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Impact of Contaminants on Microbiota: Linking the Gut–Brain Axis with Neurotoxicity

Balaguer-Trias, Jordina, Deepika, Deepika, Schuhmacher, Marta, Kumar, Vikas·2022·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph19031368PMC PMC8835190
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Abstract

Over the last years, research has focused on microbiota to establish a missing link between neuronal health and intestine imbalance. Many studies have considered microbiota as critical regulators of the gut–brain axis. The crosstalk between microbiota and the central nervous system is mainly explained through three different pathways: the neural, endocrine, and immune pathways, intricately interconnected with each other. In day-to-day life, human beings are exposed to a wide variety of contaminants that affect our intestinal microbiota and alter the bidirectional communication between the gut and brain, causing neuronal disorders. The interplay between xenobiotics, microbiota and neurotoxicity is still not fully explored, especially for susceptible populations such as pregnant women, neonates, and developing children. Precisely, early exposure to contaminants can trigger neurodevelopmental toxicity and long-term diseases. There is growing but limited research on the specific mechanisms of the microbiota–gut–brain axis (MGBA), making it challenging to understand the effect of environmental pollutants. In this review, we discuss the biological interplay between microbiota–gut–brain and analyse the role of endocrine-disrupting chemicals: Bisphenol A (BPA), Chlorpyrifos (CPF), Diethylhexyl phthalate (DEHP), and Per- and polyfluoroalkyl substances (PFAS) in MGBA perturbations and subsequent neurotoxicity. The complexity of the MGBA and the changing nature of the gut microbiota pose significant challenges for future research. However, emerging in-silico models able to analyse and interpret meta-omics data are a promising option for understanding the processes in this axis and can help prevent neurotoxicity.

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Figure 1 Maternal factors that influence neonate’s microbiota.
Figure 1 Maternal factors that influence neonate’s microbiota.CC BY · PubMed Central
Figure 2 Scheme of the interaction between the microbiota and the vagus nerve.
Figure 2 Scheme of the interaction between the microbiota and the vagus nerve.CC BY · PubMed Central
Figure 3 EDC exposure, early dysbiosis, and neurological disorders in adults.
Figure 3 EDC exposure, early dysbiosis, and neurological disorders in adults.CC BY · PubMed Central
Figure 4 DEHP exposure and microbiota modifications in newborns.
Figure 4 DEHP exposure and microbiota modifications in newborns.CC BY · PubMed Central
Figure 5 Impact of contaminants on the microbiota–gut–brain axis and linkage with neurotoxicity.
Figure 5 Impact of contaminants on the microbiota–gut–brain axis and linkage with neurotoxicity.CC BY · PubMed Central

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  112. Signore, 2021, Gut-on-Chip Microphysiological Systems: Latest Advances in the Integration of Sensing Strategies and Adoption of Mature Detection Mechanisms, Sens. Bio-Sens. Res., 33, 100443, 10.1016/j.sbsr.2021.100443 · DOI

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Metadata

Authors
Balaguer-Trias, J., Deepika, D., Schuhmacher, M., & Kumar, V.
Affiliations
Environmental Engineering Laboratory, Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain
ORCID
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2022-01-26
Volume
19
Issue
3
Pages
1368
ISSN
1660-4601
DOI
10.3390/ijerph19031368
PMC
PMC8835190
Cited by
88
References
14
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Spanish Ministry of Science, Innovation and Universities (RTI2018-095466-B-100)
  • Spanish Ministry of Science, Innovation and Universities, cofinanced by EU (PCIN-2017-012 and MCIN/AEI /10.13039/501100011033)
  • European Commission (766251)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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