Integrated genomic analyses identify ARID1A and ARID1B alterations in the childhood cancer neuroblastoma

Neuroblastomas are tumors of peripheral sympathetic neurons and are the most common solid tumor in children. To determine the genetic basis for neuroblastoma we performed whole-genome sequencing (6 cases), exome sequencing (16 cases), genome-wide rearrangement analyses (32 cases), and targeted analyses of specific genomic loci (40 cases) using massively parallel sequencing. On average each tumor had 19 somatic alterations in coding genes (range, 3–70). Among genes not previously known to be involved in neuroblastoma, chromosomal deletions and sequence alterations of chromatin remodeling genes, ARID1A and ARID1B, were identified in 8 of 71 tumors (11%) and were associated with early treatment failure and decreased survival. Using tumor-specific structural alterations, we developed an approach to identify rearranged DNA fragments in sera, providing personalized biomarkers for minimal residual disease detection and monitoring. These results highlight dysregulation of chromatin remodeling in pediatric tumorigenesis and provide new approaches for the management of neuroblastoma patients

Sodium ion channel mutations in glioblastoma patients correlate with shorter survival

<p>Abstract</p> <p>Background</p> <p>Glioblastoma Multiforme (GBM) is the most common and invasive astrocytic tumor associated with dismal prognosis. Treatment for GBM patients has advanced, but the median survival remains a meager 15 months. In a recent study, 20,000 genes from 21 GBM patients were sequenced that identified frequent mutations in ion channel genes. The goal of this study was to determine whether ion channel mutations have a role in disease progression and whether molecular targeting of ion channels is a promising therapeutic strategy for GBM patients. Therefore, we compared GBM patient survival on the basis of presence or absence of mutations in calcium, potassium and sodium ion transport genes. Cardiac glycosides, known sodium channel inhibitors, were then tested for their ability to inhibit GBM cell proliferation.</p> <p>Results</p> <p>Nearly 90% of patients showed at least one mutation in ion transport genes. GBM patients with mutations in sodium channels showed a significantly shorter survival compared to patients with no sodium channel mutations, whereas a similar comparison based on mutational status of calcium or potassium ion channel mutations showed no survival differences. Experimentally, targeting GBM cells with cardiac glycosides such as digoxin and ouabain demonstrated preferential cytotoxicity against U-87 and D54 GBM cells compared to non-tumor astrocytes (NTAs).</p> <p>Conclusions</p> <p>These pilot studies of GBM patients with sodium channel mutations indicate an association with a more aggressive disease and significantly shorter survival. Moreover, inhibition of GBM cells by ion channel inhibitors such as cardiac glycosides suggest a therapeutic strategy with relatively safe drugs for targeting GBM ion channel mutations. <b>Key Words: </b>glioblastoma multiforme, ion channels, mutations, small molecule inhibitors, cardiac glycosides.</p

Somatic mutations in the chromatin remodeling gene ARID1A occur in several tumor types

Mutations in the chromatin remodeling gene ARID1A have recently been identified in the majority of ovarian clear cell carcinomas (OCCCs). To determine the prevalence of mutations in other tumor types, we evaluated 759 malignant neoplasms including those of the pancreas, breast, colon, stomach, lung, prostate, brain, and blood (leukemias). We identified truncating mutations in 6% of the neoplasms studied; nontruncating somatic mutations were identified in an additional 0.4% of neoplasms. Mutations were most commonly found in gastrointestinal samples with 12 of 119 (10%) colorectal and 10 of 100 (10%) gastric neoplasms, respectively, harboring changes. More than half of the mutated colorectal and gastric cancers displayed microsatellite instability (MSI) and the mutations in these tumors were out‐of‐frame insertions or deletions at mononucleotide repeats. Mutations were also identified in 2–8% of tumors of the pancreas, breast, brain (medulloblastomas), prostate, and lung, and none of these tumors displayed MSI. These findings suggest that the aberrant chromatin remodeling consequent to ARID1A inactivation contributes to a variety of different types of neoplasms. Hum Mutat 33:100–103, 2012. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89516/1/humu_21633_sm_Mat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/89516/2/21633_ftp.pd

