Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 20:15:1349367.
doi: 10.3389/fmicb.2024.1349367. eCollection 2024.

High-throughput screening of the effects of 90 xenobiotics on the simplified human gut microbiota model (SIHUMIx): a metaproteomic and metabolomic study

Victor Castañeda-Monsalve  1 , Laura-Fabienne Fröhlich  2 , Sven-Bastiaan Haange  1 , Masun Nabhan Homsi  1 , Ulrike Rolle-Kampczyk  1 , Qiuguo Fu  2 , Martin von Bergen  1   3   4 , Nico Jehmlich  1
Affiliations

High-throughput screening of the effects of 90 xenobiotics on the simplified human gut microbiota model (SIHUMIx): a metaproteomic and metabolomic study

Victor Castañeda-Monsalve et al. Front Microbiol. .

Abstract

The human gut microbiota is a complex microbial community with critical functions for the host, including the transformation of various chemicals. While effects on microorganisms has been evaluated using single-species models, their functional effects within more complex microbial communities remain unclear. In this study, we investigated the response of a simplified human gut microbiota model (SIHUMIx) cultivated in an in vitro bioreactor system in combination with 96 deep-well plates after exposure to 90 different xenobiotics, comprising 54 plant protection products and 36 food additives and dyes, at environmentally relevant concentrations. We employed metaproteomics and metabolomics to evaluate changes in bacterial abundances, the production of Short Chain Fatty Acids (SCFAs), and the regulation of metabolic pathways. Our findings unveiled significant changes induced by 23 out of 54 plant protection products and 28 out of 36 food additives across all three categories assessed. Notable highlights include azoxystrobin, fluroxypyr, and ethoxyquin causing a substantial reduction (log2FC < -0.5) in the concentrations of the primary SCFAs: acetate, butyrate, and propionate. Several food additives had significant effects on the relative abundances of bacterial species; for example, acid orange 7 and saccharin led to a 75% decrease in Clostridium butyricum, with saccharin causing an additional 2.5-fold increase in E. coli compared to the control. Furthermore, both groups exhibited up- and down-regulation of various pathways, including those related to the metabolism of amino acids such as histidine, valine, leucine, and isoleucine, as well as bacterial secretion systems and energy pathways like starch, sucrose, butanoate, and pyruvate metabolism. This research introduces an efficient in vitro technique that enables high-throughput screening of the structure and function of a simplified and well-defined human gut microbiota model against 90 chemicals using metaproteomics and metabolomics. We believe this approach will be instrumental in characterizing chemical-microbiota interactions especially important for regulatory chemical risk assessments.

Keywords: SIHUMIx; chemical screening; chemical-microbe interaction; metabolomics; metaproteomics; microbiome-mediated toxicity.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Experimental overview of the screening of 90 xenobiotics with the simplified model of human microbiota (SIHUMIx). The community was inoculated in bioreactors until steady-state. Then, SIHUMIx were cultured in 96-deep-well plates at 37°C under anaerobic conditions for 24 h exposed to the individual xenobiotics. Pellets and supernatants were collected for metaproteomic and metabolomic analysis, respectively.
Figure 2
Figure 2
Impact of plant protection products, food additives, and dyes on the composition of SIHUMIx. The species abundance was measured by metaproteomics and is displayed as log2FC for (A) 14 plant protection products and (B) 17 food additives and dyes which affected at least the abundance of one species compared to the control. Statistically significant effects (Padj < 0.05) are highlighted by asterisks.
Figure 3
Figure 3
Impact of xenobiotics on SCFA concentration. Log2FC of the 8 detected SCFAs following 24 h of exposure of SIHUMIx to (A) plant protection products and (B) food additives and dyes. Statistically significant effects (Padj < 0.05) are indicated by asterisks.
Figure 4
Figure 4
Impact of xenobiotics on SIHUMIx pathways and their relationship with SCFA production. Effects of (A) ethoxyquin and (B) acid orange 7 exposure on SIHUMIx significantly (1) affected pathways and their association with changes in (2) short-chain fatty acid (SCFA) concentrations. Following exposure to the plant protection product, we observed a reduction in energy pathways, notably the metabolism of pyruvate, a key precursor for SCFAs. Acid orange 7, on the other hand, led to a decline in butyrate metabolism, resulting in a significant decrease in butyrate concentration levels in the supernatant. Statistically significant effects (Padj < 0.05) are indicated by asterisks.
Figure 5
Figure 5
Pearson’s correlation values between relative species abundance and affected pathways. Values for the relative abundance of SIHUMIx species and pathways lower than −0.6 and higher than 0.6 and Padj < 0.05 after exposure to (A) plant protection products and (B) food additives and dyes.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Aertsen A., Michiels C. W. (2004). Stress and how bacteria cope with death and survival. Crit. Rev. Microbiol. 30, 263–273. doi: 10.1080/10408410490884757, PMID: - DOI - PubMed
    1. Ampatzoglou A., Gruszecka-Kosowska A., Aguilera-Gómez M. (2022). Microbiota analysis for risk assessment of xenobiotics: toxicomicrobiomics, incorporating the gut microbiome in the risk assessment of xenobiotics and identifying beneficial components for one health. EFSA J. 20:e200915. doi: 10.2903/j.efsa.2022.e200915, PMID: - DOI - PMC - PubMed
    1. Band V. I., Weiss D. S. (2014). Mechanisms of antimicrobial peptide resistance in gram-negative bacteria. Antibiotics 4, 18–41. doi: 10.3390/antibiotics4010018, PMID: - DOI - PMC - PubMed
    1. Bao Z., Wang W., Wang X., Qian M., Jin Y. (2022). Sub-chronic Difenoconazole exposure induced gut microbiota Dysbiosis in mice. Toxics 10, 1–11. doi: 10.3390/toxics10010034, PMID: - DOI - PMC - PubMed
    1. Becker N., Kunath J., Loh G., Blaut M. (2011). Human intestinal microbiota: characterization of a simplified and stable gnotobiotic rat model. Gut Microbes 2, 25–33. doi: 10.4161/gmic.2.1.14651, PMID: - DOI - PubMed

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

LinkOut - more resources