Identification of differential gene expression profile in circulating lympho-monocytes of apparently healthy young adults in presence of cardiovascular risk factors through whole-genome transcriptomic profile analysis with oligo-RNA microarrays

Background and Aims: Preliminary evidence indicates that the gene expression profile of circulating white blood cells is significantly affected by the exposure to cardiovascular (CV) risk factors. However the few available studies have several limitations, including small sample size and narrow range of genes tested. The aim of the present thesis is to provide a review of the current literature on this relationship and the presentation of the results of a clinical research program aimed at investigating the gene expression profile at whole-genome level in peripheral blood mononuclear cells (PBMC) of healthy volunteers in presence of CV risk factors and associated biomarkers. Materials and Methods: In the clinical study, PBMC’s expression profile was evaluated using Agilent whole genome oligonucleotide mRNA microarrays in 167 apparently healthy young-adults, all volunteers in a CV risk assessment study. Enrolled subjects were free of diabetes, CV and inflammatory diseases, and did not take pharmacological medications. Data analysis was performed using methods developed to address differential expression for quantitative phenotypes and enrichment of gene sets in ranked lists of genes. Results. Differential expression profiles were identified in presence of cigarette smoking, high LDL-cholesterol concentrations, increased biomakers associated with CV risk (like IL-6 and TNF-alpha) and in presence of increased carotid intima-media thickness (IMT), a surrogate marker of atherosclerosis. In all these conditions, gene expression profile was characterized by a pro-inflammatory signature. More in detail, cigarette smoking was characterized by an over-expression of groups of genes and molecular pathways involved in the innate immunity, whereas smoking was associated with the over-expression of genes involved in the cell-mediated immunitary response and high IMT was characterized by an over-expression of genes of the mitochondrial respiratory chain. Interestingly, in subjects who smoked and had high LDL-c compared to low LDL-c non-smokers, the transcriptomic profile was characterized by the co-presence of the two bio-signatures and the enrichment of both innate and cell-mediated immunity. The gene expression profile associated with gender, age, and different proportions of leukocyte sub-populations are also presented as collateral results. Conclusions: In conclusion, in apparently healthy young adults, the presence of several CV risk factors such as cigarette smoking, high LDL-cholesterol and thickened IMT are associated with the over-expression of genes related to the inflammatory response. However, different risk factors are characterized by different transcriptomic “signatures”. Presented data and supportive literature suggest that gene expression profile in PBMCs reflects the biological processes involved in the chronic sub-clinical inflammation that is a key patho-physiological element of CV disease

Clinically driven semi-supervised class discovery in gene expression data

Abstract Motivation: Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries. Results: We develop an approach to semi-supervised class discovery. One component of our approach uses clinical sample information to constrain the search space and guide the class discovery process to yield biologically relevant partitions. A second component consists of using known biological annotation of genes to drive the search, seeking partitions that manifest strong differential expression in specific sets of genes. We develop efficient algorithmics for these tasks, implementing both approaches and combinations thereof. We show that our method is robust enough to detect known clinical parameters in accordance with expected clinical values. We also use our method to elucidate cardiovascular disease (CVD) putative risk factors. Availability: MonoClaD (Monotone Class Discovery). See http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/ Supplementary information: Supplementary data is available at http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/software.html Contact: [email protected]

Navigating Market Authorization: The Path Holoclar Took to Become the First Stem Cell Product Approved in the European Union

Gene therapy, cell therapy, and tissue engineering have the potential to revolutionize the treatment of disease and injury. Attaining marketing authorization for such advanced therapy medicinal products (ATMPs) requires a rigorous scientific evaluation by the European Medicines Agencyâauthorization is only granted if the product can fulfil stringent requirements for quality, safety, and efficacy. However, many ATMPs are being provided to patients under alternative means, such as âhospital exemptionâ schemes. Holoclar (ex vivo expanded autologous human corneal epithelial cells containing stem cells), a novel treatment for eye burns, is one of the few ATMPs to have been granted marketing authorization and is the first containing stem cells. This review highlights the differences in standards between an authorized and unauthorized medicinal product, and specifically discusses how the manufacture of Holoclar had to be updated to achieve authorization. The result is that patients will have access to a therapy that is manufactured to high commercial standards, and is supported by robust clinical safety and efficacy data. Stem Cells Translational Medicine 2018;7:146â154

SONDEO ARQUEOLÓGICO SECTOR 84 [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Correction: Identification of an early transcriptomic signature of insulin resistance and related diseases in lymphomonocytes of healthy subjects(PLoS ONE (2018)12:8(e0182559) DOI: 10.1371/journal.pone.0182559)

There is an error in the Methods section under the sub-heading “Study subjects.” The first sentence of the second paragraph should read: This study was reviewed and approved by the Institutional Ethical Committee “Comitato di Etica dell’Università degli Studi di Parma.

Biomarkers in neomark European project for oral cancers

Oral cavity cancers are the seventh tumor by diffusion worldwide with more than 90% being diagnosed as oral squamous cell carcinomas (OSCCs). According to the latestWHO statistics, OSCC accounts for 5% of the cancer deaths worldwide, being the eighth more lethal cancer entity. Early identification of cancer relapses would have the potentiality to improve the disease control and the patient survival. NeoMark is a European co-funded research project (Seventh Framework Program, Information and Communication Technologies: EU-FP7-ICT-2007-2-22483-NeoMark) that has the objective to identify relevant biomarkers of OSCC recurrence. It integrates high-throughput gene expression analysis in tumor cells and IT-assisted imaging with traditional staging and follow-up protocols to improve the recurrence risk stratification and to obtain the earlier identification of locoregional relapses. The architecture of the project is based on the following key points: – Creation of a web application tool: A unified interface that helps the storage and management of all information – NeoMark database: The heterogeneous NeoMark data (demographics and risk factors; clinical, pathological, and immunohistochemical parameters; filtered and cleaned genomic and imaging data) are stored in a single database – the Integrated Health Record Repository (IHRR) – on a central NeoMark server. The server contains the marker definition functional environment (MDFE), a data analysis module. Based on the heterogeneous input data, it estimates the likelihood of a relapse and identifies OSCC risk factors. – Imaging biomarker extraction: Several biomarkers are obtained from medical images such as CT and MRI scans (size, amount of necrosis from tumor and lymph nodes, etc.). To extract those features, a custom software tool – called the NeoMark Image Processing Tool – has specifically been developed. – Genomic data cleaning and filtering: Extraction of genomic data and filtering of genes with low data quality and of those with high number of missing values. The NeoMark system was trained and initially validated in a multicenter pilot study (three European clinical centers involved: Two in Italy and one in Spain) basing on 86 patients affected by OSCC with a minimum follow-up of 12 months. The clinicians recognized the usefulness of the disease bioprofile (or diseasespecific profile) identified by NeoMark to evaluate the risk of disease reoccurrence of a patient at diagnosis, to stratify patients affected by OSCC at baseline according to the risk of recurrence, and to reserve a “tailored therapy” to each case