The author would like to thank Tan Ke En and Dr. Cameron Bracken for their critical review of this manuscript. This work was supported by grants from Fundamental Research Grant Scheme (FRGS/1/2020/SKK0/UM/02/15) and University of Malaya High Impact Research Grant (UM.C/625/1/HIR/MOE/CHAN/02/07).

In this protocol, de-identified ribosomal RNA (rRNA)-depleted RNA-seq library datasets prepared from the influenza A virus-infected human macrophage cells were downloaded and used from the Gene Expression Omnibus (GEO) database. The entire bioinformatics pipeline from prediction to functional characterization of circRNAs is summarized in Figure 1. Each part of the pipeline is further explained in the sections below.
1. Preparation, download, and setup before data analysis
NOTE: All software packages used in this study are free and open-source.
<ol>
	<li>Downloading the required tools on the Linux platform
	<ol>
		<li>Download and install the required software and tools listed in the Table of Materials on a Linux high-performance computer using the instructions provided by the developer. 
		NOTE: Most of the tools and software have their own online GitHub pages or documentation containing instructions on installing and using their tools (refer to the Table of Materials).</li>
		<li>Download the desired RNA-seq datasets for circRNA detection and analysis from sequence archive websites (e.g., European Nucleotides Archive and Gene Expression Omnibus).</li>
		<li>Download the reference genome(s) (FASTA format) and annotation files (GTF/GFF3 format), compatible with the host from which the RNA-seq dataset was prepared. Host reference genome(s) and annotation files are usually found on online genome browsers such as the National Center for Biotechnology Information (NCBI), the University of California Santa Cruz (UCSC), and the Ensembl websites.</li>
	</ol>
	</li>
	<li>Quality checking of RNA-seq
	<ol>
		<li>Input the FASTQ files into the FASTQC program to determine the quality of RNA sequences. If the quality of the FASTQ files are low (e.g., &#60;Q20) or there is a presence of adapter sequences, further trimming might be needed using tools such as Trimmomatic29,30.</li>
	</ol>
	</li>
</ol>
2. Prediction and differential expression analysis of circRNAs using CIRIquant
NOTE: A more detailed manual on installing and performing differential expression analysis can be found in the code availability section of the CIRIquant paper31. The supplementary data also includes some of the basic commands used in this protocol.
<ol>
	<li>CircRNA predictions
	<ol>
		<li>Index the host&#39;s reference genome first using BWA and HISAT2 aligners. Then, on a Linux terminal, execute the commands bwa index32 and hisat2-build33 in the directory of the host&#39;s reference genome to index it.</li>
		<li>Next, prepare a YML config file containing the name of the file, the path of tools (BWA, HISAT2, stringtie34, samtools35), the path to the downloaded reference files (host&#39;s reference genome FASTA files, annotation files), and the path to the index files from step 2.1.1.</li>
		<li>Execute the CIRIquant tool from terminal using either the default or manual parameters. The user can specify the library type (either stranded or non-stranded) of the RNA-seq data when executing the CIRIquant tool. 
		NOTE: The library type of the RNA-seq data can be determined by knowing the type of library preparation kit used. If the identity of the library preparation kit is unknown, an RNA-seq control bioinformatic package called RSeQC36 can be used to determine the strandedness of RNA-seq data.</li>
	</ol>
	</li>
	<li>Differential expression analysis 
	NOTE: CIRIquant package includes prep_CIRIquant, prepDE.py, and CIRI_DE_replicate; therefore, no additional downloads are needed for these three tools.
	<ol>
		<li>Prepare a text file (.lst) with a list of data containing the following: 
		1st column: IDs of the RNA-seq data used in step 2.1.3 
		2nd column: path to the GTF files outputted by CIRIquant 
		3rd column: grouping of the RNA-seq data, whether it is a control or treated group.</li>
		<li>For an example, refer to Table 1 below. 
		NOTE: It is not necessary to put in the headers as they are just for reference.</li>
		<li>On the Linux terminal, run prep_CIRIquant with the text file (.lst) prepared in step 2.2.1 as an input. The run will generate a list of files: library_info.csv, circRNA_info.csv, circRNA_bsj.csv, and circRNA_ratio.csv.</li>
		<li>Prepare a second text file with a list of data containing the RNA-seq IDs and the path to their respective StringTie output. The file layout must be similar to the text file in step 2.2.1 without the grouping column.</li>
		<li>Run prepDE.py with the text file prepared in step 2.2.4 as an input to generate the gene count matrix files.</li>
		<li>Execute CIRI_DE_replicate with the library_info.csv and circRNA_bsj.csv files from step 2.2.3 and the gene_count_matrix.csv file from step 2.2.5 as inputs to output the final circRNA_de.tsv file.</li>
	</ol>
	</li>
	<li>Filtering of DE circRNAs
	<ol>
		<li>Use R (in the computer terminal or RStudio) or any spreadsheet software (e.g., Microsoft Excel) to open the circRNA_de.tsv file generated from step 2.2.6 to filter and determine the number of differentially expressed (DE) circRNAs.</li>
		<li>Filter the DE circRNAs according to the criteria LogFC &#62; |2| and FDR &#60; 0.05.</li>
		<li>Create a file named DE_circRNAs.txt to store the information of DE circRNAs.</li>
	</ol>
	</li>
</ol>
3. Characterization and annotation of predicted DE circRNAs
<ol>
	<li>Annotation status of DE circRNAs
	<ol>
		<li>Load the file named DE_circRNAs.txt in RStudio , which consists of the list of DE circRNAs filtered from step 2.3.3. Include other information such as the genomic positions (Chr, Start, End), strand orientations (+ or -), gene name, and circRNA type. Before proceeding, convert the circRNA genomic start coordinates from CIRIquant to 0-based by subtracting 1 base pair. 
		NOTE: The other information stated above can be obtained from the GTF files outputted by CIRIquant (Supplementary File 1).</li>
		<li>Determine the annotation status of the predicted DE circRNAs by downloading a library containing the genomic positions of the circRNA-database (e.g., circBase) deposited circRNAs. 
		NOTE: Assure that the genome version used to predict the circRNAs is identical to the circRNA database library before making the comparison. The circBase data file used here is freely available in the drive folder provided in Github (https://github.com/bicciatolab/Circr)37.</li>
		<li>Once both the files from step 3.1.1 and step 3.1.2 are prepared, run the R script given in Supplementary File 1. Chromosomal locations of DE circRNAs are queried to the library before assigning the status Annotated or Unannotated.</li>
