A drug repurposing method based on inhibition effect on gene regulatory network.

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

Recent advances of bioactive proteins/polypeptides in the treatment of breast cancer.

Proteins do not only serve as nutrients to fulfill the demand for food, but also are used as a source of bioactive proteins/polypeptides for regulating physical functions and promoting physical health. Female breast cancer has the highest incidence in the world and is a serious threat to women's health. Bioactive proteins/polypeptides exert strong anti-tumor effects and exhibit inhibition of multiple breast cancer cells. This review discussed the suppressing effects of bioactive proteins/polypeptides on breast cancer in vitro and in vivo, and their mechanisms of migration and invasion inhibition, apoptosis induction, and cell cycle arrest. This may contribute to providing a basis for the development of bioactive proteins/polypeptides for the treatment of breast cancer.

Level and risk assessment of selected polychlorinated biphenyls, polycyclic aromatic hydrocarbons, and organochlorine pesticides in walnut and soil.

It is unknown how hydrophobic organic contaminants (HOCs) are distributed and how they affect the environment in high-fat nuts and their planted soil. The profile of HOCs in walnut/soil system was investigated in this study. Polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and organochlorine pesticides (OCPs) were found in walnuts at concentrations of 0.67, 127, and 116 µg/kg, respectively. The target hazard quotients (THQ) of 17 PCBs, 16 PAHs, and 21 OCPs from walnut consumption by human were 0.06, 0.01, and 0.11, respectively. The highest concentrations of HOC in the soil were found in Nap and toxaphene, with concentrations of 2580 and 902 µg/kg, respectively. Bioaccumulation factors (BAF) and biota-sediment accumulation factors (BSAF) in walnuts were ranged from <0.01 to 7.04 and <0.01 to 3.83, respectively. Concentrations of most individual HOCs in soil samples were significantly correlated with soil organic matter (SOM) (p < 0.01) and minerals (p < 0.01), with maximum correlation coefficients of 0.70 (OM-PCB81) and -0.84 (P-BaP). According to this study, high-fat walnuts do not have a high bioaccumulation of HOCs from soil, and the risk of consumption is within the safe range.

Gas-particle partitioning of polycyclic aromatic hydrocarbons from oil combustion involving condensate, diesel and heavy oil.

This study focuses on the gas-particle (G-P) partitioning of 16 polycyclic aromatic hydrocarbons (PAHs) from oil combustion, which is one of the important contributors of anthropogenic PAHs but has been rarely studied. The combustions of different types of oils involving ultra-light to heavy oils were investigated, and the PAH partitioning mechanism was determined by the widely used Junge-Pankow adsorption model, Koa absorption model, and dual sorption model, respectively. The results show that the source-specific diagnostic ratios of Ant/(Ant+Phe) are between 0.09 and 0.24, the estimated regression slopes of G-P partition coefficients (KP) of the total PAHs on their sub-cooled liquid vapor pressures (PLO) are in the range of - 0.34 to - 0.25, and the predicted fractions of PAHs in the particle phase (φ) by Koa absorption model are close to the measured values, while the log KPvalues of the LMW PAHs from the combustions of diesel and heavy oil are better represented by the dual sorption model. Our findings indicate that PAHs are derived from mixed sources that include the unburned original oil and combustion products, and the PAH partitioning mechanism is governed by the process of absorption into organic matter because of the unburned oil, but both adsorption and absorption exist simultaneously in the lighter PAHs from the combustions of heavier oils (i.e., diesel and heavy oil). Based on these findings, the understanding of the fate and transport of PAH emissions and the optimization of the emergency responses to accidents such as marine oil spills would be potentially improved.

Analysis of Breast Cancer Based on the Dysregulated Network.

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.

MIMRDA: A Method Incorporating the miRNA and mRNA Expression Profiles for Predicting miRNA-Disease Associations to Identify Key miRNAs (microRNAs).

