Marine microbiomes are prolific sources of bioactive natural products of potential pharmaceutical value. This study inspected two culture collections comprising 919 host-associated marine bacteria belonging to 55 genera and several thus-far unclassified lineages to identify isolates with potentially rich secondary metabolism and antimicrobial activities. Seventy representative isolates had their genomes mined for secondary metabolite biosynthetic gene clusters (SM-BGCs) and were screened for antimicrobial activities against four pathogenic bacteria and five pathogenic Candida strains. In total, 466 SM-BGCs were identified, with antimicrobial peptide- and polyketide synthase-related SM-BGCs being frequently detected. Only 38 SM-BGCs had similarities greater than 70% to SM-BGCs encoding known compounds, highlighting the potential biosynthetic novelty encoded by these genomes. Cross-streak assays showed that 33 of the 70 genome-sequenced isolates were active against at least one Candida species, while 44 isolates showed activity against at least one bacterial pathogen. Taxon-specific differences in antimicrobial activity among isolates suggested distinct molecules involved in antagonism against bacterial versus Candida pathogens. The here reported culture collections and genome-sequenced isolates constitute a valuable resource of understudied marine bacteria displaying antimicrobial activities and potential for the biosynthesis of novel secondary metabolites, holding promise for a future sustainable production of marine drug leads.
a b s t r a c tA novel metal-free electrocatalyst for the oxygen reduction reaction (ORR) is one of the most important issues in fuel cells. Here, we report a facile method to synthesize reduced graphene oxide (rGO) decorated with nitrogen-doped carbon nanowires (rGO-CN) as an electrocatalyst for ORR. After the polymerization of polpyrrole nanowires on the rGO surface (rGO-PPy), the carbonization of rGO-PPy at 800 C affords a unique nanostructured product by the integration of rGO sheets and the N-doped carbon nanowires with high nitrogen content. The morphology of rGO-CN is confirmed by TEM analysis and the chemical composition and interaction of the prepared samples are analyzed by XPS and FT-IR analysis. The electrocatalytic activity of rGO-CN toward ORR is also evaluated by the cyclic voltammetry. It is found that the rGO-CN electrode shows superior electrocatalytic performance toward ORR, compared to rGO and rGO-PPy, which demonstrates the promising potential of rGO-CN as a carbon-based, metal-free electrocatalyst for enhancing the electrocatalytic property towards ORR. ScienceDirect j o urn al h om epa ge: www.elsev ier.com/locate/he i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 6 8 2 7 e6 8 3 4 http://dx.
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5502 Background: A number of clinicopathologic risk factors are used for survival prediction and clinical decision-making in epithelial ovarian cancer (EOC). Information from novel technologies such as gene arrays has not had an impact on patient management. We studied EOC protein signaling profiles to determine if their addition to accepted clinicopathologic factors improves their accuracy in predicting individual patient outcomes. Methods: We applied a novel functional proteomics technology, reverse phase protein array (RPPA), to quantify expression and activation of 42 steroid and kinase signaling pathway proteins in 106 high-grade EOCs from patients with stages 1–4 tumors managed with surgery and platinum-based chemotherapy. Cox regression analysis and a novel committee modeling approach were used to study the impact of functional proteomics on patient outcomes. Results: In a Cox model using only clinical variables, stage and residual disease were significantly related to overall survival. By adding the proteins to the clinical Cox model, two proteins that were significantly associated with overall survival on univariate analysis (phosphorylated-MAPK (p-MAPK; log rank p = 0.0047) and progesterone receptor (PR; log rank p = 0.027)) remained significant at the alpha=0.10 level (z-test p-values 0.074 and 0.034, respectively, when treated as binary variables according to martingale residual plots); as a result, these two proteins added to the predictive accuracy of the clinical survival model. However, using the novel committee modeling approach in test and validation EOC sets, a closest neighbor metric was applied to successfully define distinct proteins groups, each composed of nine proteins, that are predictive of specific survival times in patients with EOC. This granular approach to modeling is particularly suited to defining the molecular heterogeneity of EOC. Conclusions: EOC is a complex process with significant individual variability. Using novel approaches to functional proteomic study and statistical modeling, our striking finding is that distinct combinations of steroid and kinase signaling proteins are predictive markers of specific survival times in EOC. No significant financial relationships to disclose.
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