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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+responses+in+mouse+models+poorly+mimic+human+inflammatory+diseases.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+responses+in+mouse+models+greatly+mimic+human+inflammatory+diseases.">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., Oelgeschl&#228;ger, M., Sch&#246;nfelder, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Defining+the+optimal+animal+model+for+translational+research+using+gene+set+enrichment+analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+set+enrichment+analysis:+a+knowledge-based+approach+for+interpreting+genome-wide+expression+profiles.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KEGG+as+a+reference+resource+for+gene+and+protein+annotation.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KEGG:+kyoto+encyclopedia+of+genes+and+genomes.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Reactome+pathway+Knowledgebase.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Reactome+pathway+knowledgebase.">The Reactome pathway knowledgebase.</a> Nucleic Acids Res. 42 (Database issue), D472-D477 (2014).</li><li>Nishimura, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BioCarta.">BioCarta.</a> Biotech Software &amp; 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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+Expression+Omnibus:+NCBI+gene+expression+and+hybridization+array+data+repository.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ArrayExpress+update--simplifying+data+submissions.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sepsis:+a+roadmap+for+future+research.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BubbleGUM:+automatic+extraction+of+phenotype+molecular+signatures+and+comprehensive+visualization+of+multiple+Gene+Set+Enrichment+Analyses.">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 strategies1,2. Consequently, there is a high demand for further robust bioinformatics techniques for the analysis of complex omics data to systematically define the optimal animal model for a given human disease. Applying the best available model not only improves translational research but further contributes to animal welfare by avoiding animal experiments that might not correlate with the human situation.
The presented protocol describes a standardized approach to systematically compare omics data of different species with the aim to identify the optimal animal models and treatment protocols for a given human disorder. By the use of GSEA instead of a single-gene analysis, this protocol circumvents all problems associated with subjective setting of gene expression thresholds and gene filtering. The focus on selected pathways further allows to specifically address the (patho)physiological process of the disorder/condition in question (e.g., inflammation). Of course, the accuracy of the GSEA results depends on the quality of current gene set annotations and on whether regulation mechanisms are conserved between species. However, we hypothesize that in general the conservation is higher at pathway level than on single gene level. In addition, set enrichment approaches are more robust for comparisons of transcriptomic data between different platforms and experimental models or clinical cohorts than single-gene analyses13.
Instead of using pre-defined gene sets such as pathways, the presented approach also allows to define custom gene sets. In particular, experimental expression data can be used to identify relevant genes that are activated or inhibited in one condition (e.g., overlap of regulated human genes in clinical cohorts). The de novo defined gene sets can then be used to test for the enrichment of data from different animal models. This alternative approach avoids the &#39;detour&#39; of using annotated pathways. Further, the protocol is not restricted to the comparison of transcriptomic data, but is transferable to any omics data including proteomics and metabolomics. Nonetheless, one has to keep in mind that this approach is limited to existing omics data from mouse models and humans, and that it does not indicate how to develop new animal models. However, it represents an effective approach for the standardized interpretation of existing data, which may facilitate the careful selection of the optimal animal model and thus avoid unnecessary and misleading translational studies.

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.

