Development of a gut microbiota model for the analysis of bacterial modifications of xenobiotics

Development of a gut microbiota model for the analysis of bacterial modifications of xenobiotics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of a gut microbiota model for the analysis of bacterial modifications of xenobiotics Natalie Hager, Jan-Lorenz Weyers, Laura Falk, Marie-Christine Simon, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8136690/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The human gut microbiota influences host physiology by metabolizing xenobiotics, such as drugs, dietary additives, and environmental contaminants. To enable standardized assessment of microbial xenobiotic metabolism, we established a defined bacterial colon model from pooled human fecal samples, by preparing clean bacterial cell suspensions using density gradient centrifugation. The bacterial cell fraction remained structurally intact, allowing long-term preservation at -70°C with glycerol as a cryoprotectant. The bacteria were fully reactivated without compromising cellular integrity. Glycerol as a disturbing substrate was removed by washing steps. The system preserved microbial diversity, enzymatic activity, and metabolic functionality over 24 hours under anaerobic conditions. Unlike fecal-based models, the model was free from non-cell contaminants, which could cause unpredictable interactions between test compounds and reactive components within the fecal matrix. Enzymatic assays demonstrated hydrolytic, reductive, and proteolytic activities comparable to native feces. 16S rRNA gene sequencing confirmed taxonomic stability and compositional shifts in response to different substrates. A fiber-rich substrate proved to be optimal for maintaining the bacterial composition over 24 hours. We further applied the model to investigate the microbial biotransformation of selected model xenobiotics using high-performance liquid chromatography and mass spectrometry, revealing substrate-specific metabolite formation. A versatile addition of food ingredients, dietary supplements, pharmaceuticals, and environmental chemicals is possible to analyze their effects on the composition of the microbiota, its enzymatic activity, and the excretion of metabolites and end products. Together, this bacterial colon model provides a reproducible, high-throughput capable and ethically viable platform for pharmacomicrobiomic studies and next-generation risk assessment. Gut microbiota xenobiotics in vitro colon model toxicomicrobiomics drug metabolism physiologically based kinetic modeling (PBK) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The human body serves as a favored environment for a vast diversity of bacteria, archaea, and fungi (Schmidt et al. 2018 ). Collectively referred to as the human microflora or microbiota, these microorganisms inhabit various body sites and typically coexist with their host in a mutualistic relationship. In an adult human, the body harbors up to 10 14 prokaryotic cells (Sender et al. 2016 ), with the majority reside in the gastrointestinal tract. This complex microbial population interacts actively with the gut lymphoid tissues and the immune system of the host (Gensollen et al. 2016 ). Furthermore, the gut microbiota plays a pivotal role in maintaining intestinal health, and influencing the development of both intestinal and non-intestinal diseases. A major class of non-host-derived substances are xenobiotics, a diverse group of chemicals including pharmaceuticals, environmental pollutants, food additives, and naturally occurring compounds that may exert toxic at elevated concentrations (Soucek 2011 ). Given their structural diversity and widespread presence, the estimated interaction of more than 25,000 different compounds with the gut microbiota underscores its critical function in shaping host responses (Lindell et al. 2022 ). Unlike human metabolism, which primarily relies on hepatic oxidation and conjugation, the gut microbiome, harboring more than 3.3 million genes exceeding the human genome by over 150-fold (Qin et al. 2010 ). Hence, the gut microbiome provides a remarkably broad enzymatic repertoire, including hydrolysis, reduction, and deconjugation reactions (Zimmermann et al. 2019 ). These microbial transformations can significantly impact the half-life, bioavailability, and the pharmacokinetics of xenobiotics by altering their toxicity, metabolite formation, or pharmacological effects (Koppel et al. 2017 ). Prominent examples include the activation of the ulcerative colitis drug sulfasalazine through azo bond cleavage (Lima et al. 2024 ), the deglucuronidation of non-steroidal anti-inflammatory drugs such as diclofenac (Boelsterli et al. 2013 ), the acylation-mediated inactivation of 5-aminosalicylic acid (Deloménie et al. 2001 ), as well as the modulation of drug efficacy and toxicity through reduction, hydrolysis, and competition with host metabolic pathways, as observed for digoxin (Haiser et al. 2013 ), irinotecan (Takasuna et al. 1996 ), and acetaminophen (Clayton et al. 2009 ). By shaping the chemical landscape of the intestinal lumen, the gut microbiota affects not only systemic exposure but also presents both challenges and opportunities for contemporary chemical risk assessment. Traditional approaches, based on animal studies, clinical trials, and epidemiological data, often neglect the role of microbial