	</ol>
	</li>
	<li>Characterization of DE circRNAs
	<ol>
		<li>Use R and other spreadsheet software to summarize the number of circRNAs according to the circRNA types (i.e., exon, intron, intergenic, and antisense) and the number of genes that the circRNAs span across (1 or &#62;1) (Supplementary File 1).&#8203;NOTE: CIRIquant can only detect four types of circRNAs (exon, intron, intergenic, and antisense). Exon-intron circRNAs, also known as ElciRNAs, cannot be detected by CIRIquant.</li>
	</ol>
	</li>
</ol>
4. Predicting the circRNA-miRNA interaction using Circr
NOTE: A more detailed manual on how to install and use Circr for the circRNA-miRNA interaction analysis can be found at: https://github.com/bicciatolab/Circr37.
<ol>
	<li>Preparation of files
	<ol>
		<li>Unzip and extract the contents of the Circr.zip file after downloading it from the Circr GitHub page using the relevant software such as &#34;WinRar&#34; or &#34;7-zip&#34; into a new directory where the analysis will be conducted.</li>
		<li>Install the prerequisite software applications (miRanda, RNAhybrid, Pybedtools, and samtools) before conducting the circRNA-miRNA analysis.</li>
		<li>Reference genomes and annotation files for several organisms of interest, rRNA coordinates file, validated miRNA interaction file, and circBase circRNA files are provided by the Circr author in the Github page (https://github.com/bicciatolab/Circr)37. Upon clicking on the support files in drive folder, select the folder for the organism of interest, miRNA folder, and the circBase text file and download it.</li>
		<li>After downloading the necessary files in step 4.1.3, create a new directory named support_files in the directory mentioned in step 4.1.1. Then, unzip and extract the content into the support_files directory.</li>
		<li>Index the reference genome file of the organism of interest using the samtools faidx command (Supplementary File 1).</li>
		<li>Prepare an input file consisting of the coordinates of DE circRNAs of interest in a tab-delimited BED file, as shown in Table 2. 
		NOTE: Because circRNAs predicted by CIRIquant are not 0-based, it is necessary to minus 1 bp at the starting coordinate (as mentioned in step 3.1.1) before converting them to the BED format. The headers shown in Table 2 are just for reference and are not needed in the BED files.</li>
		<li>At this point, ensure that the expected folder tree structure for Circr analysis is as in Figure 2.</li>
	</ol>
	</li>
	<li>Running Circr.py
	<ol>
		<li>Execute Circr.py using Python 3, and as arguments, specify the circRNA input file, the FASTA genome of the organism of interest, the genome version of the selected organism, the number of threads, and the name of the output file in the command line.</li>
		<li>If the organism of interest is not provided in the drive folder listed in step 4.1.3 or if the user prefers to have a custom set of files to run the analysis, additional commands specifying the location of these files need to be included when executing Circr.py.</li>
		<li>After the Circr analysis is complete, the program outputs a circRNA-miRNA interaction file in the csv format.</li>
		<li>Filter the circRNA-miRNA interaction results according to the user-specific preference. For this study, the predictions are filtered using Rstudio according to the criteria below: 
		-Detected by all three software tools 
		-Two or more binding sites reported by both Targetscan and miRanda 
		-Identified in either the &#34;AGO&#34; or &#34;validated&#34; columns 
		-&#8203;Filter out no seed region interactions</li>
		<li>Write the circRNAs that pass the filtered conditions from step 4.2.3 into a new text file named circRNA_miRNA.txt. Such filtering can increase the confidence of the predicted interactions.</li>
	</ol>
	</li>
</ol>
5. Construction of the ceRNA network
NOTE: A detailed manual on how to use Cytoscape can be found at: http://manual.cytoscape.org/en/stable/ and https://github.com/cytoscape/cytoscape-tutorials/wiki#introduction&#160;
<ol>
	<li>Download&#160;and preparation
	<ol>
		<li>Download the latest version of Cytoscape38 from:&#160;https://cytoscape.org/download.html.</li>
		<li>Execute the installer wizard downloaded in step 5.1.1 and select the file location for the Cytoscape software.</li>
		<li>Prepare a tab-delimited file containing the circRNAs of interest and their target miRNA. The first column consists of the circRNA name; the second column specifies the type of RNA from the first column; the third column is the target miRNA; and the fourth column specifies the type of RNA from the third column. An example of the file is shown in Table 3.</li>
	</ol>
	</li>
	<li>Constructing the ceRNA network map
	<ol>
		<li>Open the Cytoscape software installed in step 5.1.2.</li>
		<li>In Cytoscape, navigate to File &#62; Import &#62; Network from File. Select the file that has been prepared in step 5.1.3.</li>
		<li>In the new tab, select the first and second column as &#34;Source Node&#34; and &#34;Source Node Attribute&#34; while select the third and fourth column as &#34;Target Node&#34; and &#34;Target Node Attribute&#34; respectively. Click OK&#160;and the network will show up on the upper right side of Cytoscape.</li>
		<li>To change the visual style of the network, press the Style button on the left side of Cytoscape.</li>
		<li>Press the arrow on the right side of Fill Color. Choose Type for the column and Discrete Mapping for the mapping type. Then, select the color desired for each of the RNA types.</li>
		<li>After changing the color, change the shape of the nodes by navigating to Shape and following step 5.2.5.</li>
	</ol>
	</li>
</ol>
6. Functional enrichment analysis
<ol>
	<li>Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for the parental gene of the circRNAs
	<ol>
		<li>Ensure the clusterProfiler39,40 and org.Hs.eg.db41 packages have been installed in Rstudio. The org.Hs.eg.db41 package is a genome-wide annotation package only for humans. If the organism of interest is another species, refer to:&#160;https://bioconductor.org/packages/release/BiocViews.html#OrgDb</li>
		<li>Import the DE_circRNA information from step 2.3.1 into the Rstudio workspace.</li>
		<li>Use the parental gene of the circRNAs provided in this file for enrichment analysis in the upcoming steps. However, if the user wishes to convert the gene symbol to other formats, such as the Entrez ID, use a function such as &#34;bitr&#34;.</li>
		<li>By using the gene ID as an input, run the GO enrichment analysis using the enrichGO function within the clusterProfiler39,40 package using default parameters.</li>
		<li>By using the gene ID as an input, run the KEGG enrichment analysis using the enrichKEGG function within the clusterProfiler39,40 package using the default parameters.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