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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-by-step protocol for the use of the GSEA software tool. An exemplary result report is given in Figure 5. Detailed GSEA enrichment results are summarized in Figure 6. For the comparison of different gene expression studies, in particular human vs. mouse studies, a contingency table is required (Figure 7). For the visualization of the results, Figure 8 shows a correlation matrix of pathway comparisons among human and mouse studies.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55768/55768fig1v2.jpg" /> 
Figure 1: GSEA Gene Expression Data File. The file contains expression values for all detectable genes (or probes), also for genes that might not be differentially expressed. The file therefore typically comprises many thousands of genes. (A) The gene expression data file includes data for each individual sample. The first line contains the labels name (here: probe ID) followed by an optional description and individual sample names (here: GSM515585, GSM515586, etc.). The remainder of the file contains expression values for each of the genes and for each sample in the dataset. (B) Alternative gene expression data format. Externally calculated group metrics (here: mean ratio) can be used for the GSEA preranked tool if individual sample data are not available. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55768/55768fig1v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55768/55768fig2.jpg" /> 
Figure 2: GSEA Phenotype File. The file combines individual samples to groups and labels the groups accordingly. The first line contains the total number of samples and further the number of groups. The third field of the first line is always &#39;1&#39;. The second line contains the name for each group. The line begins with a pound sign (#) followed by a space. The third line contains a group label for each sample (here: 0 or 1). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55768/55768fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55768/55768fig3.jpg" /> 
Figure 3: GSEA Gene Set Database File. The file defines sets of genes that are assigned to certain biological processes or categories (here: inflammatory pathways). In the GMT format, each row represents a gene set, which is defined by a name, a description, and the included genes (official HUGO gene symbols). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55768/55768fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55768/55768fig4v2.jpg" /> 
Figure 4: GSEA Software Settings. The GSEA software tool was downloaded from the Broad Institute website as a java desktop application. (A) Start menu. The left side contains the navigation menu while the right section (Home) gives a short summary of the GSEA workflow. Clicking the Load data button will open a new tab for importing the files. (B) Load data section before data import. Required files can be imported via the file browser. (C) Load data section after data import. Imported data files are listed in the Object cache and are organized to datasets (mandatory file), phenotypes (mandatory file), gene set databases (optional, if internet connection provided) and chip files (optional, if internet connection provided). Clicking on the Run GSEA button will open a new tab for setting the analysis parameters. (D) Run GSEA section. The tab for setting the analysis parameters is divided into required fields, basic fields and advanced fields. Clicking the Run button on the on the right bottom of the window will start the analysis. The progress of the analysis will then be visible in the GSEA reports section on the left bottom of the window. After finishing the analysis, the status &#39;success&#39; appears in the GSEA reports section. (E) GSEA preranked tool. Gene expression data files containing externally calculated group metrics instead of individual sample data can be analyzed via the main navigation bar. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55768/55768fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55768/55768fig5.jpg" /> 
Figure 5: GSEA Report. The GSEA report will open in a browser window that summarizes all results and selected parameters. The upper two sections of the navigation menu comprise gene set enrichment results for the defined groups (e.g., enrichment in S. aureus treated samples or healthy control samples). In that example, 42 of 65 gene sets (pathways) are activated in S. aureus treated mice, while 14 of them are significantly enriched with an FDR below 25%. Similarly, 23 of 65 gene sets (pathways) are inhibited in S. aureus treated mice, while 18 of them are significantly enriched with an FDR below 25%. Clicking on the detailed enrichment results opens an html or excel file for exporting the analysis data required for a comparison of different gene expression studies. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55768/55768fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/55768/55768fig6v2.jpg" /> 
Figure 6: Detailed Enrichment Results. (A) Exported spreadsheet file containing detailed analysis results for gene sets (pathways) that were activated in S. aureus treated mice. The spreadsheet file contains huge data for each of the analyzed gene set, including the name of the gene set, its size, its normalized enrichment score, its nominal (uncorrected) p value and its FDR value. (B) Simplified spreadsheet file that only contains information required for comparing different gene expression studies. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55768/55768fig6v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/55768/55768fig7.jpg" /> 
Figure 7: 3 x 3 Contingency Table of GSEA Results. (A) Common contingency table format for the comparison of 2 studies. (B) Exemplary numbers of regulated pathways for the comparison of a human sepsis study (GSE9960) with a murine S. aureus injection model (GSE20524). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55768/55768fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/55768/55768fig8.jpg" /> 
Figure 8: Correlation Matrix of Pathway Comparisons Between Human and Mouse Studies. The overlap of pathway regulation is shown as the gain of information that can be obtained from one (mouse) study for predicting the effects in another (human) study (blue, decrease, low correlation; red, increase, high correlation). In that example, the comparison of human with murine datasets revealed a subgroup of experimental murine models that were highly correlative to human clinical studies (studies 10 and 11, dotted line), indicating that these mouse models are best suited for mimicking the human situation. In contrast, the studies 7, 8 and 9 showed no correlation to the human disease studies. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55768/55768fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research at JoVE.com

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 ...

a-protocol-for-using-gene-set-enrichment-analysis-to-identify

Nanyang Technological University

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17.7K 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

Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of the visual system provide more empirical evidence for functional applications. Investigation into the sensory system provides information about the sensory capacity, learning and memory ability, and establishes known baseline behaviour in which to gauge deviations (Burghardt, 1977). However, unlike mammalian or avian systems, testing for learning and memory in a reptile species is difficult. Furthermore, using an operant paradigm as a psychophysical measure of sensory ability is likewise as difficult. Historically, reptilian species have responded poorly to conditioning trials because of issues related to motivation, physiology, metabolism, and basic biological characteristics. Here, I demonstrate an operant paradigm used a novel model lizard species, the Jacky dragon (Amphibolurus muricatus) and describe how to test peripheral sensitivity to salient speed and motion characteristics. This method uses an innovative approach to assessing learning and sensory capacity in lizards. I employ the use of random-dot kinematograms (RDKs) to measure sensitivity to speed, and manipulate the level of signal strength by changing the proportion of dots moving in a coherent direction. RDKs do not represent a biologically meaningful stimulus, engages the visual system, and is a classic psychophysical tool used to measure sensitivity in humans and other animals. Here, RDKs are displayed to lizards using three video playback systems. Lizards are to select the direction (left or right) in which they perceive dots to be moving. Selection of the appropriate direction is reinforced by biologically important prey stimuli, simulated by computer-animated invertebrates.

Testing visual sensitivity in lizards using an operant conditioning paradigm that employs video playback of random-dot kinematograms and computer-generated invertebrates.

Badania wizualne Wrażliwość na prędkość i kierunek ruchu w Jaszczurki

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of ...

Computer-Generated Animal Model Stimuli

Communication between animals is diverse and complex. Animals may communicate using auditory, seismic, chemosensory, electrical, or visual signals. In particular, understanding the constraints on visual signal design for communication has been of great interest. Traditional methods for investigating animal interactions have used basic observational techniques, staged encounters, or physical manipulation of morphology. Less intrusive methods have tried to simulate conspecifics using crude playback tools, such as mirrors, still images, or models. As technology has become more advanced, video playback has emerged as another tool in which to examine visual communication (Rosenthal, 2000). However, to move one step further, the application of computer-animation now allows researchers to specifically isolate critical components necessary to elicit social responses from conspecifics, and manipulate these features to control interactions. Here, I provide detail on how to create an animation using the Jacky dragon as a model, but this process may be adaptable for other species. In building the animation, I elected to use Lightwave  3D to alter object morphology, add texture, install bones, and provide comparable weight shading that prevents exaggerated movement. The animation is then matched to select motor patterns to replicate critical movement features. Finally, the sequence must rendered into an individual clip for presentation. Although there are other adaptable techniques, this particular method had been demonstrated to be effective in eliciting both conspicuous and social responses in staged interactions.

Computer-generated stimuli using the Jacky dragon as a model.

Communication between animals is diverse and complex. Animals may communicate using auditory, seismic, chemosensory, electrical, or visual signals. In ...

Automated Microfluidic Blood Lysis Protocol for Enrichment of Circulating Nucleated Cells

In this report the protocol for an automated microfluidic blood lysis device is detailed. Circulating nucleated cells (CNCs), including leukocytes and endothelial cells, provide an ideal platform for an updated status on the immune condition of an individual. The microfluidic protocol allows for enrichment of CNCs without selective cell loss and sample preparation variability due to user-mediated steps. Briefly, the protocol includes device fabrication, sample collection, device setup, and running blood through the microfluidic chamber. Within the device whole blood is rapidly mixed with deionized water for approximately 10 seconds in a 50 micron x 150 micron microfluidic channel. In this time span erythrocytes are lysed due to hypotonic conditions. Herringbone structures on the bottom of the channel ensure thorough mixing and exposure of cells to a constant environment. Remaining cells are returned to isotonic conditions at the exit of the device, fixed using 2% paraformaldehyde, centrifuged to separate erythrocyte debris from CNCs, and suspended in flow buffer for staining and analysis by flow cytometry. Results show clean flow cytometry scatter plots with CNC populations saved. Significance of this device and protocol comes in the study and understanding of disease pathogenesis by analysis of CNC populations. Hence, automation, effectiveness, and simplicity of the microfluidic protocol are demonstrated.

An automated microfluidic device was developed for circulating nucleated cell enrichment from peripheral blood via erythrocyte lysis that ensures isolation of high quality sample without cell loss.

In this report the protocol for an automated microfluidic blood lysis device is detailed. Circulating nucleated cells (CNCs), including leukocytes and ...