Identifying cancer-related miRNAs (or microRNAs) that precisely target mRNAs is important for diagnosis and treatment of cancer. Creating novel methods to identify candidate miRNAs becomes an imminent Frontier of researches in the field. One major obstacle lies in the integration of the state-of-the-art databases. Here, we introduce a novel method, MIMRDA, which incorporates the miRNA and mRNA expression profiles for predicting miRNA-disease associations to identify key miRNAs. As a proof-of-principle study, we use the MIMRDA method to analyze TCGA datasets of 20 types (BLCA, BRCA, CESE, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, SKCM, STAD, THCA and UCEC) of cancer, which identified hundreds of top-ranked miRNAs. Some (as Category 1) of them are endorsed by public databases including TCGA, miRTarBase, miR2Disease, HMDD, MISIM, ncDR and mTD; others (as Category 2) are supported by literature evidences. miR-21 (representing Category 1) and miR-1258 (representing Category 2) display the excellent characteristics of biomarkers in multi-dimensional assessments focusing on the function similarity analysis, overall survival analysis, and anti-cancer drugs' sensitivity or resistance analysis. We compare the performance of the MIMRDA method over the Limma and SPIA packages, and estimate the accuracy of the MIMRDA method in classifying top-ranked miRNAs via the Random Forest simulation test. Our results indicate the superiority and effectiveness of the MIMRDA method, and recommend some top-ranked key miRNAs be potential biomarkers that warrant experimental validations.

Identification of a novel immune-related microRNA prognostic model in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer, and one of the most common malignant tumors. Many studies have shown that certain microRNAs (miRNAs) play an important role in the occurrence and development of ccRCC. Nevertheless, the prognosis of ccRCC patients is very rarely based on these "immuno-miRs". Our aim was thus to determine the relationship between immune-related miRNA signatures and ccRCC. METHODS: We downloaded the miRNA expression data from 521 KIRC and 71 normal tissues in The Cancer Genome Atlas (TCGA). We used "limma" package and univariate Cox regression analysis to identify differentially expressed miRNAs (DEMs) that related to overall survival (OS). We applied lasso and multivariate Cox regression analyses to construct a prognostic model based on immuno-miRs. We evaluated the performance of model by using the Kaplan-Meier method. Furthermore, Cox regression analysis was used to determine independent prognostic signatures in ccRCC. RESULTS: A total of 59 significant immuno-miRs were identified. We use univariate Cox regression analysis to acquire 18 immune-related miRNAs which were markedly related to OS of ccRCC patients in the training set. We then constructed the 9-immune-related-miRNA prognostic model (miR-21, miR-342, miR-149, miR-130b, miR-223, miR-365a, miR-9-1, and miR-146b) by using lasso and multivariate Cox regression. Further analysis suggested that the immune-related prognostic model could be an independent prognostic indicator for patients with ccRCC. The prognostic performance of the 9-immune-related-miRNA prognostic model was further validated successfully in the testing set. CONCLUSIONS: We established a novel immune-based prognostic model of ccRCC based on potential prognostic immune-related miRNAs. Our results indicated that the 9-miRNA signature could be a practical and reliable prognostic tool for ccRCC.

Risk assessment and prediction for toxic heavy metals in chestnut and growth soil from China.

BACKGROUND: Toxic heavy metals (THMs) cause severe environmental hazards and threaten human health through various consumption of food stuff. However, little is known of THMs in chestnuts. In this study, the risk assessment and prediction of THMs [lead (Pb), arsenic (As), chromium (Cr), cadmium (Cd) and mercury (Hg)] in chestnuts and growth soils from China were investigated. RESULTS: The main detected THMs in chestnuts and growth soils were As and Cd. The total pollution levels of the five THMs (Nemerow pollution indexes, NPIs) were 0.062 and 1.06, respectively. The dietary risks for children were higher than those of adults, especially short-term non-carcinogenic risk. The main combined risks from the relationships between THMs were Pb-Cr (r = 0.85, P < 0.01) in chestnuts and Pb-As (r = 0.59, P < 0.01) in growth soils. The risk source was found to be the uptake effect of THMs from soil to chestnut, with the highest bioaccumulation factors (BCFs) of Cd (0.254). Several comprehensive risk models were established with the highest coefficient of determination (R2 ) of 0.79. In addition, the main contribution rates of different soil parameters to comprehensive risk of THMs uptake were 49.8% (Cd), 23.4% (pH), 13.8% (Cr) and 13.0% (organic carbon). CONCLUSION: The total pollution levels of THMs fell outside of the safety domain in growth soils. Furthermore, more attention needs to be paid to Cd pollution owing to its low environment background value and high accumulation ability. Three main soil parameters (Cr content, pH, organic carbon) played important roles in the formations and accumulations of THMs in chestnuts. © 2019 Society of Chemical Industry.

