New polyketides from the basidiomycetous fungus Pholiota sp.

Two new polyketides, pholiotones B and C (1 and 2), and four known compounds, trichodermatide D (3), vermistatin (4), dehydroaltenuene A (5) and terpestacin (6) were isolated from the crude extract of Pholiota sp. Their structures were identified by NMR and MS spectroscopic data. The absolute configurations of compounds 1 and 2 were elucidated by modified Mosher's method, electronic circular dichroism (ECD) calculations and 13C NMR calculations as well as DP4+ probability analyses. All the compounds were evaluated for their antifungal and cytotoxicity.

A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration.

The identification of the cancer driver genes is essential for personalized therapy. The mutation frequency of most driver genes is in the middle (2-20%) or even lower range, which makes it difficult to find the driver genes with low-frequency mutations. Other forms of genomic aberrations, such as copy number variations (CNVs) and epigenetic changes, may also reflect cancer progression. In this work, a method for identifying the potential cancer driver genes (iPDG) based on molecular data integration is proposed. DNA copy number variation, somatic mutation, and gene expression data of matched cancer samples are integrated. In combination with the method of iKEEG, the "key genes" of cancer are identified, and the change in their expression levels is used for auxiliary evaluation of whether the mutated genes are potential drivers. For a mutated gene, the concept of mutational effect is defined, which takes into account the effects of copy number variation, mutation gene itself, and its neighbor genes. The method mainly includes two steps: the first step is data preprocessing. First, DNA copy number variation and somatic mutation data are integrated. Then, the integrated data are mapped to a given interaction network, and the diffusion kernel is used to form the mutation effect matrix. The second step is to obtain the key genes by using the iKGGE method, and construct the connection matrix by means of the gene expression data of the key genes and mutation impact matrix of the mutated genes. Experiments on TCGA breast cancer and Glioblastoma multiforme datasets demonstrate that iPDG is effective not only to identify the known cancer driver genes but also to discover the rare potential driver genes. When measured by functional enrichment analysis, we find that these genes are clearly associated with these two types of cancers.

Inhibition of estrogen receptor reduces connexin 43 expression in breast cancers.

Connexins are widely supported as tumor suppressors due to their downregulation in cancers, nevertheless, more recent evidence suggests roles for connexins in facilitating tumor progression in later stages, including metastasis. One of the key factors regulating the expression, modification, stability, and localization of connexins is hormone receptors in hormone-dependent cancers. It is reasonable to consider that hormones/hormone receptors may modulate connexins expression and play critical roles in the cellular control of connexins during breast cancer progression. In estrogen receptor (ER)-positive breast cancers, tamoxifen and fulvestrant are widely used therapeutic agents and are considered to alter ER signaling. In this present study, we investigated the effects of fulvestrant and tamoxifen in Cx43 expression, and we also explored the role of Cx43 in ER-positive breast cancer migration and the relationship between Cx43 and ER. The involvement of estrogen/ER in Cx43 modulation was further verified by administering tyrosine kinase inhibitors and chemotherapeutic agents. We found that inhibition of ER promoted the binding of E3 ligase Nedd4 to Cx43, leading to Cx43 ubiquitination. Furthermore, inhibition of ER by fulvestrant and tamoxifen phosphorylated p38 MAPK, and inhibition of Rac, MKK3/6, and p38 reversed fulvestrant-reduced Cx43 expression. These findings suggest that Cx43 expression which may positively regulate cell migration is ER-dependent in ER-positive breast cancer cells.

Triterpenic Acids from Potentilla parvifolia and Their Protective Effects against Okadaic Acid Induced Neurotoxicity in Differentiated SH-SY5Y Cells.

Six triterpenic acids were separated and purified from the ethyl acetate extractive fraction of ethanol extracts of Potentilla parvifolia FISCH. using a variety of chromatographic methods. The neuroprotective effects of these triterpenoids were investigated in the present study, in which the okadaic acid induced neurotoxicity in human neuroblastoma SH-SY5Y cells were used as an Alzheimer's disease cell model in vitro. The cell model was established with all trans-retinoic acid (5 µmol/L, 4 d) and okadaic acid (40 nmol/L, 6 h) treatments to induce tau phosphorylation and synaptic atrophy. Subsequently, the neuroprotective effects of these triterpenic acids were evaluated in vitro by this cell model. Results from the Western blot and morphology analysis suggested that compounds 3-6 had the better neuroprotective effects. Furthermore, we tested the level of mitochondrial reactive oxygen species and mitochondrial membrane potential of these compounds in SH-SY5Y cells by flow cytometry technology to investigate the potential neuroprotective mechanism of these compounds. All of the results indicated that maybe the mechanism of compounds 5 and 6 is to protect the cell from mitochondrial oxidative stress injuries.

