The microRNA analysis portal is a next-generation tool for exploring and analyzing miRNA-focused data in the literature.

MicroRNAs constitute a class of noncoding small RNAs involved in the posttranscriptional regulation of many biological pathways. In recent years, microRNAs have also been associated with regulation across kingdoms, demonstrating that exogenous miRNAs can function in mammals in a fashion similar to mammalian miRNAs. The growing interest in microRNAs and the increasing amount of literature and molecular and biomedical data available make it difficult to identify records of interest and keep up to date with novel findings. For these reasons, we developed the microRNA Analysis Portal (MAP). MAP selects relevant miRNA-focused articles from PubMed, links biomedical and molecular data and applies bioinformatics modules. At the time of this writing, MAP represents the richest, most complete and integrated database focused on microRNAs. MAP also integrates an updated version of MirCompare (2.0), a computational platform used for selecting plant microRNAs on the basis of their ability to regulate mammalian genes. Both MAP and MirCompare functionalities were used to predict that microRNAs from Moringa oleifera have putative roles across kingdoms by regulating human genes coding for proteins of the immune system. Starting from a selection of 94 human microRNAs, MirCompare selected 6 Moringa oleifera functional homologs. The subsequent prediction of human targets and areas of functional enrichment highlighted the central involvement of these genes in regulating immune system processes, particularly the host-virus interaction processes in hepatitis B, cytomegalovirus, papillomavirus and coronavirus. This case of use showed how MAP can help to perform complex queries without any computational background. MAP is available at http://stablab.uniroma2.it/MAP .

The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis.

Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research.

Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer.

Widespread mammographic screening programs and improved self-monitoring allow for breast cancer to be detected earlier than ever before. Breast-conserving surgery is a successful treatment for select women. However, up to 40% of women develop local recurrence after surgery despite apparently tumor-free margins. This suggests that morphologically normal breast may harbor early alterations that contribute to increased risk of cancer recurrence. We conducted a comprehensive transcriptomic and proteomic analysis to characterize 57 fresh-frozen tissues from breast cancers and matched histologically normal tissues resected proximal to (<2 cm) and distant from (5-10 cm) the primary tumor, using tissues from cosmetic reduction mammoplasties as baseline. Four distinct transcriptomic subtypes are identified within matched normal tissues: metabolic; immune; matrisome/epithelial-mesenchymal transition, and non-coding enriched. Key components of the subtypes are supported by proteomic and tissue composition analyses. We find that the metabolic subtype is associated with poor prognosis (p < 0.001, HR6.1). Examination of genes representing the metabolic signature identifies several genes able to prognosticate outcome from histologically normal tissues. A subset of these have been reported for their predictive ability in cancer but, to the best of our knowledge, these have not been reported altered in matched normal tissues. This study takes an important first step toward characterizing matched normal tissues resected at pre-defined margins from the primary tumor. Unlocking the predictive potential of unexcised tissue could prove key to driving the realization of personalized medicine for breast cancer patients, allowing for more biologically-driven analyses of tissue margins than morphology alone.

HiPPO and PANDA: Two Bioinformatics Tools to Support Analysis of Mass Cytometry Data.

High-dimensional mass cytometry (Cytometry by Time-Of-Flight; CyTOF) is a multiparametric single-cell approach that allows for more than 40 parameters to be evaluated simultaneously, opening the possibility to dissect cellular heterogeneity and elucidate functional interactions between different cell types. However, the complexity of these data makes analysis and interpretation daunting. We created High-throughput Population Profiler (HiPPO), a tool that reduces the complexity of the CyTOF data and allows homogeneous clusters of cells to be visualized in an intuitive manner. Each subpopulation is mapped to the Population Analysis Database (PANDA), an open-source, manually curated database containing protein expression profiles for selected markers of primary cells, allowing for cell type abundance in the analyzed samples to be monitored. Custom cell definitions can be submitted for targeted identifications. All cell clusters, regardless of their annotation status, are available for further analyses. HiPPO also conducts nonparametric tests to determine whether differences in protein expression levels between conditions are significant. HiPPO strikes a balance between diagnostic power and computational burden. Its minimal computational footprint allows for subpopulations in a heterogeneous sample to be identified and quantified quickly.

Publisher Correction: Identification of microRNAs and relative target genes in Moringa oleifera leaf and callus.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification of microRNAs and relative target genes in Moringa oleifera leaf and callus.

