This work was financed by the German Federal Institute for Risk Assessment (BfR).

<ol><li>Seok, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genomic+responses+in+mouse+models+poorly+mimic+human+inflammatory+diseases%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+U+S+A%22%5BJournal%5D)">Genomic responses in mouse models poorly mimic human inflammatory diseases.</a> Proc Natl Acad Sci U S A. 110 (9), 3507-3512 (2013).</li>
<li>Takao, K., Miyakawa, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genomic+responses+in+mouse+models+greatly+mimic+human+inflammatory+diseases%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+U+S+A%22%5BJournal%5D)">Genomic responses in mouse models greatly mimic human inflammatory diseases.</a> Proc Natl Acad Sci U S A. 112 (4), 1167-1172 (2015).</li>
<li>Weidner, C., Steinfath, M., Opitz, E., Oelgeschläger, M., Schönfelder, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Defining+the+optimal+animal+model+for+translational+research+using+gene+set+enrichment+analysis%5BTitle%5D)+AND+%22EMBO+Mol+Med%22%5BJournal%5D)">Defining the optimal animal model for translational research using gene set enrichment analysis.</a> EMBO Mol Med. 8 (8), 831-838 (2016).</li>
<li>Subramanian, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Gene+set+enrichment+analysis%3A+a+knowledge-based+approach+for+interpreting+genome-wide+expression+profiles%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+U+S+A%22%5BJournal%5D)">Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.</a> Proc Natl Acad Sci U S A. 102 (43), 15545-15550 (2005).</li>
<li>Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., Tanabe, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((KEGG+as+a+reference+resource+for+gene+and+protein+annotation%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">KEGG as a reference resource for gene and protein annotation.</a> Nucleic Acids Res. 44 (D1), D457-D462 (2016).</li>
<li>Kanehisa, M., Goto, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((KEGG%3A+kyoto+encyclopedia+of+genes+and+genomes%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">KEGG: kyoto encyclopedia of genes and genomes.</a> Nucleic Acids Res. 28 (1), 27-30 (2000).</li>
<li>Fabregat, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+Reactome+pathway+Knowledgebase%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">The Reactome pathway Knowledgebase.</a> Nucleic Acids Res. 44 (D1), D481-D487 (2016).</li>
<li>Croft, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+Reactome+pathway+knowledgebase%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">The Reactome pathway knowledgebase.</a> Nucleic Acids Res. 42 (Database issue), D472-D477 (2014).</li>
<li>Nishimura, D. <a target="_blank" href="http://online.liebertpub.com/doi/abs/10.1089/152791601750294344">BioCarta.</a> Biotech Software & Internet Report. 2 (3), 117-120 (2001).</li>
<li>Edgar, R., Domrachev, M., Lash, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Gene+Expression+Omnibus%3A+NCBI+gene+expression+and+hybridization+array+data+repository%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.</a> Nucleic Acids Res. 30 (1), 207-210 (2002).</li>
<li>Kolesnikov, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ArrayExpress+update--simplifying+data+submissions%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">ArrayExpress update--simplifying data submissions.</a> Nucleic Acids Res. 43 (Database issue), D1113-D1116 (2015).</li>
<li>Cohen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sepsis%3A+a+roadmap+for+future+research%5BTitle%5D)+AND+%22Lancet+Infect+Dis%22%5BJournal%5D)">Sepsis: a roadmap for future research.</a> Lancet Infect Dis. 15 (5), 581-614 (2015).</li>
<li>Spinelli, L., Carpentier, S., Montanana Sanchis, F., Dalod, M., Vu Manh, T. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((BubbleGUM%3A+automatic+extraction+of+phenotype+molecular+signatures+and+comprehensive+visualization+of+multiple+Gene+Set+Enrichment+Analyses%5BTitle%5D)+AND+%22BMC+Genomics%22%5BJournal%5D)">BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses.</a> BMC Genomics. 16 (1), 814(2015).</li>
</ol>

The authors declare that they have no competing financial interests.

Animal models have long been applied for the investigation of disease mechanisms and the development of novel therapeutic strategies. However, skepticism regarding the predictivity of animal models started to spread following failures of clinical trials12. Furthermore, controversial discussions about appropriate strategies for analyzing and interpreting big omics data from preclinical trials were raised by opposite conclusions drawn from the same data after applying differing data analysis strateg...

