Metagenomic Identification of Uncommon Meningoencephalitis Pathogens and Development of Peptide-based Immunoassays for Early Diagnosis: A Prospective Observational Study Research Protocol

Abstract

Introduction: Meningoencephalitis (ME) is an inflammatory condition of the meninges and brain parenchyma, remains a major diagnostic challenge. Metagenomic Next-Generation Sequencing (mNGS) has emerged as a promising tool for unbiased detection of viral, bacterial, fungal, and parasitic pathogens often missed by conventional methods. It also enables identification of regional pathogen profiles to guide development of locally tailored diagnostics. However, mNGS-based studies from India, particularly Central India, remain limited. Need of the study: The lack of diagnostic tests for uncommon and regionally prevalent pathogens causing ME in Central India contributes to a high burden of undiagnosed cases. Even in well-equipped healthcare settings, a proportion of ME cases remain without confirmed aetiology due to diverse pathogens and overlapping clinical presentations. Addressing this gap is essential for improving aetiological diagnosis and guiding appropriate treatment. Therefore, the present study is designed to identify these uncommon pathogens using mNGS approach and to develop targeted, clinically applicable diagnostic tools to improve early detection and patient management. Aim: To characterise the aetiology of pathogens in ME cases using mNGS and to develop peptide-based immunoassays for uncommon pathogens enabling early diagnosis. Materials and Methods: The present prospective observational study will be conducted at the Neurological Intensive Care Unit (NICU) of Central India Institute of Medical Sciences (CIIMS), Nagpur, Maharashtra, India from April 2026 to April 2028, enrolling 146 clinically suspected ME patients (18-60 years). Cerebrospinal Fluid (CSF) samples will be analysed using conventional and metagenomic approaches. Patients will be categorised into undiagnosed, confirmed, and non infectious groups. Peptide-based Enzyme-Linked Immunosorbent Assay (ELISA) will be developed based on mNGS findings. Statistical analysis will include sensitivity, specificity, and Receiver Operating Characteristic (ROC) curve evaluation.