MicroRNAs, a class of small, non-coding RNAs, play important roles in plant growth, development and stress response by negatively regulating gene expression. Moringa oleifera Lam. plant has many medical and nutritional uses; however, little attention has been dedicated to its potential for the bio production of active compounds. In this study, 431 conserved and 392 novel microRNA families were identified and 9 novel small RNA libraries constructed from leaf, and cold stress treated callus, using high-throughput sequencing technology. Based on the M. oleifera genome, the microRNA repertoire of the seed was re-evaluated. qRT-PCR analysis confirmed the expression pattern of 11 conserved microRNAs in all groups. MicroRNA159 was found to be the most abundant conserved microRNA in leaf and callus, while microRNA393 was most abundantly expressed in the seed. The majority of predicted microRNA target genes were transcriptional factors involved in plant reproduction, growth/development and abiotic/biotic stress response. In conclusion, this is the first comprehensive analysis of microRNAs in M. oleifera leaf and callus which represents an important addition to the existing M. oleifera seed microRNA database and allows for possible exploitation of plant microRNAs induced with abiotic stress, as a tool for bio-enrichment with pharmacologically important phytochemicals.

SMAC, a computational system to link literature, biomedical and expression data.

High-throughput technologies have produced a large amount of experimental and biomedical data creating an urgent need for comprehensive and automated mining approaches. To meet this need, we developed SMAC (SMart Automatic Classification method): a tool to extract, prioritise, integrate and analyse biomedical and molecular data according to user-defined terms. The robust ranking step performed on Medical Subject Headings (MeSH) ensures that papers are prioritised based on specific user requirements. SMAC then retrieves any related molecular data from the Gene Expression Omnibus and performs a wide range of bioinformatics analyses to extract biological insights. These features make SMAC a robust tool to explore the literature around any biomedical topic. SMAC can easily be customised/expanded and is distributed as a Docker container ( https://hub.docker.com/r/hfx320/smac ) ready-to-use on Windows, Mac and Linux OS. SMAC's functionalities have already been adapted and integrated into the Breast Cancer Now Tissue Bank bioinformatics platform and the Pancreatic Expression Database.

Olea europaea small RNA with functional homology to human miR34a in cross-kingdom interaction of anti-tumoral response.

Functional foods include compounds with nutritional and health properties. The human diet could play a stronger role in cancer prevention. Only a few studies have described the presence of plant small RNA, in humans who were fed with plant foods, which demonstrated the ability of these molecules to modulate consumer's genes and evidenced the existence of a plant-animal regulation. Through in silico prediction, Olea europaea small RNAs (sRs), which had been previously reported as miRNAs, were identified, each with functional homology to hsa-miR34a. According to this initial funding, we investigated the ability of oeu-sRs to regulate tumorigenesis in human cells. The transfection of these synthetic oeu-sRs reduced the protein expression of hsa-miR34a mRNA targets, increased apoptosis and decreased proliferation in different tumor cells; by contrast, no effect was observed in PBMCs from healthy donors. The introduction of oeu-small RNA in hsa-miR34a-deficient tumor cells restores its function, whereas cells with normal expression of endogenous hsa-miR34a remained unaffected. The natural oeu-small RNAs that were extracted from O. europaea drupes induce the same effects as synthetic sRs. Careful research on the small RNA sequences executed for mapping and annotation in the genome of O. europaea var. Sylvestris and var. Farga led to the hypothesis that RNA fragments with functional homology to human miRNAs could be generated from the degradation of regions of RNA transcripts. These results indicate the possibility of developing novel natural non-toxic drugs that contain active plant-derived tumor-suppressing small RNA with functional homology to hsa-miRNAs and that can support antineoplastic strategies.

The Pancreatic Expression Database: 2018 update.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) continues to be a major resource for mining pancreatic -omics data a decade after its initial release. Here, we present recent updates to PED and describe its evolution into a comprehensive resource for extracting, analysing and integrating publicly available multi-omics datasets. A new analytical module has been implemented to run in parallel with the existing literature mining functions. This analytical module has been created using rich data content derived from pancreas-related specimens available through the major data repositories (GEO, ArrayExpress) and international initiatives (TCGA, GENIE, CCLE). Researchers have access to a host of functions to tailor analyses to meet their needs. Results are presented using interactive graphics that allow the molecular data to be visualized in a user-friendly manner. Furthermore, researchers are provided with the means to superimpose layers of molecular information to gain greater insight into alterations and the relationships between them. The literature-mining module has been improved with a redesigned web appearance, restructured query platforms and updated annotations. These updates to PED are in preparation for its integration with the Pancreatic Cancer Research Fund Tissue Bank (PCRFTB), a vital resource of pancreas cancer tissue for researchers to support and promote cutting-edge research.

BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking.

Here, we present an update of Breast Cancer Now Tissue Bank bioinformatics, a rich platform for the sharing, mining, integration and analysis of breast cancer data. Its modalities provide researchers with access to a centralised information gateway from which they can access a network of bioinformatic resources to query findings from publicly available, in-house and experimental data generated using samples supplied from the Breast Cancer Now Tissue Bank. This in silico environment aims to help researchers use breast cancer data to their full potential, irrespective of any bioinformatics barriers. For this new release, a complete overhaul of the IT and bioinformatic infrastructure underlying the portal has been conducted and a host of novel analytical modules established. We developed and adopted an automated data selection and prioritisation system, expanded the data content and included tissue and cell line data generated from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia, designed a host of novel analytical modalities and enhanced the query building process. Furthermore, the results are presented in an interactive format, providing researchers with greater control over the information on which they want to focus. Breast Cancer Now Tissue Bank bioinformatics can be accessed at http://bioinformatics.breastcancertissuebank.org/.

Activation of the Pro-Oxidant PKCßII-p66Shc Signaling Pathway Contributes to Pericyte Dysfunction in Skeletal Muscles of Patients With Diabetes With Critical Limb Ischemia.

Critical limb ischemia (CLI), foot ulcers, former amputation, and impaired regeneration are independent risk factors for limb amputation in subjects with diabetes. The present work investigates whether and by which mechanism diabetes negatively impacts on functional properties of muscular pericytes (MPs), which are resident stem cells committed to reparative angiomyogenesis. We obtained muscle biopsy samples from patients with diabetes who were undergoing major limb amputation and control subjects. Diabetic muscles collected at the rim of normal tissue surrounding the plane of dissection showed myofiber degeneration, fat deposition, and reduction of MP vascular coverage. Diabetic MPs (D-MPs) display ultrastructural alterations, a differentiation bias toward adipogenesis at the detriment of myogenesis and an inhibitory activity on angiogenesis. Furthermore, they have an imbalanced redox state, with downregulation of the antioxidant enzymes superoxide dismutase 1 and catalase, and activation of the pro-oxidant protein kinase C isoform ß-II (PKCßII)-dependent p66Shc signaling pathway. A reactive oxygen species scavenger or, even more effectively, clinically approved PKCßII inhibitors restore D-MP angiomyogenic activity. Inhibition of the PKCßII-dependent p66Shc signaling pathway could represent a novel therapeutic approach for the promotion of muscle repair in individuals with diabetes.

Bioinformatics Prediction and Experimental Validation of MicroRNAs Involved in Cross-Kingdom Interaction.

MicroRNAs (miRNAs) are a class of small noncoding RNAs that act as efficient post-transcriptional regulators of gene expression. In 2012, the first cross-kingdom miRNA-based interaction had been evidenced, demonstrating that exogenous miRNAs act in a manner of mammalian functional miRNAs. Starting from this evidence, we defined the concept of cross-kingdom functional homology between plant and mammalian miRNAs as a needful requirement for vegetal miRNA to explicit a regulation mechanism into the host mammalian cell, comparable to the endogenous one. Then, we proposed a new dedicated algorithm to compare plant and mammalian miRNAs, searching for functional sequence homologies between them, and we developed a web software called MirCompare. We also predicted human genes regulated by the selected plant miRNAs, and we determined the role of exogenous miRNAs in the perturbation of intracellular interaction networks. Finally, as already performed by Pirrò and coworkers, the ability of MirCompare to select plant miRNAs with functional homologies with mammalian ones has been experimentally confirmed by evaluating the ability of mol-miR168a to downregulate the protein expression of SIRT1, when its mimic is transfected into human hepatoma cell line G2 (HEPG2) cells. This tool is implemented into a user-friendly web interface, and the access is free to public through the website http://160.80.35.140/MirCompare.

Deep Proteomics of Breast Cancer Cells Reveals that Metformin Rewires Signaling Networks Away from a Pro-growth State.

