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Integrated (epi)-Genomic Analyses Identify Subgroup-Specific Therapeutic Targets in CNS Rhabdoid Tumors.

Integrated (epi)-Genomic Analyses Identify Subgroup-Specific Therapeutic Targets in CNS Rhabdoid Tumors.

Cancer Cell. 2016 Dec 12;30(6):891-908

Authors: Torchia J, Golbourn B, Feng S, Ho KC, Sin-Chan P, Vasiljevic A, Norman JD, Guilhamon P, Garzia L, Agamez NR, Lu M, Chan TS, Picard D, de Antonellis P, Khuong-Quang DA, Planello AC, Zeller C, Barsyte-Lovejoy D, Lafay-Cousin L, Letourneau L, Bourgey M, Yu M, Gendoo DM, Dzamba M, Barszczyk M, Medina T, Riemenschneider AN, Morrissy AS, Ra YS, Ramaswamy V, Remke M, Dunham CP, Yip S, Ng HK, Lu JQ, Mehta V, Albrecht S, Pimentel J, Chan JA, Somers GR, Faria CC, Roque L, Fouladi M, Hoffman LM, Moore AS, Wang Y, Choi SA, Hansford JR, Catchpoole D, Birks DK, Foreman NK, Strother D, Klekner A, Bognár L, Garami M, Hauser P, Hortobágyi T, Wilson B, Hukin J, Carret AS, Van Meter TE, Hwang EI, Gajjar A, Chiou SH, Nakamura H, Toledano H, Fried I, Fults D, Wataya T, Fryer C, Eisenstat DD, Scheinemann K, Fleming AJ, Johnston DL, Michaud J, Zelcer S, Hammond R, Afzal S, Ramsay DA, Sirachainan N, Hongeng S, Larbcharoensub N, Grundy RG, Lulla RR, Fangusaro JR, Druker H, Bartels U, Grant R, Malkin D, McGlade CJ, Nicolaides T, Tihan T, Phillips J, Majewski J, Montpetit A, Bourque G, Bader GD, Reddy AT, Gillespie GY, Warmuth-Metz M, Rutkowski S, Tabori U, Lupien M, Brudno M, Schüller U, Pietsch T, Judkins AR, Hawkins CE, Bouffet E, Kim SK, Dirks PB, Taylor MD, Erdreich-Epstein A, Arrowsmith CH, De Carvalho DD, Rutka JT, Jabado N, Huang A

Abstract
We recently reported that atypical teratoid rhabdoid tumors (ATRTs) comprise at least two transcriptional subtypes with different clinical outcomes; however, the mechanisms underlying therapeutic heterogeneity remained unclear. In this study, we analyzed 191 primary ATRTs and 10 ATRT cell lines to define the genomic and epigenomic landscape of ATRTs and identify subgroup-specific therapeutic targets. We found ATRTs segregated into three epigenetic subgroups with distinct genomic profiles, SMARCB1 genotypes, and chromatin landscape that correlated with differential cellular responses to a panel of signaling and epigenetic inhibitors. Significantly, we discovered that differential methylation of a PDGFRB-associated enhancer confers specific sensitivity of group 2 ATRT cells to dasatinib and nilotinib, and suggest that these are promising therapies for this highly lethal ATRT subtype.

PMID: 27960086 [PubMed - in process]



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Clarifying intact 3D tissues on a microfluidic chip for high-throughput structural analysis.

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Clarifying intact 3D tissues on a microfluidic chip for high-throughput structural analysis.

Proc Natl Acad Sci U S A. 2016 Dec 12;:

Authors: Chen YY, Silva PN, Syed AM, Sindhwani S, Rocheleau JV, Chan WC

Abstract
On-chip imaging of intact three-dimensional tissues within microfluidic devices is fundamentally hindered by intratissue optical scattering, which impedes their use as tissue models for high-throughput screening assays. Here, we engineered a microfluidic system that preserves and converts tissues into optically transparent structures in less than 1 d, which is 20× faster than current passive clearing approaches. Accelerated clearing was achieved because the microfluidic system enhanced the exchange of interstitial fluids by 567-fold, which increased the rate of removal of optically scattering lipid molecules from the cross-linked tissue. Our enhanced clearing process allowed us to fluorescently image and map the segregation and compartmentalization of different cells during the formation of tumor spheroids, and to track the degradation of vasculature over time within extracted murine pancreatic islets in static culture, which may have implications on the efficacy of beta-cell transplantation treatments for type 1 diabetes. We further developed an image analysis algorithm that automates the analysis of the vasculature connectivity, volume, and cellular spatial distribution of the intact tissue. Our technique allows whole tissue analysis in microfluidic systems, and has implications in the development of organ-on-a-chip systems, high-throughput drug screening devices, and in regenerative medicine.