metabolism in modulating xenobiotic fate (Stevanoska et al. 2024 ). In response, emerging fields such as toxicomicrobiomics aim to elucidate the bidirectional interactions between gut microbes and toxicants (Abdelsalam et al. 2020 ). Incorporating microbiome dynamics into physiologically based kinetic models (PBK models) has the potential to improve the accuracy of exposure and risk predictions. This underscores the need for advanced gut microbiome models that more accurately reflect human microbial diversity and functional capacity. Such standardized systems are not only critical for mechanistic understanding but also hold promise for improving preclinical screening and regulatory safety assessment of xenobiotics. Extensive efforts have been made to create experimental in vitro and ex vivo models of the human intestine, enabling the analysis of gastrointestinal pathophysiology and the intricate involvement of bacteria in these processes (Stevanoska et al. 2024 ). However, for certain applications, such as screening, standardization, and integration into higher-throughput pipelines, simpler and more accessible models are essential. To address this need, we developed a streamlined and defined in vitro bacterial colon model optimized for reproducibility, ease of use, and compatibility with routine xenobiotic screening workflows. Together, these features make the system particularly suitable for rapid, high-throughput applications in toxicomicrobiomics, including studies on how xenobiotics are transformed by the gut microbiota. Material and Methods Chemicals All reagents, chemicals, and substrates were obtained from commercial suppliers including Carl Roth GmbH + Co. KG (Karlsruhe, Germany), Sigma-Aldrich (Darmstadt, Germany), as well as Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan), BLDpharm Co., Ltd. (Shanghai, China), Chemos GmbH & Co. KG (Regenstauf, Germany), and Cayman Chemical Company (Ann Arbor, MI, USA). The gases CO 2 (99.9%), H 2 (99.9%), and N 2 (99.9%) were obtained from Air Liquide (Düsseldorf, Germany). All substances were of analytical grade or higher and were used without further purification. Study design The objective of this study was to develop and validate a new and standardized in vitro colon model based on bacterial communities isolated from human feces. The primary objective was to preserve the taxonomic diversity and metabolic functionality of the gut microbiota while enabling controlled and reproducible functional assays. Diluted fecal samples from healthy individuals were used to obtain both untreated reference inocula and standardized bacterial preparations for the colon model. To minimize interindividual variability, donors were randomly assigned to groups of three, and their samples were pooled under anaerobic conditions, allowing for randomization at both the collection and application stages. Sample collection Fecal samples were obtained from 20 healthy male and female volunteers aged 20 to 60 years who had not received antibiotic treatment within the six months prior to recruitment. The study protocol was approved by the Ethics Committee of the University of Bonn and is registered in the German Clinical Trials Register (registration number DRKS00036882, registered on 26 September 2025; https://drks.de/search/de/trial/DRKS00036882 ), and all procedures were conducted in accordance with the relevant guidelines and regulations. Each participant collected two fecal samples using a standardized collection kit. Immediately after collection, 5 g of fresh stool were diluted in 50 mL of pre-anaerobized modified SHIME® saline buffer (29.8 mM NaHCO 3 , 34.2 mM NaCl, 18.7 mM NH 4 Cl, 49.5 mM K 2 HPO 4 , 49.8 mM KH 2 PO 4 , 0.07 mM CaCl 2 x 2H 2 O, 0.03 mM MgSO 4 x 7H 2 O, pH 7.0), which had been flushed with N 2 /CO 2 (80%/20%) and sealed in serum flasks with butyl rubber stoppers (Van De Wiele et al. 2015 ). Following sample addition, the flasks were flushed twice with 50 mL of 80% N 2 and 20% CO 2 , to remove the air in the headspace. The samples were stored at 4°C until further processing (< 2 h). Sample processing- for the in vitro bacterial colon model Our model was based on the isolation of intact bacterial communities from human feces. Bacterial isolation was performed using a modified density gradient centrifugation protocol first described by Hevia et al. ( 2015 ). To detach bacteria adhering to particulate matter and embedded within fecal biofilms, 0.001% (w/v) cetyltrimethylammonium bromide (CTAB) was added to each diluted fecal sample 30 min prior to further processing (Macfarlane et al. 1997 ). All steps involving open sample handling were conducted under strictly anaerobic conditions (79% N 2 , 19% CO 2 , 2% H 2 ) within a vinyl anaerobic chamber (Coy Laboratory Products, Grass Lake, Michigan, USA). Samples from three donors were randomly pooled for each preparation to reduce variability among individuals. From each pooled and diluted fecal suspension, 15 mL were layered onto 3.5 mL of 55% (w/v) Nycodenz® (Serumwerk Bernburg AG, Bernburg, Germany) in anaerobic centrifuge tubes. The samples were then centrifuged at 4,000 x g for 30 min at room temperature. Following centrifugation, three distinct layers were obtained: an upper layer containing extracellular enzymes and soluble components, an intermediate opaque layer composed of bacterial cells, and a lower layer consisting of insoluble debris. To further