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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Integrative Transcriptomic Analysis Reveals Distinct and Shared Host Responses in Dengue and Chikungunya Infections.

Mostafa Rezapour¹, Thomas D Shupe¹, David A Ornelles²

¹Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.

International Journal of Molecular Sciences

|June 26, 2026

Summary

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This study identifies key host gene signatures to accurately distinguish dengue virus (DENV) and chikungunya virus (CHIKV) infections. These molecular signatures offer improved diagnostic potential for co-circulating arboviruses.

Area of Science:

* Molecular biology and virology
* Transcriptomics and bioinformatics
* Infectious disease diagnostics

Background:

* Dengue virus (DENV) and chikungunya virus (CHIKV) infections present with similar symptoms, complicating diagnosis and management.
* Accurate differentiation between DENV and CHIKV is crucial for effective patient care and epidemiological surveillance.
* Host gene expression patterns offer potential biomarkers for distinguishing viral infections.

Purpose of the Study:

* To develop an integrative transcriptomic framework for identifying host gene signatures differentiating DENV, CHIKV, and healthy states.
* To establish robust and reproducible gene signatures using cross-validation.
* To evaluate the diagnostic performance of identified gene signatures.

Main Methods:

Keywords:

RNA sequencing chikungunya consensus signatures dengue feature selection interferon response predictive modeling transcriptomics

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

* Analysis of publicly available RNA sequencing (RNA-seq) datasets from human blood samples.
* Application of a cross-validation design with Generalized Linear Models with Quasi-Likelihood F-tests and Magnitude-Altitude Scoring (GLMQL-MAS) and Cross-Magnitude-Altitude Scoring (Cross-MAS) for differential expression and signature integration.
* Dimensionality reduction and multinomial logistic regression for classification performance assessment.

Main Results:

* A small subset of host genes demonstrated high accuracy (0.97 balanced accuracy) in distinguishing between dengue, chikungunya, and healthy states.
* Both infections elicited a shared antiviral response involving interferon signaling and innate immunity.
* Distinct virus-specific signatures were identified: dengue associated with cell-cycle/DNA replication, chikungunya with inflammatory/immune signaling (NF-kappaB, TLR).

Conclusions:

* The study presents a validated framework for integrative transcriptomic analysis to identify diagnostic host-response signatures.
* Compact and reproducible gene signatures show strong discriminative power for DENV and CHIKV.
* Further validation in independent cohorts is necessary before clinical application of these signatures.