Impact of childhood trauma on sensorimotor gating in Chinese patients with chronic schizophrenia.

We investigated the relationship between childhood trauma (CT) and sensorimotor gating in Chinese patients diagnosed with chronic schizophrenia. Seventy-five patients were assessed with the Childhood Trauma Questionnaire-Short Form (CTQ-SF), and then the modified paradigm, perceived spatial separation-induced prepulse inhibition (PSS PPI) and the perceived spatial co-location PPI (PSC PPI or classical PPI) were applied to test sensorimotor gating. Startling stimuli (90 dB) were presented either alone or preceded by discrete prepulse stimuli of 4 dB in a background 60-dB noise level. Associations between CT and various PPI paradigms were statistically analyzed. Univariate analysis revealed the absence of a significant correlation between CT and PPI paradigms (p > 0.05). However, multiple linear regression analyses revealed that sexual abuse and the positive and negative syndrome scale (PANSS) score were negatively correlated with PSS PPI (p = 0.029 and 0.008, respectively). On the other hand, female sex and history of smoking were positively correlated with PSS PPI (p = 0.044 and 0.043, respectively). In conclusion, the results of this study suggest that CT can be a predisposing factor that affects sensorimotor gating in schizophrenia patients.

Signaling pathway impact analysis by incorporating the importance and specificity of genes (SPIA-IS).

rlying biology of differentially expressed genes and proteins. Although various approaches have been proposed to identify cancer-related pathways, most of them only partially consider the influence of those differentially expressed genes, such as the gene numbers, their perturbation in the signaling transduction, and the interaction between genes. Signaling-pathway impact analysis (SPIA) provides a convenient framework which considers both the classical enrichment analysis and the actual perturbation on a given pathway. In this study, we extended previous proposed SPIA by incorporating the importance and specificity of genes (SPIA-IS). We applied this approach to six datasets for colorectal cancer, lung cancer, and pancreatic cancer. Results from these datasets showed that the proposed SPIA-IS could effectively improve the performance of the original SPIA in identifying cancer-related pathways.

Prolactin related symptoms during risperidone maintenance treatment: results from a prospective, multicenter study of schizophrenia.

BACKGROUND: This study aimed to investigate prolactin related symptoms (PRS) in individuals with schizophrenia during risperidone maintenance treatment for one year, as well as to identify the risk factors for PRS. METHODS: In a multicenter, randomized, controlled, longitudinal study, clinically stabilized schizophrenia patients (N = 374) were randomized to a no-dose-reduction group (N = 129) and 4-week (N = 125) and 26-week (N = 120) reduction groups, in which the original dose was followed by a 50 % reduction over 8 weeks and subsequently maintained. PRS were assessed via a scale of prolactin related adverse events, which included 16 items: menstrual cycle, menstrual period, menstrual volume, menstrual irregularities, amenorrhea, dysmenorrhea, postpartum lactation, gynecomastia, breast tenderness, sexual dysfunction, decreased sexual desire, erectile dysfunction, ejaculatory dysfunction, impotence, increased body hair, and acne. The occurrence of PRS was assessed at baseline and monthly for six months, followed by every two months. A mixed model was used. RESULTS: PRS at baseline were reported in 18.4, 15.0, and 14.0 % of the 4-week, 26-week, and no-dose-reduction groups, respectively. Female gender, younger age at onset, and the Positive and Negative Syndrome Scale (PANSS) total scores at entry predicted the development of PRS. The mixed model indicated that PRS were more severe in females and at a high dose. In the 237 patients who remained in the study after one year, the incidence of PRS decreased to 9.6, 11.1, and 7.6 % in the 4-week, 26-week, and no-dose-reduction groups, respectively. CONCLUSION: These findings indicate that the PRS severity was alleviated during the one year treatment period because of the dose reduction. Attention should focus on the side effects of hyperprolactinemia during long-term treatment, especially with a high dose, females, younger age at onset, and more severe patients. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00848432 . Registered February 19, 2009.