To illustrate the utility of this protocol, RNA-seq from influenza A virus-infected human macrophage cells was used as an example. CircRNAs functioning as potential miRNA sponges in host-pathogen interactions and their GO and KEGG functional enrichment within a host were investigated. Although there are a variety of circRNA tools available online, each of them is a standalone package that does not interact with one another. Here, we put together few of the tools that are required for circRNA prediction and quantification...

Host-pathogen interactions represent a complex interplay between the pathogens and host organisms, which triggers the hosts&#39; innate immune responses that eventually result in the removal of invading pathogens1,2. During&#160;pathogenic infections, a multitude of the host immune genes is regulated to inhibit the replication and release of pathogens. For example, common interferon-stimulated genes (ISGs) regulated upon pathogenic infections include ADAR1, IFIT1, IFIT2, IFIT3, ISG20, RIG-I, and OASL3,4. Besides protein-coding genes, studies have also reported that non-coding RNAs such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs) also play a role and are regulated concurrently during pathogenic infections5,6,7. In contrast to protein-coding genes that mainly encode proteins as functional molecules, non-coding RNAs (ncRNAs) are known to function as regulators of genes at transcriptional and post-transcriptional levels. However, studies involving the participation of non-coding RNAs, particularly circRNAs, in regulating the hosts&#39; immune genes are not well reported compared to the protein-coding genes.
CircRNAs are widely characterized by their covalently closed continuous loop structure, which is generated through a non-canonical splicing process called back-splicing8. The process of back-splicing, unlike the splicing process of cognate linear RNAs, involves the ligation of the downstream donor site to the upstream acceptor site, forming a circular-shaped structure. Currently, three different back-splicing mechanisms for the biogenesis of circRNAs have been proposed. These are RNA binding protein (RBP) mediated circularization9,10, intron-pairing-driven circularization11, and lariat-driven circularization12,13,14. Given that circRNAs are connected end-to-end in a circular structure, they tend to be naturally resistant to normal exonuclease digestions and, thus, are considered to be more stable than their linear counterparts15. Another common characteristic exhibited by circRNAs includes the cell or tissue type-specific expression in hosts16.
As implied by their unique structure and cell or tissue-specific expression, circRNAs have been discovered to play important biological functions in cells. To date, one of the prominent functions of circRNAs is their role as microRNA (miRNA) sponges17,18. This regulatory role of circRNAs occurs through the complementary binding of circRNA nucleotides with the seed region of miRNAs. Such a circRNA-miRNA interaction inhibits the miRNAs&#39; normal regulatory functions on target mRNAs, thus regulating the expression of genes19,20. Additionally, circRNAs are also known to regulate gene expression by interacting with RNA binding proteins (RBPs) and forming RNA-protein complexes21. Although circRNAs are classified as non-coding RNAs, there is also evidence that circRNAs can act as templates for protein translation22,23,24.
Recently, circRNAs have been demonstrated to play pivotal roles in regulating the host-pathogen interactions, particularly between the hosts and viruses. Generally, host circRNAs are assumed to assist in regulating the host&#39;s immune responses to eliminate the invading pathogens. An example of&#160;circRNA that promotes host immune responses is circRNA_0082633, reported by Guo et al.25. This circRNA enhances type I interferon (IFN) signaling within A549 cells, which helps to suppress influenza virus replication25. Moreover, Qu et al.&#160;also reported a human intronic circRNA, called circRNA AIVR, that promotes immunity by regulating the expression of CREB-binding protein (CREBBP), a signal transducer of IFN-&#946;26,27. However, circRNAs that are known to promote the pathogenesis of disease upon infection also exist. For example, Yu et al. recently reported the role played by a circRNA spliced from the GATA zinc finger domain containing the 2A gene (circGATAD2A) in promoting the H1N1 virus replication through the inhibition of host cell autophagy28.
