force comes mostly from the interaction between the incident field and the electric quadrupole excited in the core-shell particle. It is worth noting that the force expression within the dipole approximation commonly used for Rayleigh particles is invalid in our situation, even the particle is within the Rayleigh regime. In addition, both the optical potential energy and the optical trapping stiffness for the core-shell particle exhibit a great enhancement by two orders of magnitude stronger than a conventional dielectric particle and thus is favorable to a stable optical trapping. These results may extend the application range of optical tweezers and enrich optical manipulation techniques.

Modulated pattern scanning microscopy.

In confocal microscopy, the effective optical transfer function (OTFeff) with Gaussian plane wave illumination covers very few high-frequency components, which prohibits further improvement of the resolution. We propose modulated pattern scanning microscopy (MPSM) to achieve super-resolution imaging. In MPSM, the phase of the illumination beam is modulated to reassign the OTFeff in the Fourier domain. The phase mask is designed using an optimization algorithm to obtain the fluorescence emission pattern with rich high-frequency components. Then, the postprocessing algorithms are adapted to retrieve the super-resolved images from the modulated recordings. Simulation and experiment demonstrate that MPSM increases the resolution approximately 1.3 times better than confocal microscopy. Compared with conventional deconvolution, MPSM exhibits a higher signal-to-noise ratio.

Combination of tea polyphenols and proanthocyanidins prevents menopause-related memory decline in rats via increased hippocampal synaptic plasticity by inhibiting p38 MAPK and TNF-α pathway.

OBJECTIVE: Many studies have examined the beneficial effects of tea polyphenols (TP) and proanthocyanidins (PC) on the memory impairment in different animal models. However, the combined effects of them on synaptic, memory dysfunction and molecular mechanisms have been poorly studied, especially in the menopause-related memory decline in rats. METHODS: In this rat study, TP and PC were used to investigate their protective effects on memory decline caused by inflammation. We characterized the learning and memory abilities, synaptic plasticity, AMPAR, phosphorylation of the p38 protein, TNF-ɑ, structural synaptic plasticity-related indicators in the hippocampus. RESULTS: The results showed that deficits of learning and memory in OVX + D-gal rats, which was accompanied by dendrites and synaptic morphology damage, and increased expression of Aß1-42 and inflammation. The beneficial effects of TP and PC treatment were found to prevent memory loss and significantly improve synaptic structure and functional plasticity. TP+PC combination shows more obvious advantages than intervention alone. TP and PC treatment improved behavioral performance, the hippocampal LTP damage and the shape and number of dendrites, dendritic spines and synapses, reduced the burden of Aß and decreased the inflammation in hippocampus. In addition, TP and PC treatment decreased the expressions of Iba-1, TNF-α, TNFR1, and TRAF2. CONCLUSIONS: These results provided a novel evidence TP combined with PC inhibits p38 MAPK pathway, suppresses the inflammation in hippocampus, and increase the externalization of AMPAR, which may be one of the mechanisms to improve synaptic plasticity and memory in the menopause-related memory decline rats.

Emerging photodynamic/sonodynamic therapies for urological cancers: progress and challenges.

Photodynamic therapy (PDT), and sonodynamic therapy (SDT) that developed from PDT, have been studied for decades to treat solid tumors. Compared with other deep tumors, the accessibility of urological tumors (e.g., bladder tumor and prostate tumor) makes them more suitable for PDT/SDT that requires exogenous stimulation. Due to the introduction of nanobiotechnology, emerging photo/sonosensitizers modified with different functional components and improved physicochemical properties have many outstanding advantages in cancer treatment compared with traditional photo/sonosensitizers, such as alleviating hypoxia to improve quantum yield, passive/active tumor targeting to increase drug accumulation, and combination with other therapeutic modalities (e.g., chemotherapy, immunotherapy and targeted therapy) to achieve synergistic therapy. As WST11 (TOOKAD® soluble) is currently clinically approved for the treatment of prostate cancer, emerging photo/sonosensitizers have great potential for clinical translation, which requires multidisciplinary participation and extensive clinical trials. Herein, the latest research advances of newly developed photo/sonosensitizers for the treatment of urological cancers, and the efficacy, as well as potential biological effects, are highlighted. In addition, the clinical status of PDT/SDT for urological cancers is presented, and the optimization of the photo/sonosensitizer development procedure for clinical translation is discussed.

Protein-Protein Interaction Prediction Based on Spectral Radius and General Regression Neural Network.