Signalling pathway impact analysis based on the strength of interaction between genes.

Signalling pathway analysis is a popular approach that is used to identify significant cancer-related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (-1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer-related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.

Subpathway Analysis based on Signaling-Pathway Impact Analysis of Signaling Pathway.

Pathway analysis is a common approach to gain insight from biological experiments. Signaling-pathway impact analysis (SPIA) is one such method and combines both the classical enrichment analysis and the actual perturbation on a given pathway. Because this method focuses on a single pathway, its resolution generally is not very high because the differentially expressed genes may be enriched in a local region of the pathway. In the present work, to identify cancer-related pathways, we incorporated a recent subpathway analysis method into the SPIA method to form the "sub-SPIA method." The original subpathway analysis uses the k-clique structure to define a subpathway. However, it is not sufficiently flexible to capture subpathways with complex structure and usually results in many overlapping subpathways. We therefore propose using the minimal-spanning-tree structure to find a subpathway. We apply this approach to colorectal cancer and lung cancer datasets, and our results show that sub-SPIA can identify many significant pathways associated with each specific cancer that other methods miss. Based on the entire pathway network in the Kyoto Encyclopedia of Genes and Genomes, we find that the pathways identified by sub-SPIA not only have the largest average degree, but also are more closely connected than those identified by other methods. This result suggests that the abnormality signal propagating through them might be responsible for the specific cancer or disease.

Aluminum-centered tetrahedron-octahedron transition in advancing Al-Sb-Te phase change properties.

Group IIIA elements, Al, Ga, or In, etc., doped Sb-Te materials have proven good phase change properties, especially the superior data retention ability over popular Ge2Sb2Te5, while their phase transition mechanisms are rarely investigated. In this paper, aiming at the phase transition of Al-Sb-Te materials, we reveal a dominant rule of local structure changes around the Al atoms based on ab initio simulations and nuclear magnetic resonance evidences. By comparing the local chemical environments around Al atoms in respective amorphous and crystalline Al-Sb-Te phases, we believe that Al-centered motifs undergo reversible tetrahedron-octahedron reconfigurations in phase transition process. Such Al-centered local structure rearrangements significantly enhance thermal stability of amorphous phase compared to that of undoped Sb-Te materials, and facilitate a low-energy amorphization due to the weak links among Al-centered and Sb-centered octahedrons. Our studies may provide a useful reference to further understand the underlying physics and optimize performances of all IIIA metal doped Sb-Te phase change materials, prompting the development of NOR/NAND Flash-like phase change memory technology.

Methylation of a phosphatase specifies dephosphorylation and degradation of activated brassinosteroid receptors.

Internalization of cell surface receptors, followed by either recycling back to the plasma membrane or degradation, is crucial for receptor homeostasis and signaling. The plant brassinosteroid (BR) receptor, BRASSINOSTEROID INSENSITIVE 1 (BRI1), undergoes constitutive cycling between the plasma membrane and the internal membranes. We show that protein phosphatase 2A (PP2A) dephosphorylated BRI1 and that Arabidopsis thaliana rcn1, a mutant for a PP2A subunit, caused an increase in BRI1 abundance and BR signaling. We report the identification, in A. thaliana, of a suppressor of bri1, sbi1, which caused selective accumulation of BR-activated BRI1, but not the BR co-receptor BAK1 (BRI1-ASSOCIATED KINASE 1), in the membranous compartment. SBI1 mRNA was induced by BRs, and SBI1 encodes a leucine carboxylmethyltransferase (LCMT) that methylated PP2A and controlled its membrane-associated subcellular localization. We propose that BRs increase production of SBI1, which methylates PP2A, thus facilitating its association with activated BRI1. This leads to receptor dephosphorylation and degradation, and thus to the termination of BR signaling.

Development of a Mab-based heterologous immunoassay for the broad-selective determination of organophosphorus pesticides.