To effectively study circRNAs, a genome-wide circRNA prediction algorithm is usually implemented, followed by an in silico characterization of the predicted circRNA candidates before any functional studies can be carried out. Such a bioinformatics approach to predict and characterize circRNAs is less costly and more time efficient. It helps to refine the number of candidates to be functionally studied and could potentially lead to novel findings. Here, we provide a detailed bioinformatics-based protocol for the in silico identification, characterization and functional annotation of circRNAs during the host-pathogen interactions. The protocol includes the identification and quantification of circRNAs from RNA-sequencing datasets, annotation via circBase, and the characterization of the circRNA candidates in terms of circRNA types, number of overlapping genes, and predicted circRNA-miRNA interactions. This study also provides the functional annotation of the circRNA parental genes through Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Bedtools</td>
			<td>GitHub</td>
			<td>https://github.com/arq5x/bedtools2/</td>
			<td>Referring to section 4.1.2. Needed for Circr.</td>
		</tr>
		<tr>
			<td>BWA</td>
			<td>Burrows-Wheeler Aligner</td>
			<td>http://bio-bwa.sourceforge.net/</td>
			<td>Referring to section 2.1.1 and 2.1.2. Needed to run CIRIquant, and to index the genome</td>
		</tr>
		<tr>
			<td>Circr</td>
			<td>GitHub</td>
			<td>https://github.com/bicciatolab/Circr</td>
			<td>Referring to section 4. Use to predict the miRNA binding sites</td>
		</tr>
		<tr>
			<td>CIRIquant</td>
			<td>GitHub</td>
			<td>https://github.com/bioinfo-biols/CIRIquant</td>
			<td>Referring to section 2.1.3. To predict circRNAs</td>
		</tr>
		<tr>
			<td>Clusterprofiler</td>
			<td>GitHub</td>
			<td>https://github.com/YuLab-SMU/clusterProfiler</td>
			<td>Referring to section 7. For GO and KEGG functional enrichment</td>
		</tr>
		<tr>
			<td>CPU</td>
			<td>Intel</td>
			<td>&#160;Intel(R) Xeon(R) CPU E5-2620 V2 @ 2.10 GHz&#160;&#160; Cores: 6-core CPU Memory: 65 GB Graphics card: NVIDIA GK107GL (QUADRO K2000)&#160;</td>
			<td>Specifications used to run this entire protocol.</td>
		</tr>
		<tr>
			<td>Cytoscape</td>
			<td>Cytoscape</td>
			<td>https://cytoscape.org/download.html</td>
			<td>Referring to section 5.2. Needed to plot ceRNA network</td>
		</tr>
		<tr>
			<td>FastQC</td>
			<td>Babraham Bioinformatics</td>
			<td>https://www.bioinformatics.babraham.ac.uk/projects/fastqc/</td>
			<td>Referring to section 1.2.1. Quality checking on Fastq files</td>
		</tr>
		<tr>
			<td>HISAT2</td>
			<td></td>
			<td>http://daehwankimlab.github.io/hisat2/</td>
			<td>Referring to section 2.1.1 and 2.1.2. Needed to run CIRIquant, and to index the genome</td>
		</tr>
		<tr>
			<td>Linux</td>
			<td>Ubuntu 20.04.5 LTS (Focal Fossa)</td>
			<td>https://releases.ubuntu.com/focal/</td>
			<td>Needed to run the entire protocol. Other Ubuntu versions may still be valid to carry out the protocol.</td>
		</tr>
		<tr>
			<td>miRanda</td>
			<td></td>
			<td>http://www.microrna.org/microrna/getDownloads.do</td>
			<td>Referring to section 4.1.2. Needed for Circr</td>
		</tr>
		<tr>
			<td>Pybedtools</td>
			<td>pybedtools 0.8.2</td>
			<td>https://pypi.org/project/pybedtools/</td>
			<td>Needed for BED file genomic manipulation</td>
		</tr>
		<tr>
			<td>Python</td>
			<td>Python 2.7 and 3.6 or abover</td>
			<td>https://www.python.org/downloads/</td>
			<td>To run necessary library modules</td>
		</tr>
		<tr>
			<td>R</td>
			<td>The Comprehensive R Archive Network</td>
			<td>https://cran.r-project.org/</td>
			<td>To manipulate dataframes</td>
		</tr>
		<tr>
			<td>RNAhybrid</td>
			<td>BiBiServ</td>
			<td>https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid</td>
			<td>Referring to section 4.1.2. Needed for Circr</td>
		</tr>
		<tr>
			<td>RStudio</td>
			<td>RStudio</td>
			<td>https://www.rstudio.com/</td>
			<td>A workspace to run R</td>
		</tr>
		<tr>
			<td>samtools&#160;</td>
			<td>SAMtools</td>
			<td>http://www.htslib.org/</td>
			<td>Referring to section 2.1.2. Needed to run CIRIquant</td>
		</tr>
		<tr>
			<td>StringTie</td>
			<td>Johns Hopkins University: Center for Computational Biology</td>
			<td>http://ccb.jhu.edu/software/stringtie/index.shtml</td>
			<td>Referring to section 2.1.2. Needed to run CIRIquant</td>
		</tr>
		<tr>
			<td>TargetScan</td>
			<td>GitHub</td>
			<td>https://github.com/nsoranzo/targetscan</td>
			<td>Referring to section 4.1.2. Needed for Circr</td>
		</tr>
	</tbody>
</table>

