GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq data is identifying the cell types within tissue to estimate the quantitative composition of cell populations. Due to the massive amount of available scRNA-seq data, automatic classification approaches for cell typing, based on the most recent deep learning technology, are needed. Here, we present the gene ontology-driven wide and deep learning (GOWDL) model for classifying cell types in several tissues. GOWDL implements a hybrid architecture that considers the functional annotations found in Gene Ontology and the marker genes typical of specific cell types. We performed cross-validation and independent external testing, comparing our algorithm with 12 other state-of-the-art predictors. Classification scores demonstrated that GOWDL reached the best results over five different tissues, except for recall, where we got about 92% versus 97% of the best tool. Finally, we presented a case study on classifying immune cell populations in breast cancer using a hierarchical approach based on GOWDL.

A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.

Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among them, such as interactions. Due to the current availability of a huge amount of biological data, it is very important to consider in silico analysis methods based on, for example, machine learning, that could take advantage of the inner graph structure of the data in order to improve the quality of the results. In this scenario, graph neural networks (GNNs) are recent computational approaches that directly deal with graph-structured data. In this paper, we present a GNN network for the analysis of siRNA-mRNA interaction networks. siRNAs, in fact, are small RNA molecules that are able to bind to target genes and silence them. These events make siRNAs key molecules as RNA interference agents in many biological interaction networks related to severe diseases such as cancer. In particular, our GNN approach allows for the prediction of the siRNA efficacy, which measures the siRNA's ability to bind and silence a gene target. Tested on benchmark datasets, our proposed method overcomes other machine learning algorithms, including the state-of-the-art predictor based on the convolutional neural network, reaching a Pearson correlation coefficient of approximately 73.6%. Finally, we proposed a case study where the efficacy of a set of siRNAs is predicted for a gene of interest. To the best of our knowledge, GNNs were used for the first time in this scenario.

Transcriptomic Analyses Reveal 2 and 4 Family Members of Cytochromes P450 (CYP) Involved in LPS Inflammatory Response in Pharynx of Ciona robusta.

Cytochromes P450 (CYP) are enzymes responsible for the biotransformation of most endogenous and exogenous agents. The expression of each CYP is influenced by a unique combination of mechanisms and factors including genetic polymorphisms, induction by xenobiotics, and regulation by cytokines and hormones. In recent years, Ciona robusta, one of the closest living relatives of vertebrates, has become a model in various fields of biology, in particular for studying inflammatory response. Using an in vivo LPS exposure strategy, next-generation sequencing (NGS) and qRT-PCR combined with bioinformatics and in silico analyses, compared whole pharynx transcripts from naïve and LPS-exposed C. robusta, and we provide the first view of cytochrome genes expression and miRNA regulation in the inflammatory response induced by LPS in a hematopoietic organ. In C. robusta, cytochromes belonging to 2B,2C, 2J, 2U, 4B and 4F subfamilies were deregulated and miRNA network interactions suggest that different conserved and species-specific miRNAs are involved in post-transcriptional regulation of cytochrome genes and that there could be an interplay between specific miRNAs regulating both inflammation and cytochrome molecules in the inflammatory response in C. robusta.

2,2'4,4'-Tetrabromodiphenyl Ether (PBDE-47) Modulates the Intracellular miRNA Profile, sEV Biogenesis and Their miRNA Cargo Exacerbating the LPS-Induced Pro-Inflammatory Response in THP-1 Macrophages.

The 2,2'4,4'-tetrabromodiphenyl ether (PBDE-47) is one of the most prominent PBDE congeners detected in the environment and in animal and human tissues. Animal model experiments suggested the occurrence of PBDE-induced immunotoxicity leading to different outcomes and recently we demonstrated that this substance can impair macrophage and basophil activities. In this manuscript, we decided to further examine the effects induced by PBDE-47 treatment on innate immune response by looking at the intracellular expression profile of miRNAs as well as the biogenesis, cargo content and activity of human M(LPS) macrophage cell-derived small extracellular vesicles (sEVs). Microarray and in silico analysis demonstrated that PBDE-47 can induce some epigenetic effects in M(LPS) THP-1 cells modulating the expression of a set of intracellular miRNAs involved in biological pathways regulating the expression of estrogen-mediated signaling and immune responses with particular reference to M1/M2 differentiation. In addition to the cell-intrinsic modulation of intracellular miRNAs, we demonstrated that PBDE-47 could also interfere with the biogenesis of sEVs increasing their number and selecting a de novo population of sEVs. Moreover, PBDE-47 induced the overload of specific immune related miRNAs in PBDE-47 derived sEVs. Finally, culture experiments with naïve M(LPS) macrophages demonstrated that purified PBDE-47 derived sEVs can modulate macrophage immune response exacerbating the LPS-induced pro-inflammatory response inducing the overexpression of the IL-6 and the MMP9 genes. Data from this study demonstrated that PBDE-47 can perturb the innate immune response at different levels modulating the intracellular expression of miRNAs but also interfering with the biogenesis, cargo content and functional activity of M(LPS) macrophage cell-derived sEVs.