Inhibition and Dispersal of Pseudomonas aeruginosa Biofilms by Combination Treatment with Escapin Intermediate Products and Hydrogen Peroxide.

Escapin is an l-amino acid oxidase that acts on lysine to produce hydrogen peroxide (H2O2), ammonia, and equilibrium mixtures of several organic acids collectively called escapin intermediate products (EIP). Previous work showed that the combination of synthetic EIP and H2O2 functions synergistically as an antimicrobial toward diverse planktonic bacteria. We initiated the present study to investigate how the combination of EIP and H2O2 affected bacterial biofilms, using Pseudomonas aeruginosa as a model. Specifically, we examined concentrations of EIP and H2O2 that inhibited biofilm formation or fostered disruption of established biofilms. High-throughput assays of biofilm formation using microtiter plates and crystal violet staining showed a significant effect from pairing EIP and H2O2, resulting in inhibition of biofilm formation relative to biofilm formation in untreated controls or with EIP or H2O2 alone. Similarly, flow cell analysis and confocal laser scanning microscopy revealed that the EIP and H2O2 combination reduced the biomass of established biofilms relative to that of the controls. Area layer analysis of biofilms posttreatment indicated that disruption of biomass occurs down to the substratum. Only nanomolar to micromolar concentrations of EIP and H2O2 were required to impact biofilm formation or disruption, and these concentrations are significantly lower than those causing bactericidal effects on planktonic bacteria. Micromolar concentrations of EIP and H2O2 combined enhanced P. aeruginosa swimming motility compared to the effect of either EIP or H2O2 alone. Collectively, our results suggest that the combination of EIP and H2O2 may affect biofilms by interfering with bacterial attachment and destabilizing the biofilm matrix.

Sulforaphane prevents rat cardiomyocytes from hypoxia/reoxygenation injury in vitro via activating SIRT1 and subsequently inhibiting ER stress.

AIM: Sulforaphane (SFN), a natural dietary isothiocyanate, is found to exert beneficial effects for cardiovascular diseases. This study aimed to investigate the mechanisms underlying the protective effects of SFN in a model of myocardial hypoxia/reoxygenation (H/R) injury in vitro. METHODS: Cultured neonatal rat cardiomyocytes pretreated with SFN were subjected to 3-h hypoxia followed by 3-h reoxygenation. Cell viability and apoptosis were detected. Caspase-3 activity and mitochondrial membrane potential (ΔΨm) was measured. The expression of ER stress-related apoptotic proteins were analyzed with Western blot analyses. Silent information regulator 1 (SIRT1) activity was determined with SIRT1 deacetylase fluorometric assay kit. RESULTS: SFN (0.1-5 µmol/L) dose-dependently improved the viability of cardiomyocytes, diminished apoptotic cells and suppressed caspase-3 activity. Meanwhile, SFN significantly alleviated the damage of ΔΨm and decreased the expression of ER stress-related apoptosis proteins (GRP78, CHOP and caspase-12), elevating the expression of SIRT1 and Bcl-2/Bax ratio in the cardiomyocytes. Co-treatment of the cardiomyocytes with the SIRT1-specific inhibitor Ex-527 (1 µmol/L) blocked the SFN-induced cardioprotective effects. CONCLUSION: SFN prevents cardiomyocytes from H/R injury in vitro most likely via activating SIRT1 pathway and subsequently inhibiting the ER stress-dependent apoptosis.

Molecular cancer classification using a meta-sample-based regularized robust coding method.

MOTIVATION: Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data. RESULTS: In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual. CONCLUSIONS: Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.

Diagnostic prediction of complex diseases using phase-only correlation based on virtual sample template.

