An OSA score may help identify those patients who should have a full sleep evaluation.

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Acute myeloid leukemia (AML) the most common acute leukemia in adults is characterized by various cytogenetic and molecular abnormalities. However, the genetic etiology of the disease is not yet fully understood. MicroRNAs (miRNA) are small noncoding RNAs which regulate the expression of target mRNAs both at transcriptional and translational level. In recent years, miRNAs have been identified as a novel mechanism in gene regulation, which show variable expression during myeloid differentiation. We studied miRNA expression of leukemic blasts of 29 cases of newly diagnosed and genetically defined AML using quantitative reverse transcription polymerase chain reaction (RT-PCR) for 365 human miRNA. We showed that miRNA expression profiling reveals distinctive miRNA signatures that correlate with cytogenetic and molecular subtypes of AML. Specific miRNAs with consolidated role on cell proliferation and differentiation such as miR-155, miR-221, let-7, miR-126 and miR-196b appear to be associated with particular subtypes. We observed a significant differentially expressed miRNA profile that characterizes two subgroups of AML with different mechanism of leukemogenesis: core binding factor (CBF) and cytogenetically normal AML with mutations in the genes of NPM1 and FLT3-ITD. We demonstrated, for the first time, the inverse correlation of expression levels between miRNA and their targets in specific AML genetic groups. We suggest that miRNA deregulation may act as complementary hit in the multisteps mechanism of leukemogenesis offering new therapeutic strategies. Am. J. Hematol. 85:331-339, 2010. V

Human epidermis is continuously exposed to environmental mutagenic hazard and is the most frequent target of human cancer. How the epidermis coordinates proliferation with differentiation to maintain homeostasis, even in hyperproliferative conditions, is unclear. For instance, overactivation of the proto-oncogene MYC in keratinocytes stimulates differentiation. Here we explore the cell cycle regulation as proliferating human keratinocytes commit to terminal differentiation upon loss of anchorage or overactivation of MYC. The S-phase of the cell cycle is deregulated as mitotic regulators are inhibited in the onset of differentiation. Experimental inhibition of mitotic kinase cdk1 or kinases of the mitosis spindle checkpoint Aurora B or Pololike Kinase, triggered keratinocyte terminal differentiation. Furthermore, hyperactivation of the cell cycle by overexpressing the DNA replication regulator Cyclin E induced mitosis failure and differentiation. Inhibition of Cyclin E by shRNAs attenuated the induction of differentiation by MYC. In addition, we present evidence that Cyclin E induces DNA damage and the p53 pathway. The results provide novel clues for the mechanisms committing proliferative keratinocytes to differentiate, with implications for tissue homeostasis maintenance, HPV amplification and tumorigenesis.

BackgroundAn open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them.ResultsTo test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data.ConclusionsIn this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2182-6) contains supplementary material, which is available to authorized users.

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MotivationNon-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks.ResultsWe tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%.ConclusionThe proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

We have investigated the effects of sex steroids, estradiol (E2), and testosterone (T) on the synthesis of tumor necrosis factor alpha (TNF-alpha) and interleukin-10 (IL-10) in phorbol-myristate-acetate (PMA)-differentiated human monoblastic U937 cells. The ability of both hormones to modulate the viability and programmed cell death of macrophage-like PMA-differentiated U937 cells was also inspected. E2 increased TNF-alpha synthesis, whereas T had no effect on the production of this cytokine. The combination of E2 and its antagonist tamoxifen or ICI-182,789 completely abolished the induction of TNF-alpha, while combination of T and its antagonist Casodex (CSDX) did not significantly affect TNF-alpha production by U937 cells. Exposure of cells to E2 resulted in a dose-dependent decrease of IL-10 synthesis, while again T did not show any detectable effect. In addition, E2 induced a significant increase of apoptosis in macrophage-like U937 cells and this increase was inhibited by the simultaneous addition of either tamoxifen or ICI-182. In contrast, T alone or in combination with CSDX did not modify apoptotic rates of U937 cells. This evidence, taken together, suggests that estrogens, but not androgens, exert a pro-inflammatory action through the modulation of TNF-alpha and IL-10, and regulate the immune effector cells by the induction of programmed cell death.