Thermally Methanol Oxidation via the Mn₁@Co₃O₄(111) Facet: Non-CO Reaction Pathway

Co3O4, as the support of single-atom catalysts, is effective in electron-structure modulation to get distinct methanol adsorption behaviors and adjustable reaction pathways for the methanol oxidation reaction. Herein, we considered the facets that constitute a Co vacancy of the Co3O4(111) facet and a foreign metal atom M (M = Fe, Ni, Cu, Ru, Rh, Pd, Ag, Os, Ir, Pt, Au, Mn) leading to single-atom catalysts. The Mn1@Co3O4(111) facet is the facet considered the most favorable among all of the possible terminations. Oxygen adsorption, decomposition, and its co-adsorption with methanol are the vital steps of methanol oxidation at the exposed Mn1@Co3O4(111) facet, giving rise to the stable configuration: two O* and one CH3OH* adsorbates. Then, the Mn1@Co3O4(111) facet activates the O–H and C–H bonds within CH3OH*, advances CH3O* → H2CO* → HCOO* → COO*, and releases the products H2, H2O, and CO2 consecutively

Pathway crosstalk among NAGenes-enriched pathways.

<p>Nodes represent pathways and edges represent crosstalk between pathways. Node size corresponds to the number of NAGenes found in the corresponding pathway. Node color corresponds to the P_BH-value of the corresponding pathway. Darker color indicates lower P_BH-value. Edge width corresponds to the score of the related pathways. Node shape indicates pathway categories, with ellipse for neurodevelopment, diamond for immune, triangle for metabolism, square for other pathways.</p

Analyzing the pathways enriched in genes associated with nicotine dependence in the context of human protein–protein interaction network

<p>Nicotine dependence is the primary addictive stage of cigarette smoking. Although a lot of studies have been performed to explore the molecular mechanism underlying nicotine dependence, our understanding on this disorder is still far from complete. Over the past decades, an increasing number of candidate genes involved in nicotine dependence have been identified by different technical approaches, including the genetic association analysis. In this study, we performed a comprehensive collection of candidate genes reported to be genetically associated with nicotine dependence. Then, the biochemical pathways enriched in these genes were identified by considering the gene’s propensity to be related to nicotine dependence. One of the most widely used pathway enrichment analysis approach, over-representation analysis, ignores the function non-equivalence of genes in candidate gene set and may have low discriminative power in identifying some dysfunctional pathways. To overcome such drawbacks, we constructed a comprehensive human protein–protein interaction network, and then assigned a function weighting score to each candidate gene based on their network topological features. Evaluation indicated the function weighting score scheme was consistent with available evidence. Finally, the function weighting scores of the candidate genes were incorporated into pathway analysis to identify the dysfunctional pathways involved in nicotine dependence, and the interactions between pathways was detected by pathway crosstalk analysis. Compared to conventional over-representation-based pathway analysis tool, the modified method exhibited improved discriminative power and detected some novel pathways potentially underlying nicotine dependence. In summary, we conducted a comprehensive collection of genes associated with nicotine dependence and then detected the biochemical pathways enriched in these genes using a modified pathway enrichment analysis approach with function weighting score of candidate genes integrated. Our results may provide insight into the molecular mechanism underlying nicotine dependence.</p

Gene Ontology terms enriched in nicotine addiction-related genes (NAGenes).

<p>a. Only GO terms with hierarchical level≥4 and containing 5 or more nicotine addiction-related genes are shown.</p><p>b. Number of genes in the 220 nicotine addiction-related genes and also in the category</p><p>c. P-value were calculated by hypergeometric test</p><p>d. P_BH-value were adjusted by Benjamini & Hochberg (BH) method</p><p>Gene Ontology terms enriched in nicotine addiction-related genes (NAGenes).</p

A Highly Sensitive Method for Quantitative Determination of L-Amino Acid Oxidase Activity Based on the Visualization of Ferric-Xylenol Orange Formation

<div><p>L-amino acid oxidase (LAAO) has important biological roles in many organisms, thus attracting great attention from researchers to establish its detection methods. In this study, a new quantitative in-gel determination of LAAO activity based on ferric-xylenol orange (Fe^IIIXO) formation was established. This method showed that due to the conversion of Fe^II to Fe^III by H₂O₂ and subsequent formation of Fe^IIIXO complex halo in agar medium, the logarithm of H₂O₂ concentration from 5 to 160 µM was linearly correlated to the diameter of purplish red Fe^IIIXO halo. By extracting the LAAO-generated H₂O₂ concentration, the LAAO activity can be quantitatively determined. This Fe^IIIXO agar assay is highly sensitive to detect H₂O₂ down to micromolar range. More importantly, it is easy to handle, cheap, reproducible, convenient and accurate. Coupled with SDS-PAGE, it can directly be used to determine the number and approximate molecular weight of LAAO in one assay. All these features make this in-gel Fe^IIIXO assay useful and convenient as a general procedure for following enzyme purification, assaying fractions from a column, or observing changes in activity resulting from enzyme modifications, hence endowing this method with broad applications.</p></div

Pathways and Networks-Based Analysis of Candidate Genes Associated with Nicotine Addiction

<div><p>Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction.</p></div

<i>lao</i> gene expression in mutants and wild-type strain Rf-1.

<p>Mean difference of relative expression of <i>lao</i> gene between mutant and wild type was statistically analyzed by ANOVA (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122741#pone.0122741.s001" target="_blank">S1 Dataset</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122741#pone.0122741.s002" target="_blank">S2 Dataset</a>). “*” and “***” represent significant (P<0.05) and extremely significant (P<0.001), respectively.</p

Table 1. The diameters of the purplish red halos under different concentrations of H₂O₂.

<p>Table 1. The diameters of the purplish red halos under different concentrations of H₂O₂.</p

Bacterial strains and plasmids used in this study.

<p>Bacterial strains and plasmids used in this study.</p

Pathways enriched in nicotine addiction-related genes (NAGenes) (top 20 pathways).

<p>^a P-value were calculated by Fisher’s exact test</p><p>^b P_BH-value were adjusted by Benjamini & Hochberg (BH) method</p><p>^c 220 nicotine addiction-related genes included in the pathway</p><p>Pathways enriched in nicotine addiction-related genes (NAGenes) (top 20 pathways).</p