T Cells Expressing Receptor Recombination/Revision Machinery Are Detected in the Tumor Microenvironment and Expanded in Genomically Over-unstable Models.

Tumors undergo dynamic immunoediting as part of a process that balances immunologic sensing of emerging neoantigens and evasion from immune responses. Tumor-infiltrating lymphocytes (TIL) comprise heterogeneous subsets of peripheral T cells characterized by diverse functional differentiation states and dependence on T-cell receptor (TCR) specificity gained through recombination events during their development. We hypothesized that within the tumor microenvironment (TME), an antigenic milieu and immunologic interface, tumor-infiltrating peripheral T cells could reexpress key elements of the TCR recombination machinery, namely, Rag1 and Rag2 recombinases and Tdt polymerase, as a potential mechanism involved in the revision of TCR specificity. Using two syngeneic invasive breast cancer transplantable models, 4T1 and TS/A, we observed that Rag1, Rag2, and Dntt in situ mRNA expression characterized rare tumor-infiltrating T cells. In situ expression of the transcripts was increased in coisogenic Mlh1-deficient tumors, characterized by genomic overinstability, and was also modulated by PD-1 immune-checkpoint blockade. Through immunolocalization and mRNA hybridization analyses, we detected the presence of rare TDT+RAG1/2+ cells populating primary tumors and draining lymph nodes in human invasive breast cancer. Analysis of harmonized single-cell RNA-sequencing data sets of human cancers identified a very small fraction of tumor-associated T cells, characterized by the expression of recombination/revision machinery transcripts, which on pseudotemporal ordering corresponded to differentiated effector T cells. We offer thought-provoking evidence of a TIL microniche marked by rare transcripts involved in TCR shaping.

ceRNA Network Regulation of TGF-ß, WNT, FOXO, Hedgehog Pathways in the Pharynx of Ciona robusta.

The transforming growth factor-ß (TGF-ß) family of cytokines performs a multifunctional signaling, which is integrated and coordinated in a signaling network that involves other pathways, such as Wintless, Forkhead box-O (FOXO) and Hedgehog and regulates pivotal functions related to cell fate in all tissues. In the hematopoietic system, TGF-ß signaling controls a wide spectrum of biological processes, from immune system homeostasis to the quiescence and self-renewal of hematopoietic stem cells (HSCs). Recently an important role in post-transcription regulation has been attributed to two type of ncRNAs: microRNAs and pseudogenes. Ciona robusta, due to its philogenetic position close to vertebrates, is an excellent model to investigate mechanisms of post-transcriptional regulation evolutionarily highly conserved in immune homeostasis. The combined use of NGS and bioinformatic analyses suggests that in the pharynx, the hematopoietic organ of Ciona robusta, the Tgf-ß, Wnt, Hedgehog and FoxO pathways are involved in tissue homeostasis, as they are in human. Furthermore, ceRNA network interactions and 3'UTR elements analyses of Tgf-ß, Wnt, Hedgehog and FoxO pathways genes suggest that different miRNAs conserved (cin-let-7d, cin-mir-92c, cin-mir-153), species-specific (cin-mir-4187, cin-mir-4011a, cin-mir-4056, cin-mir-4150, cin-mir-4189, cin-mir-4053, cin-mir-4016, cin-mir-4075), pseudogenes (ENSCING00000011392, ENSCING00000018651, ENSCING00000007698) and mRNA 3'UTR elements are involved in post-transcriptional regulation in an integrated way in C. robusta.

MiRNA therapeutics based on logic circuits of biological pathways.