Animal models are widely used to study human diseases, because of their assumed similarity to humans in terms of genetics, anatomy, and physiology. Moreover, animal models often serve as gatekeepers to clinical therapies and can have a huge impact on the success of translational research. Careful selection of the optimal animal model can reduce the number of misleading animal studies. Recently, the relevance of animal models for translational research has been controversially discussed, particularly because analyzing the same datasets obtained from human inflammatory diseases and related mouse models led to contradictory conclusions 1,2. This discussion revealed a fundamental problem during analyzing omics data: standardized approaches for systematic data analysis are needed in order to reduce biased gene selection and to increase the robustness of interspecies comparisons 3.
Traditionally, the analysis of transcriptomics data (and other omics data) is done at the single-gene level and includes an initial step of gene selection based on stringent cut-off parameters (e.g., fold change &#62;2.0, p value &#60;0.05). However, the setting of initial cut-off parameters often is subjective, arbitrary and not biologically justified, and can even lead to opposite conclusions1,2. Furthermore, initial gene selection generally restricts the analysis to a few highly up- and downregulated genes and is thus not sensitive enough to include the majority of genes that were differentially expressed to a lesser extent.
With the rise of the genomics era in the early 2000s and the increasing knowledge of biological pathways and contexts, alternative statistical approaches were developed that allowed to circumvent the limitations of single-gene level analyses. Gene set enrichment analysis (GSEA)4, which is one of the widely accepted methods for the analysis of transcriptomics data, makes use of a-priori defined groups of genes (e.g., signaling pathways, proximal location on a chromosome etc.). GSEA first maps all detected unfiltered genes to the intended gene sets (e.g., pathways), irrespective of their individual change in expression. This approach thus also includes moderately regulated genes that would otherwise be lost with single-gene level analyses. The additive change in expression within gene sets is subsequently performed using running sum statistics.
Despite its wide use in medical research, GSEA and related set enrichment approaches are not self-evidently taken into account for the analysis of complex omics data. Here, we describe a protocol for comparing omics data from human samples with those from mouse models in order to identify the ideal model for translational studies. We demonstrate the applicability of the protocol based on a collection of mouse models that are used for mimicking human inflammatory disorders. However, this analysis pipeline is not restricted to human-mouse comparisons and is amendable to further research questions.

<table><tbody><tr><td>Excel</td><td>Microsoft Corporation</td><td></td><td></td></tr></tbody></table>

a protocol for using gene set enrichment analysis to identify the appropriate animal model for translational research

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. A major reason for the discrepancies between different gene expression analyses is the arbitrary filtering of differentially expressed genes. Furthermore, the comparison of single genes between different species and platforms often is limited by technical variance, leading to misinterpretation of the con/discordance between data from human and animal models. Thus, standardized approaches for systematic data analysis are needed. To overcome subjective gene filtering and ineffective gene-to-gene comparisons, we recently demonstrated that gene set enrichment analysis (GSEA) has the potential to avoid these problems. Therefore, we developed a standardized protocol for the use of GSEA to distinguish between appropriate and inappropriate animal models for translational research. This protocol is not suitable to predict how to design new model systems a-priori, as it requires existing experimental omics data. However, the protocol describes how to interpret existing data in a standardized manner in order to select the most suitable animal model, thus avoiding unnecessary animal experiments and misleading translational studies.

The GSEA workflow and screenshots of exemplary data are demonstrated. Figure 1 shows the gene expression data file that contains the transcriptomic data of interest. For every study a descriptive phenotype file is required that is shown in Figure 2. Annotated gene sets (e.g., pathways) are defined in the gene set database file (Figure 3). Figure 4 shows a step-b...

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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research. 
This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison ...

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Research

JoVE Journal

Biology

17.8K Views.  German Federal Institute for Risk Assessment (BfR). We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research. This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.

Video: A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Genetics

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform

Early embryonic loss is a large contributor to infertility in cattle. Moreover, bovine becomes an interesting model to study human preimplantation embryo development due to their similar developmental process. Although genetic factors are known to affect early embryonic development, the discovery of such factors has been a serious challenge. Microarray technology allows quantitative measurement and gene expression profiling of transcript levels on a genome-wide basis. One of the main decisions that have to be made when planning a microarray experiment is whether to use a one- or two-color approach. Two-color design increases technical replication, minimizes variability, improves sensitivity and accuracy as well as allows having loop designs, defining the common reference samples. Although microarray is a powerful biological tool, there are potential pitfalls that can attenuate its power. Hence, in this technical paper we demonstrate an optimized protocol for RNA extraction, amplification, labeling, hybridization of the labeled amplified RNA to the array, array scanning and data analysis using the two-color analysis strategy.

