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Peer-reviewedCC BY 4.0Frontiers in Microbiology

From correlation to causation: dissecting the detection methodologies of oral bacterial-fungal crosstalk in health and disease

  • Yixin Zhang1
  • Nanyan Bian1
  • Zhuojun Dong2
  • Biao Ren1
  • Juan Li1,3
  1. 1State Key Laboratory of Oral Diseases, West China School of Stomatology, National Clinical Research Center for Oral Diseases, Med-X Center for Manufacturing, Sichuan University, Chengdu, China
  2. 2Department of Orthodontics, Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, Sichuan, China
  3. 3Department of Orthodontics, State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Abstract

In the oral microecosystem, cross-kingdom interactions between bacteria and fungi are pivotal ecological events that determine the equilibrium between oral health and disease. Historically, technical limitations have confined research to describing correlations in microbial community composition, hindering the elucidation of underlying pathogenic mechanisms. This review aims to systematically outline the evolution of detection technologies for oral bacterial-fungal interactions and critically discuss the paradigm shift from identifying “associative correlations” to deciphering “causal mechanisms.” The literature search encompassed studies ranging from traditional culture and molecular identification to high-throughput sequencing for ecological network construction, as well as cutting-edge multi-omics integration and high-resolution imaging technologies. Beyond cataloguing technical advances, we critically evaluate the level of causal inference supported by each methodological class, with particular attention to temporality, confounding, ecological simplification, and the need for perturbational validation in disease-relevant models. Representative studies are used to illustrate how evidence can progress from co-occurrence and spatial proximity to functional dependency and mechanism.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026from,
  title = {{From correlation to causation: dissecting the detection methodologies of oral bacterial-fungal crosstalk in health and disease}},
  author = {Yixin Zhang and Nanyan Bian and Zhuojun Dong and Biao Ren and Juan Li},
  journal = {Frontiers in Microbiology},
  year = {2026},
  month = oct,
  doi = {10.3389/fmicb.2026.1937704},
  url = {https://doi.org/10.3389/fmicb.2026.1937704},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - From correlation to causation: dissecting the detection methodologies of oral bacterial-fungal crosstalk in health and disease
AU  - Yixin Zhang
AU  - Nanyan Bian
AU  - Zhuojun Dong
AU  - Biao Ren
AU  - Juan Li
PY  - 2026
DA  - 2026/10/12
JO  - Frontiers in Microbiology
DO  - 10.3389/fmicb.2026.1937704
UR  - https://doi.org/10.3389/fmicb.2026.1937704
AB  - In the oral microecosystem, cross-kingdom interactions between bacteria and fungi are pivotal ecological events that determine the equilibrium between oral health and disease. Historically, technical limitations have confined research to describing correlations in microbial community composition, hindering the elucidation of underlying pathogenic mechanisms. This review aims to systematically outline the evolution of detection technologies for oral bacterial-fungal interactions and critically discuss the paradigm shift from identifying “associative correlations” to deciphering “causal mechanisms.” The literature search encompassed studies ranging from traditional culture and molecular identification to high-throughput sequencing for ecological network construction, as well as cutting-edge multi-omics integration and high-resolution imaging technologies. Beyond cataloguing technical advances, we critically evaluate the level of causal inference supported by each methodological class, with particular attention to temporality, confounding, ecological simplification, and the need for perturbational validation in disease-relevant models. Representative studies are used to illustrate how evidence can progress from co-occurrence and spatial proximity to functional dependency and mechanism.
SN  - 1664-302X
ER  -