REVIEW article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
This article is part of the Research TopicSurveillance and Control of Wildlife Diseases: Integrating Ecology, Pathology, and Public HealthView all 11 articles
Predictive Multi-omic Biomarkers for Urban Zoonotic Spillover Detection: An Integrative Review
Provisionally accepted
Iliana C. Martínez-Ortiz1
Igor Garcia-Atutxa2
Javier Ignacio Sanchez-Villamil1
Carlos Machain-Williams3
Miguel Ángel Reyes-López4
Francisca Villanueva-Flores1*- 1Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Boulevard de la Tecnología, 1036 Z-1, P 2/2, 62790, Xochitepec, Mexico
- 2Catholic University San Antonio of Murcia, Guadalupe, Spain
- 3Estudios en Una Salud, Unidad Profesional Interdisciplinaria de Ingeniería Campus Palenque, Instituto Politécnico Nacional, Carretera Federal 199, Nueva Esperanza, 29960, Palenque, Mexico
- 4Centro de Biotecnología Genómica. Laboratory of Conservation Medicine, Instituto Politécnico Nacional, Blvd. Del Maestro SN, Narciso Mendoza, 88710, Reynosa, Tamaulipas, Mexico
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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 modelling 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.
Keywords: Multi-omics surveillance, Urban wildlife reservoirs, Zoonotic spillover detection, One Health approach, predictive biomarkers
Received: 07 Oct 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Martínez-Ortiz, Garcia-Atutxa, Sanchez-Villamil, Machain-Williams, Reyes-López and Villanueva-Flores. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Francisca Villanueva-Flores
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