Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm.

In the field of neuroimaging, more studies of abnormalities in brain regions of the autism spectrum disorder (ASD) usually focused on two brain regions connected, and less on abnormalities of higher-order interactions of brain regions. To explore the complex relationships of brain regions, we used the partial entropy decomposition (PED) algorithm to capture higher-order interactions by computing the higher-order dependencies of all three brain regions (triads). We proposed a method for examining the effect of individual brain regions on triads based on the PED and surrogate tests. The key triads were discovered by analyzing the effects. Further, the hypergraph modularity maximization algorithm revealed the higher-order brain structures, of which the link between right thalamus and left thalamus in ASD was more loose compared with the typical control (TC). Redundant key triad (left cerebellum crus 1 and left precuneus and right inferior occipital gyrus) exhibited a discernible attenuation in interaction in ASD, while the synergistic key triad (right cerebellum crus 1 and left postcentral gyrus and left lingual gyrus) indicated a notable decline. The results of classification model further confirmed the potential of the key triads as diagnostic biomarkers.

CDCS-TCM: A framework based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicine, with the feature of synergistic effects of multi-component, multi-pathway and multi-target, plays an important role in the treatment of cancer, cardiovascular and cerebrovascular diseases, etc. However, chemical components in traditional Chinese medicine are complex and most of the pharmacological mechanisms remain unclear, especially the relationships of chemical components change during the metabolic process. AIM OF STUDY: Our aim is to provide a method based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine. MATERIALS AND METHODS: We proposed a framework named CDCS-TCM to analyze the causality and dynamic correlation between substances in the metabolic process of traditional Chinese medicine. Our method mainly consists two parts. The first part is to discover the local and global causality by the causality network. The second part is to investigate the dynamic correlations and identify the essential substance by dynamic substance correlation network. RESULTS: We developed a CDCS-TCM method to analyze the causality and dynamic correlation of substances. Using the XiangDan Injection for ischemic stroke as an example, we have identified the important substances in the metabolic process including substance pairs with strong causality and the dynamic changes of the core effector substance clusters. CONCLUSION: The proposed framework will be useful for exploring the correlations of active ingredients in traditional Chinese medicine more effectively and will provide a new perspective for the elucidation of drug action mechanisms and the new drug discovery.

Prediction of Drug-Target Interactions with High-Quality Negative Samples and A Network-Based Deep Learning Framework.

Identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared to traditional experimental methods, computer-based methods for predicting DTIs can significantly reduce the time and financial burdens of drug development. In recent years, numerous machine learning-based methods have been proposed for predicting potential DTIs. However, a common limitation among these methods is the absence of high-quality negative samples. Moreover, the effective extraction of multisource information of drugs and proteins for DTI prediction remains a significant challenge. In this paper, we investigated two aspects: the selection of high-quality negative samples and the construction of a high-performance DTI prediction framework. Specifically, we found two types of hidden biases when randomly selecting negative samples from unlabeled drug-protein pairs and proposed a negative sample selection approach based on complex network theory. Furthermore, we proposed a novel DTI prediction method named HNetPa-DTI, which integrates topological information from the drug-protein-disease heterogeneous network and gene ontology (GO) and pathway annotation information of proteins. Specifically, we extracted topological information of the drug-protein-disease heterogeneous network using heterogeneous graph neural networks, and obtained GO and pathway annotation information of proteins from the GO term semantic similarity networks, GO term-protein bipartite networks, and pathway-protein bipartite network using graph neural networks. Experimental results show that HNetPa-DTI outperforms the baseline methods on four types of prediction tasks, demonstrating the superiority of our method. Our code and datasets are available at https://github.com/study-czx/HNetPa-DTI.

The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding.