RNAi revised - Target mRNA-dependent enhancement of gene silencing

The discovery of RNA interference (RNAi) gave rise to the development of new nucleic acid-based technologies as powerful investigational tools and potential therapeutics. Mechanistic key details of RNAi in humans need to be deciphered yet, before such approaches take root in biomedicine and molecular therapy. We developed and validated an in silico-based model of siRNA-mediated RNAi in human cells in order to link in vitro-derived pre-steady state kinetic data with a quantitative and time-resolved understanding of RNAi on the cellular level. The observation that product release by Argonaute 2 is accelerated in the presence of an excess of target RNA in vitro inspired us to suggest an associative mechanism for the RNA slicer reaction where incoming target mRNAs actively promote dissociation of cleaved mRNA fragments. This novel associative model is compatible with highmultiple turnover rates of RNAibased gene silencing in living cells and accounts for target mRNA concentration-dependent enhancement of the RNAi machinery

Analysis of multiplex gene expression maps obtained by voxelation

BackgroundGene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions.ResultsTo analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum.ConclusionThe experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists

Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model

<p>Abstract</p> <p>Background</p> <p>Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Binomial or Poisson distribution. However, tag counts observed across multiple libraries (for example, one or more groups of biological replicates) have additional variance that cannot be accommodated by this assumption alone. Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance. Here, a Poisson mixture model, which assumes excess variability arises from sampling a mixture of distinct components, is proposed and the merits of this model are discussed and evaluated.</p> <p>Results</p> <p>The goodness of fit of the Poisson mixture model on 15 sets of biological SAGE replicates is compared to the previously proposed hierarchical gamma-Poisson (negative binomial) model, and a substantial improvement is seen. In further support of the mixture model, there is observed: 1) an increase in the number of mixture components needed to fit the expression of tags representing more than one transcript; and 2) a tendency for components to cluster libraries into the same groups. A confidence score is presented that can identify tags that are differentially expressed between groups of SAGE libraries. Several examples where this test outperforms those previously proposed are highlighted.</p> <p>Conclusion</p> <p>The Poisson mixture model performs well as a) a method to represent SAGE data from biological replicates, and b) a basis to assign significance when testing for differential expression between multiple groups of replicates. Code for the R statistical software package is included to assist investigators in applying this model to their own data.</p

ProbFAST: Probabilistic Functional Analysis System Tool

<p>Abstract</p> <p>Background</p> <p>The post-genomic era has brought new challenges regarding the understanding of the organization and function of the human genome. Many of these challenges are centered on the meaning of differential gene regulation under distinct biological conditions and can be performed by analyzing the Multiple Differential Expression (MDE) of genes associated with normal and abnormal biological processes. Currently MDE analyses are limited to usual methods of differential expression initially designed for paired analysis.</p> <p>Results</p> <p>We proposed a web platform named ProbFAST for MDE analysis which uses Bayesian inference to identify key genes that are intuitively prioritized by means of probabilities. A simulated study revealed that our method gives a better performance when compared to other approaches and when applied to public expression data, we demonstrated its flexibility to obtain relevant genes biologically associated with normal and abnormal biological processes.</p> <p>Conclusions</p> <p>ProbFAST is a free accessible web-based application that enables MDE analysis on a global scale. It offers an efficient methodological approach for MDE analysis of a set of genes that are turned on and off related to functional information during the evolution of a tumor or tissue differentiation. ProbFAST server can be accessed at <url>http://gdm.fmrp.usp.br/probfast</url>.</p

Transcriptome analyses of the Giardia lamblia life cycle

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Molecular and Biochemical Parasitology 174 (2010): 62-65, doi:10.1016/j.molbiopara.2010.05.010.We quantified mRNA abundance from 10 stages in the Giardia lamblia life cycle in vitro using Serial Analysis of Gene Expression (SAGE). 163 abundant transcripts were expressed constitutively. 71 transcripts were upregulated specifically during excystation and 42 during encystation. Nonetheless, the transcriptomes of cysts and trophozoites showed major differences. SAGE detected co-expressed clusters of 284 transcripts differentially expressed in cysts and excyzoites and 287 transcripts in vegetative trophozoites and encysting cells. All clusters included known genes and pathways as well as proteins unique to Giardia or diplomonads. SAGE analysis of the Giardia life cycle identified a number of kinases, phosphatases, and DNA replication proteins involved in excystation and encystation, which could be important for examining the roles of cell signaling in giardial differentiation. Overall, these data pave the way for directed gene discovery and a better understanding of the biology of Giardia lamblia.BJD, DSR, and FDG were supported by NIH grants AI42488, GM61896, DK35108, and AI051687. DP and SGS were supported by grants from the Swedish Natural Science Research Council, the Swedish Medical Research Council, and the Karolinska Institutet. AGM, SRB, SPP, and MJC were supported by NIH grant AI51089 and by the Marine Biological Laboratory’s Program in Global Infectious Diseases, funded by the Ellison Medical Foundation