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform

Microarray technology allows quantitative measurement and gene expression profiling of transcripts on a genome-wide basis. Therefore, this protocol provides an optimized technical procedure in a two-color custom made bovine array using Day 7 bovine embryos to demonstrate the feasibility of using low amount of total RNA.

Early embryonic loss is a large contributor to infertility in cattle. Moreover, bovine becomes an interesting model to study human preimplantation embryo ...

Developmental Biology

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

Performing biological experiments in space requires special accommodations and procedures to ensure that these investigations are performed effectively and efficiently. Moreover, given the infrequency of these experiments it is imperative that their impacts be maximized. The rapid advancement of omics technologies offers an opportunity to dramatically increase the volume of data produced from precious spaceflight specimens. To capitalize on this, NASA has developed the GeneLab platform to provide unrestricted access to spaceflight omics data and encourage its widespread analysis. Rodents (both rats and mice) are common model organisms used by scientists to investigate space-related biological impacts. The enclosure that house rodents during spaceflight are called Rodent Habitats (formerly Animal Enclosure Modules), and are substantially different from standard vivarium cages in their dimensions, air flow, and access to water and food. In addition, due to environmental and atmospheric conditions on the International Space Station (ISS), animals are exposed to a higher CO2 concentration. We recently reported that mice in the Rodent Habitats experience large changes in their transcriptome irrespective of whether animals were on the ground or in space. Furthermore, these changes were consistent with a hypoxic response, potentially driven by higher CO2 concentrations. Here we describe how a typical rodent experiment is performed in space, how omics data from these experiments can be accessed through the GeneLab platform, and how to identify key factors in this data. Using this process, any individual can make critical discoveries that could change the design of future space missions and activities.

The NASA GeneLab platform provides unfettered access to precious omics data from biological spaceflight experiments. We describe how a typical mouse experiment is conducted in space and how data from such experiments can be accessed and analyzed.

Performing biological experiments in space requires special accommodations and procedures to ensure that these investigations are performed effectively ...

A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure

As genomes of a wider variety of animals become available, there is an increasing need for tools that can capture dynamic epigenetic changes in these animal models. The rat is one particular model animal where an epigenetic tool can complement many pharmacological and behavioral studies to provide insightful mechanistic information. To this end, we adapted the SureSelect Target Capture System (referred to as Methyl-Seq) for the rat, which can assess DNA methylation levels across the rat genome. The rat design targeted promoters, CpG islands, island shores, and GC-rich regions from all RefSeq genes. 
To implement the platform on a rat experiment, male Sprague Dawley rats were exposed to chronic variable stress for 3 weeks, after which blood samples were collected for genomic DNA extraction. Methyl-Seq libraries were constructed from the rat DNA samples by shearing, adapter ligation, target enrichment, bisulfite conversion, and multiplexing. Libraries were sequenced on a next-generation sequencing platform and the sequenced reads were analyzed to identify DMRs between DNA of stressed and unstressed rats. Top candidate DMRs were independently validated by bisulfite pyrosequencing to confirm the robustness of the platform.
Results demonstrate that the rat Methyl-Seq platform is a useful epigenetic tool that can capture methylation changes induced by exposure to stress.

Here, we describe the protocol and implementation of Methyl-Seq, an epigenomic platform, using a rat model to identify epigenetic changes associated with chronic stress exposure. Results demonstrate that the rat Methyl-Seq platform is capable of detecting methylation differences that arise from stress exposure in rats.

As genomes of a wider variety of animals become available, there is an increasing need for tools that can capture dynamic epigenetic changes in these ...

