scholarly journals Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map

2017 ◽  
Author(s):  
Ian Smith ◽  
Peyton G Greenside ◽  
David Wadden ◽  
Itay Tirosh ◽  
Ted Natoli ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
Large Scale ◽  
Gene Signature ◽  
Loss Of Function ◽  
Connectivity Map ◽  
Rnai Technology ◽  
Short Palindromic Repeat ◽  
Short Hairpin Rnas ◽  
Target Effects

AbstractThe application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss of function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that miRNA-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a Consensus Gene Signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 sgRNAs in 6 cells lines, and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function.

scholarly journals Cross-Species RNAi Rescue Platform in Drosophila melanogaster

Genetics ◽  
2009 ◽  
Vol 183 (3) ◽  
pp. 1165-1173 ◽  
Author(s):  
Shu Kondo ◽  
Matthew Booker ◽  
Norbert Perrimon
Keyword(s):  
Cell Culture ◽  
Drosophila Melanogaster ◽  
Large Scale ◽  
Transgenic Animals ◽  
Selection Marker ◽  
Bacterial Artificial Chromosomes ◽  
Loss Of Function ◽  
Artificial Chromosomes ◽  
Target Effects

RNAi-mediated gene knockdown in Drosophila melanogaster is a powerful method to analyze loss-of-function phenotypes both in cell culture and in vivo. However, it has also become clear that false positives caused by off-target effects are prevalent, requiring careful validation of RNAi-induced phenotypes. The most rigorous proof that an RNAi-induced phenotype is due to loss of its intended target is to rescue the phenotype by a transgene impervious to RNAi. For large-scale validations in the mouse and Caenorhabditis elegans, this has been accomplished by using bacterial artificial chromosomes (BACs) of related species. However, in Drosophila, this approach is not feasible because transformation of large BACs is inefficient. We have therefore developed a general RNAi rescue approach for Drosophila that employs Cre/loxP-mediated recombination to rapidly retrofit existing fosmid clones into rescue constructs. Retrofitted fosmid clones carry a selection marker and a phiC31 attB site, which facilitates the production of transgenic animals. Here, we describe our approach and demonstrate proof-of-principle experiments showing that D. pseudoobscura fosmids can successfully rescue RNAi-induced phenotypes in D. melanogaster, both in cell culture and in vivo. Altogether, the tools and method that we have developed provide a gold standard for validation of Drosophila RNAi experiments.

scholarly journals Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Gene Networks ◽  
Large Scale ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Gene Profiles ◽  
Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

scholarly journals A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis

2019 ◽  
Vol 15 ◽  
pp. 117693431983849 ◽  
Author(s):  
Mengying Sheng ◽  
Xueying Xie ◽  
Jun Wang ◽  
Wanjun Gu
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Large Scale ◽  
Gene Signature ◽  
Multiple Sources ◽  
Cancer Subtypes ◽  
Cancer Management ◽  
Lung Cancer Diagnosis ◽  
Gene Expression Signatures

Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers. We tested their performance in several independent cohorts, which confirmed their robust prognostic and diagnostic power. Furthermore, we showed that the proposed gene expression signatures were independent of several traditional clinical indicators in lung cancer management. Our results suggest that the pathway-based strategy is useful to identify transcriptomic biomarkers from large-scale gene expression datasets that were collected from multiple sources.

Large Scale Transient Gene Expression in Mammalian Cells

2006 ◽  
pp. 113-116
Author(s):  
E. -J. Schlaeger ◽  
K. Christensen ◽  
G. Schmid ◽  
N. Schaub ◽  
B. Wipf ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
Large Scale ◽  
Transient Gene Expression ◽  
Expression In Mammalian Cells

scholarly journals Measuring error rates in genomic perturbation screens: gold standards for human functional genomics

2014 ◽  
Author(s):  
Traver Hart ◽  
Kevin R. Brown ◽  
Fabrice Sircoulomb ◽  
Robert Rottapel ◽  
Jason Moffat
Keyword(s):  
Mammalian Cells ◽  
Large Scale ◽  
Cellular Proliferation ◽  
Gene Knockout ◽  
Error Rates ◽  
Measuring Error ◽  
Technological Advancement ◽  
Loss Of Function ◽  
False Discovery Rates ◽  
Gold Standard Reference

