# Integrating DNA/RNA microbe detection and host response for accurate diagnosis, treatment and prognosis of childhood infectious meningitis and encephalitis

## TL;DR

This study introduces a new mNGS pipeline that improves the diagnosis and prognosis of childhood infectious meningitis and encephalitis by detecting pathogens and host responses simultaneously.

## Contribution

The novel contribution is an optimized mNGS pipeline that integrates DNA/RNA pathogen detection and host gene expression analysis in a single test for IM.

## Key findings

- The pipeline detected RNA viruses and pathogens like Echovirus E30 and HHV-7 that conventional methods miss.
- Antibiotic resistance genes in Escherichia coli, Acinetobacter baumannii, and Group B Streptococcus were identified.
- Machine-learning models were built to detect sample contamination and predict poor prognosis in bacterial meningitis.

## Abstract

Infectious meningitis/encephalitis (IM) is a severe neurological disease that can be caused by bacterial, viral, and fungal pathogens. IM suffers high morbidity, mortality, and sequelae in childhood. Metagenomic next-generation sequencing (mNGS) can potentially improve IM outcomes by sequencing both pathogen and host responses and increasing the diagnosis accuracy.

Here we developed an optimized mNGS pipeline named comprehensive mNGS (c-mNGS) to monitor DNA/RNA pathogens and host responses simultaneously and applied it to 142 cerebrospinal fluid samples. According to retrospective diagnosis, these samples were classified into three categories: confirmed infectious meningitis/encephalitis (CIM), suspected infectious meningitis/encephalitis (SIM), and noninfectious controls (CTRL).

Our pipeline outperformed conventional methods and identified RNA viruses such as Echovirus E30 and etiologic pathogens such as HHV-7, which would not be clinically identified via conventional methods. Based on the results of the c-mNGS pipeline, we successfully detected antibiotic resistance genes related to common antibiotics for treating Escherichia coli, Acinetobacter baumannii, and Group B Streptococcus. Further, we identified differentially expressed genes in hosts of bacterial meningitis (BM) and viral meningitis/encephalitis (VM). We used these genes to build a machine-learning model to pinpoint sample contaminations. Similarly, we also built a model to predict poor prognosis in BM.

This study developed an mNGS-based pipeline for IM which measures both DNA/RNA pathogens and host gene expression in a single assay. The pipeline allows detecting more viruses, predicting antibiotic resistance, pinpointing contaminations, and evaluating prognosis. Given the comparable cost to conventional mNGS, our pipeline can become a routine test for IM.

The online version contains supplementary material available at 10.1186/s12967-024-05370-w.