Autism spectrum disorder (ASD) is a neurological and developmental disorder and its early diagnosis is a challenging task. The dynamic brain network (DBN) offers a wealth of information for the diagnosis and treatment of ASD. Mining the spatio-temporal characteristics of DBN is critical for finding dynamic communication across brain regions and, ultimately, identifying the ASD diagnostic biomarker. We proposed the dgEmbed-KNN and the Aggregation-SVM diagnostic models, which use the spatio-temporal information from DBN and interactive information among brain regions represented by dynamic graph embedding. The classification accuracies show that dgEmbed-KNN model performs slightly better than traditional machine learning and deep learning methods, while the Aggregation-SVM model has a very good capacity to diagnose ASD using aggregation brain network connections as features. We discovered over- and under-connections in ASD at the level of dynamic connections, involving brain regions of the postcentral gyrus, the insula, the cerebellum, the caudate nucleus, and the temporal pole. We also found abnormal dynamic interactions associated with ASD within/between the functional subnetworks, including default mode network, visual network, auditory network and saliency network. These can provide potential DBN biomarkers for ASD identification.

DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features.

Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.

[Investigation of the mechanism of action and identification of candidate traditional Chinese medicines for the treatment of ischemic stroke in the Danshen-Jiangxiang pair based on drug-target-disease association network].

The therapeutic efficacy of Danshen and Jiangxiang in the treatment of ischemic stroke (IS) is relatively significant. Studying the mechanism of action of Danshen and Jiangxiang in the treatment of IS can effectively identify candidate traditional Chinese medicines (TCM) with efficacy. However, it is challenging to analyze the effector substances and explain the mechanism of action of Danshen-Jiangxiang from a systematic perspective using traditional pharmacological approaches. In this study, a systematic study was conducted based on the drug-target-symptom-disease association network using complex network theory. On the basis of the association information about Danshen, Jiangxiang and IS, the protein-protein interaction (PPI) network and the "drug pair-pharmacodynamic ingredient-target-IS" network were constructed. The different topological features of the networks were analyzed to identify the core pharmacodynamic ingredients including formononetin in Jiangxiang, cryptotanshinone and tanshinone IIA in Danshen as well as core target proteins such as prostaglandin G/H synthase 2, retinoic acid receptor RXR-alpha, sodium channel protein type 5 subunit alpha, prostaglandin G/H synthase 1 and beta-2 adrenergic receptor. Further, a method for screening IS candidates based on TCM symptoms was proposed to identify key TCM symptoms and syndromes using the "drug pair-TCM symptom-syndrome-IS" network. The results showed that three TCMs, namely Puhuang, Sanleng and Zelan, might be potential therapeutic candidates for IS, which provided a theoretical reference for the development of drugs for the treatment of IS.

GCNA-Cluster: A Gene Co-Expression Network Alignment to Cluster Cancer Patients Algorithm for Identifying Subtypes of Pancreatic Ductal Adenocarcinoma.

Cancer heterogeneity makes it necessary to use different treatment strategies for patients with the same pathological features. Accurate identification of cancer subtypes is a crucial step in this approach. The current studies of pancreatic ductal adenocarcinoma (PDAC) subtypes mainly focus on single genes and ignore the synergistic effects of genes. Here we proposed a network alignment algorithm GCNA-cluster to cluster patients based on gene co-expression networks. We constructed weighted gene co-expression networks for patients and aligned the networks of two patients to estimate the similarity of patients and their cancer subtypes. A scoring function is defined to measure the network alignment result and the score can indicate the similarity between patients. Then, the patients are clustered based on their similarities. We validated the accuracy of the algorithm on the GEO-PDAC dataset with real labels, and the experimental results show that the GCNA-cluster algorithm has better results than classical cancer subtyping algorithms. In addition, the GCNA-cluster algorithm applied to the TCGA-PDAC dataset identified two subtypes based on the Silhouette Coefficient. Biomarkers identified for the PDAC subtypes hint to cell growth, cell cycle or apoptosis as targets for new therapeutic strategies.

RSC-based differential model with correlation removal for improving multi-omics clustering.