in silico identification and characterization of circrnas during host-pathogen interactions

Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with&#160;pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.

The protocol enlisted in the previous section was modified and configured to suit the Linux OS system. The main reason is that most module libraries and packages involved in the analysis of circRNAs can only work on the Linux platform. In this analysis, de-identified ribosomal RNA (rRNA)-depleted RNA-seq library datasets prepared from the Influenza A virus-infected human macrophage cells were downloaded from the GEO database42 and used to generate the representative results.
<p class="jove_con...

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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

The protocol submitted here explains the complete in silico pipeline needed to predict and functionally characterize circRNAs from RNA-sequencing transcriptome data studying host-pathogen interactions.

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as ...

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1.5K Views.  Universiti Malaya. The protocol submitted here explains the complete in silico pipeline needed to predict and functionally characterize circRNAs from RNA-sequencing transcriptome data studying host-pathogen interactions.

Video: In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Identification and Characterization of Protein Glycosylation using Specific Endo- and Exoglycosidases

Glycosylation, the addition of covalently linked sugars, is a major post-translational modification of proteins that can significantly affect processes such as cell adhesion, molecular trafficking, clearance, and signal transduction1-4. In eukaryotes, the most common glycosylation modifications in the secretory pathway are additions at consensus asparagine residues (N-linked); or at serine or threonine residues (O-linked) (Figure 1). Initiation of N-glycan synthesis is highly conserved in eukaryotes, while the end products can vary greatly among different species, tissues, or proteins. Some glycans remain unmodified ("high mannose N-glycans") or are further processed in the Golgi ("complex N-glycans"). Greater diversity is found for O-glycans, which start with a common N-Acetylgalactosamine (GalNAc) residue in animal cells but differ in lower organisms1.
The detailed analysis of the glycosylation of proteins is a field unto itself and requires extensive resources and expertise to execute properly. However a variety of available enzymes that remove sugars (glycosidases) makes possible to have a general idea of the glycosylation status of a protein in a standard laboratory setting. Here we illustrate the use of glycosidases for the analysis of a model glycoprotein: recombinant human chorionic gonadotropin beta (hCG&beta;), which carries two N-glycans and four O-glycans 5. The technique requires only simple instrumentation and typical consumables, and it can be readily adapted to the analysis of multiple glycoprotein samples.
Several enzymes can be used in parallel to study a glycoprotein. PNGase F is able to remove almost all types of N-linked glycans6,7. For O-glycans, there is no available enzyme that can cleave an intact oligosaccharide from the protein backbone. Instead, O-glycans are trimmed by exoglycosidases to a short core, which is then easily removed by O-Glycosidase. The Protein Deglycosylation Mix contains PNGase F, O-Glycosidase, Neuraminidase (sialidase), &beta;1-4 Galactosidase, and &beta;-N-Acetylglucosaminidase. It is used to simultaneously remove N-glycans and some O-glycans8 . Finally, the Deglycosylation Mix was supplemented with a mixture of other exoglycosidases (&alpha;-N-Acetylgalactosaminidase, &alpha;1-2 Fucosidase, &alpha;1-3,6 Galactosidase, and &beta;1-3 Galactosidase ), which help remove otherwise resistant monosaccharides that could be present in certain O-glycans.
SDS-PAGE/Coomasie blue is used to visualize differences in protein migration before and after glycosidase treatment. In addition, a sugar-specific staining method, ProQ Emerald-300, shows diminished signal as glycans are successively removed. This protocol is designed for the analysis of small amounts of glycoprotein (0.5 to 2 &mu;g), although enzymatic deglycosylation can be scaled up to accommodate larger quantities of protein as needed.

