Systems-wide RNAi analysis of CASP8AP2/FLASH shows transcriptional deregulation of the replication-dependent histone genes and extensive effects on the transcriptome of colorectal cancer cells

<p>Abstract</p> <p>Background</p> <p>Colorectal carcinomas (CRC) carry massive genetic and transcriptional alterations that influence multiple cellular pathways. The study of proteins whose loss-of-function (LOF) alters the growth of CRC cells can be used to further understand the cellular processes cancer cells depend upon for survival.</p> <p>Results</p> <p>A small-scale RNAi screen of ~400 genes conducted in SW480 CRC cells identified several candidate genes as required for the viability of CRC cells, most prominently <it>CASP8AP2</it>/<it>FLASH</it>. To understand the function of this gene in maintaining the viability of CRC cells in an unbiased manner, we generated gene specific expression profiles following RNAi. Silencing of <it>CASP8AP2</it>/<it>FLASH </it>resulted in altered expression of over 2500 genes enriched for genes associated with cellular growth and proliferation. Loss of CASP8AP2/FLASH function was significantly associated with altered transcription of the genes encoding the replication-dependent histone proteins as a result of the expression of the non-canonical polyA variants of these transcripts. Silencing of <it>CASP8AP2</it>/<it>FLASH </it>also mediated enrichment of changes in the expression of targets of the NFκB and MYC transcription factors. These findings were confirmed by whole transcriptome analysis of <it>CASP8AP2</it>/<it>FLASH </it>silenced cells at multiple time points. Finally, we identified and validated that CASP8AP2/FLASH LOF increases the expression of neurofilament heavy polypeptide (NEFH), a protein recently linked to regulation of the AKT1/ß-catenin pathway.</p> <p>Conclusions</p> <p>We have used unbiased RNAi based approaches to identify and characterize the function of CASP8AP2/FLASH, a protein not previously reported as required for cell survival. This study further defines the role CASP8AP2/FLASH plays in the regulating expression of the replication-dependent histones and shows that its LOF results in broad and reproducible effects on the transcriptome of colorectal cancer cells including the induction of expression of the recently described tumor suppressor gene <it>NEFH</it>.</p

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression

Activation of Type 1 Cannabinoid Receptor (CB1R) promotes neurogenesis in murine subventricular zone cell cultures

The endocannabinoid system has been implicated in the modulation of adult neurogenesis. Here, we describe the effect of type 1 cannabinoid receptor (CB1R) activation on self-renewal, proliferation and neuronal differentiation in mouse neonatal subventricular zone (SVZ) stem/progenitor cell cultures. Expression of CB1R was detected in SVZ-derived immature cells (Nestin-positive), neurons and astrocytes. Stimulation of the CB1R by (R)-(+)-Methanandamide (R-m-AEA) increased self-renewal of SVZ cells, as assessed by counting the number of secondary neurospheres and the number of Sox2+/+ cell pairs, an effect blocked by Notch pathway inhibition. Moreover, R-m-AEA treatment for 48 h, increased proliferation as assessed by BrdU incorporation assay, an effect mediated by activation of MAPK-ERK and AKT pathways. Surprisingly, stimulation of CB1R by R-m-AEA also promoted neuronal differentiation (without affecting glial differentiation), at 7 days, as shown by counting the number of NeuN-positive neurons in the cultures. Moreover, by monitoring intracellular calcium concentrations ([Ca2+](i)) in single cells following KCl and histamine stimuli, a method that allows the functional evaluation of neuronal differentiation, we observed an increase in neuronal-like cells. This proneurogenic effect was blocked when SVZ cells were co-incubated with R-m-AEA and the CB1R antagonist AM 251, for 7 days, thus indicating that this effect involves CB1R activation. In accordance with an effect on neuronal differentiation and maturation, R-m-AEA also increased neurite growth, as evaluated by quantifying and measuring the number of MAP2-positive processes. Taken together, these results demonstrate that CB1R activation induces proliferation, self-renewal and neuronal differentiation from mouse neonatal SVZ cell cultures.Fundacao para a Ciencia e a Tecnologia - Portugal [POCTI/SAU-NEU/68465/2006, PTDC/SAU-NEU/104415/2008, PTDC/SAU-NEU/101783/2008, POCTI/SAU-NEU/110838/2009]; Fundacao Calouste Gulbenkian [96542]; Fundacao para a Ciencia e Tecnologiainfo:eu-repo/semantics/publishedVersio

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making

Objectives: The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). / Study Design and Setting: Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach. / Results: Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose–response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either “off-the-shelf” or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines. / Conclusion: This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care–related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics)