MOTIVATION: Complex diseases induce perturbations to interaction and regulation networks in living systems, resulting in dynamic equilibrium states that differ for different diseases and also normal states. Thus identifying gene expression patterns corresponding to different equilibrium states is of great benefit to the diagnosis and treatment of complex diseases. However, it remains a major challenge to deal with the high dimensionality and small size of available complex disease gene expression datasets currently used for discovering gene expression patterns. RESULTS: Here we present a phase-only correlation (POC) based classification method for recognizing the type of complex diseases. First, a virtual sample template is constructed for each subclass by averaging all samples of each subclass in a training dataset. Then the label of a test sample is determined by measuring the similarity between the test sample and each template. This novel method can detect the similarity of overall patterns emerged from the differentially expressed genes or proteins while ignoring small mismatches. CONCLUSIONS: The experimental results obtained on seven publicly available complex disease datasets including microarray and protein array data demonstrate that the proposed POC-based disease classification method is effective and robust for diagnosing complex diseases with regard to the number of initially selected features, and its recognition accuracy is better than or comparable to other state-of-the-art machine learning methods. In addition, the proposed method does not require parameter tuning and data scaling, which can effectively reduce the occurrence of over-fitting and bias.

Soil and vegetation characteristics during the formation of typical Ligularia virgaurea degraded grassland.

To understand the formation process of typical poisonous plant degraded grassland, we studied the cha-racteristics of vegetation and soil during the gradual expansion of Ligularia virgaurea into the native grassland of Qinghai-Tibet Plateau. The results showed that population density, plant height, coverage, and biomass of L. virgaurea increased during the formation of L. virgaurea degraded grassland. In comparison with native grassland, the degraded grassland had higher total aboveground biomass (113.9%), soil total nitrogen concentration (61.0%), NH4+-N (77.9%), organic carbon concentration (45.3%), available phosphorus concentration (78.8%) as well as soil microbial biomass carbon (42.1%) and nitrogen (47.4%), but lower NO3--N (40.1%) and species richness (28.5%) and aboveground biomass (45.7%) of other species beyond L. virgaurea. The extremely strong abilities of interspecific inhibition and morphological plasticity of L. virgaurea, as well as efficient nutrient accumulation and utilization were the keys to its successful expansion, which facilitated the formation of typical L. virgaurea degraded grassland.

Maternal di-(2-ethylhexyl) phthalate exposure elicits offspring IFN-λ upregulation: Insights from birth cohort, murine model, and in vitro mechanistic analysis.

Maternal exposure to di-(2-ethylhexyl)-phthalate (DEHP), an environmental endocrine disruptor, may lead to developmental immunotoxicity in offspring. The causal relationship and underlying mechanism require further study. A subset of Taiwan Maternal and Infant Cohort Study data (n = 283) was analyzed and found a significant association between urinary DEHP metabolite levels from the third trimester of pregnancy and plasma levels of IL-28A and IL-29, named IFNλs, in cord blood. A trans-maternal murine model mimicking human DEHP exposure way showed that bone marrow-derived dendritic cells from maternal DEHP-exposed F1 offspring secreted higher IL-28A levels than control cells, indicating a potential causal relationship. Human bronchial epithelial cell lines treated with DEHP or its primary metabolite, mono-(2-ethyl-5-hexyl) phthalate (MEHP), expressed significantly higher levels of IFNλs mRNA or protein than controls. MEHP's effect on IFNλs expression was blocked by peroxisome proliferator-activated receptor α (PPARα) and PPARγ antagonists, and inhibited by a histone acetyltransferase inhibitor or a histone methyltransferase inhibitor. Chromatin immunoprecipitation assay showed that MEHP treatment promoted histone modifications at H3 and H4 proteins at the promoter regions of Il28a and Il29 genes. These results suggest maternal DEHP exposure could result in high IFNλ expression in offspring, and the health risk of early-life exposure requires further investigation.

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification.

BACKGROUND: Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. RESULTS: This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. CONCLUSIONS: It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network.

Burrow characteristics and ecological significance of Marmota himalayana in the northeastern Qinghai-Tibetan Plateau.