Using specific glycosidases to remove sugars from glycoproteins followed by SDS-PAGE is a valuable method to detect glycan modifications on protein samples and is a good choice for initial glycobiology studies. Changes following deglycosylation can be detected as shifts in gel mobility or by staining with glycan sensitive reagents.

Glycosylation, the addition of covalently linked sugars, is a major post-translational modification of proteins that can significantly affect processes ...

Rapid Genetic Analysis of Epithelial-Mesenchymal Signaling During Hair Regeneration

Hair follicle morphogenesis, a complex process requiring interaction between epithelia-derived keratinocytes and the underlying mesenchyme, is an attractive model system to study organ development and tissue-specific signaling. Although hair follicle development is genetically tractable, fast and reproducible analysis of factors essential for this process remains a challenge. Here we describe a procedure to generate targeted overexpression or shRNA-mediated knockdown of factors using lentivirus in a tissue-specific manner. Using a modified version of a hair regeneration model 5, 6, 11, we can achieve robust gain- or loss-of-function analysis in primary mouse keratinocytes or dermal cells to facilitate study of epithelial-mesenchymal signaling pathways that lead to hair follicle morphogenesis. We describe how to isolate fresh primary mouse keratinocytes and dermal cells, which contain dermal papilla cells and their precursors, deliver lentivirus containing either shRNA or cDNA to one of the cell populations, and combine the cells to generate fully formed hair follicles on the backs of nude mice. This approach allows analysis of tissue-specific factors required to generate hair follicles within three weeks and provides a fast and convenient companion to existing genetic models.

Tissue-specific analysis of a hair follicle regeneration model using lentivirus to mediate gain- or loss-of-function.

Hair follicle morphogenesis, a complex process requiring interaction between epithelia-derived keratinocytes and the underlying mesenchyme, is an ...

Live Imaging of Apoptotic Cell Clearance during Drosophila Embryogenesis

The proper elimination of unwanted or aberrant cells through apoptosis and subsequent phagocytosis (apoptotic cell clearance) is crucial for normal development in all metazoan organisms. Apoptotic cell clearance is a highly dynamic process intimately associated with cell death; unengulfed apoptotic cells are barely seen in vivo under normal conditions. In order to understand the different steps of apoptotic cell clearance and to compare 'professional' phagocytes - macrophages and dendritic cells to 'non-professional' - tissue-resident neighboring cells, in vivo live imaging of the process is extremely valuable. Here we describe a protocol for studying apoptotic cell clearance in live Drosophila embryos. To follow the dynamics of different steps in phagocytosis we use specific markers for apoptotic cells and phagocytes. In addition, we can monitor two phagocyte systems in parallel: 'professional' macrophages and 'semi-professional' glia in the developing central nervous system (CNS). The method described here employs the Drosophila embryo as an excellent model for real time studies of apoptotic cell clearance.

Live Imaging of Apoptotic Cell Clearance during Drosophila Embryogenesis

Here we describe an effective method for studying dynamics of apoptotic cell clearance in vivo. This method employs live Drosophila embryos as a powerful model for monitoring phagocytosis of apoptotic cells using specific labeling of apoptotic cells and phagocytes.

The  proper elimination of unwanted or aberrant cells through apoptosis and  subsequent phagocytosis (apoptotic cell clearance) is crucial for normal  ...

Pyrosequencing for Microbial Identification and Characterization

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate bacterial strains and detect genetic mutations that confer resistance to anti-microbial agents. The advantages of pyrosequencing for microbiology applications include rapid and reliable high-throughput screening and accurate identification of microbes and microbial genome mutations. Pyrosequencing involves sequencing of DNA by synthesizing the complementary strand a single base at a time, while determining the specific nucleotide being incorporated during the synthesis reaction. The reaction occurs on immobilized single stranded template DNA where the four deoxyribonucleotides (dNTP) are added sequentially and the unincorporated dNTPs are enzymatically degraded before addition of the next dNTP to the synthesis reaction. Detection of the specific base incorporated into the template is monitored by generation of chemiluminescent signals. The order of dNTPs that produce the chemiluminescent signals determines the DNA sequence of the template. The real-time sequencing capability of pyrosequencing technology enables rapid microbial identification in a single assay. In addition, the pyrosequencing instrument, can analyze the full genetic diversity of anti-microbial drug resistance, including typing of SNPs, point mutations, insertions, and deletions, as well as quantification of multiple gene copies that may occur in some anti-microbial resistance patterns.