Genetic architecture of gene expression in ovine skeletal muscle

In livestock populations the genetic contribution to muscling is intensively monitored in the progeny of industry sires and used as a tool in selective breeding programs. The genes and pathways conferring this genetic merit are largely undefined. Genetic variation within a population has potential, amongst other mechanisms, to alter gene expression via cis- or trans-acting mechanisms in a manner that impacts the functional activities of specific pathways that contribute to muscling traits. By integrating sire-based genetic merit information for a muscling trait with progeny-based gene expression data we directly tested the hypothesis that there is genetic structure in the gene expression program in ovine skeletal muscle. Results The genetic performance of six sires for a well defined muscling trait, longissimus lumborum muscle depth, was measured using extensive progeny testing and expressed as an Estimated Breeding Value by comparison with contemporary sires. Microarray gene expression data were obtained for longissimus lumborum samples taken from forty progeny of the six sires (4-8 progeny/sire). Initial unsupervised hierarchical clustering analysis revealed strong genetic architecture to the gene expression data, which also discriminated the sire-based Estimated Breeding Value for the trait. An integrated systems biology approach was then used to identify the major functional pathways contributing to the genetics of enhanced muscling by using both Estimated Breeding Value weighted gene co-expression network analysis and a differential gene co-expression network analysis. The modules of genes revealed by these analyses were enriched for a number of functional terms summarised as muscle sarcomere organisation and development, protein catabolism (proteosome), RNA processing, mitochondrial function and transcriptional regulation. Conclusions This study has revealed strong genetic structure in the gene expression program within ovine longissimus lumborum muscle. The balance between muscle protein synthesis, at the levels of both transcription and translation control, and protein catabolism mediated by regulated proteolysis is likely to be the primary determinant of the genetic merit for the muscling trait in this sheep population. There is also evidence that high genetic merit for muscling is associated with a fibre type shift toward fast glycolytic fibres. This study provides insight into mechanisms, presumably subject to strong artificial selection, that underpin enhanced muscling in sheep populations

Genomic imbalances in 5918 malignant epithelial tumors: an explorative meta-analysis of chromosomal CGH data

BACKGROUND: Chromosomal abnormalities have been associated with most human malignancies, with gains and losses on some genomic regions associated with particular entities. METHODS: Of the 15429 cases collected for the Progenetix molecular-cytogenetic database, 5918 malignant epithelial neoplasias analyzed by chromosomal Comparative Genomic Hybridization (CGH) were selected for further evaluation. For the 22 clinico-pathological entities with more than 50 cases, summary profiles for genomic imbalances were generated from case specific data and analyzed. RESULTS: With large variation in overall genomic instability, recurring genomic gains and losses were prominent. Most entities showed frequent gains involving 8q2, while gains on 20q, 1q, 3q, 5p, 7q and 17q were frequent in different entities. Loss "hot spots" included 3p, 4q, 13q, 17p and 18q among others. Related average imbalance patterns were found for clinically distinct entities, e.g. hepatocellular carcinomas (ca.) and ductal breast ca., as well as for histologically related entities (squamous cell ca. of different sites). CONCLUSION: Although considerable case-by-case variation of genomic profiles can be found by CGH in epithelial malignancies, a limited set of variously combined chromosomal imbalances may be typical for carcinogenesis. Focus on the respective regions should aid in target gene detection and pathway deduction

Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival

<p>Abstract</p> <p>Background</p> <p>Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival.</p> <p>Methods</p> <p>A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers.</p> <p>Results</p> <p>A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. <it>Pik3r1</it>, <it>E2f3, Akr1c3</it>, <it>Csf1</it>, <it>Jag2</it>, <it>Plcg1</it>, <it>Rpl37a</it>, <it>Sod2</it>, <it>Topors</it>, <it>Hras</it>, <it>Mdm2, Camk2g</it>, <it>Fstl1</it>, <it>Il13ra1</it>, <it>Mtap </it>and <it>Tp53 </it>were associated with multiple survival events.</p> <p>Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for <it>Syne1</it>, <it>Pdcd4</it>, <it>Ighg1</it>, <it>Tgfa</it>, <it>Pla2g7</it>, and <it>Paics</it>. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. <it>C2</it>, <it>Egfr</it>, <it>Prkcb</it>, <it>Igf2bp3</it>, and <it>Gdf10 </it>had gender-dependent associations; <it>Sox10</it>, <it>Rps20</it>, <it>Rab31</it>, and <it>Vav3 </it>had race-dependent associations; <it>Chi3l1</it>, <it>Prkcb</it>, <it>Polr2d</it>, and <it>Apool </it>had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death.</p> <p>Conclusions</p> <p>Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.</p