Name: In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions (Video) | JoVE Journal
Uploaded: 2022-10-21T15:00:00.000+00:00
Duration: 10 min 27 s
Description: Discover an in silico pipeline for predicting and characterizing circular RNAs from RNA-sequencing data during host-pathogen interactions. This computational method enables differential expression analysis and functional enrichment for clinical biomarker discovery.

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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><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>&amp;nbsp;Intel(R) Xeon(R) CPU E5-2620 V2 @ 2.10 GHz&amp;nbsp;&amp;nbsp; Cores: 6-core CPU Memory: 65 GB Graphics card: NVIDIA GK107GL (QUADRO K2000)&amp;nbsp;</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&amp;nbsp;</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

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...

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.

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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Discover an in silico pipeline for predicting and characterizing circular RNAs from RNA-sequencing data during host-pathogen interactions. This computational method enables differential expression analysis and functional enrichment for clinical biomarker discovery.

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

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