# Predictive multi-omic biomarkers for urban zoonotic spillover detection: an integrative review

## TL;DR

This paper proposes a multi-omics framework to detect zoonotic spillover risks in urban wildlife, integrating genetic, microbial, and transcriptomic data for early pathogen detection.

## Contribution

The novel contribution is an integrative, cross-validated multi-omics framework combining host and pathogen biomarkers for predictive zoonotic spillover detection.

## Key findings

- Convergent molecular signatures like miRNAs and MHC diversity shifts are linked to infection across species and pathogens.
- A workflow from non-invasive sampling to predictive modeling enhances detection of known and cryptic pathogens.
- Key barriers include field preservation and standardization, with proposed solutions like open-access databases.

## Abstract

Urban wildlife is an overlooked yet critical component of zoonotic disease surveillance, especially in biodiversity hotspots where human–animal interfaces accelerate spillover risk. This review synthesizes five complementary omics layers: Host microRNAs, host–pathogen genetic markers, bacterial microbiome profiling, viromics, and host transcriptomics into a single predictive framework for early spillover detection. Across taxa and pathogen classes, we highlight convergent molecular signatures of infection, from receptor polymorphisms and shifts in MHC diversity to pathogen-responsive miRNAs, high-risk bacterial genera, novel viral sequences, and transcriptomic profiles associated with pathogen tolerance. By integrating these biomarkers into a cross-validated, multi-omics architecture, we outline a workflow from non-invasive sampling to predictive modeling that enhances sensitivity for detecting both known and cryptic pathogens. We also identify key barriers, including Field preservation, cross-species assay standardization, and bioinformatics capacity, and propose practical solutions, such as interoperable pipelines and open-access databases. This integrative approach shifts surveillance from reactive detection to anticipatory risk profiling, providing a transformative tool for One Health strategies aimed at forecasting and preventing zoonotic epidemics.

Flowchart titled “Multi-omics as a tool for zoonosis detection” showing two main categories: Metagenomic and Transcriptomic. Metagenomic includes 16S; 18S ITS and Viromics. 16S; 18S ITS covers broad biological samples and detects pathogens, while Viromics involves a supplementary PCR assay with stable samples. Transcriptomic includes miRNAs and RNA-Seq. miRNAs analyze various samples to identify infection markers, whereas RNA-Seq requires a supplementary RT qPCR assay and shows lower stability.