Biochemistry

Genome-Scale Screening to Identify and Compare Therapeutic Targets in Tumors

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), MPNSTs are the most common malignancy and the leading cause of death. These rare and aggressive soft-tissue sarcomas offer a stark future, with 5-year disease-free survival rates of 34-60%. Treatment options for individuals with MPNSTs are disappointingly limited, with disfiguring surgery being the foremost treatment option. Many once-promising therapies such as tipifarnib, an inhibitor of Ras signaling, have failed clinically. Likewise, phase II clinical trials with erlotinib, which targets the epidermal growth factor (EFGR), and sorafenib, which targets the vascular endothelial growth factor receptor (VEGF), platelet-derived growth factor receptor (PDGF), and Raf, in combination with standard chemotherapy, have also failed to produce a response in patients. 
In recent years, functional genomic screening methods combined with genetic profiling of cancer cell lines have proven useful for identifying essential cytoplasmic signaling pathways and the development of target-specific therapies. In the case of rare tumor types, a variation of this approach known as cross-species comparative oncogenomics is increasingly being used to identify novel therapeutic targets. In cross-species comparative oncogenomics, genetic profiling and functional genomics are performed in genetically engineered mouse (GEM) models and the results are then validated in the rare human specimens and cell lines that are available. 
This paper describes how to identify candidate driver gene mutations in human and mouse MPNST cells using whole exome sequencing (WES). We then describe how to perform genome-scale shRNA screens to identify and compare critical signaling pathways in mouse and human MPNST cells and identify druggable targets in these pathways. These methodologies provide an effective approach to identifying new therapeutic targets in a variety of human cancer types.

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens 

We have developed a cross-species comparative oncogenomics approach utilizing genomic analyses and functional genomic screens to identify and compare therapeutic targets in tumors arising in genetically engineered mouse models and the corresponding human tumor type.

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome ...

Cancer Research

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers easy identification and characterization of these cell types, prerequisites to study their specific functions. This article describes the multiplex single cell reverse transcription polymerase chain reaction (RT-PCR) protocol, which allows, after patch-clamp recording in slices, to detect simultaneously the expression of tens of genes in a single cell. This simple method can be implemented with morphological characterization and is widely applicable to determine the phenotypic traits of various cell types and their particular cellular environment, such as in the vicinity of blood vessels. The principle of this protocol is to record a cell with the patch-clamp technique, to harvest and reverse transcribe its cytoplasmic content, and to detect qualitatively the expression of a predefined set of genes by multiplex PCR. It requires a careful design of PCR primers and intracellular patch-clamp solution compatible with RT-PCR. To ensure a selective and reliable transcript detection, this technique also requires appropriate controls from cytoplasm harvesting to amplification steps. Although precautions discussed here must be strictly followed, virtually any electrophysiological laboratory can use the multiplex single cell RT-PCR technique.

This protocol describes the critical steps and precautions required to perform single cell multiplex reverse transcription polymerase chain reaction after patch-clamp. This technique is a simple and effective method to analyze the expression profile of a predetermined set of genes from a single cell characterized by patch-clamp recordings.

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers ...

Neuroscience

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present methods for the development of assays to query potentially clinically significant nonsynonymous changes using in vivo complementation in zebrafish. Zebrafish (Danio rerio) are a useful animal system due to their experimental tractability; embryos are transparent to enable facile viewing, undergo rapid development ex vivo, and can be genetically manipulated.1 These aspects have allowed for significant advances in the analysis of embryogenesis, molecular processes, and morphogenetic signaling. Taken together, the advantages of this vertebrate model make zebrafish highly amenable to modeling the developmental defects in pediatric disease, and in some cases, adult-onset disorders. Because the zebrafish genome is highly conserved with that of humans (~70% orthologous), it is possible to recapitulate human disease states in zebrafish. This is accomplished either through the injection of mutant human mRNA to induce dominant negative or gain of function alleles, or utilization of morpholino (MO) antisense oligonucleotides to suppress genes to mimic loss of function variants. Through complementation of MO-induced phenotypes with capped human mRNA, our approach enables the interpretation of the deleterious effect of mutations on human protein sequence based on the ability of mutant mRNA to rescue a measurable, physiologically relevant phenotype. Modeling of the human disease alleles occurs through microinjection of zebrafish embryos with MO and/or human mRNA at the 1-4 cell stage, and phenotyping up to seven days post fertilization (dpf). This general strategy can be extended to a wide range of disease phenotypes, as demonstrated in the following protocol. We present our established models for morphogenetic signaling, craniofacial, cardiac, vascular integrity, renal function, and skeletal muscle disorder phenotypes, as well as others.

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.

Here,  we present methods for the development of assays to query potentially clinically  significant nonsynonymous changes using in  vivo complementation ...