Burrows provide burrowing animals with a place to hibernate, reproduce, and avoid predators and harsh weather conditions and thus have a vital impact on their survival. However, the general physical characteristics and ecological functions of Marmota himalayana burrows as well as whether there are differences in burrow traits under different terrains (e.g., sunny slopes, shady slopes, and flat areas) are not well understood. From July to August 2019 (warm season), we used unmanned aerial vehicles to fly at low altitudes and slow speeds to locate 131 M. himalayana burrows (45 on shaded slopes, 51 on sunny slopes, and 35 on flat areas) in the northeastern Qinghai-Tibetan Plateau region. We then measured the physical characteristics (burrow density, entrance size, first tunnel length, volume, orientation, and plant characteristics near the burrow entrance) of these burrows on site. We found that terrain had a substantial influence on burrow density, orientation, and entrance size and on the angle of the burrow entrance; species richness had a substantial impact on path density and tunnel volume. The physical parameters of the M. himalayana burrows showed that they function to protect the marmots from natural enemies and bad weather, provide good drainage, and maintain a stable microclimate around the entrance. We discuss the ability of burrowing animals (e.g., M. himalayana) to adapt to the external environment based on their burrow characteristics.

Effect of sheep grazing on seed circulation on the Loess Plateau.

In grazing ecosystems, mature seeds fall directly to the soil to form the soil seed bank (SSB), or are ingested by grazing livestock to become part of the dung seed bank (DSB; i.e., seed circulation). Both the SSB and DSB form the basis for the natural regeneration of vegetation. However, little is known about the relationships between the SSB, DSB, and aboveground vegetation (AGV) community under different stocking rates (SRs). This study investigated the relationships between the SSB, seeds in Tan sheep (Ovis aries) dung, and AGV at different SRs (0, 2.7, 5.3, and 8.7 sheep ha-1) in a semiarid region of the Loess Plateau in China. We found that Tan sheep grazing increased the species richness heterogeneity of grassland vegetation, and negatively influenced the density of AGV. Under natural conditions, 17 species from soil-borne seeds and 10 species from Tan sheep dung germinated. There was low species similarity between the soil and DSBs and AGV. Sheep SR and the seed banks (soil and dung) were negatively correlated with AGV. Seeds are cycled from herbage to livestock to soil during cold season grazing; the seasonal nature of this seed dispersal is an adaptation to harsh, semiarid environments. Increased seed bank diversity under sheep grazing facilitates grassland regeneration on the Loess Plateau, similarly to other semiarid regions globally.

Identification of Cancer Driver Modules Based on Graph Clustering from Multiomics Data.

A major challenge in cancer genomics is to identify cancer driver genes and modules. Most existing methods to identify cancer driver modules (iCDM) identify groups of genes whose somatic mutational patterns exhibit either mutual exclusivity or high coverage of patient samples, without considering other biological information from multiomics data sets. Here we integrate mutual exclusivity, coverage, and protein-protein interaction information to construct an edge-weighted network, and present a graph clustering approach based on symmetric non-negative matrix factorization to iCDM. iCDM was tested on pan-cancer data and the results were compared with those from several advanced computational methods. Our approach outperformed other methods in recovering known cancer driver modules, and the identified driver modules showed high accuracy in classifying normal and tumor samples.

DMFMDA: Prediction of Microbe-Disease Associations Based on Deep Matrix Factorization Using Bayesian Personalized Ranking.

Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. First, the disease one-hot encoding is fed into neural network, which is transformed into a low-dimensional dense vector in implicit semantic space via embedding layer, and so is microbe. Then, matrix factorization is realized by neural network with embedding layer. Furthermore, our model synthesizes the non-linear modeling advantages of multi-layer perceptron based on the linear modeling advantages of matrix factorization. Finally, different from other methods using square error loss function, Bayesian Personalized Ranking optimizes the model from a ranking perspective to obtain the optimal model parameters, which makes full use of the unobserved data. Experiments show that DMFMDA reaches average AUCs of 0.9091 and 0.9103 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-state-of-art methods. In case studies, 10, 9 and 9 out of top-10 candidate microbes are verified by recently published literature for asthma, inflammatory bowel disease and colon cancer, respectively. In conclusion, DMFMDA is successful application of deep learning in the prediction of microbe-disease association.

Neighborhood rough set reduction-based gene selection and prioritization for gene expression profile analysis and molecular cancer classification.

Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers.

Exploring the potential microRNA sponge interactions of breast cancer based on some known interactions.

MicroRNA (miRNA) sponges' regulatory mechanisms play an important role in developing human cancer. Herein, we develop a new method to explore potential miRNA sponge interactions (EPMSIs) for breast cancer. Based on some known interactions, and a matching gene expression profile, EPMSIs explored other potential miRNA sponge interactions for breast cancer. Every interaction is inferred with a value representing interaction intensity. Then, we apply a clustering algorithm called BCPlaid to potential interactions. Ten modules are identified; nine of them are closely associated with biological enrichments. When we employ a classification algorithm to separate normal and tumor samples in each module, each module demonstrates powerful classification performance. Furthermore, EPMSI illustrates a new method to explore the miRNA sponge regulatory network for breast cancer by applying its superior performance.

