EDITORIAL article
Front. Bioeng. Biotechnol.
Sec. Industrial Biotechnology
This article is part of the Research TopicTrigger the Microbiome Changes in Foods via Metagenomic Technologies: From Diagnostic to Potential Changes in Product Safety or Quality Risk ProfilesView all 5 articles
Editorial: Trigger the Microbiome Changes in Foods via Metagenomic Technologies: From Diagnostic to Potential Changes in Product Safety or Quality Risk Profiles
Provisionally accepted
- 1Universidad Técnica del Norte, Ibarra, Ecuador
- 2Dirección de Innovación, Instituto Nacional de Biodiversidad, Quito, Pichincha, Ecuador, Quito, Ecuador
- 3Jiangsu University, Zhenjiang, China
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Food safety, Food quality, Antibiotic resistant genes (ARG), Metal resistant genes (MRG), Fruits, Vegetables From raw agricultural inputs to the finished consumer product, the microbial communities of 20 our food production systems are a crucial factor in determining both quality and safety. For 21 many years, food safety diagnostics relied on labor-intensive, culture-based techniques that 22 only captured a small portion of the microbial reality. However, the integration of metagenomic 23 technologies to accurately characterize entire food microbiomes is on the verge of a disruptive 24 revolution, as demonstrated by the studies gathered in this Research Topic. The use of 25 metagenomic technologies offers unmatched chances for proactive risk assessment and quality 26 control by taking us beyond straightforward hazard detection and toward a comprehensive 27 understanding of the microbial mechanisms that determine the quality of our food. Metagenomics is useful for much more than just diagnostics; it gives us the information we 50 need to better understand the fundamental processes that underlie the emergence and spread of 51 microorganisms. With microbial interactions in mind, metagenomics enables a system-wide 52 understanding of interrelationships rather than approaching food safety as a linear "hazard-by-53 these technologies generate increasingly massive and complex datasets, the integration of 87 Artificial Intelligence (AI) and Machine Learning (ML) will become essential for predictive 88 modeling of pathogen risk or fermentation outcomes. Furthermore, these techniques are 89 expanding into new domains, such as the use of metabarcoding for food authentication to 90 combat fraud and ensure traceability. This Research Topic thus provides a snapshot of a field 91 in rapid transition: from cataloging microbes to understanding their function, and from reactive 92 diagnostics to a predictive, system-wide science.
Keywords: antibiotic resistant genes (ARG), food quality, Food Safety, Fruits, Long-read metagenomics, metabarcoding, Metal resistant genes (MRG), Next-generation sequencing
Received: 12 Dec 2025; Accepted: 15 Dec 2025.
Copyright: © 2025 Tenea, Jarrín and Lin. 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: Gabriela N. Tenea
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