Multi-omics clustering plays an important role in cancer subtyping. However, the data of different kinds of omics are often related, these correlations may reduce the clustering algorithm performance. It is crucial to eliminate the unexpected redundant information caused by these correlations between different omics. We proposed RSC-based differential model with correlation removal for improving multi-omics clustering (RSC-MCR). This method first introduced RSC to calculate the pairwise correlations of all features, and decomposed it to obtain the pairwise correlations of different omics features, thus built the connection between different omics based on the pairwise correlations of different omics features. Then, to remove the redundant correlation, we designed a differential model to calculate the degree of difference between the original feature matrix and the correlation matrix which contained the most relevant information between different omics. We compared the performance of RSC-MCR with decorrelation methods on different clustering methods (CC, FCM, SNF, NMF, LRAcluster). The experimental results on five cancer datasets show the efficiency of the RSC-MCR as well as improvements over other decorrelation methods.

A 70­RNA model based on SVR and RFE for predicting the pancreatic cancer clinical prognosis.

Researches on the prognosis of pancreatic cancer is of great significance to improve the patient treatment effect and survival. Current researches mainly focus on the prediction of the survival status and the determination of prognostic markers. Each patient has its own characteristics, there is no report about the prediction of survival time. However, accurate prediction of survival time is critical for personalized medicine. In this paper, a hybrid algorithm of Support Vector Regression (SVR) and Recursive Feature Elimination (RFE) was used to construct a quantitative prediction model of Overall Survival (OS) for pancreatic cancer patients, 70 RNAs related to OS were determined, including 33 mRNAs, 28 lncRNAs, and 9 miRNAs. The results of 10-fold cross-validation (R2 is 0.9693) and the generalization ability (R2 is 0.9666) showed that the model has reliable predictive performance and these 70 RNAs are important factors influencing the OS of pancreatic cancer patients. To further study the relationship between RNA-RNA interaction and the survival, competitive endogenous RNA (ceRNA) regulation network was constructed. Degree centrality, betweenness centrality and closeness centrality of nodes in the ceRNA network showed that hsa-mir-570, hsa-mir-944, hsa-mir-6506, hsa-mir-3136, MMP16, PLGLB2, HPGD, FUT1, MFSD2A, SULT1E1, SLC13A5, ZNF488, F2RL2, TNFRSF8, TNFSF11, FHDC1, ISLR2 and THSD7B are hub nodes, which are key RNAs closely determining the OS of pancreatic cancer patients.

Determination of the key ccRCC-related molecules from monolayer network to three-layer network.

Clear cell renal cell carcinoma (ccRCC), with an increasing incidence rate, is one of the ubiquitous cancers. Its pathogenic factors are complicated and the molecular mechanism is not clear. It is essential to analyze the potential key genes related to ccRCC carcinogenesis. In this study, the differentially expressed mRNAs, miRNAs and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) of ccRCC were screened from TCGA database. Then the miRNA-mRNA network, lncRNA-miRNA network and lncRNA-mRNA network were constructed by online database or WGCNA algorithm. Topology attributes of these monolayer networks showed that hsa-mir-155, hsa-mir-200c, hsa-mir-122, hsa-mir-506, hsa-mir-216b, hsa-mir-141, lncRNA AC137723.1 and AC021074.3 are the crucial genes related with the regulatory effects on the proliferation, metastasis and invasion of ccRCC cells. Subsequently, these three monolayer networks were integrated into a lncRNA-miRNA-mRNA multilayer network. Considering node degree, closeness centrality and betweenness centrality, we found hsa-mir-122 is screened out as the only crucial gene in three-layer network. In order to better illustrate the effect of hsa-mir-122 on ccRCC, the lncRNA-hsa-mir-122-mRNA network was constructed with hsa-mir-122 as the center. Pathway analysis of the unique target gene GALNT3 linked to hsa-mir-122 showed that GALNT3 influenced the metabolic process of mucin type O-Glycan biosynthesis. LncRNA AC090377.1 is the unique gene that has target genes among lncRNAs with clinical significance that linked to hsa-mir-122 in the lncRNA-hsa-mir-122-mRNA network. Pathway analysis of AC090377.1 suggested that GUCY2F enriched in phototransduction pathway associated with retina. From monolayer network to three-layer network, hsa-mir-122 is identified as an important molecule in the oncogenesis and progression of ccRCC, offering new strategies to further study of the carcinogenic mechanism of ccRCC.

