Diagnostic and prognostic potential of MiR-379/656 MicroRNA cluster in molecular subtypes of breast cancer

Introduction: Breast cancer is the most frequently diagnosed cancer globally and is one of the most important contributors to cancer-related deaths. Earlier diagnosis is known to reduce mortality, and better biomarkers are needed. MiRNA clusters often co-express and target mRNAs in a coordinated fashion, perturbing entire pathways; they thus merit further exploration for diagnostic or prognostic use. MiR-379/656, at chromosome 14q32, is the second largest miRNA cluster in the human genome and implicated in various malignancies including glioblastoma, melanoma, gastrointestinal tumors and ovarian cancer highlighting its potential importance. In this study, we focus on the diagnostic and prognostic potentials of MiR-379/656 in breast cancer and its molecular subtypes. Materials and Methods: We analyzed miRNA and mRNA next generation sequencing data from 903 primary tumors and 90 normal controls (source: The Cancer Genome Atlas). The differential expression profile between tumor and normal was analyzed using DeSEQ2. Penalized logistic regression modelling (lasso regression) was used to assess the predictive potential of MiR-379/656 expression for tumor and normal samples. The association between MiR-379/656 expression and overall patient survival was studied using Cox Proportional-Hazard Model. The target mRNAs (validated) of MiR-379/656 were annotated via pathway enrichment analysis to understand the biological significance of the cluster in breast cancer. Results: The differential expression analysis for 1390 miRNAs (miRnome) revealed 310 upregulated (22.3%) and 176 downregulated (12.66%) miRNAs in breast cancer patients compared with controls. For MiR-379/656, 32 miRNAs (32/42; 76%) were downregulated. The MiR-379/656 cluster was found to be the most differentially expressed cluster in the human genome (p 10−30). The Basal and Luminal B subtypes showed at least 83% (35/42) of the miRNAs to be downregulated. The binomial model prioritized 15 miRNAs, which distinguished breast cancer patients from controls with 99.15 ± 0.58% sensitivity and 77.78 ± 5.24% specificity. Overall, the Basal and Luminal B showed the most effective predictive power with respect to the 15 prioritized miRNAs at MiR-379/656 cluster. The decreased expression of MiR-379/656 was found to be associated with poorer clinical outcome in Basal and Luminal B subtypes, increasing tumor stage and tumor size/extent, and overall patient survival. Pathway enrichment for the validated targets of MiR-379/656 was significant for cancer-related pathways, especially DNA repair, transcriptional regulation by p53 and cell cycle checkpoints (adjusted p-value 0.05). Conclusions: Genome informatics analysis of high throughput data for MiR-379/656 cluster has shown that a subset of 15 miRNAs from MiR-379/656 cluster can be used for the diagnostic and prognostic purpose of breast cancer and its subtypes—especially in Basal and Luminal B

Predicting novel candidate human obesity genes and their site of action by systematic functional screening in Drosophila.

This is the final version. Available from This is the final version. Available from [publisher] via the DOI in this record.  via the DOI in this record. SCOOP and INTERVAL WES data are accessible from EGA (EGAS00001000124 and EGAS00001000825). All other data are available in the manuscript or the supplementary materialsThe discovery of human obesity-associated genes can reveal new mechanisms to target for weight loss therapy. Genetic studies of obese individuals and the analysis of rare genetic variants can identify novel obesity-associated genes. However, establishing a functional relationship between these candidate genes and adiposity remains a significant challenge. We uncovered a large number of rare homozygous gene variants by exome sequencing of severely obese children, including those from consanguineous families. By assessing the function of these genes in vivo in Drosophila, we identified 4 genes, not previously linked to human obesity, that regulate adiposity (itpr, dachsous, calpA, and sdk). Dachsous is a transmembrane protein upstream of the Hippo signalling pathway. We found that 3 further members of the Hippo pathway, fat, four-jointed, and hippo, also regulate adiposity and that they act in neurons, rather than in adipose tissue (fat body). Screening Hippo pathway genes in larger human cohorts revealed rare variants in TAOK2 associated with human obesity. Knockdown of Drosophila tao increased adiposity in vivo demonstrating the strength of our approach in predicting novel human obesity genes and signalling pathways and their site of action.Wellcome Trust Senior Investigator AwardWellcome TrustCRU

