Topiramate inhibits the proliferation of bladder cancer cells via PI3K/AKTR signaling pathway

Purpose: To explore new treatment options for bladder cancer (BC) based on topiramate (TPM).Methods: The MTT assay and flow cytometry were used to determine the effect of topiramate on partial growth-related malignant phenotype of BC cells. Expression levels of apoptosis-related biomarkers and signaling pathway-related factors were analyzed using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting. In vivo experiments were conducted to investigate the role of TPM on tumor growth in mice with bladder cancer.Results: The MTT results showed that topiramate blocked the growth of BC cells (p < 0.05). Growth inhibition was positively correlated with TPM concentration. Flow cytometry results revealed that bladder cancer cell apoptosis rose with increase in TPM concentration, while the mRNAs of apoptosisassociated factors Bcl-2 and Mcl-1 were down-regulated in a concentration-based manner by TPM (p < 0.05). Western blot assay indicated that Bax and Caspase-3 proteins were up-regulated, and the higher the concentration of TPM, the more significant the protein expression levels (p < 0.05).Conclusion: Topiramate (TPM) slows down the rate of growth of BC cells and accelerates their rate of apoptosis through the regulation of P13K/AKT/mTOR signaling pathway. Thus, the compound has potentials for development as an anti-bladder cancer agent

Bioelectricity generation by wetland plant-sediment microbial fuel cells (P-SMFC) and effects on the transformation and mobility of arsenic and heavy metals in sediment.

Two wetland plant-sediment microbial fuel cell systems (PSM1 and PSM2) and one wetland sediment microbial fuel cell system (SM) were constructed to investigate their electricity production performance and the simultaneous migration and transformation of arsenic and heavy metals in sediment and overlying water, arsenic and heavy metals uptake by plants. The bioelectricity generation was monitored for 175 days, and sediment samples were collected at three time points (64, 125 and 200 days) for the analysis. The results showed that plants improved the efficiency of the electricity production by the fuel cell system. The average output voltage was: PSM1 (0.32 V) > PSM2 (0.28 V) > SM (0.24 V)(P ≤ 0.05).The electricity production of the electrodes and the introduction of plants affected the mobility and transformation of As, Zn and Cd in the sediment, which contributed to their stability in the sediment and reduced the release of these metals into the overlying water column. The bioelectricity production process affected the bioavailability of arsenic and heavy metals in the sediment and attenuated metal uptake by plants, which indicated the potential for remediation of arsenic and heavy metals pollution in sediment

Physicochemical Properties of Various 2-Hydroxyethylammonium Sulfonate -Based Protic Ionic Liquids and Their Potential Application in Hydrodeoxygenation

In order to obtain the regularities of physicochemical properties of hydroxy protic ionic liquids (PILs) and broaden their potential application, a series of 2-hydroxyethylammonium sulfonate-based PILs were synthesized through proton transfer reaction and characterized by NMR and FT-IR and elemental analysis. Their phase transfer behavior (Tm) and initial decomposition point (Td) were characterized by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. Meanwhile, the regularities of density (ρ), viscosity (η) and electrical conductivity (σ) of synthesized PILs at different temperatures were measured. The results indicated that their physicochemical properties were tightly related with their structures and the interactions between cations and anions. In addition, the dissociation constants (pKa) of synthesized PILs were obtained by acid-base titration, which revealed that all synthesized PILs had pKa exceeding 7 and their cations were the crux of determining the pKa value. Moreover, several synthesized PILs with a low melting temperature also showed potential application in the deoxidation reaction of cyclohexanol, as they had conversion rates approximating 100% and the selectivity of cyclohexane or cyclohexene was about 80%

A Novel Evolution-Based Method for Detecting Gene-Gene Interactions

BACKGROUND: The rapid advance in large-scale SNP-chip technologies offers us great opportunities in elucidating the genetic basis of complex diseases. Methods for large-scale interactions analysis have been under development from several sources. Due to several difficult issues (e.g., sparseness of data in high dimensions and low replication or validation rate), development of fast, powerful and robust methods for detecting various forms of gene-gene interactions continues to be a challenging task. METHODOLOGY/PRINCIPAL FINDINGS: In this article, we have developed an evolution-based method to search for genome-wide epistasis in a case-control design. From an evolutionary perspective, we view that human diseases originate from ancient mutations and consider that the underlying genetic variants play a role in differentiating human population into the healthy and the diseased. Based on this concept, traditional evolutionary measure, fixation index (Fst) for two unlinked loci, which measures the genetic distance between populations, should be able to reveal the responsible genetic interplays for disease traits. To validate our proposal, we first investigated the theoretical distribution of Fst by using extensive simulations. Then, we explored its power for detecting gene-gene interactions via SNP markers, and compared it with the conventional Pearson Chi-square test, mutual information based test and linkage disequilibrium based test under several disease models. The proposed evolution-based method outperformed these compared methods in dominant and additive models, no matter what the disease allele frequencies were. However, its performance was relatively poor in a recessive model. Finally, we applied the proposed evolution-based method to analysis of a published dataset. Our results showed that the P value of the Fst -based statistic is smaller than those obtained by the LD-based statistic or Poisson regression models. CONCLUSIONS/SIGNIFICANCE: With rapidly growing large-scale genetic association studies, the proposed evolution-based method can be a promising tool in the identification of epistatic effects

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

BACKGROUND: It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. RESULTS: In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. CONCLUSION: We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs

Towards precise classification of cancers based on robust gene functional expression profiles

BACKGROUND: Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. RESULTS: Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. CONCLUSION: This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches

BACKGROUND: Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression. RESULTS: The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene × gene and gene × environment interactions. There was evidence to suggest the existence of gene × environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene × gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one. CONCLUSION: We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene × gene or gene × environment interactions

Electrochemical Generation of Catalytically Active Edge Sites in C₂N‐Type Carbon Materials for Artificial Nitrogen Fixation

The electrochemical nitrogen reduction reaction (NRR) to ammonia (NH₃) is a potentially carbon‐neutral and decentralized supplement to the established Haber–Bosch process. Catalytic activation of the highly stable dinitrogen molecules remains a great challenge. Especially metal‐free nitrogen‐doped carbon catalysts do not often reach the desired selectivity and ammonia production rates due to their low concentration of NRR active sites and possible instability of heteroatoms under electrochemical potential, which can even contribute to false positive results. In this context, the electrochemical activation of nitrogen‐doped carbon electrocatalysts is an attractive, but not yet established method to create NRR catalytic sites. Herein, a metal‐free C₂N material (HAT‐700) is electrochemically etched prior to application in NRR to form active edge‐sites originating from the removal of terminal nitrile groups. Resulting activated metal‐free HAT‐700‐A shows remarkable catalytic activity in electrochemical nitrogen fixation with a maximum Faradaic efficiency of 11.4% and NH₃ yield of 5.86 µg mg⁻¹cat h⁻¹. Experimental results and theoretical calculations are combined, and it is proposed that carbon radicals formed during activation together with adjacent pyridinic nitrogen atoms play a crucial role in nitrogen adsorption and activation. The results demonstrate the possibility to create catalytically active sites on purpose by etching labile functional groups prior to NRR