AbstractTechnological advancement has opened the door to systematic genetics in mammalian cells. Genome-scale loss-of-function screens can assay fitness defects induced by partial gene knockdown, using RNA interference, or complete gene knockout, using new CRISPR techniques. These screens can reveal the basic blueprint required for cellular proliferation. Moreover, comparing healthy to cancerous tissue can uncover genes that are essential only in the tumor; these genes are targets for the development of specific anticancer therapies. Unfortunately, progress in this field has been hampered by offtarget effects of perturbation reagents and poorly quantified error rates in large-scale screens. To improve the quality of information derived from these screens, and to provide a framework for understanding the capabilities and limitations of CRISPR technology, we derive gold-standard reference sets of essential and nonessential genes, and provide a Bayesian classifier of gene essentiality that outperforms current methods on both RNAi and CRISPR screens. Our results indicate that CRISPR technology is more sensitive than RNAi, and that both techniques have nontrivial false discovery rates that can be mitigated by rigorous analytical methods.

scholarly journals sigQC: A procedural approach for standardising the evaluation of gene signatures

2017 ◽  
Author(s):  
Andrew Dhawan ◽  
Alessandro Barberis ◽  
Wei-Chen Cheng ◽  
Enric Domingo ◽  
Catharine West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
Large Scale ◽  
Gene Expression Signature ◽  
R Package ◽  
Gene Signature ◽  
Gene Signatures ◽  
Expression Variability ◽  
Procedural Approach ◽  
Standard Quality

AbstractWith the increase in next generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools, poised to make a large impact on the diagnosis, management and prognosis for a number of diseases. Increasingly, it is becoming necessary to determine whether a gene expression signature may apply to a dataset, but no standard quality control methodology exists. In this work, we introduce the first protocol, implemented in an R package sigQC, enabling a streamlined methodological and standardised approach for the quality control validation of gene signatures on independent data sets. The emphasis in this work is in showing the critical quality control steps involved in the generation of a clinically and biologically useful, transportable gene signature, including ensuring sufficient expression, variability, and autocorrelation of a signature. We demonstrate the application of the protocol in this work, showing how the outputs created from sigQC may be used for the evaluation of gene signatures on large-scale gene expression data in cancer.

scholarly journals Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map

PLoS Biology ◽  
2017 ◽  
Vol 15 (11) ◽  
pp. e2003213 ◽  
Author(s):  
Ian Smith ◽  
Peyton G. Greenside ◽  
Ted Natoli ◽  
David L. Lahr ◽  
David Wadden ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Large Scale ◽  
Connectivity Map

scholarly journals A DNA vector-based RNAi technology to suppress gene expression in mammalian cells

2002 ◽  
Vol 99 (8) ◽  
pp. 5515-5520 ◽  
Author(s):  
G. Sui ◽  
C. Soohoo ◽  
E. B. Affar ◽  
F. Gay ◽  
Y. Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
Rnai Technology ◽  
Dna Vector ◽  
Expression In Mammalian Cells

scholarly journals A data mining paradigm for identifying key factors in biological processes using gene expression data

2018 ◽  
Author(s):  
Jin Li ◽  
Le Zheng ◽  
Akihiko Uchiyama ◽  
Lianghua Bin ◽  
Theodora M. Mauro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Biological Data ◽  
Epidermal Differentiation ◽  
Biological Processes ◽  
Loss Of Function ◽  
Key Factors ◽  
Primary Analysis ◽  
Epidermal Development

AbstractA large volume of biological data is being generated for studying mechanisms of various biological processes. These precious data enable large-scale computational analyses to gain biological insights. However, it remains a challenge to mine the data efficiently for knowledge discovery. The heterogeneity of these data makes it difficult to consistently integrate them, slowing down the process of biological discovery. We introduce a data processing paradigm to identify key factors in biological processes via systematic collection of gene expression datasets, primary analysis of data, and evaluation of consistent signals. To demonstrate its effectiveness, our paradigm was applied to epidermal development and identified many genes that play a potential role in this process. Besides the known epidermal development genes, a substantial proportion of the identified genes are still not supported by gain- or loss-of-function studies, yielding many novel genes for future studies. Among them, we selected a top gene for loss-of-function experimental validation and confirmed its function in epidermal differentiation, proving the ability of this paradigm to identify new factors in biological processes. In addition, this paradigm revealed many key genes in cold-induced thermogenesis using data from cold-challenged tissues, demonstrating its generalizability. This paradigm can lead to fruitful results for studying molecular mechanisms in an era of explosive accumulation of publicly available biological data.

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