Using the Gene Pulser MXcell Electroporation System to Transfect Primary Cells with High Efficiency

It is becoming increasingly apparent that electroporation is the most effective way to introduce plasmid DNA or siRNA into primary cells. The Gene Pulser MXcell electroporation system and Gene Pulser electroporation buffer (Bio-Rad) were specifically developed to easily transfect nucleic acids into mammalian cells and difficult-to-transfect cells, such as primary and stem cells. We will demonstrate how to perform a simple experiment to quickly identify the best electroporation conditions. We will demonstrate how to run several samples through a range of electroporation conditions so that an experiment can be conducted at the same time as optimization is performed. We will also show how optimal conditions identified using 96-well electroporation plates can be used with standard electroporation cuvettes, facilitating the switch from electroporation plates to electroporation cuvettes while maintaining the same electroporation efficiency. In the video, we will also discuss some of the key factors that can lead to the success or failure of electroporation experiments.

This procedure shows how to use the Gene Pulser MXcell electroporation system to rapidly and easily identify the best electroporation conditions for mouse embryonic fibroblasts (MEFs) or other primary cells. Considerations for troubleshooting are also discussed in the associated video.

It is becoming increasingly apparent that electroporation is the most effective way to introduce plasmid DNA or siRNA into primary cells. The Gene Pulser ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

Isolation of Ribosome Bound Nascent Polypeptides in vitro to Identify Translational Pause Sites Along mRNA

The rate of translational elongation is non-uniform. mRNA secondary structure, codon usage and mRNA associated proteins may alter ribosome movement on the messagefor review see 1. However, it's now widely accepted that synonymous codon usage is the primary cause of non-uniform translational elongation rates1. Synonymous codons are not used with identical frequency. A bias exists in the use of synonymous codons with some codons used more frequently than others2. Codon bias is organism as well as tissue specific2,3. Moreover, frequency of codon usage is directly proportional to the concentrations of cognate tRNAs4. Thus, a frequently used codon will have higher multitude of corresponding tRNAs, which further implies that a frequent codon will be translated faster than an infrequent one. Thus, regions on mRNA enriched in rare codons (potential pause sites) will as a rule slow down ribosome movement on the message and cause accumulation of nascent peptides of the respective sizes5-8. These pause sites can have functional impact on the protein expression, mRNA stability and protein foldingfor review see 9. Indeed, it was shown that alleviation of such pause sites can alter ribosome movement on mRNA and subsequently may affect the efficiency of co-translational (in vivo) protein folding1,7,10,11. To understand the process of protein folding in vivo, in the cell, that is ultimately coupled to the process of protein synthesis it is essential to gain comprehensive insights into the impact of codon usage/tRNA content on the movement of ribosomes along mRNA during translational elongation.
Here we describe a simple technique that can be used to locate major translation pause sites for a given mRNA translated in various cell-free systems6-8. This procedure is based on isolation of nascent polypeptides accumulating on ribosomes during in vitro translation of a target mRNA. The rationale is that at low-frequency codons, the increase in the residence time of the ribosomes results in increased amounts of nascent peptides of the corresponding sizes. In vitro transcribed mRNA is used for in vitro translational reactions in the presence of radioactively labeled amino acids to allow the detection of the nascent chains. In order to isolate ribosome bound nascent polypeptide complexes the translation reaction is layered on top of 30% glycerol solution followed by centrifugation. Nascent polypeptides in polysomal pellet are further treated with ribonuclease A and resolved by SDS PAGE. This technique can be potentially used for any protein and allows analysis of ribosome movement along mRNA and the detection of the major pause sites. Additionally, this protocol can be adapted to study factors and conditions that can alter ribosome movement and thus potentially can also alter the function/conformation of the protein.

Isolation of Ribosome Bound Nascent Polypeptides in vitro to Identify Translational Pause Sites Along mRNA

A technique to identify translational pause sites on mRNA is described. This procedure is based on isolation of nascent polypeptides accumulating on ribosomes during in vitro translation of a target mRNA, followed by the size analysis of the nascent chains using a denaturing gel electrophoresis.

The rate of translational elongation is non-uniform. mRNA secondary structure, codon usage and mRNA associated proteins may alter ribosome movement on the ...