PMID: 27956625 [PubMed - as supplied by publisher]



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RNAcompete methodology and application to determine sequence preferences of unconventional RNA-binding proteins.

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RNAcompete methodology and application to determine sequence preferences of unconventional RNA-binding proteins.

Methods. 2016 Dec 09;:

Authors: Ray D, C H Ha K, Nie K, Zheng H, Hughes TR, Morris QD

Abstract
RNA-binding proteins (RBPs) participate in diverse cellular processes and have important roles in human development and disease. The human genome, and that of many other eukaryotes, encodes hundreds of RBPs that contain canonical sequence-specific RNA-binding domains (RBDs) as well as numerous other unconventional RNA binding proteins (ucRBPs). ucRBPs physically associate with RNA but lack common RBDs. The degree to which these proteins bind RNA, in a sequence specific manner, is unknown. Here, we provide a detailed description of both the laboratory and data processing methods for RNAcompete, a method we have previously used to analyze the RNA binding preferences of hundreds of RBD-containing RBPs, from diverse eukaryotes. We also determine the RNA-binding preferences for two human ucRBPs, NUDT21 and CNBP, and use this analysis to exemplify the RNAcompete pipeline. The results of our RNAcompete experiments are consistent with independent RNA-binding data for these proteins and demonstrate the utility of RNAcompete for analyzing the growing repertoire of ucRBPs.

PMID: 27956239 [PubMed - as supplied by publisher]



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Systematic Identification of Oncogenic EGFR Interaction Partners.

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Systematic Identification of Oncogenic EGFR Interaction Partners.

J Mol Biol. 2016 Dec 09;:

Authors: Petschnigg J, Kotlyar M, Blair L, Jurisica I, Stagljar I, Ketteler R

Abstract
The Epidermal Growth Factor Receptor (EGFR) is a receptor tyrosine kinase that - once activated upon ligand binding - leads to receptor dimerization, recruitment of protein complexes and activation of multiple signaling cascades. The EGFR is frequently overexpressed or mutated in various cancers leading to aberrant signaling and tumor growth. Hence, identification of interaction partners that bind to mutated EGFR can help identify novel targets for drug discovery. Here, we used a systematic approach to identify novel proteins that are involved in cancerous EGFR-signaling. Using a combination of high-content imaging and a mammalian membrane two-hybrid protein-protein interaction (MaMTH) method, we identified 8 novel interaction partners of EGFR, out of which half strongly interacted with oncogenic, hyperactive EGFR variants. One of these, TACC3, stabilizes EGFR on the cell surface, which results in an increase in downstream signaling via the MAPK and AKT pathway. Depletion of TACC3 from cells using shRNA knockdown or small molecule targeting reduced mitogenic signaling in non-small cell lung cancer cell lines, suggesting that targeting TACC3 has potential as a new therapeutic approach for non-small cell lung cancer.

PMID: 27956147 [PubMed - as supplied by publisher]



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Machine learning and computer vision approaches for phenotypic profiling.

Machine learning and computer vision approaches for phenotypic profiling.

J Cell Biol. 2016 Dec 09;:

Authors: Grys BT, Lo DS, Sahin N, Kraus OZ, Morris Q, Boone C, Andrews BJ

Abstract
With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.

PMID: 27940887 [PubMed - as supplied by publisher]



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The profile of adsorbed plasma and serum proteins on methacrylic acid copolymer beads: Effect on complement activation.

The profile of adsorbed plasma and serum proteins on methacrylic acid copolymer beads: Effect on complement activation.

