Melanoma treatment with the BRAF V600E inhibitor vemurafenib provides therapeutic benefits but the common emergence of drug resistance remains a challenge. We generated A375 melanoma cells resistant to vemurafenib with the goal of investigating changes in miRNA expression patterns that might contribute to resistance. Increased expression of miR-204-5p and miR-211-5p occurring in vemurafenib-resistant cells was determined to impact vemurafenib response. Their expression was rapidly affected by vemurafenib treatment through RNA stabilization. Similar effects were elicited by MEK and ERK inhibitors but not AKT or Rac inhibitors. Ectopic expression of both miRNA in drug-naïve human melanoma cells was sufficient to confer vemurafenib resistance and more robust tumor growth Conversely, silencing their expression in resistant cells inhibited cell growth. Joint overexpression of miR-204-5p and miR-211-5p durably stimulated Ras and MAPK upregulation after vemurafenib exposure. Overall, our findings show how upregulation of miR-204-5p and miR-211-5p following vemurafenib treatment enables the emergence of resistance, with potential implications for mechanism-based strategies to improve vemurafenib responses. Identification of miRNAs that enable resistance to BRAF inhibitors in melanoma suggests a mechanism-based strategy to limit resistance and improve clinical outcomes. .

BackgroundGenomic and proteomic analysis are potent tools for metabolic characterization of microorganisms. Although cellulose usually triggers cellulase production in cellulolytic fungi, the secretion of the different enzymes involved in polymer conversion is subjected to different factors, depending on growth conditions. These enzymes are key factors in biomass exploitation for second generation bioethanol production. Although highly effective commercial cocktails are available, they are usually deficient for β-glucosidase activity, and genera like Penicillium and Talaromyces are being explored for its production.ResultsThis article presents the description of Talaromyces amestolkiae as a cellulase-producer fungus that secretes high levels of β-glucosidase. β-1,4-endoglucanase, exoglucanase, and β-glucosidase activities were quantified in the presence of different carbon sources. Although the two first activities were only induced with cellulosic substrates, β-glucosidase levels were similar in all carbon sources tested. Sequencing and analysis of the genome of this fungus revealed multiple genes encoding β-glucosidases. Extracellular proteome analysis showed different induction patterns. In all conditions assayed, glycosyl hydrolases were the most abundant proteins in the supernatants, albeit the ratio of the diverse enzymes from this family depended on the carbon source. At least two different β-glucosidases have been identified in this work: one is induced by cellulose and the other one is carbon source-independent. The crudes induced by Avicel and glucose were independently used as supplements for saccharification of slurry from acid-catalyzed steam-exploded wheat straw, obtaining the highest yields of fermentable glucose using crudes induced by cellulose.ConclusionsThe genome of T. amestolkiae contains several genes encoding β-glucosidases and the fungus secretes high levels of this activity, regardless of the carbon source availability, although its production is repressed by glucose. Two main different β-glucosidases have been identified from proteomic shotgun analysis. One of them is produced under different carbon sources, while the other is induced in cellulosic substrates and is a good supplement to Celluclast in saccharification of pretreated wheat straw.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-017-0844-7) contains supplementary material, which is available to authorized users.

The small noncoding RNAs (sncRNAs) are considered as post-transcriptional key regulators of male germ cell development. In addition to microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs), other sncRNAs generated from small nucleolar RNAs (snoRNAs), tRNAs, or rRNAs processing may also play important regulatory roles in spermatogenesis. By next-generation sequencing (NGS), we characterized the sncRNA populations detected at three milestone stages in male germ differentiation: primordial germ cells (PGCs), pubertal spermatogonia cells, and mature spermatozoa. To assess their potential transmission through the spermatozoa during fertilization, the sncRNAs of mouse oocytes and zygotes were also analyzed. Both, microRNAs and snoRNA-derived small RNAs are abundantly expressed in PGCs but transiently replaced by piRNAs in spermatozoa and endo-siRNAs in oocytes and zygotes. Exhaustive analysis of miRNA sequence variants also shows an increment of noncanonical microRNA forms along male germ cell differentiation. RNAs-derived from tRNAs and rRNAs interacting with PIWI proteins are not generated by the ping-pong pathway and could be a source of primary piRNAs. Moreover, our results strongly suggest that the small RNAs-derived from tRNAs and rRNAs are interacting with PIWI proteins, and specifically with MILI. Finally, computational analysis revealed their potential involvement in post-transcriptional regulation of mRNA transcripts suggesting functional convergence among different small RNA classes in germ cells and zygotes.

Omics data integration is already a reality. However, few omics-based algorithms show enough predictive ability to be implemented into clinics or public health domains. Clinical/epidemiological data tend to explain most of the variation of health-related traits, and its joint modeling with omics data is crucial to increase the algorithm’s predictive ability. Only a small number of published studies performed a “real” integration of omics and non-omics (OnO) data, mainly to predict cancer outcomes. Challenges in OnO data integration regard the nature and heterogeneity of non-omics data, the possibility of integrating large-scale non-omics data with high-throughput omics data, the relationship between OnO data (i.e., ascertainment bias), the presence of interactions, the fairness of the models, and the presence of subphenotypes. These challenges demand the development and application of new analysis strategies to integrate OnO data. In this contribution we discuss different attempts of OnO data integration in clinical and epidemiological studies. Most of the reviewed papers considered only one type of omics data set, mainly RNA expression data. All selected papers incorporated non-omics data in a low-dimensionality fashion. The integrative strategies used in the identified papers adopted three modeling methods: Independent, conditional, and joint modeling. This review presents, discusses, and proposes integrative analytical strategies towards OnO data integration.