Biology subjects

Probst, S. I.

Publications and source records attributed to Probst, S. I..

4 recordsLinked to original sources

bioRxiv2026

Bidirectional interactions between gut microbiota and fluorochemical biotransformation and bioactivity

Fluorinated chemicals are increasingly prevalent in pharmaceuticals and agrochemicals, yet their influence on the human gut microbiome and the potential for microbial biotransformation to alter therapeutic and toxicological profiles remain poorly understood. Here, we investigated the bidirectional relationship between 15 structurally diverse fluorinated chemicals and the gut microbiota by using an ex vivo high-throughput fermentation system. Screening revealed that flutamide, fluazinam, and pretomanid were consistently biotransformed across the donor microbiomes, while other compounds showed substantial inter-individual variability in degradation. Furthermore, exposure to fluorinated chemicals induced compound-specific shifts in microbial diversity and community composition, demonstrating their capacity to alter gut microbial ecology. Using a computational workflow combining in silico biotransformation predictions with untargeted LC-MS/MS analysis, we identified nitroreduction as the primary gut microbial transformation across all three compounds. Single-strain experiments confirmed that the nitroreduction of flutamide to flu-6, previously attributed only to hepatic metabolism, is a widespread capacity among gut bacterial strains. Finally, in vitro cytotoxicity assays and in silico modelling further revealed flu-6 to be a less hepatotoxic derivative than the parent compound, suggesting a potential detoxifying role for the gut microbiota. Together, these findings establish an integrated ex vivo, in vitro, and in silico approach for assessing the bidirectional interactions between fluorinated chemicals and the gut microbiome.

pharmacology and toxicology↗

bioRxiv2026

An enzyme-level benchmark based on environmental bacterial laccases for predicting contaminant fate in water

Bacterial laccases are widespread multicopper oxidases whose roles in the fate of anthropogenic chemicals in aquatic environments remain poorly understood. Here, we integrate metagenomic analysis, a miniaturized high-throughput assay and machine learning to establish an enzyme-level benchmark for predicting biotransformation of wastewater-relevant trace organic contaminants by laccase-mediator systems. Using a laccase from an ammonia-oxidizing bacterium as a model enzyme, we screened 183 compounds and identified 38 that underwent significant removal. Following phylogenetic analysis of environmental homologs, we expressed and purified two additional laccases from the bacterial methanotrophic phylum Methylmirabilota and an archaeal phylum Thermoproteota, demonstrating the activity of this enzyme family across domains. Graph convolutional network models trained on the dataset achieved up to 78% accuracy in classifying degradable versus persistent chemicals, while quantum-chemical descriptors highlighted key electronic properties governing oxidation. This bottom-up approach to enzyme-chemical interactions establishes a trajectory towards predicting contaminant persistence in engineered and natural waters.

biochemistry↗

bioRxiv2024

Machine learning reveals signatures of promiscuous microbial amidases for micropollutant biotransformations

Organic micropollutants - including pharmaceuticals, personal care products, pesticides and food additives - are prevalent in the environment and have unknown and potentially toxic effects. Humans are a direct source of micropollutants as the majority of pharmaceuticals are primarily excreted through urine. Urine contains its own microbiota with the potential to catalyze micropollutant biotransformations. Amidase signature (AS) enzymes are known for their promiscuous activity in micropollutant biotransformations, but the potential for AS enzymes from the urinary microbiota to transform micropollutants is not known. Moreover, characterization of AS enzymes to identify key chemical and enzymatic features predictive of biotransformation profiles is critical for developing benign-by-design chemicals and micropollutant removal strategies. In this study, we biochemically characterized a new AS enzyme with arylamidase activity from a urine isolate, Lacticaseibacillus rhamnosus, and demonstrated its capability to hydrolyze pharmaceuticals and other micropollutants. To uncover the signatures of AS enzyme-substrate specificity, we then designed a targeted enzyme library consisting of 40 arylamidase homologs from diverse urine isolates and tested it against 17 structurally diverse compounds. We found that 16 out of the 40 enzymes showed activity on at least one substrate and exhibited diverse substrate specificities, with the most promiscuous enzymes active on nine different substrates. Using an interpretable gradient boosting machine learning model, we identified chemical and amino acid features predictive of arylamidase biotransformations. Key chemical features from our substrates included the molecular weight of the amide carbonyl substituent and the number of charges in the molecule. Important amino acid features were found to be located on the protein surface and four predictive residues were located in close proximity of the substrate tunnel entrance. Overall, this work highlights the understudied role of urine-derived microbial arylamidases and contributes to enzyme sequence-structure-substrate-based predictions of micropollutant biotransformations.

biochemistry↗

bioRxiv2024

Enzymatic carbon-fluorine bond cleavage by human gut microbes

Fluorinated compounds are used for agrochemical, pharmaceutical, and numerous industrial applications, resulting in global contamination. In many molecules, fluorine is incorporated to enhance the half-life and improve bioavailability. Fluorinated compounds enter the human body through food, water, and xenobiotics including pharmaceuticals, exposing gut microbes to these substances. The human gut microbiota is known for its xenobiotic biotransformation capabilities, but it was not previously known whether gut microbial enzymes could break carbon-fluorine bonds, potentially altering the toxicity of these compounds. Here, through the development of a rapid, miniaturized fluoride detection assay for whole-cell screening, we discovered active gut microbial defluorinases. We biochemically characterized enzymes from diverse human gut microbial classes including Clostridia, Bacilli, and Coriobacteriia, with the capacity to hydrolyze (di)fluorinated organic acids and a fluorinated amino acid. Whole-protein alanine scanning, molecular dynamics simulations, and chimeric protein design enabled the identification of a disordered C-terminal protein segment involved in defluorination activity. Domain swapping exclusively of the C-terminus conferred defluorination activity to a non-defluorinating dehalogenase. To advance our understanding of the structural and sequence differences between defluorinating and non-defluorinating dehalogenases, we trained machine learning models which identified protein termini as important features. Models trained on 41-amino acid segments from protein C-termini alone predicted defluorination activity with 83% accuracy (compared to 95% accuracy based on full-length protein features). This work is relevant for therapeutic interventions and environmental and human health by uncovering specificity-determining signatures of fluorine biochemistry from the gut microbiome. SignificanceHumans have introduced carbon-fluorine bonds into numerous manufactured compounds, including pharmaceuticals, leading to the formation of toxic fluorinated byproducts. While the human gut microbiota is known for its ability to metabolize drugs, its encoded capacity to break the strong carbon-fluorine chemical bond was previously unknown. Here we discovered that human gut microbial enzymes are capable of cleaving carbon-fluorine bonds. We developed a 96-well colorimetric fluoride assay amenable to bacterial culture-based screening. We additionally conducted whole-protein alanine scanning mutagenesis and identified through machine learning that flexible C-terminal loop residues were predictive of defluorination. Taken in the context of flexible regions of other enzyme families known to perform fluorine chemistry, this work supports using convergent structural features to predict defluorination specificity.

biochemistry↗