BACKGROUND: In silico experiments, with the aid of computer simulation, speed up the process of in vitro or in vivo experiments. Cancer therapy design is often based on signalling pathway. MicroRNAs (miRNA) are small non-coding RNA molecules. In several kinds of diseases, including cancer, hepatitis and cardiovascular diseases, they are often deregulated, acting as oncogenes or tumor suppressors. miRNA therapeutics is based on two main kinds of molecules injection: miRNA mimics, which consists of injection of molecules that mimic the targeted miRNA, and antagomiR, which consists of injection of molecules inhibiting the targeted miRNA. Nowadays, the research is focused on miRNA therapeutics. This paper addresses cancer related signalling pathways to investigate miRNA therapeutics. RESULTS: In order to prove our approach, we present two different case studies: non-small cell lung cancer and melanoma. KEGG signalling pathways are modelled by a digital circuit. A logic value of 1 is linked to the expression of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. All possible relationships provided by a signalling pathway are modelled by logic gates. Mutations, derived according to the literature, are introduced and modelled as well. The modelling approach and analysis are widely discussed within the paper. MiRNA therapeutics is investigated by the digital circuit analysis. The most effective miRNA and combination of miRNAs, in terms of reduction of pathogenic conditions, are obtained. A discussion of obtained results in comparison with literature data is provided. Results are confirmed by existing data. CONCLUSIONS: The proposed study is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation of a cancer pathway. Using this simulation, the most effective combination of drugs and miRNAs for mutated cancer therapy design are obtained and these results were validated by the literature. The proposed modelling and analysis approach can be applied to each human disease, starting from the corresponding signalling pathway.

miRTissue: a web application for the analysis of miRNA-target interactions in human tissues.

BACKGROUND: microRNAs act as regulators of gene expression interacting with their gene targets. Current bioinformatics services, such as databases of validated miRNA-target interactions and prediction tools, usually provide interactions without any information about what tissue that interaction is more likely to appear nor information about the type of interactions, causing mRNA degradation or translation inhibition respectively. RESULTS: In this work, we introduce miRTissue, a web application that combines validated miRNA-target interactions with statistical correlation among expression profiles of miRNAs, genes and proteins in 15 different human tissues. Validated interactions are taken from the miRTarBase database, while expression profiles are downloaded from The Cancer Genome Atlas repository. As a result, the service provides a tissue-specific characterisation of each couple of miRNA and gene together with its statistical significance (p-value). The inclusion of protein data also allows providing the type of interaction. Moreover, miRTissue offers several views for analysing interactions, focusing for example on the comparison between different cancer types or different tissue conditions. All the results are freely downloadable in the most common formats. CONCLUSIONS: miRTissue fills a gap concerning current bioinformatics services related to miRNA-target interactions because it provides a tissue-specific context to each validated interaction and the type of interaction itself. miRTissue is easily browsable allowing the user to select miRNAs, genes, cancer types and tissue conditions. The results can be sorted according to p-values to immediately identify those interactions that are more likely to occur in a given tissue. miRTissue is available at http://tblab.pa.icar.cnr.it/mirtissue.html.

BioGraph: a web application and a graph database for querying and analyzing bioinformatics resources.

BACKGROUND: Several 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. DESCRIPTION: Here, 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. CONCLUSION: We 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.

Potential Role of ANGPTL4 in the Cross Talk between Metabolism and Cancer through PPAR Signaling Pathway.

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, PPARß/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.

Analysis of miRNA expression profiles in breast cancer using biclustering.

BACKGROUND: MicroRNAs (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. RESULTS: We 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. CONCLUSIONS: The 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.

Driver mutations and differential sensitivity to targeted therapies: a new approach to the treatment of lung adenocarcinoma.

The adenocarcinoma of the lung has recently shown peculiar molecular characteristics, which relate with both carcinogenesis and response to targeted drugs. Several molecular alterations have been defined as "driver mutations". These are responsible for both the initiation and maintenance of the malignancy. The epidermal growth factor receptor (EGFR) pathway is the main regulator of cell function and cancer development. It has a widely defined role in the occurrence of driver mutations. Up till now EGFR gene mutations, KRAS gene mutations and EML4-ALK fusion genes are the most widely recognized alterations involved in both the biology and the clinical management of lung adenocarcinoma. In this review we report the molecular bases that have led to the clinical application of the detection for such genetic impairments. Subsequently we discuss the clinical studies regarding the prognostic role and the predictive value for response to anti-EGFR tyrosine kinase inhibitors (TKI) of the same mutations. We also provide a potential algorithm as a guide in the choice of the best treatment for patients with adenocarcinoma.

The Clinical Significance of Unknown Sequence Variants in BRCA Genes.

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.