Identification of subtype specific biomarkers of clear cell renal cell carcinoma using random forest and greedy algorithm.

Suitable biomarkers can be good indicator for cancer subtype. To find biomarkers that can accurately distinguish clear cell renal cell carcinoma (ccRCC) subtypes, we first determined ccRCC subtypes based on the expression of mRNA, miRNA and lncRNA, named clear cell type 1 (ccluster1) and 2 (ccluster2), using three unsupervised clustering algorithms. Besides being associated with the expression pattern derived from the single type of RNA, the differences between subtypes are relevant to the interactions between RNAs. Then, based on ceRNA network, the optimal combination features are selected using random forest and greedy algorithm. Further, in survival-related sub-ceRNA, competing gene pairs centering on miR-106a, miR-192, miR-193b, miR-454, miR-32, miR-98, miR-143, miR-145, miR-204, miR-424 and miR-1271 can also well identify ccluster1 and ccluster2 with prediction accuracy over 92%. These subtype-specific features potentially enhance the accuracy with which machine learning methods predict specific ccRCC subtypes. Simultaneously, the changes of miR-106 and OIP5-AS1 affect cell proliferation and the prognosis of ccluster1. The changes of miR-145 and FAM13A-AS1 in ccluster2 have an effect on cell invasion, apoptosis, migration and metabolism function. Here miR-192 displays a unique characteristic in both subtypes. Two subtypes also display notable differences in diverse pathways. Tumors belonging to ccluster1 are characterized by Fc gamma R-mediated phagocytosis pathway that affects tissue remodeling and repair, whereas those belonging to ccluster2 are characterized by EGFR tyrosine kinase inhibitor resistance pathway that participates in regulation of cell homeostasis. In conclusion, identifying these gene pairs can shed light on therapeutic mechanisms of ccRCC subtypes.

Probing the Relation Between Community Evolution in Dynamic Residue Interaction Networks and Xylanase Thermostability.

Residue-residue interactions are the basis of protein thermostability. The molecular conformations of Streptomyces lividans xylanase (xyna_strli) and Thermoascus aurantiacus xylanase (xyna_theau) at 300K, 325K and 350K were obtained by Molecular Dynamics (MD) simulations. Dynamic weighted residue interaction networks were constructed and the rigid-communities were detected using the ESPRA algorithm and the Evolving Graph+Fast-Newman algorithm. The residues in the rigid-communities are primarily located in loop2, short helixes α2', α3', α4' and helixes α3 and α4. Thus, the rigid-community is close to the N-terminus of xylanase, which is usually stabilized to increase thermostability using site-directed mutagenesis. The evolution of the rigid-community with increasing temperature shows a stable synergistic interaction between loop2, α2', α3' and α4' in xyna_theau. In particular, the short helixes α2' and α3' form a "thermo helix" to promote thermostability. In addition, tight global interactions between loop2, α2', α3', α3, α4' and α4 of xyna_theau are identified, consisting mainly of hydrogen bonds, van der Waals forces and π-π stacking. These residue interactions are more resistant to high temperatures than those in xyna_strli. Robust residue interactions within these secondary structures are key factors influencing xyna_strli and xyna_theau thermostability. Analyzing the rigid-community can elucidate the cooperation of secondary structures, which cannot be discovered from sequence and 3D structure alone.

The Quantitative Structure-Activity Relationships between GABAA Receptor and Ligands based on Binding Interface Characteristic.

BACKGROUND: Quantitative Structure Activity Relationship (QSAR) methods based on machine learning play a vital role in predicting biological effect. OBJECTIVE: Considering the characteristics of the binding interface between ligands and the inhibitory neurotransmitter Gamma-Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of ligands that bind to the human GABAA receptor. METHODS: After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked ligand molecular descriptors. Three QSAR models are built using a gradient boosting regression tree algorithm based on the different combinations of docked ligand molecular descriptors and ligand receptor interaction characteristics. RESULTS: The features of the optimal QSAR model contain both the docked ligand molecular descriptors and ligand-receptor interaction characteristics. The Leave-One-Out-Cross-Validation (Q2 LOO) of the optimal QSAR model is 0.8974, the Coefficient of Determination (R2) for the testing set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this model to predict the pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02 and 0.03, respectively. CONCLUSION: We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features, which can help to build the QSAR model more accurately.

Identification of the complex regulatory relationships related to gastric cancer from lncRNA-miRNA-mRNA network.

The oncogenesis and progression of gastric cancer are closely correlated with the complex regulatory relationships among messenger RNA (mRNA), long noncoding RNA (lncRNA), and microRNA (miRNA). After constructing the gastric cancer lncRNA-miRNA-mRNA regulatory network, we analyzed the network topology properties and found that lncRNA ADAMTS9-AS2 and C20orf166-AS1 and miRNA hsa-mir-204 are key nodes. Further functional enrichment analysis and survival analysis were performed on these key nodes and the RNAs interacting with them. We found that CHRM2, ANGPT2, and COL1A1 interacting with ADAMTS9-AS2 are enriched in the PI3K-Akt signaling pathway, and low expression of the ADAMTS9-AS2 is closely related to the prognosis of patients. Abnormal expression of CACNA1H, FLNA, and FLNC interacting with lncRNA C20orf166-AS1 is associated with MAPK signaling pathway in gastric cancer. In addition, the downregulated miRNA hsa-mir-204 promotes invasion and proliferation of gastric cancer cells by regulating the abnormal expression of mRNAs (CHRDL1 and NPTX1) and lncRNAs (ADAMTS9-AS2, NKX2-1-AS1, TLR8-AS1, and VCAN-AS1). This study systematically analyzed the lncRNA-miRNA-mRNA regulatory network of gastric cancer, which not only has a new understanding of the pathogenesis of gastric cancer, but also provides new insights for the early diagnosis and treatment of gastric cancer.

Identification of Key Features of CNS Drugs Based on SVM and Greedy Algorithm.

INTRODUCTION: The research and development of drugs, related to the central nervous system (CNS) diseases is a long and arduous process with high cost, long cycle and low success rate. Identification of key features based on available CNS drugs is of great significance for the discovery of new drugs. METHODS: In this paper, based on the PaDEL descriptors of CNS drugs and non-CNS drugs, a support vector machine (SVM) model was constructed to identify the key features of CNS drugs. Firstly, the random forest algorithm was used to rank descriptors according to the feature significance that contributes to the identification of CNS drugs. Then, a reliable SVM model was constructed, and the optimal combination of descriptors was determined based on greedy algorithm and recursive feature elimination method. RESULTS: It was found, based on the optimal combination of 40 descriptors, the prediction accuracy of CNS drugs and non-CNS drugs reached 94.2% and 94.4% respectively. CONCLUSION: nF11HeteroRing, AATSC3v, SpMin6_Bhi, maxdssC, AATS4v, E1v, E3e, GATS5s, minsOH and minHBint4 are the key features to distinguish between CNS drugs and non-CNS drugs.

Polygala tenuifolia-Acori tatarinowii herbal pair as an inspiration for substituted cinnamic α-asaronol esters: Design, synthesis, anticonvulsant activity, and inhibition of lactate dehydrogenase study.