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate bacterial strains, and detect genetic mutations that confer resistance to anti-microbial agents. In this video, the procedure for microbial amplicon generation, amplicon pyrosequencing, and DNA sequence analysis will be demonstrated.

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate ...

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

We developed computational de novo protein design methods capable of tackling several important areas of protein design. To disseminate these methods we present Protein WISDOM, an online tool for protein design (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>). Starting from a structural template, design of monomeric proteins for increased stability and complexes for increased binding affinity can be performed.

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific ...

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. Saccharomyces cerevisiae, or budding yeast, is a model eukaryote for which a complete collection of ~6,000 gene deletion mutants and hypomorphic essential gene mutants are commercially available. These collections of mutants can be used to systematically detect chemical-gene interactions, i.e. genes necessary to tolerate a chemical. This information, in turn, reports on the likely mode of action of the compound. Here we describe a protocol for the rapid identification of chemical-genetic interactions in budding yeast. We demonstrate the method using the chemotherapeutic agent 5-fluorouracil (5-FU), which has a well-defined mechanism of action. Our results show that the nuclear TRAMP RNA exosome and DNA repair enzymes are needed for proliferation in the presence of 5-FU, which is consistent with previous microarray based bar-coding chemical genetic approaches and the knowledge that 5-FU adversely affects both RNA and DNA metabolism. The required validation protocols of these high-throughput screens are also described.

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Here we present a cost-effective method for defining chemical-genetic interactions in budding yeast. The approach is built on fundamental techniques in yeast molecular biology and is well suited for the mechanistic interrogation of small to medium collections of chemicals and other media environments.

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. ...

Chromatin Immunoprecipitation Assay for the Identification of Arabidopsis Protein-DNA Interactions In Vivo

Intricate gene regulatory networks orchestrate biological processes and developmental transitions in plants. Selective transcriptional activation and silencing of genes mediate the response of plants to environmental signals and developmental cues. Therefore, insights into the mechanisms that control plant gene expression are essential to gain a deep understanding of how biological processes are regulated in plants. The chromatin immunoprecipitation (ChIP) technique described here is a procedure to identify the DNA-binding sites of proteins in genes or genomic regions of the model species Arabidopsis thaliana. The interactions with DNA of proteins of interest such as transcription factors, chromatin proteins or posttranslationally modified versions of histones can be efficiently analyzed with the ChIP protocol. This method is based on the fixation of protein-DNA interactions in vivo, random fragmentation of chromatin, immunoprecipitation of protein-DNA complexes with specific antibodies, and quantification of the DNA associated with the protein of interest by PCR techniques. The use of this methodology in Arabidopsis has contributed significantly to unveil transcriptional regulatory mechanisms that control a variety of plant biological processes. This approach allowed the identification of the binding sites of the Arabidopsis chromatin protein EBS to regulatory regions of the master gene of flowering FT. The impact of this protein in the accumulation of particular histone marks in the genomic region of FT was also revealed through ChIP analysis.

Chromatin Immunoprecipitation Assay for the Identification of Arabidopsis Protein-DNA Interactions In Vivo

Chromatin immunoprecipitation is a powerful technique for the identification of DNA binding sites of Arabidopsis proteins in vivo. This procedure includes chromatin cross-linking and fragmentation, immunoprecipitation with selective antibodies against the protein of interest, and qPCR analysis of bound DNA. We describe a simple ChIP assay optimized for Arabidopsis plants.

Intricate gene regulatory networks orchestrate biological processes and developmental transitions in plants. Selective transcriptional activation and ...

Plant Promoter Analysis: Identification and Characterization of Root Nodule Specific Promoter in the Common Bean

The upstream sequences of gene coding sequences are termed as promoter sequences. Studying the expression patterns of promoters are very significant in understanding the gene regulation and spatiotemporal expression patterns of target genes. On the other hand, it is also critical to establish promoter evaluation tools and genetic transformation techniques that are fast, efficient, and reproducible. In this study, we investigated the spatiotemporal expression pattern of the rhizobial symbiosis-specific nodule inception (NIN) promoter of Phaseolus vulgaris in the transgenic hairy roots. Using plant genome databases and analysis tools we identified, isolated, and cloned the P. vulgaris NIN promoter in a transcriptional fusion to the chimeric reporter &#946;-glucuronidase (GUS) GUS-enhanced::GFP. Further, this protocol describes a rapid and versatile system of genetic transformation in the P. vulgaris using Agrobacterium rhizogenes induced hairy roots. This system generates &#8805;2 cm hairy roots at 10 to 12 days after transformation. Next, we assessed the spatiotemporal expression of NIN promoter in Rhizobium inoculated hairy roots at periodic intervals of post-inoculation. Our results depicted by GUS activity show that the NIN promoter was active during the process of nodulation. Together, the present protocol demonstrates how to identify, isolate, clone, and characterize a plant promoter in the common bean hairy roots. Moreover, this protocol is easy to use in non-specialized laboratories.

