Re-analysis of microarray data reveals insights into altered transcriptional activity of TH17 and TReg signalling in psoriasis

Identifying differential expression of genes in psoriatic and healthy skin by microarray data analysis is a key approach to understand the pathogenesis of psoriasis. Analysis of more than one dataset to identify genes commonly upregulated reduces the likelihood of false positives and narrows down the possible signature genes. Genes controlling the critical balance between T helper 17 and regulatory T cells are of special interest in psoriasis. Our objectives were to identify genes that are consistently upregulated in lesional skin from three published microarray datasets. We carried out a reanalysis of gene expression data extracted from three experiments on samples from psoriatic and nonlesional skin using the same stringency threshold and software and further compared the expression levels of 92 genes related to the T helper 17 and regulatory T cell signaling pathways. We found 73 probe sets representing 57 genes commonly upregulated in lesional skin from all datasets. These included 26 probe sets representing 20 genes that have no previous link to the etiopathogenesis of psoriasis. These genes may represent novel therapeutic targets and surely need more rigorous experimental testing to be validated. Our analysis also identified 12 of 92 genes known to be related to the T helper 17 and regulatory T cell signaling pathways, and these were found to be differentially expressed in the lesional skin samples.

Development of a genotyping microarray for Usher syndrome

Usher syndrome, a combination of retinitis pigmentosa (RP) and sensorineural hearing loss with or without vestibular dysfunction, displays a high degree of clinical and genetic heterogeneity. Three clinical subtypes can be distinguished, based on the age of onset and severity of the hearing impairment, and the presence or absence of vestibular abnormalities. Thus far, eight genes have been implicated in the syndrome, together comprising 347 protein-coding exons.

Preparation of planar retinal specimens:verification by histology, mRNA profiling, and proteome analysis

Elucidation of the transcriptome and proteome of the normal retina will be difficult since it is comprised of at least 55 different cell types. However the characteristic layered cellular anatomy of the retina makes it amenable to planar sectioning, enabling the generation of enriched retinal cell populations. The aim of this study was to validate a reproducible method for preparing enriched retinal layers from porcine retina.

Automated, high performance, flow-through chemiluminescence microarray for the multiplexed detection of phycotoxins

A novel multiplexed immunoassay for the analysis of phycotoxins in shellfish samples has been developed. Therefore, a regenerable chemiluminescence (CL) microarray was established which is able to analyze automatically three different phycotoxins (domoic acid (DA), okadaic acid (OA) and saxitoxin (STX)) in parallel on the analysis platform MCR3. As a test format an indirect competitive immunoassay format was applied. These phycotoxins were directly immobilized on an epoxy-activated PEG chip surface. The parallel analysis was enabled by the simultaneous addition of all analytes and specific antibodies on one microarray chip. After the competitive reaction, the CL signal was recorded by a CCD camera. Due to the ability to regenerate the toxin microarray, internal calibrations of phycotoxins in parallel were performed using the same microarray chip, which was suitable for 25 consecutive measurements. For the three target phycotoxins multi-analyte calibration curves were generated. In extracted shellfish matrix, the determined LODs for DA, OA and STX with values of 0.5±0.3 µg L(-1), 1.0±0.6 µg L(-1), and 0.4±0.2 µg L(-1) were slightly lower than in PBS buffer. For determination of toxin recoveries, the observed signal loss in the regeneration was corrected. After applying mathematical corrections spiked shellfish samples were quantified with recoveries for DA, OA, and STX of 86.2%, 102.5%, and 61.6%, respectively, in 20 min. This is the first demonstration of an antibody based phycotoxin microarray.

Monitoring a toxic bloom of Alexandrium minutum using novel microarray and multiplex surface plasmon resonance biosensor technology

Blooms of Alexandrium occur annually during the summer months in the North Channel of Cork Harbour on the south coast of Ireland. This study monitored an extensive bloom of the toxin producing Alexandrium minutum during the summer of 2011 with the use of the MIDTAL (Microarrays for the Detection of Toxic Algae) microarray and a prototype multiplex surface plasmon resonance (multi SPR) biosensor. Microarray signal intensities and toxin results from three testing platforms of the prototype multi SPR biosensor, commercial (CER) enzyme-linked immunosorbent assay (ELISA) and high performance liquid chromatography (HPLC) were compared against light microscopy counts. The main aim was to demonstrate the use of these methodologies to support national monitoring agencies by providing a faster and more accurate means of identifying and quantifying the harmful phytoplankton community and their toxins in natural water samples. Both the microarray signals and multi SPR biosensor results followed a significant trend with light microscopy results and both techniques indicated detection limits of <4000 cells of A. minutum in natural seawater samples.