A MIQE-Compliant Real-Time PCR Assay for Aspergillus Detection

PMCID: PMC3393739This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Interferon Impedes an Early Step of Hepatitis Delta Virus Infection

Hepatitis delta virus (HDV) infects hepatocytes, the major cell type of the liver. Infection of the liver may be either transient or chronic. The prognosis for patients with chronic HDV infection is poor, with a high risk of cirrhosis and hepatocellular carcinoma. The best antiviral therapy is weekly administration for at least one year of high doses of interferon alpha. This efficacy of interferon therapy has been puzzling in that HDV replication in transfected cell lines is reported as insensitive to administration of interferon alpha or gamma. Similarly, this study shows that even when an interferon response was induced by transfection of poly(IC) into a cell line, HDV RNA accumulation was only modestly inhibited. However, when the HDV replication was initiated by infection of primary human hepatocytes, simultaneous addition of interferons alpha or gamma at 600 units/ml, a concentration comparable to that achieved in treated patients, the subsequent HDV RNA accumulation was inhibited by at least 80%. These interferon treatments were shown to produce significant time-dependent increases of host response proteins such as for Stat-1, phosphoStat-1, Mx1/2/3 and PKR, and yet interferon pretreatment of hepatocytes did not confer an increased inhibition of HDV replication over interferon treatment at the time of (or after) infection. These and other data support the interpretation that interferon action against HDV replication can occur and is largely mediated at the level of entry into primary human hepatocytes. Thus in vivo, the success of long-term interferon therapy for chronic HDV, may likewise involve blocking HDV spread by interfering with the initiation of productive infection of naïve hepatocytes

ECIL-6 guidelines for the treatment of invasive candidiasis, aspergillosis and mucormycosis in leukemia and hematopoietic stem cell transplant patients

The European Conference on Infections in Leukemia (ECIL) provides recommendations for diagnostic strategies and prophylactic, pre-emptive or targeted therapy strategies for various types of infection in patients with hematologic malignancies or hematopoietic stem cell transplantation recipients. Meetings are held every two years since 2005 and evidence-based recommendations are elaborated after evaluation of the literature and discussion among specialists of nearly all European countries. In this manuscript, the ECIL group presents the 2015-update of the recommendations for the targeted treatment of invasive candidiasis, aspergillosis and mucormycosis. Current data now allow a very strong recommendation in favor of echinocandins for first-line therapy of candidemia irrespective of the underlying predisposing factors. Anidulafungin has been given the same grading as the other echinocandins for hemato-oncological patients. The beneficial role of catheter removal in candidemia is strengthened. Aspergillus guidelines now recommend the use of either voriconazole or isavuconazole for first-line treatment of invasive aspergillosis, while first-line combination antifungal therapy is not routinely recommended. As only few new data were published since the last ECIL guidelines, no major changes were made to mucormycosis recommendations

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

Innovation capacity in the healthcare sector and historical anchors: examples from the UK, Switzerland and the US

Innovation is an integral part of economic development in developed economies. In the post 2008 period, a key policy agenda is that of sustainable development, which calls for innovation in all aspects of value-chains. In this paper, we focus on innovation from the biotech—pharma perspective to see whether or not this will lead to a sustainable future for the regions where there are clusters of firms in this sector. We examine data from a recently completed European Union study of innovation in the Healthcare sector from the UK and Switzerland, countries with an historical base in pharma, to understand how innovation pathways vary at the regional level in the broader life sciences, which incorporate biotech and more. Innovation in the healthcare sector in two regions, Oxfordshire in the UK and Zurich in Switzerland are compared. We contextualize our discussion by drawing on studies that focus on the sector in the US, specifically Boston. The analytical framework comprises three elements: innovation systems and national and regional economic development theories are the first two, followed by approaches which consider organizational or institutional activity. This framework is used to help explain and understand the complexity of how innovation is organized at the sub-national level. The overall context is that it is increasing becoming a condition for government financing of research that it has more immediate application in industry or have the possibility of commercialisation (e.g., translational research)

Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites

Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice-Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice-phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice-Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice-Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC-Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice-phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community