Promoter expression analyses are crucial to improving the understanding of gene regulation and the spatiotemporal expression of target genes. Herein we present a protocol to identify, isolate, and clone a plant promoter. Further, we describe the characterization of the nodule-specific promoter in the common bean hairy roots.

The upstream sequences of gene coding sequences are termed as promoter sequences. Studying the expression patterns of promoters are very significant in ...

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water deficit or pathogens plays a crucial role. Identifying small molecules that regulate stomatal movement can therefore contribute to understanding the physiological basis by which plants adapt to their environment. Large-scale screening approaches that have been used to identify regulators of stomatal movement have potential limitations: some rely heavily on the abscisic acid (ABA) hormone signaling pathway, therefore excluding ABA-independent mechanisms, while others rely on the observation of indirect, long-term physiological effects such as plant growth and development. The screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging. Since evaporation of water through transpiration results in leaf surface cooling, thermal imaging provides a non-invasive approach to investigate changes in stomatal conductance over time. In this protocol, Helianthus annuus seedlings are grown hydroponically and then treated by root feeding, in which the primary root is cut and dipped into the chemical being tested. Thermal imaging followed by statistical analysis of cotyledonary temperature changes over time allows for the identification of bioactive molecules modulating stomatal aperture. Our proof-of-concept experiments demonstrate that a chemical can be carried from the cut root to the cotyledon of the sunflower seedling within 10 minutes. In addition, when plants are treated with ABA as a positive control, an increase in leaf surface temperature can be detected within minutes. Our method thus allows the efficient and rapid identification of novel molecules regulating stomatal aperture.

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

We provide a method for identifying modulators of foliar transpiration by large-scale screening of a compound library.

Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water ...

Identification, Isolation, and Characterization of Fibro-Adipogenic Progenitors (FAPs) and Myogenic Progenitors (MPs) in Skeletal Muscle in the Rat

Fibro-adipogenic Progenitors (FAPs) are resident interstitial cells in skeletal muscle that, together with myogenic progenitors (MPs), play a key role in muscle homeostasis, injury, and repair. Current protocols for FAPs identification and isolation use flow cytometry/fluorescence-activated cell sorting (FACS) and studies evaluating their function in vivo to date have been undertaken exclusively in mice. The larger inherent size of the rat allows for a more comprehensive analysis of FAPs in skeletal muscle injury models, especially in severely atrophic muscle or when investigators require substantial tissue mass to conduct multiple downstream assays. The rat additionally provides a larger selection of muscle functional assays that do not require animal sedation or sacrifice, thus minimizing morbidity and animal use by enabling serial assessments. The flow cytometry/FACS protocols optimized for mice are species specific, notably restricted by the characteristics of commercially available antibodies. They have not been optimized for separating FAPs from rat or highly fibrotic muscle. A flow cytometry/FACS protocol for the identification and isolation of FAPs and MPs from both healthy and denervated rat skeletal muscle was developed, relying on the differential expression of surface markers CD31, CD45, Sca-1, and VCAM-1. As rat-specific, flow cytometry-validated primary antibodies are severely limited, in-house conjugation of the antibody targeting Sca-1 was performed. Using this protocol, successful Sca-1 conjugation was confirmed, and flow cytometric identification of FAPs and MPs was validated by cell culture and immunostaining of FACS-isolated FAPs and MPs. Finally, we report a novel FAPs time-course in a prolonged (14 week) rat denervation model. This method provides the investigators the ability to study FAPs in a novel animal model.

Identification, Isolation, and Characterization of Fibro-Adipogenic Progenitors FAPs and Myogenic Progenitors MPs in Skeletal Muscle in the Rat

This protocol outlines a method to isolate Fibro-adipogenic progenitors (FAPs) and myogenic progenitors (MPs) from rat skeletal muscle. Utilization of the rat in muscle injury models provides increased tissue availability from atrophic muscle for the analysis and a larger repertoire of validated methods to assess muscle strength and gait in free-moving animals.

Fibro-adipogenic Progenitors (FAPs) are resident interstitial cells in skeletal muscle that, together with myogenic progenitors (MPs), play a key role in ...

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

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Identification and Characterization of Protein Glycosylation using Specific Endo- and Exoglycosidases

Rapid Genetic Analysis of Epithelial-Mesenchymal Signaling During Hair Regeneration

Live Imaging of Apoptotic Cell Clearance during Drosophila Embryogenesis

Pyrosequencing for Microbial Identification and Characterization

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Chromatin Immunoprecipitation Assay for the Identification of Arabidopsis Protein-DNA Interactions In Vivo

Plant Promoter Analysis: Identification and Characterization of Root Nodule Specific Promoter in the Common Bean

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Identification, Isolation, and Characterization of Fibro-Adipogenic Progenitors (FAPs) and Myogenic Progenitors (MPs) in Skeletal Muscle in the Rat