Biomaterials. 2016 Nov 25;118:74-83

Authors: Wells LA, Guo H, Emili A, Sefton MV

Abstract
Polymer beads made of 45% methacrylic acid co methyl methacrylate (MAA beads) promote vascular regenerative responses in contrast to control materials without methacrylic acid (here polymethyl methacrylate beads, PMMA). In vitro and in vivo studies suggest that MAA copolymers induce differences in macrophage phenotype and polarization and inflammatory responses, presumably due to protein adsorption differences between the beads. To explore differences in protein adsorption in an unbiased manner, we used high resolution shotgun mass spectrometry to identify and compare proteins that adsorb from human plasma or serum onto MAA and PMMA beads. From plasma, MAA beads adsorbed many complement proteins, such as C1q, C4-related proteins and the complement inhibitor factor H, while PMMA adsorbed proteins, such as albumin, C3 and apolipoproteins. Because of the differences in complement protein adsorption, follow-up studies focused on using ELISA to assess complement activation. When incubated in serum, MAA beads generated significantly lower levels of soluble C5b9 and C3a/C3adesarg in comparison to PMMA beads, indicating a decrease in complement activation with MAA beads. The differences in adsorbed protein on the two materials likely alter subsequent cell-material interactions that ultimately result in different host responses and local vascularization.

PMID: 27940384 [PubMed - as supplied by publisher]



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Functional characterisation of long intergenic non-coding RNAs through genetic interaction profiling in Saccharomyces cerevisiae.

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Functional characterisation of long intergenic non-coding RNAs through genetic interaction profiling in Saccharomyces cerevisiae.

BMC Biol. 2016 Dec 07;14(1):106

Authors: Kyriakou D, Stavrou E, Demosthenous P, Angelidou G, San Luis BJ, Boone C, Promponas VJ, Kirmizis A

Abstract
BACKGROUND: Transcriptome studies have revealed that many eukaryotic genomes are pervasively transcribed producing numerous long non-coding RNAs (lncRNAs). However, only a few lncRNAs have been ascribed a cellular role thus far, with most regulating the expression of adjacent genes. Even less lncRNAs have been annotated as essential hence implying that the majority may be functionally redundant. Therefore, the function of lncRNAs could be illuminated through systematic analysis of their synthetic genetic interactions (GIs).
RESULTS: Here, we employ synthetic genetic array (SGA) in Saccharomyces cerevisiae to identify GIs between long intergenic non-coding RNAs (lincRNAs) and protein-coding genes. We first validate this approach by demonstrating that the telomerase RNA TLC1 displays a GI network that corresponds to its well-described function in telomere length maintenance. We subsequently performed SGA screens on a set of uncharacterised lincRNAs and uncover their connection to diverse cellular processes. One of these lincRNAs, SUT457, exhibits a GI profile associating it to telomere organisation and we consistently demonstrate that SUT457 is required for telomeric overhang homeostasis through an Exo1-dependent pathway. Furthermore, the GI profile of SUT457 is distinct from that of its neighbouring genes suggesting a function independent to its genomic location. Accordingly, we show that ectopic expression of this lincRNA suppresses telomeric overhang accumulation in sut457Δ cells assigning a trans-acting role for SUT457 in telomere biology.
CONCLUSIONS: Overall, our work proposes that systematic application of this genetic approach could determine the functional significance of individual lncRNAs in yeast and other complex organisms.

PMID: 27927215 [PubMed - in process]



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A 17-gene stemness score for rapid determination of risk in acute leukaemia.

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A 17-gene stemness score for rapid determination of risk in acute leukaemia.

Nature. 2016 12 15;540(7633):433-437

Authors: Ng SW, Mitchell A, Kennedy JA, Chen WC, McLeod J, Ibrahimova N, Arruda A, Popescu A, Gupta V, Schimmer AD, Schuh AC, Yee KW, Bullinger L, Herold T, Görlich D, Büchner T, Hiddemann W, Berdel WE, Wörmann B, Cheok M, Preudhomme C, Dombret H, Metzeler K, Buske C, Löwenberg B, Valk PJ, Zandstra PW, Minden MD, Dick JE, Wang JC

Abstract
Refractoriness to induction chemotherapy and relapse after achievement of remission are the main obstacles to cure in acute myeloid leukaemia (AML). After standard induction chemotherapy, patients are assigned to different post-remission strategies on the basis of cytogenetic and molecular abnormalities that broadly define adverse, intermediate and favourable risk categories. However, some patients do not respond to induction therapy and another subset will eventually relapse despite the lack of adverse risk factors. There is an urgent need for better biomarkers to identify these high-risk patients before starting induction chemotherapy, to enable testing of alternative induction strategies in clinical trials. The high rate of relapse in AML has been attributed to the persistence of leukaemia stem cells (LSCs), which possess a number of stem cell properties, including quiescence, that are linked to therapy resistance. Here, to develop predictive and/or prognostic biomarkers related to stemness, we generated a list of genes that are differentially expressed between 138 LSC(+) and 89 LSC(-) cell fractions from 78 AML patients validated by xenotransplantation. To extract the core transcriptional components of stemness relevant to clinical outcomes, we performed sparse regression analysis of LSC gene expression against survival in a large training cohort, generating a 17-gene LSC score (LSC17). The LSC17 score was highly prognostic in five independent cohorts comprising patients of diverse AML subtypes (n = 908) and contributed greatly to accurate prediction of initial therapy resistance. Patients with high LSC17 scores had poor outcomes with current treatments including allogeneic stem cell transplantation. The LSC17 score provides clinicians with a rapid and powerful tool to identify AML patients who do not benefit from standard therapy and who should be enrolled in trials evaluating novel upfront or post-remission strategies.