Spherical aberration after implantation of an aspheric versus a spherical intraocular lens in high myopia.

PURPOSE: To compare visual performance and wavefront aberration in high myopia implanted with an aspheric intraocular lens (IOL) and a spherical IOL. METHODS: In this prospective investigation, 31 highly myopic patients were randomized to receive two IOL types: aspheric IOL (Acri.Smart 36A, 22 eyes) and spherical IOL (Rayner Superflex 620H, 23 eyes). Complete ophthalmological examination including best-corrected visual acuity (BCVA) and corneal aberration (Humphrey corneal topography) were performed preoperatively, 1 and 3 months postoperatively. Ocular aberration (WASCA wavefront analyser) was performed 1 and 3 months postoperatively. Contrast sensitivity under different lighting condition (CSV-1000) was performed 3 months postoperatively. RESULTS: The aspheric IOL group and the spherical IOL group did not differ in baseline characteristics, including corneal spherical aberration Z(4) (0) (for 5-mm pupil diameter 0.13 +/- 0.06 vs. 0.15 +/- 0.08 microm, P = 0.317; for 6-mm pupil diameter 0.30 +/- 0.11 vs. 0.29 +/- 0.13 microm, P = 0.764). Compared with the spherical IOL group, the aspheric IOL group showed statistically significant less induction of Z(4) (0) of total ocular aberration at a pupil size of 5 and 6 mm 3 months postoperatively (0.07 +/- 0.06 vs. 0.12 +/- 0.06 microm, P = 0.017; 0.17 +/- 0.11 vs. 0.27 +/- 0.12 microm, P = 0.010), but not for 4-mm pupil (0.03 +/- 0.04 vs. 0.02 +/- 0.04 microm, P = 0.54). The BCVA and contrast sensitivity were not statistically different between the two groups postoperatively. CONCLUSIONS: The aspheric IOL induces significantly less spherical aberration than the spherical IOL after implantation in high myopia. Implantation of an aspheric IOL may reduce spherical aberration in high myopia, but clinically superior vision is not achieved.

An Integrated Framework for Identifying Mutated Driver Pathway and Cancer Progression.

Next-generation sequencing (NGS) technologies provide amount of somatic mutation data in a large number of patients. The identification of mutated driver pathway and cancer progression from these data is a challenging task because of the heterogeneity of interpatient. In addition, cancer progression at the pathway level has been proved to be more reasonable than at the gene level. In this paper, we introduce an integrated framework to identify mutated driver pathways and cancer progression (iMDPCP) at the pathway level from somatic mutation data. First, we use uncertainty coefficient to quantify mutual exclusivity on gene driver pathways and develop a computational framework to identify mutated driver pathways based on the adaptive discrete differential evolution algorithm. Then, we construct cancer progression model for driver pathways based on the Bayesian Network. Finally, we evaluate the performance of iMDPCP on real cancer somatic mutation datasets. The experimental results indicate that iMDPCP is more accurate than state-of-the-art methods according to the enrichment of KEGG pathways, and it also provides new insights on identifying cancer progression at the pathway level.

Prediction of Potential Associations Between MicroRNA and Disease Based on Bayesian Probabilistic Matrix Factorization Model.

The association between microRNAs (miRNAs) and diseases is significant to understand the development and progression of many human diseases. Given the cost and complexity of biological experiments, the computational method for predicting the potential association between miRNAs and disease will be an effective complement. In this article, we have developed a model (microRNA and disease based on Bayesian probabilistic matrix factorization, MDBPMF) based on a fully Bayesian treatment of the probabilistic matrix factorization to find potential associations between miRNAs and diseases by using the HMDDv2.0 database, which contains proven miRNA-disease associations. We show that Bayesian probabilistic matrix factorization models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the HMDDv2.0 database. MDBPMF achieves reliable prediction with an average area under receiver operating characteristic curve of 0.8755 for eight complex diseases based on fivefold cross-validation, which indeed outperforms the state-of-the-art method. In addition, a case study of lung cancer further verifies the utility of our method.