Development of a planar waveguide microarray for the monitoring and early detection of five harmful algal toxins in water and cultures

A novel multiplex microarray has been developed for the detection of five groups of harmful algal and cyanobacterial toxins found in marine, brackish, and freshwater environments including domoic acid (DA), okadaic acid (OA, and analogues), saxitoxin (STX, and analogues), cylindrospermopsin (CYN) and microcystins (MC, and analogues). The sensitivity and specificity were determined and feasibility to be used as a screening tool investigated. Results for algal/cyanobacterial cultures (n = 12) and seawater samples (n = 33) were compared to conventional analytical methods, such as high performance liquid chromatography (HPLC) and liquid chromatography tandem mass spectrometry (LC-MS/MS). Detection limits for the 15 min assay were 0.37, 0.44, 0.05, 0.08, and 0.40 ng/mL for DA, OA, STX, CYN, and MC, respectively. The correlation of data obtained from the microarray compared to conventional analysis for the 12 cultures was r(2) = 0.83. Analysis of seawater samples showed that 82, 82, 70, 82, and 12% of samples were positive (>IC20) compared to 67, 55, 36, 0, and 0% for DA, OA, STX, CYN, and MC, respectively, for conventional analytical methods. The discrepancies in results can be attributed to the enhanced sensitivity and cross-reactivity profiles of the antibodies in the MBio microarray. The feasibility of the microarray as a rapid, easy to use, and highly sensitive screening tool has been illustrated for the five-plex detection of biotoxins. The research demonstrates an early warning screening assay to support national monitoring agencies by providing a faster and more accurate means of identifying and quantifying harmful toxins in water samples.

Digital pathology and image analysis in tissue biomarker research

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

DNA methylation subgroups and the CpG island methylator phenotype in gastric cancer: a comprehensive profiling approach

BACKGROUND: Methylation-induced silencing of promoter CpG islands in tumor suppressor genes plays an important role in human carcinogenesis. In colorectal cancer, the CpG island methylator phenotype (CIMP) is defined as widespread and elevated levels of DNA methylation and CIMP+ tumors have distinctive clinicopathological and molecular features. In contrast, the existence of a comparable CIMP subtype in gastric cancer (GC) has not been clearly established. To further investigate this issue, in the present study we performed comprehensive DNA methylation profiling of a well-characterised series of primary GC.

METHODS: The methylation status of 1,421 autosomal CpG sites located within 768 cancer-related genes was investigated using the Illumina GoldenGate Methylation Panel I assay on DNA extracted from 60 gastric tumors and matched tumor-adjacent gastric tissue pairs. Methylation data was analysed using a recursively partitioned mixture model and investigated for associations with clinicopathological and molecular features including age, Helicobacter pylori status, tumor site, patient survival, microsatellite instability and BRAF and KRAS mutations.

RESULTS: A total of 147 genes were differentially methylated between tumor and matched tumor-adjacent gastric tissue, with HOXA5 and hedgehog signalling being the top-ranked gene and signalling pathway, respectively. Unsupervised clustering of methylation data revealed the existence of 6 subgroups under two main clusters, referred to as L (low methylation; 28% of cases) and H (high methylation; 72%). Female patients were over-represented in the H tumor group compared to L group (36% vs 6%; P = 0.024), however no other significant differences in clinicopathological or molecular features were apparent. CpG sites that were hypermethylated in group H were more frequently located in CpG islands and marked for polycomb occupancy.

CONCLUSIONS: High-throughput methylation analysis implicates genes involved in embryonic development and hedgehog signaling in gastric tumorigenesis. GC is comprised of two major methylation subtypes, with the highly methylated group showing some features consistent with a CpG island methylator phenotype.