Metformin is the most frequently prescribed drug for type 2 diabetes. In addition to its hypoglycemic effects, metformin also lowers cancer incidence. This anti-cancer activity is incompletely understood. Here, we profiled the metformin-dependent changes in the proteome and phosphoproteome of breast cancer cells using high-resolution mass spectrometry. In total, we quantified changes of 7,875 proteins and 15,813 phosphosites after metformin changes. To interpret these datasets, we developed a generally applicable strategy that overlays metformin-dependent changes in the proteome and phosphoproteome onto a literature-derived network. This approach suggested that metformin treatment makes cancer cells more sensitive to apoptotic stimuli and less sensitive to pro-growth stimuli. These hypotheses were tested in vivo; as a proof-of-principle, we demonstrated that metformin inhibits the p70S6K-rpS6 axis in a PP2A-phosphatase dependent manner. In conclusion, analysis of deep proteomics reveals both detailed and global mechanisms that contribute to the anti-cancer activity of metformin.

MicroRNA from Moringa oleifera: Identification by High Throughput Sequencing and Their Potential Contribution to Plant Medicinal Value.

Moringa oleifera is a widespread plant with substantial nutritional and medicinal value. We postulated that microRNAs (miRNAs), which are endogenous, noncoding small RNAs regulating gene expression at the post-transcriptional level, might contribute to the medicinal properties of plants of this species after ingestion into human body, regulating human gene expression. However, the knowledge is scarce about miRNA in Moringa. Furthermore, in order to test the hypothesis on the pharmacological potential properties of miRNA, we conducted a high-throughput sequencing analysis using the Illumina platform. A total of 31,290,964 raw reads were produced from a library of small RNA isolated from M. oleifera seeds. We identified 94 conserved and two novel miRNAs that were validated by qRT-PCR assays. Results from qRT-PCR trials conducted on the expression of 20 Moringa miRNA showed that are conserved across multiple plant species as determined by their detection in tissue of other common crop plants. In silico analyses predicted target genes for the conserved miRNA that in turn allowed to relate the miRNAs to the regulation of physiological processes. Some of the predicted plant miRNAs have functional homology to their mammalian counterparts and regulated human genes when they were transfected into cell lines. To our knowledge, this is the first report of discovering M. oleifera miRNAs based on high-throughput sequencing and bioinformatics analysis and we provided new insight into a potential cross-species control of human gene expression. The widespread cultivation and consumption of M. oleifera, for nutritional and medicinal purposes, brings humans into close contact with products and extracts of this plant species. The potential for miRNA transfer should be evaluated as one possible mechanism of action to account for beneficial properties of this valuable species.

Characterization by mass cytometry of different methods for the preparation of muscle mononuclear cells.

Biological processes that are mediated by cell-cell interactions in heterogeneous populations are best approached by methods that have single cell resolution. Most of these methods rely on the preparation, from solid tissues, of cell suspensions by enzymatic digestion, followed by analysis of single cell reactivity to an antibody panel that allows the discrimination of cell populations and characterization of their activation state. Thus for any specific biological problem, both efficient and at the same time mild, protocols for cell separation, together with tissue specific panels of antibodies, need to be developed and optimized. Here we characterize an antibody panel that permits the discrimination of mononuclear muscle cell populations by mass cytometry and use it to characterize the cell populations obtained by three different cell extraction procedures from muscle fibers. We show that our panel of antibodies, albeit limited and incomplete, is sufficient to discriminate most of the mononuclear muscle cell populations and that each cell extraction method yields heterogeneous cell populations with a different relative abundance of the distinct cell types.

SIGNOR: a database of causal relationships between biological entities.

Assembly of large biochemical networks can be achieved by confronting new cell-specific experimental data with an interaction subspace constrained by prior literature evidence. The SIGnaling Network Open Resource, SIGNOR (available on line at http://signor.uniroma2.it), was developed to support such a strategy by providing a scaffold of prior experimental evidence of causal relationships between biological entities. The core of SIGNOR is a collection of approximately 12,000 manually-annotated causal relationships between over 2800 human proteins participating in signal transduction. Other entities annotated in SIGNOR are complexes, chemicals, phenotypes and stimuli. The information captured in SIGNOR can be represented as a signed directed graph illustrating the activation/inactivation relationships between signalling entities. Each entry is associated to the post-translational modifications that cause the activation/inactivation of the target proteins. More than 4900 modified residues causing a change in protein concentration or activity have been curated and linked to the modifying enzymes (about 351 human kinases and 94 phosphatases). Additional modifications such as ubiquitinations, sumoylations, acetylations and their effect on the modified target proteins are also annotated. This wealth of structured information can support experimental approaches based on multi-parametric analysis of cell systems after physiological or pathological perturbations and to assemble large logic models.