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of the disease.&#160;To study such processes, researchers can use high-throughput sequencing techniques (RNA-seq) that measure the dynamic changes of the host transcriptome at different stages of infection, clinical outcomes, or disease severity.This investigation can lead to a better understanding of the diseases, as well as uncovering potential drug targets and treatments. The protocol presented here describes a complete pipeline to analyze RNA-sequencing data from raw reads to functional analysis. The pipeline is divided into five steps: (1) quality control of the data; (2) mapping and annotation of genes; (3) statistical analysis to identify differentially expressed genes and co-expressed genes; (4) determination of the molecular degree of the perturbation of samples; and (5) functional analysis. Step 1 removes technical artifacts that may impact the quality of downstream analyses. In step 2, genes are mapped and annotated according to standard library protocols.&#160;The statistical analysis in step 3 identifies genes that are differentially expressed or co-expressed in infected samples, in comparison with non-infected ones. Sample variability and the presence of potential biological outliers are verified using the molecular degree of perturbation approach in step 4. Finally, the functional analysis in step 5 reveals the pathways associated with the disease phenotype. The presented pipeline aims to support researchers through the RNA-seq data analysis from host-pathogen interaction studies and drive future&#160;in vitro or in vivo experiments, that are essential to understand the molecular mechanism of infections.

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches.

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of ...

Immunology and Infection

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays focus on transcriptional regulation, being essentially aimed at the identification of promoter-targeting molecules. As a result, post-transcriptional mechanisms are largely uncovered by gene expression targeted drug development. Here we describe a cell-based assay aimed at investigating the role of the 3&#39; untranslated region (3&#8217; UTR) in the modulation of the fate of its mRNA, and at identifying compounds able to modify it. The assay is based on the use of a luciferase reporter construct containing the 3&#8217; UTR of a gene of interest stably integrated into a disease-relevant cell line. The protocol is divided into two parts, with the initial focus on the primary screening aimed at the identification of molecules affecting luciferase activity after 24 hr of treatment. The second part of the protocol describes the counter-screening necessary to discriminate compounds modulating luciferase activity specifically through the 3&#8217; UTR. In addition to the detailed protocol and representative results, we provide important considerations about the assay development and the validation of the hit(s) on the endogenous target. The described cell-based reporter gene assay will allow scientists to identify molecules modulating protein levels via post-transcriptional mechanisms dependent on a 3&#8217; UTR.

Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3&#8217; UTR.

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays ...

Xenopus laevis as a Model to Identify Translation Impairment

Protein synthesis is a fundamental process to gene expression impacting diverse biological processes notably adaptation to environmental conditions. The initiation step, which involves the assembly of the ribosomal subunits at the mRNA initiation codon, involved initiation factor including eIF4G1. Defects in this rate limiting step of translation are linked to diverse disorders. To study the potential consequences of such deregulations, Xenopus laevis oocytes constitute an attractive model with high degrees of conservation of essential cellular and molecular mechanisms with human. In addition, during meiotic maturation, oocytes are transcriptionally repressed and all necessary proteins are translated from preexisting, maternally derived mRNAs. This inexpensive model enables exogenous mRNA to become perfectly integrated with an effective translation. Here is described a protocol for assessing translation with a factor of interest (here eIF4G1) using stored maternal mRNA that are the first to be polyadenylated and translated during oocyte maturation as a physiological readout. At first, mRNA synthetized by in vitro transcription of plasmids of interest (here eIF4G1) are injected in oocytes and kinetics of oocyte maturation by Germinal Vesicle Breakdown detection is determined. The studied maternal mRNA target is the serine/threonine-protein-kinase mos. Its polyadenylation and its subsequent translation are investigated together with the expression and phosphorylation of proteins of the mos signaling cascade involved in oocyte maturation. Variations of the current protocol to put forward translational defects are also proposed to emphasize its general applicability. In light of emerging evidence that aberrant protein synthesis may be involved in the pathogenesis of neurological disorders, such a model provides the opportunity to easily assess this impairment and identify new targets.

Xenopus laevis as a Model to Identify Translation Impairment

Protein synthesis control occurs mainly at the translation initiation step, deficiencies in which are linked to diverse disorders. To better understand their etiology, we described here a protocol using Xenopus laevis oocytes assessing the translation of mos transcript in the presence of a mutant of translation initiation factor eIF4G1.

Protein synthesis is a fundamental process to gene expression impacting diverse biological processes notably adaptation to environmental conditions. The ...

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Summary

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Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

In Vivo Modeling of the Morbid Human Genome using Danio rerio

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