Host Response Profiling in Infectious Disease Diagnostics

Host response profiling harnesses the body’s own immune signals to diagnose and characterise infections, shifting the focus from direct pathogen detection to analysis of host-derived biomarkers. By measuring patterns of gene expression, protein abundance or other molecular readouts in blood or tissue samples, researchers can distinguish bacterial from viral infections, predict disease severity and monitor treatment response. Advances in transcriptomics and proteomics, coupled with machine learning, have enabled the identification of multi-analyte signatures that greatly exceed the accuracy of single-marker assays. Such approaches promise rapid point-of-care tests that guide antibiotic stewardship, improve patient outcomes and reduce healthcare costs. Global efforts now encompass high-throughput sequencing, mass spectrometry and multiplex immunoassays across diverse populations, including paediatric, critical-care and community settings. Integration of temporal sampling and computational modelling addresses challenges of dynamic immune responses, while emerging epigenetic and single-cell techniques refine cellular contributions to infection. By offering early detection, prognostic stratification and pathogen-agnostic screening, host response profiling stands at the forefront of precision diagnostics in infectious disease.

Research from Nature Portfolio

A human viral challenge study with SARS-CoV-2 employed high-frequency blood sampling to map interferon-stimulated gene dynamics, revealing that MX1 expression peaks rapidly across immune cell types and may identify infection before conventional PCR positivity. In contrast, IFI27 shows a delayed, myeloid-restricted response, offering sustained diagnostic accuracy later in illness. These distinct temporal and cell-specific profiles suggest tailored uses: MX1 for early isolation or antiviral allocation and IFI27 for broader case finding during symptomatic phases.

In sepsis, a community-based consortium analysed transcriptomic data from multiple cohorts to derive prognostic models for 30-day mortality. Independent teams generated gene-expression signatures with AUROCs of 0.77–0.89, validated across community- and hospital-acquired sepsis cohorts. Combining these molecular scores with clinical severity indices significantly improved risk stratification, laying the groundwork for molecular bedside tests that inform early therapeutic decisions in critically ill patients.

Research from all publishers

A multi-cohort proteomic and machine learning study in febrile children integrated data from targeted and untargeted platforms to identify a six-protein signature distinguishing bacterial from viral infections. Validation by Luminex and ELISA assays demonstrated diagnostic AUROCs of 89–94%, supporting development of a rapid blood-based point-of-care test to optimise antibiotic use in paediatric fever.

Earlier work applied bioinformatic screening of circulating immune proteins followed by quantitative evaluation in over 1,000 patients, leading to a three-protein panel—TRAIL, IP-10 and C-reactive protein—that achieved an AUROC of 0.94. This signature outperformed single markers, remained robust across various pathogens, symptom onsets and clinical presentations, and informed strategies to curb antibiotic misuse in acute febrile illness.

Host Response Profiling in Infectious Disease Diagnostics publication trend

The graph below shows the total number of articles in host response profiling in infectious disease diagnostics across all publications each year (not limited to Nature Index journals).

Technical terms

Host response profiling: Analysis of immune-derived molecular signals (genes, proteins) to infer infection status.
Transcriptomics: Study of the complete set of RNA transcripts in a sample, reflecting gene‐expression dynamics.
Proteomics: Large‐scale analysis of protein abundance and modifications to characterise biological processes.
Interferon-stimulated genes (ISG): Genes induced by interferon signalling, pivotal in antiviral defence.
Area under the receiver operating characteristic curve (AUROC): Metric quantifying diagnostic test accuracy, with 1.0 denoting perfect discrimination.
Machine learning classifier: Computational model trained on molecular data to assign diagnostic or prognostic labels.
Point-of-care test (POCT): Rapid diagnostic assay performed near the patient, delivering timely clinical results.

References

1. SARS-CoV-2 human challenge reveals biomarkers that discriminate early and late phases of respiratory viral infections. Nature Communications (2024).
2. A community approach to mortality prediction in sepsis via gene expression analysis. Nature Communications (2018).
3. A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study. The Lancet Digital Health (2023).
4. A Novel Host-Proteome Signature for Distinguishing between Acute Bacterial and Viral Infections. PLOS ONE (2015).