Inspired by the traditional Chinese herbal pair of Polygala tenuifolia-Acori Tatarinowii for treating epilepsy, 33 novel substituted cinnamic α-asaronol esters and analogues were designed by Combination of Traditional Chinese Medicine Molecular Chemistry (CTCMMC) strategy, synthesized and tested systematically not only for anticonvulsant activity in three mouse models but also for LDH inhibitory activity. Thereinto, 68-70 and 75 displayed excellent and broad spectra of anticonvulsant activities with modest ability in preventing neuropathic pain, as well as low neurotoxicity. The protective indices of these four compounds compared favorably with stiripentol, lacosamide, carbamazepine and valproic acid. 68-70 exhibited good LDH1 and LDH5 inhibitory activities with noncompetitive inhibition type, and were more potent than stiripentol. Notably, 70, as a representative agent, was also shown as a moderately positive allosteric modulator at human α1ß2γ2 GABAA receptors (EC50 46.3 ±â€¯7.3 µM). Thus, 68-70 were promising candidates for developing into anti-epileptic drugs, especially for treatment of refractory epilepsies such as Dravet syndrome.

Thermostability of Lipase A and Dynamic Communication Based on Residue Interaction Network.

OBJECTIVE: Dynamic communication caused by mutation affects protein stability. The main objective of this study is to explore how mutations affect communication and to provide further insight into the relationship between heat resistance and signal propagation of Bacillus subtilis lipase (Lip A). METHODS: The relationship between dynamic communication and Lip A thermostability is studied by long-time MD simulation and residue interaction network. The Dijkstra algorithm is used to get the shortest path of each residue pair. Subsequently, time-series frequent paths and spatio-temporal frequent paths are mined through an Apriori-like algorithm. RESULTS: Time-series frequent paths show that the communication between residue pairs, both in wild-type lipase (WTL) and mutant 6B, becomes chaotic with an increase in temperature; however, more residues in 6B can maintain stable communication at high temperature, which may be associated with the structural rigidity. Furthermore, spatio-temporal frequent paths reflect the interactions among secondary structures. For WTL at 300K, ß7, αC, αB, the longest loop, αA and αF contact frequently. The 310-helix between ß3 and αA is penetrated by spatio-temporal frequent paths. At 400K, only αC can be frequently transmitted. For 6B, when at 300K, αA and αF are in more tight contact by spatio-temporal frequent paths though I157M and N166Y. Moreover, the rigidity of the active site His156 and the C-terminal of Lip A are increased, as reflected by the spatio-temporal frequent paths. At 400K, αA and αF, 310-helix between ß3 and αA, the longest loop, and the loop where the active site Asp133 is located can still maintain stable communication. CONCLUSION: From the perspective of residue dynamic communication, it is obviously found that mutations cause changes in interactions between secondary structures and enhance the rigidity of the structure, contributing to the thermal stability and functional activity of 6B.

Identification of the Key Factors Related to Bladder Cancer by lncRNA-miRNA-mRNA Three-Layer Network.

Bladder cancer is the most common malignant tumor of the urinary system, and it has high incidence, high degree of malignancy, and easy recurrence after surgery. The etiology and pathogenesis of bladder cancer are not fully understood, but more and more studies have shown that its development may be regulated by some core molecules. To identify key molecules in bladder cancer, we constructed a three-layer network by merging lncRNA-miRNA regulatory network, miRNA-mRNA regulatory network, and lncRNA-mRNA coexpression network, and further analyzed the topology attributes of the network including the degree, betweenness centrality and closeness centrality of nodes. We found that miRNA-93 and miRNA-195 are controllers for a three-layer network and regulators of numerous target genes associated with bladder cancer. Functional enrichment analysis of their target mRNAs revealed that miRNA-93 and miRNA-195 may be closely related to bladder cancer by disturbing the homeostasis of the cell cycle or HTLV-I infection. In addition, since E2F1 and E2F2 are enriched in various KEGG signaling pathways, we conclude that they are important target genes of miRNA-93, and participate in the apoptotic process by forming a complex with a certain protein or transcription factor activity, sequence-specific DNA binding in bladder cancer. Similarly, AKT3 is an important target gene of miRNA-195, its expression is associated with PI3K-Akt-mTOR signaling pathway and AMPK-mTOR signaling pathway. Therefore, we speculate that AKT3 may participate in proliferation and apoptosis of bladder cancer cells through these pathways, and ultimately affect the biological behavior of tumor cells. Furthermore, through survival analysis, we found that miRNA-195 and miRNA-93 are associated with poor prognosis of bladder cancer. And the Kaplan-Meier curve showed that 24 mRNAs and nine lncRNAs are closely related to overall survival of bladder cancer.