PMID: 27926740 [PubMed - indexed for MEDLINE]



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Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism.

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Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism.

Nature. 2016 12 15;540(7633):423-427

Authors: Parikshak NN, Swarup V, Belgard TG, Irimia M, Ramaswami G, Gandal MJ, Hartl C, Leppa V, Ubieta LT, Huang J, Lowe JK, Blencowe BJ, Horvath S, Geschwind DH

Abstract
Autism spectrum disorder (ASD) involves substantial genetic contributions. These contributions are profoundly heterogeneous but may converge on common pathways that are not yet well understood. Here, through post-mortem genome-wide transcriptome analysis of the largest cohort of samples analysed so far, to our knowledge, we interrogate the noncoding transcriptome, alternative splicing, and upstream molecular regulators to broaden our understanding of molecular convergence in ASD. Our analysis reveals ASD-associated dysregulation of primate-specific long noncoding RNAs (lncRNAs), downregulation of the alternative splicing of activity-dependent neuron-specific exons, and attenuation of normal differences in gene expression between the frontal and temporal lobes. Our data suggest that SOX5, a transcription factor involved in neuron fate specification, contributes to this reduction in regional differences. We further demonstrate that a genetically defined subtype of ASD, chromosome 15q11.2-13.1 duplication syndrome (dup15q), shares the core transcriptomic signature observed in idiopathic ASD. Co-expression network analysis reveals that individuals with ASD show age-related changes in the trajectory of microglial and synaptic function over the first two decades, and suggests that genetic risk for ASD may influence changes in regional cortical gene expression. Our findings illustrate how diverse genetic perturbations can lead to phenotypic convergence at multiple biological levels in a complex neuropsychiatric disorder.

PMID: 27919067 [PubMed - indexed for MEDLINE]



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Proteomic analysis of the human KEOPS complex identifies C14ORF142 as a core subunit homologous to yeast Gon7.

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Proteomic analysis of the human KEOPS complex identifies C14ORF142 as a core subunit homologous to yeast Gon7.

Nucleic Acids Res. 2016 Nov 29;:

Authors: Wan LC, Maisonneuve P, Szilard RK, Lambert JP, Ng TF, Manczyk N, Huang H, Laister R, Caudy AA, Gingras AC, Durocher D, Sicheri F

Abstract
The KEOPS/EKC complex is a tRNA modification complex involved in the biosynthesis of N(6)-threonylcarbamoyladenosine (t(6)A), a universally conserved tRNA modification found on ANN-codon recognizing tRNAs. In archaea and eukaryotes, KEOPS is composed of OSGEP/Kae1, PRPK/Bud32, TPRKB/Cgi121 and LAGE3/Pcc1. In fungi, KEOPS contains an additional subunit, Gon7, whose orthologs outside of fungi, if existent, remain unidentified. In addition to displaying defective t(6)A biosynthesis, Saccharomyces cerevisiae strains harboring KEOPS mutations are compromised for telomere homeostasis, growth and transcriptional co-activation. To identify a Gon7 ortholog in multicellular eukaryotes as well as to uncover KEOPS-interacting proteins that may link t(6)A biosynthesis to the diverse set of KEOPS mutant phenotypes, we conducted a proteomic analysis of human KEOPS. This work identified 152 protein interactors, one of which, C14ORF142, interacted strongly with all four KEOPS subunits, suggesting that it may be a core component of human KEOPS. Further characterization of C14ORF142 revealed that it shared a number of biophysical and biochemical features with fungal Gon7, suggesting that C14ORF142 is the human ortholog of Gon7. In addition, our proteomic analysis identified specific interactors for different KEOPS subcomplexes, hinting that individual KEOPS subunits may have additional functions outside of t(6)A biosynthesis.

PMID: 27903914 [PubMed - as supplied by publisher]



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