The angiopoietin-like 4 (ANGPTL4) protein belongs to a superfamily of secreted proteins structurally related to factors modulating angiogenesis known as angiopoietins. At first, ANGPTL4 has been identified as an adipokine exclusively involved in lipid metabolism, because of its prevalent expression in liver and adipose tissue. This protein regulates lipid metabolism by inhibiting lipoprotein lipase (LPL) activity and stimulating lipolysis of white adipose tissue (WAT), resulting in increased levels of plasma triglycerides (TG) and fatty acids. Subsequently, ANGPTL4 has been shown to be involved in several nonmetabolic and metabolic conditions, both physiological and pathological, including angiogenesis and vascular permeability, cell differentiation, tumorigenesis, glucose homoeostasis, lipid metabolism, energy homeostasis, wound healing, inflammation, and redox regulation. The transcriptional regulation of ANGPTL4 can be modulated by several transcription factors, including PPARα, PPARβ/δ, PPARγ, and HIF-1α, and nutritional and hormonal conditions. Several studies showed that high levels of ANGPTL4 are associated with poor prognosis in patients with various solid tumors, suggesting an important role in cancer onset and progression, metastasis, and anoikis resistance. Here, we have discussed the potential role of ANGPTL4 in mediating the cross talk between metabolic syndromes, such as diabetes and obesity, and cancer through regulation of its expression by PPARs.

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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.

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.

Germline mutations in BRCA1/2 genes are responsible for a large proportion of hereditary breast and/or ovarian cancers. Many highly penetrant predisposition alleles have been identified and include frameshift or nonsense mutations that lead to the translation of a truncated protein. Other alleles contain missense mutations, which result in amino acid substitution and intronic variants with splicing effect. The discovery of variants of uncertain/unclassified significance (VUS) is a result that can complicate rather than improve the risk assessment process. VUSs are mainly missense mutations, but also include a number of intronic variants and in-frame deletions and insertions. Over 2,000 unique BRCA1 and BRCA2 missense variants have been identified, located throughout the whole gene (Breast Cancer Information Core Database (BIC database)). Up to 10–20% of the BRCA tests report the identification of a variant of uncertain significance. There are many methods to discriminate deleterious/high-risk from neutral/low-risk unclassified variants (i.e., analysis of the cosegregation in families of the VUS, measure of the influence of the VUSs on the wild-type protein activity, comparison of sequence conservation across multiple species), but only an integrated analysis of these methods can contribute to a real interpretation of the functional and clinical role of the discussed variants. The aim of our manuscript is to review the studies on BRCA VUS in order to clarify their clinical relevance.

BackgroundMicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in human breast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level.ResultsWe adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm.ConclusionsThe obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response.

The close phylogenetic relationship between Ciona robusta and vertebrates makes it a powerful model for studying innate immunity and the evolution of immune genes. To elucidate the nature and dynamics of the immune response, the molecular mechanisms by which bacterial infection is detected and translated into inflammation and how potential pattern recognition receptors (PRRs) are involved in pathogen recognition in tunicate C. robusta (formerly known as Ciona intestinalis), we applied an approach combining bacterial infections, next-generation sequencing, qRT-PCR, bioinformatics and in silico analyses (criteria of a p-value < 0.05 and FDR < 0.05). A STRING analysis indicated a functional link between components of the Tlr/MyD88-dependent signalling pathway (Tlr2, MyD88, and Irak4) and components of the Nf-κB signalling pathway (Nf-κB, IκBα, and Ikkα) (p-value < 0.05, FDR < 0.05). A qRT-PCR analysis of immune genes selected from transcriptome data revealed Mif as more frequently expressed in the inflammatory response than inflammation mediator or effector molecules (e.g., Il-17s, Tnf-α, Tgf-β, Mmp9, Tlrs, MyD88, Irak4, Nf-κB, and galectins), suggesting close interplay between Mif cytokines and Nf-κB signalling pathway components in the biphasic activation of the inflammatory response. An in silico analyses of the 3′-UTR of Tlr2, MyD88, IκBα, Ikk, and Nf-κB transcripts showed the presence of GAIT elements, which are known to play key roles in the regulation of immune gene-specific translation in humans. These findings provide a new level of understanding of the mechanisms involved in the regulation of the C. robusta inflammatory response induced by LPS and suggest that in C. robusta, as in humans, a complex transcriptional and post-transcriptional control mechanism is involved in the regulation of several inflammatory genes.

BackgroundSeveral online databases provide a large amount of biomedical data of different biological entities. These resources are typically stored in systems implementing their own data model, user interface and query language. On the other hand, in many bioinformatics scenarios there is often the need to use more than one resource. The availability of a single bioinformatics platform that integrates many biological resources and services is, for those reasons a fundamental issue.DescriptionHere, we present BioGraph, a web application that allows to query, visualize and analyze biological data belonging to several online available sources. BioGraph is built upon our previously developed graph database called BioGraphDB, that integrates and stores heterogeneous biological resources and make them available by means of a common structure and a unique query language. BioGraph implements state-of-the-art technologies and provides pre-compiled bioinformatics scenarios, as well as the possibility to perform custom queries and obtaining an interactive and dynamic visualization of results.ConclusionWe present a case study about functional analysis of microRNA in breast cancer in order to demonstrate the functionalities of the system. BioGraph is freely available at http://biograph.pa.icar.cnr.it. Source files are available on GitHub at https://github.com/IcarPA-TBlab/BioGraph