The Relation Between Lipase Thermostability and Dynamics of Hydrogen Bond and Hydrogen Bond Network Based on Long Time Molecular Dynamics Simulation.

BACKGROUND: Compared with the wild type of lipase (WTL), mutant lipase 6B has twelve mutations (A15S, F17S, A20E, N89Y, G111D, L114P, A132D, M134E, M137P, I157M, S163P, N166Y). The melting temperature of 6B (78.2°C) is much higher than that of WTL (56°C). Hydrogen bond (HB) play an important role in stabilizing the protein. It is important to analyze how mutations affect hydrogen bond and hydrogen bond network and explain how hydrogen bond and hydrogen bond network affect lipase thermostability by the change of the intensity of HB and HB networks with temperature changing. OBJECTIVE: Study the dynamics of HB and HB networks to find that how HBs and HB networks change over time and over temperature in WTL and 6B. METHOD: Long time MD simulations of WTL and 6B are carried out to analyze how mutations affect hydrogen bond and hydrogen bond network. All proteins were simulated at 300K, 325K, 350K, 375K, 400K for 300ns respectively. The definition of HB is that the distance between acceptor and donor is smaller than a cutoff 3.0 Å and the angle between Donor-H and H-Acceptor is larger than 120o. If two or more HBs connect together, they formed HB network. In the network, residues that formed HB represent nodes, the HB interactions between residues represent edges. The persistence value of HB is computed by . RESULTS: The persistence values of HBs formed by mutations A15S, A20E, G111D, M137P, N166Y are significantly different from that of WTL. HB Glu20-Ser24, Asp111-Asp144, Leu160-Tyr166 and Lys170-Tyr166 are important to stabilize 6B. In addition, the HB networks dynamics show that there are three HB networks are more stable in mutants than that in WTL. The first HB network makes ß3, ß5, loop and 310-helix closely connect with each other at mutants. The second HB network increases the rigidity of the loop, αC, ß3 and ß5. The third HB network enhances the interaction between loops, αB and αC. CONCLUSION: The higher HB persistence value generally means that the HB is more stable. These mutations directly improve the stability of these HBs referring to their persistence values, which show that mutations strengthen the ability of HBs to withstand high temperature and then stabilize the secondary structure. It is thus clear that the mutations change the stability of HBs and the HB networks, which are responsible for increasing protein thermostability.

Communities in the iron superoxide dismutase amino acid network.

Amino acid networks (AANs) analysis is a new way to reveal the relationship between protein structure and function. We constructed six different types of AANs based on iron superoxide dismutase (Fe-SOD) three-dimensional structure information. These Fe-SOD AANs have clear community structures when they were modularized by different methods. Especially, detected communities are related to Fe-SOD secondary structures. Regular structures show better correlations with detected communities than irregular structures, and loops weaken these correlations, which suggest that secondary structure is the unit element in Fe-SOD folding process. In addition, a comparative analysis of mesophilic and thermophilic Fe-SOD AANs' communities revealed that thermostable Fe-SOD AANs had more highly associated community structures than mesophilic one. Thermophilic Fe-SOD AANs also had more high similarity between communities and secondary structures than mesophilic Fe-SOD AANs. The communities in Fe-SOD AANs show that dense interactions in modules can help to stabilize thermophilic Fe-SOD.