Sub-nA spatially resolved conductivity profiling of surface and interface defects in ceria films

Spatial variability of conductivity in ceria is explored using scanning probe microscopy (SPM) with galvanostatic control. Ionically blocking electrodes are used to probe the conductivity under opposite polarities to reveal possible differences in the defect structure across a thin film of CeO2. Data suggests the existence of a large spatial inhomogeneity that could give rise to constant phase elements during standard electrochemical characterization, potentially affecting the overall conductivity of films on the macroscale. The approach discussed here can also be utilized for other mixed ionic electronic conductor (MIEC) systems including memristors and electroresistors, as well as physical systems such as ferroelectric tunneling barriers.

Identification of systemic immune response markers through metabolomic profiling of plasma from calves given an intra-nasally delivered respiratory vaccine

Vaccination procedures within the cattle industry are important disease control tools to minimize economic and welfare burdens associated with respiratory pathogens. However, new vaccine, antigen and carrier technologies are required to combat emerging viral strains and enhance the efficacy of respiratory vaccines, particularly at the point of pathogen entry. New technologies, specifically metabolomic profiling, could be applied to identify metabolite immune-correlates representative of immune protection following vaccination aiding in the design and screening of vaccine candidates. This study for the first time demonstrates the ability of untargeted UPLC-MS metabolomic profiling to identify metabolite immune correlates characteristic of immune responses following mucosal vaccination in calves. Male Holstein Friesian calves were vaccinated with Pfizer Rispoval® PI3 + RSV intranasal vaccine and metabolomic profiling of post-vaccination plasma revealed 12 metabolites whose peak intensities differed significantly from controls. Plasma levels of glycocholic acid, N-[(3α,5β,12α)-3,12-Dihydroxy-7,24-dioxocholan-24-yl]glycine, uric acid and biliverdin were found to be significantly elevated in vaccinated animals following secondary vaccine administration, whereas hippuric acid significantly decreased. In contrast, significant upregulation of taurodeoxycholic acid and propionylcarnitine levels were confined to primary vaccine administration. Assessment of such metabolite markers may provide greater information on the immune pathways stimulated from vaccine formulations and benchmarking early metabolomic responses to highly immunogenic vaccine formulations could provide a means for rapidly assessing new vaccine formulations. Furthermore, the identification of metabolic systemic immune response markers which relate to specific cell signaling pathways of the immune system could allow for targeted vaccine design to stimulate key pathways which can be assessed at the metabolic level.

Genome-wide DNA profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome

Marginal zone B-cell lymphomas (MZLs) have been divided into 3 distinct subtypes (extranodal MZLs of mucosa-associated lymphoid tissue [MALT] type, nodal MZLs, and splenic MZLs). Nevertheless, the relationship between the subtypes is still unclear. We performed a comprehensive analysis of genomic DNA copy number changes in a very large series of MZL cases with the aim of addressing this question. Samples from 218 MZL patients (25 nodal, 57 MALT, 134 splenic, and 2 not better specified MZLs) were analyzed with the Affymetrix Human Mapping 250K SNP arrays, and the data combined with matched gene expression in 33 of 218 cases. MALT lymphoma presented significantly more frequently gains at 3p, 6p, 18p, and del(6q23) (TNFAIP3/A20), whereas splenic MZLs was associated with del(7q31), del(8p). Nodal MZLs did not show statistically significant differences compared with MALT lymphoma while lacking the splenic MZLs-related 7q losses. Gains of 3q and 18q were common to all 3 subtypes. del(8p) was often present together with del(17p) (TP53). Although del(17p) did not determine a worse outcome and del(8p) was only of borderline significance, the presence of both deletions had a highly significant negative impact on the outcome of splenic MZLs.

An Evidential Improvement for Gender Profiling.

CCTV systems are broadly deployed in the present world. To ensure
in-time reaction for intelligent surveillance, it is a fundamental task for real-world
applications to determine the gender of people of interest. However, normal video
algorithms for gender profiling (usually face profiling) have three drawbacks.
First, the profiling result is always uncertain. Second, for a time-lasting gender
profiling algorithm, the result is not stable. The degree of certainty usually varies, sometimes even to the extent that a male is classified as a female, and vice versa. Third, for a robust profiling result in cases were a person’s face is not visible, other features, such as body shape, are required. These algorithms may provide different recognition results - at the very least, they will provide different degrees of certainties. To overcome these problems, in this paper, we introduce an evidential approach that makes use of profiling results from multiple algorithms over a period of time. Experiments show that this approach does provide better results than single profiling results and classic fusion results.