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The actinobacteria Streptomyces wadayamensis A23, an endophitic strain, was recently sequenced and previous work showed qualitatively that the strain inhibits the growth of some pathogens. Herein we report the genome analysis of S. wadayamensis which reveals several antibiotic biosynthetic pathways. Using mass spectrometry, we were able to identify desferoxamines, several antimycins and candicidin, as predicted. Additionally, it was possible to confirm that the biosynthetic machinery of the strain when compared to identified known metabolites is far underestimated. As suggested by biochemical qualitative tests, genome encoded information reveals that the strain A23 has high capability to produce antibiotics.

Polyketides and non-ribosomal peptides are natural products widely found in bacteria, fungi and plants. The biological activities associated with these metabolites have attracted special attention in biopharmaceutical studies. Polyketide synthases act similarly to fatty acids synthetases and the whole multi-enzymatic set coordinating precursor and extending unit selection and reduction levels during chain growth. Acting in a similarly orchestrated model, non-ribosomal peptide synthetases biosynthesize NRPs. PKSs-I and NRPSs enzymatic modules and domains are collinearly organized with the parent gene sequence. This arrangement allows the use of degenerated PCR primers to amplify targeted regions in the genes corresponding to specific enzymatic domains such as ketosynthases and acyltransferases in PKSs and adenilation domains in NRPSs. Careful analysis of these short regions allows the classifying of a set of organisms according to their potential to biosynthesize PKs and NRPs. In this work, the biosynthetic potential of a set of 13 endophytic actinobacteria from Citrus reticulata for producing PKs and NRP metabolites was evaluated. The biosynthetic profile was compared to antimicrobial activity. Based on the inhibition promoted, 4 strains were considered for cluster analysis. A PKS/NRPS phylogeny was generated in order to classify some of the representative sequences throughout comparison with homologous genes. Using this approach, a molecular fingerprint was generated to help guide future studies on the most promising strains.

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.

Hybrid Bayesian networks efficiently encode a joint probability distribution over a set of continuous and discrete variables. Several approaches have been recently proposed for working with hybrid Bayesian networks, e.g., mixtures of truncated basis functions, mixtures of truncated exponentials or mixtures of polynomials (MoPs). We present a method for learning MoP approximations of probability densities from data using a linear combination of B-splines. Maximum likelihood estimators of the mixing coefficients of the linear combination are computed, and model selection is performed using a penalized likelihood criterion, i.e., the BIC score. Artificial examples are used to analyze the behaviour of the method according to different criteria, like the quality of the approximations and the number of pieces in the MoP. Also, we study the use of the proposed method as anon-parametric density estimation technique in naive Bayes (NB) classifiers. Results on real datasets show that the non-parametric NB classifier using MoPs is comparable to the kernel density-based NB and better than Gaussian or discrete NB classifiers.

Background: Endometrial cancer (EC) is the 8th leading cause of cancer death amongst American women. Most ECs are endometrioid, serous, or clear cell carcinomas, or an admixture of histologies. Serous and clear ECs are clinically aggressive tumors for which alternative therapeutic approaches are needed. The purpose of this study was to search for somatic mutations in the tyrosine kinome of serous and clear cell ECs, because mutated kinases can point to potential therapeutic targets. Methods: In a mutation discovery screen, we PCR amplified and Sanger sequenced the exons encoding the catalytic domains of 86 tyrosine kinases from 24 serous, 11 clear cell, and 5 mixed histology ECs. For somatically mutated genes, we next sequenced the remaining coding exons from the 40 discovery screen tumors and sequenced all coding exons from another 72 ECs (10 clear cell, 21 serous, 41 endometrioid). We assessed the copy number of mutated kinases in this cohort of 112 tumors using quantitative real time PCR, and we used immunoblotting to measure expression of these kinases in endometrial cancer cell lines. Results: Overall, we identified somatic mutations in TNK2 (tyrosine kinase non-receptor, 2) and DDR1 (discoidin domain receptor tyrosine kinase 1) in 5.3% (6 of 112) and 2.7% (3 of 112) of ECs. Copy number gains of TNK2 and DDR1 were identified in another 4.5% and 0.9% of 112 cases respectively. Immunoblotting confirmed TNK2 and DDR1 expression in endometrial cancer cell lines. Three of five missense mutations in TNK2 and one of two missense mutations in DDR1 are predicted to impact protein function by two or more in silico algorithms. The TNK2P761Rfs*72 frameshift mutation was recurrent in EC, and the DDR1R570Q missense mutation was recurrent across tumor types. Conclusions: This is the first study to systematically search for mutations in the tyrosine kinome in clear cell endometrial tumors. Our findings indicate that high-frequency somatic mutations in the catalytic domains of the tyrosine kinome are rare in clear cell ECs. We uncovered ten new mutations in TNK2 and DDR1 within serous and endometrioid ECs, thus providing novel insights into the mutation spectrum of each gene in EC.

Mountains play a key role in the provision of nature’s contributions to people (NCP) worldwide that support societies’ quality of life. Simultaneously, mountains are threatened by multiple drivers of change. Due to the complex interlinkages between biodiversity, quality of life and drivers of change, research on NCP in mountains requires interdisciplinary approaches. In this study, we used the conceptual framework of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and the notion of NCP to determine to what extent previous research on ecosystem services in mountains has explored the different components of the IPBES conceptual framework. We conducted a systematic review of articles on ecosystem services in mountains published up to 2016 using the Web of Science and Scopus databases. Descriptive statistical and network analyses were conducted to explore the level of research on the components of the IPBES framework and their interactions. Our results show that research has gradually become more interdisciplinary by studying higher number of NCP, dimensions of quality of life, and indirect drivers of change. Yet, research focusing on biodiversity, regulating NCP and direct drivers has decreased over time. Furthermore, despite the fact that research on NCP in mountains becoming more policy-oriented over time, mainly in relation to payments for ecosystem services, institutional responses remained underexplored in the reviewed studies. Finally, we discuss the relevant knowledge gaps that should be addressed in future research in order to contribute to IPBES., PLoS ONE, 14 (6), ISSN:1932-6203