Protein-protein interaction (PPI) not only plays a critical role in cell life activities, but also plays an important role in discovering the mechanism of biological activity, protein function, and disease states. Developing computational methods is of great significance for PPIs prediction since experimental methods are time-consuming and laborious. In this paper, we proposed a PPI prediction algorithm called GRNN-PPI only using the amino acid sequence information based on general regression neural network and two feature extraction methods. Specifically, we designed a new feature extraction method named Mutation Spectral Radius (MSR) to extract evolutionary information by the BLOSUM62 matrix. Meanwhile, we integrated another feature extraction method, autocorrelation description, which can completely extract information on physicochemical properties and protein sequences. The principal component analysis was applied to eliminate noise, and the general regression neural network was adopted as a classifier. The prediction accuracy of the yeast, human, and Helicobacter pylori1 (H. pylori1) data sets were 97.47%, 99.63%, and 99.97%, respectively. In addition, we also conducted experiments on two important PPI networks and six independent data sets. All results were significantly higher than some state-of-the-art methods used for comparison, showing that our method is feasible and robust.

MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities.

BACKGROUND: Microbes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments. METHODS: In this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities. RESULTS: The AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature. CONCLUSIONS: All the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data.

BACKGROUND: The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field. RESULTS: In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy. CONCLUSIONS: The proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information.

Identification of protein-protein interactions (PPIs) plays an essential role in the understanding of protein functions and cellular biological activities. However, the traditional experiment-based methods are time-consuming and laborious. Therefore, developing new reliable computational approaches has great practical significance for the identification of PPIs. In this paper, a novel prediction method is proposed for predicting PPIs using graph energy, named PPI-GE. Particularly, in the process of feature extraction, we designed two new feature extraction methods, the physicochemical graph energy based on the ionization equilibrium constant and isoelectric point and the contact graph energy based on the contact information of amino acids. The dipeptide composition method was used for order information of amino acids. After multi-information fusion, principal component analysis (PCA) was implemented for eliminating noise and a robust weighted sparse representation-based classification (WSRC) classifier was applied for sample classification. The prediction accuracies based on the five-fold cross-validation of the human, Helicobacter pylori (H. pylori), and yeast data sets were 99.49%, 97.15%, and 99.56%, respectively. In addition, in five independent data sets and two significant PPI networks, the comparative experimental results also demonstrate that PPI-GE obtained better performance than the compared methods.

[Effects of resveratrol and soy isoflavones on learning and memory ability and expression of glucose transporter in aging model rats].

OBJECTIVE: To investigate the effects of resveratrol combined with soy isoflavones on avoidance memory, number of neuron-specific nuclear protein(NeuN) positive cells and expressions of glucose transporter(GLUT)1 and GLUT3 in hippocampus of aging model rats. METHODS: A total of 60 female SD rats were randomly divided into 6 groups including sham control group, aging model group, 80 mg/kg resveratrol group, 160 mg/kg soy isoflavones group, 80 mg/kg resveratrol +160 mg/kg soy isoflavones group, 0. 8 mg/kg estradiol valerate group. The aging model rats was induced by ovariectomy combined with intraperitoneal injection of 100 mg/kg D-galactose. Intragastric administration was performed once a day for 12 weeks. The avoidance task was measured by the shuttle box test. The NeuN expression were measured by the immunofluorescence. The genes and proteins expression of GLUT1 and GLUT3 in rat hippocampus were detected by real-time PCR and Western blot, respectively. RESULTS: Compared with the sham control group, the avoidance latency in the aging model group was prolonged, and the active avoidance response rate and the total avoidance response rate were decreased. The number of NeuN positive cells decreased and the expression levels of GLUT1 and GLUT3 genes and proteins were decreased(P<0. 05). Compared with the aging model group, the escape latency significantly declined(P<0. 01), but the rates of active avoidance response and total avoidance response increased, the number of NeuN positive cells increased significantly, the expression levels of GLUT1 and GLUT3 genes and proteins up-regulated in the rats of the three intervention groups(P<0. 05 or P<0. 01). Compared with the soy isoflavones group, the active avoidance response rate was increased in the combined group(P<0. 05). In comparison with those for the resveratrol group, the avoidance latency was shortened and the active avoidance response rate was increased, the number of NeuN positive cells and the expression levels of GLUT3 gene and protein were significantly increased in the combined group(P<0. 05). There was no significant difference between the combined intervention group and the estradiol valerate group(P>0. 05). CONCLUSION: Resveratrol and soy isoflavones alone and in combination can improve the learning and memory ability of aging rat models. The mechanism may be related to up-regulating the expression of GLUT1 and GLUT3 genes and proteins in the hippocampus, promoting the transmembrane transport of glucose and reducing neuronal loss.