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

Summary

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.

Abstract

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 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.

Introduction

Host-pathogen interactions represent a complex interplay between the pathogens and host organisms, which triggers the hosts' innate immune responses that eventually result in the removal of invading pathogens^1,2. During 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 OASL^3,4. Besides protein-coding genes, studies have also ....

Protocol

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.......

Discussion

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.......

Materials

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