A broad-selective monoclonal antibody (Mab) for organophosphorus (OP) pesticides was raised using heterologous indirect enzyme-linked immunosorbent assay (ELISA) to screen hybridomas. On the basis of this Mab, five coating antigens were used to develop homologous and heterologous indirect competitive ELISAs. With the most suitable competitor, a sensitive and broad-selective ELISA was developed. The IC(50) values were estimated to be 20.32 ng/mL for parathion, 21.44 ng/mL for methyl-parathion, 42.15 ng/mL for fenitrothion, and 58.85 ng/mL for isocarbophos. Spike recoveries were between 70.52 and 103.27% for the detection of single pesticide residues of the four OP pesticides in purple-clayed paddy soil. Moreover, the chosen ELISA was then applied to the detection of mixtures of parathion and methyl-parathion in soil samples. The average recovery and coefficient of variation were 80.91 and 4.82%, respectively. Results proved that this broad-selective ELISA would be useful for the multiresidue determination of OP pesticides.

The impact of male circumcision on HIV incidence and cost per infection prevented: a stochastic simulation model from Rakai, Uganda.

OBJECTIVES: To estimate the impact of male circumcision on HIV incidence, the number of procedures per HIV infection averted, and costs per infection averted. METHODS: A stochastic simulation model with empirically derived parameters from a cohort in Rakai, Uganda was used to estimate HIV incidence, assuming that male circumcision reduced the risks of HIV acquisition with rate ratios (RR) ranging from 0.3 to 0.6 in men, their female partners, and in both sexes combined, with circumcision coverage 0-100%. The reproductive number (R0) was also estimated. The number of HIV infections averted per circumcision was estimated from the incident cases in the absence of surgery minus the projected number of incident cases over 10 years following circumcision. The cost per procedure ($69.00) was used to estimate the cost per HIV infection averted. RESULTS: Baseline HIV incidence was 1.2/100 person-years. Male circumcision could markedly reduce HIV incidence in this population, particularly if there was preventative efficacy in both sexes. Under many scenarios, with RR < or = 0.5, circumcision could reduce R0 to < 1.0 and potentially abort the epidemic. The number of surgeries per infection averted over 10 years was 19-58, and the costs per infection averted was $1269-3911, depending on the efficacy of circumcision for either or both sexes, assuming 75% service coverage. However, behavioral disinhibition could offset any benefits of circumcision. CONCLUSION: Male circumcision could have substantial impact on the HIV epidemic and provide a cost-effective prevention strategy if benefits are not countered by behavioral disinhibition.

Serum protein MALDI profiling to distinguish upper aerodigestive tract cancer patients from control subjects.

BACKGROUND: There are no reliable blood markers for the early detection and monitoring of aerodigestive tract tumors. Recent studies have suggested that serum protein patterns may be able to distinguish cancer patients from control subjects. METHODS: We used matrix-assisted laser desorption and ionization (MALDI) mass spectroscopy to obtain serum protein patterns from patients with head and neck cancer (n = 99) or lung cancer (n = 92) and from control subjects (n = 143) at risk for the development of these cancers. From the mass spectra, we predicted the cancer status of patients using a simple classification procedure based on a t test feature selection and linear discriminant analysis (LDA). We cross-validated the data with 200 random data simulations to establish a range of the LDA tuning parameter, which was used to construct receiver operating characteristic (ROC) curves. RESULTS: Average total protein levels were higher in case patients than in control subjects, although the differences were not statistically significant. Ten individual m/z peaks, from 5 to 111 kd, appeared frequently in head and neck cancer patients but not in control subjects. Using the 45 top predictors, selected by spectral mass and LDA, we observed that ROC curves differed from those expected under the null hypothesis, suggesting that spectral profiles from the sera of patients with head and neck cancer statistically significantly differed from the sera of control subjects. The model developed on head and neck cancer patients could also be used to identify patients with lung cancer. CONCLUSIONS: The pattern of protein spectra in total serum reliably distinguished cancer case patients from control subjects. Incorporation of MALDI assays into prospective longitudinal trials to assess the true predictive values of protein spectra in cancer detection is needed.