High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Summary

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

Abstract

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

Introduction

Arboviruses, such as dengue, yellow fever, chikungunya, and zika, have been widely associated with several endemic outbreaks and have emerged as one of the main pathogens responsible for infecting humans in the last decades^1,2. Individuals infected with the chikungunya virus (CHIKV) often have fever, headache, rash, polyarthralgia, and arthritis^3,4,5. Viruses can subvert the gene expression of the cell and influence various host signaling pathways. Recently, blood transcriptome studies utilized RNA-seq to identify the....

Protocol

The samples used in this protocol were approved by the ethics committees from both the Department of Microbiology of the Institute of Biomedical Sciences at the University of São Paulo and the Federal University of Sergipe (Protocols: 54937216.5.0000.5467 and 54835916.2.0000.5546, respectively).

1. Docker desktop installation

NOTE: Steps to prepare the Docker environment are different among the operating systems (OSs). Therefore, Mac users must follow steps listed as 1.1, Linux users must follow steps listed as 1.2, and Windows users must follow steps listed as 1.3.

1. Install on th....

Results

The computing environment for transcriptome analyses was created and configured on the Docker platform. This approach allows beginner Linux users to use Linux terminal systems without a priori management knowledge. The Docker platform uses the resources of the host OS to create a service container that includes specific users' tools (Figure 1B). A container based on the Linux OS Ubuntu 20.04 distribution was created and it was fully configured for transcriptomic analyses, which is accessible.......

Discussion

The preparation of the sequencing libraries is a crucial step toward answering biological questions in the best possible way. The type of transcripts of interest of the study will guide which type of sequencing library will be chosen and drive bioinformatic analyses. For example, from the sequencing of a pathogen and host interaction, according to the type of sequencing, it is possible to identify sequences from both or just from the host transcripts.

Next-generation sequencing equipment, e.g........

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CEMiTool	Computational Systems Biology Laboratory	1.12.2	Discovery and the analysis of co-expression gene modules in a fully automatic manner, while providing a user-friendly HTML report with high-quality graphs.
EdgeR	Bioconductor (Maintainer: Yunshun Chen [yuchen at wehi.edu.au])	3.30.3	Differential expression analysis of RNA-seq expression profiles with biological replication
EnhancedVolcano	Bioconductor (Maintainer: Kevin Blighe [kevin at clinicalbioinformatics.co.uk])	1.6.0	Publication-ready volcano plots with enhanced colouring and labeling
FastQC	Babraham Bioinformatics	0.11.9	Aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing
FeatureCounts	Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research	2.0.0	Assign mapped sequencing reads to specified genomic features
MDP	Computational Systems Biology Laboratory	1.8.0	Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls
R	R Core Group	4.0.3	Programming language and free software environment for statistical computing and graphics
STAR	Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research	2.7.6a	Aligner designed to specifically address many of the challenges of RNA-seq data mapping using a strategy to account for spliced alignments
Bowtie2	Johns Hopkins University	2.4.2	Ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences
Trimmomatic	THE USADEL LAB	0.39	Trimming adapter sequence tasks for Illumina paired-end and single-ended data
Get Docker	Docker	20.10.2	Create a bioinformatic environment reproducible and predictable (https://docs.docker.com/get-docker/)
WSL2-Kernel	Windows	NA	https://docs.microsoft.com/en-us/windows/wsl/wsl2-kernel
Get Docker Linux	Docker	NA	https://docs.docker.com/engine/install/ubuntu/
Docker Linux Repository	Docker	NA	https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository
MDP Website	Computational Systems Biology Laboratory	NA	https://mdp.sysbio.tools
Enrichr Website	MaayanLab	NA	https://maayanlab.cloud/Enrichr/
webCEMiTool	Computational Systems Biology Laboratory	NA	https://cemitool.sysbio.tools/
gProfiler	Bioinformatics, Algorithmics and Data Mining Group	NA	https://biit.cs.ut.ee/gprofiler/gost
goseq	Bioconductor (Maintainer: Matthew Young [my4 at sanger.ac.uk])	NA	http://bioconductor.org/packages/release/bioc/html/goseq.html
SRA NCBI study	NCBI	NA	https://www.ncbi.nlm.nih.gov/bioproject/PRJNA507472/