Using SecM Arrest Sequence as a Tool to Isolate Ribosome Bound Polypeptides

Extensive research has provided ample evidences suggesting that protein folding in the cell is a co-translational process1-5. However, the exact pathway that polypeptide chain follows during co-translational folding to achieve its functional form is still an enigma. In order to understand this process and to determine the exact conformation of the co-translational folding intermediates, it is essential to develop techniques that allow the isolation of RNCs carrying nascent chains of predetermined sizes to allow their further structural analysis.
SecM (secretion monitor) is a 170 amino acid E. coli protein that regulates expression of the downstream SecA (secretion driving) ATPase in the secM-secA operon6. Nakatogawa and Ito originally found that a 17 amino acid long sequence (150-FSTPVWISQAQGIRAGP-166) in the C-terminal region of the SecM protein is sufficient and necessary to cause stalling of SecM elongation at Gly165, thereby producing peptidyl-glycyl-tRNA stably bound to the ribosomal P-site7-9. More importantly, it was found that this 17 amino acid long sequence can be fused to the C-terminus of virtually any full-length and/or truncated protein thus allowing the production of RNCs carrying nascent chains of predetermined sizes7. Thus, when fused or inserted into the target protein, SecM stalling sequence produces arrest of the polypeptide chain elongation and generates stable RNCs both in vivo in E. coli cells and in vitro in a cell-free system. Sucrose gradient centrifugation is further utilized to isolate RNCs.
The isolated RNCs can be used to analyze structural and functional features of the co-translational folding intermediates. Recently, this technique has been successfully used to gain insights into the structure of several ribosome bound nascent chains10,11. Here we describe the isolation of bovine Gamma-B Crystallin RNCs fused to SecM and generated in an in vitro translation system.

We describe here a technique that is now routinely used to isolate stably bound ribosome nascent chain complexes (RNCs). This technique takes advantage of the discovery that a 17 amino acid long SecM "arrest sequence" can halt translation elongation in a prokaryotic (E. coli) system, when inserted into (or fused to the C-terminus) of virtually any protein.

Extensive research has provided ample evidences suggesting that protein folding in the cell is a co-translational process1-5. However, the exact pathway ...

A Protocol for Lentiviral Transduction and Downstream Analysis of Intestinal Organoids

Intestinal crypt-villus structures termed organoids, can be kept in sustained culture three dimensionally when supplemented with the appropriate growth factors. Since organoids are highly similar to the original tissue in terms of homeostatic stem cell differentiation, cell polarity and presence of all terminally differentiated cell types known to the adult intestinal epithelium, they serve as an essential resource in experimental research on the epithelium. The possibility to express transgenes or interfering RNA using lentiviral or retroviral vectors in organoids has increased opportunities for functional analysis of the intestinal epithelium and intestinal stem cells, surpassing traditional mouse transgenics in speed and cost. In the current video protocol we show how to utilize transduction of small intestinal organoids with lentiviral vectors illustrated by use of doxycylin inducible transgenes, or IPTG inducible short hairpin RNA for overexpression or gene knockdown. Furthermore, considering organoid culture yields minute cell counts that may even be reduced by experimental treatment, we explain how to process organoids for downstream analysis aimed at quantitative RT-PCR, RNA-microarray and immunohistochemistry. Techniques that enable transgene expression and gene knock down in intestinal organoids contribute to the research potential that these intestinal epithelial structures hold, establishing organoid culture as a new standard in cell culture.

In this video protocol we give a step by step explanation of lentiviral transduction in organoids of primary intestinal epithelium and of processing and downstream analysis of these cultures by quantitative RT-PCR, RNA-microarray and immunohistochemistry.

Intestinal crypt-villus structures termed organoids, can be kept in sustained culture three dimensionally when supplemented with the appropriate growth ...

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 ...

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone function is mediated by extensive post-translational modification by a myriad of nuclear proteins. These modifications are critical for nuclear integrity as they regulate chromatin structure and recruit enzymes involved in gene regulation, DNA repair and chromosome condensation. Even though a large part of the scientific community adopts antibody-based techniques to characterize histone PTM abundance, these approaches are low throughput and biased against hypermodified proteins, as the epitope might be obstructed by nearby modifications. This protocol describes the use of nano liquid chromatography (nLC) and mass spectrometry (MS) for accurate quantification of histone modifications. This method is designed to characterize a large variety of histone PTMs and the relative abundance of several histone variants within single analyses. In this protocol, histones are derivatized with propionic anhydride followed by digestion with trypsin to generate peptides of 5 - 20 aa in length. After digestion, the newly exposed N-termini of the histone peptides are derivatized to improve chromatographic retention during nLC-MS. This method allows for the relative quantification of histone PTMs spanning four orders of magnitude.

This protocol outlines a fully integrated workflow for characterizing histone post-translational modifications using mass spectrometry (MS). The workflow includes histone purification from cell cultures or tissues, histone derivatization and digestion, MS analysis using nano-flow liquid chromatography and instructions for data analysis. The protocol is designed for completion within 2 - 3 days.

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone ...