# An Integrated Host-Microbe Gene Classifier to Predict SARS-CoV-2 and Severe Disease in Children with Respiratory Viral Coinfections

> **NIH NIH R01** · ARKANSAS CHILDREN'S HOSPITAL RES INST · 2024 · $694,403

## Abstract

PROJECT SUMMARY
Respiratory viral coinfections (RVCIs) are more common in children than adults and have become increasingly
important due to the emergence of SARS-COV-2. However, the distinction between coinfection and codetection
remains unclear. Further, the association between finding multiple viruses using our current clinical methods and
the effect on clinical outcomes is nebulous. Understanding positive viral testing has become crucial with the
ongoing spread of SARS-CoV-2 and the treatment algorithms that are expensive or deleterious to patients with
other viruses. Currently, clinicians struggle to identify the dominant virus inducing the host immune response in
RVCIs in children. Our group has developed gene classifiers to identify adults with SARS-CoV-2 compared to
other viruses using nasopharyngeal swabs. Further, we are the first to develop a host/microbe classifier for
pediatric patients on the ventilator that will distinguish lower respiratory tract infections using lower airway
sampling. Here, our objective is to identify patient features, coinfecting viruses, microbial contributions, and host
responses that enhance disease severity in SARS-CoV-2–infected children. As the only pediatric hospital in the
state, Arkansas Children's Hospital is an ideal site for studying SARS-CoV-2 RVCIs. We aim to leverage our
unique multi-institutional collaborative team with extensive experience applying metagenomic next-generation
sequencing to simultaneously evaluate viral and host genetic material from clinically obtained nasopharyngeal
specimens. This approach identifies host–virus interactions and allows us to assess their impact on immune
responses and disease severity during RVCIs. We hypothesize that a combination of patient characteristics, viral
features, and host immune responses will predispose a child to more severe disease. We also expect to identify
an immunologic fingerprint for SARS-CoV-2 that can be used to identify it as the “infecting virus” in children with
codetections. The impact of this study is significant, and the multidisciplinary team that this proposal brings
together is experienced. By employing epidemiologic, -omic, and computational approaches, we will identify
immunologic fingerprints of SARS-CoV-2 infections in children, which can help identify the “infecting virus” when
multiple viruses are detected. Further, this study will provide distinction regarding the clinical implications of
codetections of viruses, including SARS-CoV-2, in children. The findings will support future clinical trials
evaluating treatment options based on the immunologic fingerprints that we identify.

## Key facts

- **NIH application ID:** 10859250
- **Project number:** 1R01HL173537-01
- **Recipient organization:** ARKANSAS CHILDREN'S HOSPITAL RES INST
- **Principal Investigator:** Joshua Kennedy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $694,403
- **Award type:** 1
- **Project period:** 2024-06-01 → 2029-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10859250

## Citation

> US National Institutes of Health, RePORTER application 10859250, An Integrated Host-Microbe Gene Classifier to Predict SARS-CoV-2 and Severe Disease in Children with Respiratory Viral Coinfections (1R01HL173537-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10859250. Licensed CC0.

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