Global analysis of aberrant pre-mRNA splicing in glioblastoma using exon expression arrays

<p>Abstract</p> <p>Background</p> <p>Tumor-predominant splice isoforms were identified during comparative <it>in silico </it>sequence analysis of EST clones, suggesting that global aberrant alternative pre-mRNA splicing may be an epigenetic phenomenon in cancer. We used an exon expression array to perform an objective, genome-wide survey of glioma-specific splicing in 24 GBM and 12 nontumor brain samples. Validation studies were performed using RT-PCR on glioma cell lines, patient tumor and nontumor brain samples.</p> <p>Results</p> <p>In total, we confirmed 14 genes with glioma-specific splicing; seven were novel events identified by the exon expression array (<it>A2BP1, BCAS1, CACNA1G, CLTA, KCNC2, SNCB</it>, and <it>TPD52L2</it>). Our data indicate that large changes (> 5-fold) in alternative splicing are infrequent in gliomagenesis (< 3% of interrogated RefSeq entries). The lack of splicing changes may derive from the small number of splicing factors observed to be aberrantly expressed.</p> <p>Conclusion</p> <p>While we observed some tumor-specific alternative splicing, the number of genes showing exclusive tumor-specific isoforms was on the order of tens, rather than the hundreds suggested previously by <it>in silico </it>mining. Given the important role of alternative splicing in neural differentiation, there may be selective pressure to maintain a majority of splicing events in order to retain glial-like characteristics of the tumor cells.</p

Recommendations for In Vitro and In Vivo Testing of Magnetic Nanoparticle Hyperthermia Combined with Radiation Therapy

Magnetic nanoparticle (MNP)-mediated hyperthermia (MH) coupled with radiation therapy (RT) is a novel approach that has the potential to overcome various practical difficulties encountered in cancer treatment. In this work, we present recommendations for the in vitro and in vivo testing and application of the two treatment techniques. These recommendations were developed by the members of Working Group 3 of COST Action TD 1402: Multifunctional Nanoparticles for Magnetic Hyperthermia and Indirect Radiation Therapy (“Radiomag”). The purpose of the recommendations is not to provide definitive answers and directions but, rather, to outline those tests and considerations that a researcher must address in order to perform in vitro and in vivo studies. The recommendations are divided into 5 parts: (a) in vitro evaluation of MNPs; (b) in vitro evaluation of MNP-cell interactions; (c) in vivo evaluation of the MNPs; (d) MH combined with RT; and (e) pharmacokinetic studies of MNPs. Synthesis and characterization of the MNPs, as well as RT protocols, are beyond the scope of this wor

SMRT Sequencing of Paramecium Bursaria Chlorella Virus-1 Reveals Diverse Methylation Stability in Adenines Targeted by Restriction Modification Systems

Chloroviruses (family Phycodnaviridae) infect eukaryotic, freshwater, unicellular green algae. A unique feature of these viruses is an abundance of DNA methyltransferases, with isolates dedicating up to 4.5% of their protein coding potential to these genes. This diversity highlights just one of the long-standing values of the chlorovirus model system; where group-wide epigenomic characterization might begin to elucidate the function(s) of DNA methylation in large dsDNA viruses. We characterized DNA modifications in the prototype chlorovirus, PBCV-1, using single-molecule real time (SMRT) sequencing (aka PacBio). Results were compared to total available sites predicted in silico based on DNA sequence alone. SMRT-software detected N6-methyl-adenine (m6A) at GATC and CATG recognition sites, motifs previously shown to be targeted by PBCV-1 DNA methyltransferases M.CviAI and M. CviAII, respectively. At the same time, PacBio analyses indicated that 10.9% of the PBCV-1 genome had large interpulse duration ratio (ipdRatio) values, the primary metric for DNA modification identification. These events represent 20.6x more sites than can be accounted for by all available adenines in GATC and CATG motifs, suggesting base or backbone modifications other than methylation might be present. To define methylation stability, we cross-compared methylation status of each GATC and CATG sequence in three biological replicates and found ∼81% of sites were stably methylated, while ∼2% consistently lack methylation. The remaining 17% of sites were stochastically methylated. When methylation status was analyzed for both strands of each target, we show that palindromes existed in completely non-methylated states, fully-methylated states, or hemi-methylated states, though GATC sites more often lack methylation than CATG sequences. Given that both sequences are targeted by not just methyltransferases, but by restriction endonucleases that are together encoded by PBCV-1 as virus-originating restriction modification (RM) systems, there is strong selective pressure to modify all target sites. The finding that most instances of non-methylation are associated with hemi-methylation is congruent with observations that hemi-methylated palindromes are resistant to cleavage by restriction endonucleases. However, sites where hemi-methylation is conserved might represent a unique regulatory function for PBCV-1. This study serves as a baseline for future investigation into the epigenomics of chloroviruses and their giant virus relatives

Exploring the impact of group identity at university on psychological and behavioural outcomes

With respect to supporting student well-being and success, the current research developed a peer support scheme, built on the principles of Social Identity Theory (SIT). This was targeted towards first year undergraduate psychology students, in which measures of collective identity, sense of belonging, group efficacy, happiness and resilience were obtained, along with attendance and academic attainment. Following one academic year of being part of our peer support scheme, participants (N= 90) completed a questionnaire and consented to their attendance and attainment data to be used. It was found that students’ collective identity was positively related to their sense of belonging, group efficacy beliefs and happiness. Further, the sense of belonging was a reliable predictor of happiness, but not attendance or academic attainment. Therefore, there is some evidence to suggest that a SIT-driven peer support scheme can support students’ psychosocial well-being, although more is needed to ascertain whether this could be developed further to observe any course-related outcomes. Theoretical contributions to SIT are therefore presented, in which the insights can be applied to Higher Education beyond the UK

Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility.

Genome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk

GIBA: a clustering tool for detecting protein complexes

Background: During the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings. Methods: In this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes. Results: The efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices. Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results. Conclusion: GIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein - protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods