Type 2 diabetes, obesity, and metabolic syndrome are pathologies where insulin resistance plays a central role, and that affect a large population worldwide. These pathologies are usually associated with a dysregulation of insulin secretion leading to a chronic exposure of the tissues to high insulin levels (i.e. hyperinsulinemia), which diminishes the concentration of key downstream elements, causing insulin resistance. The complexity of the study of insulin resistance arises from the heterogeneity of the metabolic states where it is observed. To contribute to the understanding of the mechanisms triggering insulin resistance, we have developed a zebrafish model to study insulin metabolism and its associated disorders. Zebrafish larvae appeared to be sensitive to human recombinant insulin, becoming insulin-resistant when exposed to a high dose of the hormone. Moreover RNA-seq-based transcriptomic profiling of these larvae revealed a strong downregulation of a number of immune-relevant genes as a consequence of the exposure to hyperinsulinemia. Interestingly, as an exception, the negative immune modulator protein tyrosine phosphatase nonreceptor type 6 (ptpn6) appeared to be upregulated in insulin-resistant larvae. Knockdown of ptpn6 was found to counteract the observed downregulation of the immune system and insulin signaling pathway caused by hyperinsulinemia. These results indicate that ptpn6 is a mediator of the metabolic switch between insulin-sensitive and insulin-resistant states. Our zebrafish model for hyperinsulinemia has therefore demonstrated its suitability for discovery of novel regulators of insulin resistance. In addition, our data will be very useful in further studies of the function of immunological determinants in a non-obese model system.

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BackgroundAlthough the responses to many pathogen associated molecular patterns (PAMPs) in cell cultures and extracted organs are well characterized, there is little known of transcriptome responses to PAMPs in whole organisms. To characterize this in detail, we have performed RNAseq analysis of responses of zebrafish embryos to injection of PAMPs in the caudal vein at one hour after exposure. We have compared two ligands that in mammals have been shown to specifically activate the TLR2 and TLR5 receptors: Pam3CSK4 and flagellin, respectively.ResultsWe identified a group of 80 common genes that respond with high stringency selection to stimulations with both PAMPs, which included several well-known immune marker genes such as il1b and tnfa. Surprisingly, we also identified sets of 48 and 42 genes that specifically respond to either Pam3CSK4 or flagellin, respectively, after a comparative filtering approach. Remarkably, in the Pam3CSK4 specific set, there was a set of transcription factors with more than 2 fold-change, as confirmed by qPCR analyses, including cebpb, fosb, nr4a1 and egr3. We also showed that the regulation of the Pam3CSK4 and flagellin specifically responding sets is inhibited by knockdown of tlr2 or tlr5, respectively.ConclusionsOur studies show that Pam3CSK4 and flagellin can stimulate the Tlr2 and Tlr5 signaling pathways leading to common and specific responses in the zebrafish embryo system.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1740-9) contains supplementary material, which is available to authorized users.

Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.

BackgroundThe function of Toll-like receptor 2 (TLR2) in host defense against pathogens, especially Mycobacterium tuberculosis (Mtb) is poorly understood. To investigate the role of TLR2 during mycobacterial infection, we analyzed the response of tlr2 zebrafish mutant larvae to infection with Mycobacterium marinum (Mm), a close relative to Mtb, as a model for tuberculosis. We measured infection phenotypes and transcriptome responses using RNA deep sequencing in mutant and control larvae.Resultstlr2 mutant embryos at 2 dpf do not show differences in numbers of macrophages and neutrophils compared to control embryos. However, we found substantial changes in gene expression in these mutants, particularly in metabolic pathways, when compared with the heterozygote tlr2+/− control. At 4 days after Mm infection, the total bacterial burden and the presence of extracellular bacteria were higher in tlr2−/− larvae than in tlr2+/−, or tlr2+/+ larvae, whereas granuloma numbers were reduced, showing a function of Tlr2 in zebrafish host defense. RNAseq analysis of infected tlr2−/− versus tlr2+/− shows that the number of up-regulated and down-regulated genes in response to infection was greatly diminished in tlr2 mutants by at least 2 fold and 10 fold, respectively. Analysis of the transcriptome data and qPCR validation shows that Mm infection of tlr2 mutants leads to decreased mRNA levels of genes involved in inflammation and immune responses, including il1b, tnfb, cxcl11aa/ac, fosl1a, and cebpb. Furthermore, RNAseq analyses revealed that the expression of genes for Maf family transcription factors, vitamin D receptors, and Dicps proteins is altered in tlr2 mutants with or without infection. In addition, the data indicate a function of Tlr2 in the control of induction of cytokines and chemokines, such as the CXCR3-CXCL11 signaling axis.ConclusionThe transcriptome and infection burden analyses show a function of Tlr2 as a protective factor against mycobacteria. Transcriptome analysis revealed tlr2-specific pathways involved in Mm infection, which are related to responses to Mtb infection in human macrophages. Considering its dominant function in control of transcriptional processes that govern defense responses and metabolism, the TLR2 protein can be expected to be also of importance for other infectious diseases and interactions with the microbiome.