purify the extracellular enzyme (exoenzyme) fraction, the supernatant was centrifuged at 18,000 x g for 3 min at 4°C. The resulting preparation was then sterile-filtered three times through 0.22 µm syringe filters to ensure complete removal of remaining microbial cells. The sterile exoenzyme fraction was stored anaerobically at -70°C until further use. The middle layer, which contained the gut bacteria, was collected and referred to as the bacterial colon model. The isolated microbiota was then diluted in sterile 50% (v/v) glycerol and stored at -70°C under anaerobic conditions. Prior to use in experiments, the cryopreserved cells were washed three times with modified SHIME® saline (18,000 x g, 3 min, 4°C) to remove any remaining Nykodenz®, glycerol, and particulate material. The final cell suspension was adjusted to an optical density at 600 nm (OD 600 ) of 10, corresponding to approximately 1.43 × 10 10 cells mL -1 , as determined microscopically using a Neubauer counting chamber (Hager et al. 2025 ). This microbial density was about 16% of the mean in vivo bacterial concentration in the human colon, estimated at 8.8 × 10 10 cells mL -1 (assuming a fecal density of 1.04 g mL -1 (Sender et al. 2016 )). The system can be upscaled to achieve cell numbers corresponding to up to 50% of the in vivo abundance of colonic bacteria. Since this washing procedure eliminates all extracellular enzymes, a targeted enzymatic regeneration step was necessary to restore the microbial metabolic functionality. Therefore, distinct substrate formulations were tested for their ability to support functional recovery while preserving the taxonomic integrity and metabolic activity. Substrate conditions included (i) a no-substrate control, (ii) a complex fiber-rich formulation composed of mucin, pectin, and xylan (each at 1 g L -1 ), (iii) a Western-style formulation containing all fiber components plus starch (1 g L -1 ), protein sources (casein and peptone, 0.5 g L -1 each), saturated and unsaturated fatty acids (palmitic, stearic, oleic, and linoleic acid, 0.25 g L -1 each), short-chain fatty acids (SCFAs; acetate 10 mM; propionate and butyrate 2.5 mM each), and bile salts (0.5 g l -1 ) as well as (iv) the same Western-style mixture without bile salts. Due to the limited purity of commercially available polysaccharides, xylan, mucin, and pectin were purified by triple ethanol precipitation (100 mL of a 10% solution in 900 mL 99.9% ethanol), followed by lyophilization and purity verification using HPLC analysis. All regeneration incubations were carried out for 24 h at 37°C under strictly anaerobic conditions. Microscopic imaging To evaluate the morphological characteristics of the isolated bacterial colon model, the samples were examined using bright-field microscopy. To immobilize the cells, 1.5% (w/v) low-melting-point agarose was mixed with SHIME® saline and melted at 70°C. The melted agarose was dispensed onto concave microscope slides (Thermo Fisher Scientific, Walthman, MA, USA) and covered with a coverslip to allow solidification. Washed bacterial suspensions (2–5 µL) were then applied to the agarose surface and covered with a coverslip. Images were acquired using a Nikon Ti2-E inverted microscope equipped with a 160x oil-immersion objective and a Prime BSI sCMOS camera (Teledyne Photometrics, Tucson, AZ, USA; pixel size 107 nm). 16S rRNA amplicon sequencing for microbial community profiling Microbial community profiling was performed by 16S rRNA gene amplicon sequencing using the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA). Total genomic DNA was extracted from 150 mg fecal material or 100 µL of microbial cell pellet (1 x 10 9 cells) using the Quick-DNA™ Fecal/Soil Microbe Microprep Kit (Zymo Research, Irvine, CA, USA), following the manufacturer’s instructions. Mechanical lysis was performed via bead beating on a vortex mixer equipped with a horizontal adapter. DNA was eluted in a final volume of 50 µL. Concentrations were measured using a NanoDrop™ 2000c Spectrophotometer (Eppendorf, Hamburg, Germany), and stored at − 20°C until further analysis. Amplicon libraries targeting the V3-V4 region of the 16S rRNA gene were generated using the primer pair Bakt_341F (5′-CCTACGGGNGGCWGCAG-3′) and Bakt_805R (5′-GACTACHVGGGTATCTAATCC-3′). PCR amplification was carried out using 2 × KAPA HiFi HotStart ReadyMix polymerase (Roche, Mannheim, Germany) under the following cycling conditions: 22 cycles of 95°C (denaturation), 55°C (annealing), and 72°C (extension), followed by a final elongation step. To attach Illumina Nextera XT adapter sequences, a second PCR was performed using indexed primers. Amplicons were purified using AMPure XP beads (Beckman Coulter, Krefeld, Germany), and quantified by the Qubit™ dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA), as well as normalized and pooled manually. The pooled libraries were denatured in 0.2 N NaOH, diluted to 10 pM, and spiked with 20% PhiX control DNA (Illumina, San Diego, CA, USA) prior to sequencing. Sequencing was performed on an Illumina MiSeq platform using the MiSeq Reagent Kit v3 with 2 × 300 bp paired-end chemistry. Raw sequence reads were demultiplexed using MiSeq Reporter v2.5 and further processed with the QIIME2 pipeline (version 2021.4). Quality control and denoising were performed using the DADA2 plugin (Callahan et al. 2016 ), applying a minimum quality threshold of Q30. Forward and reverse reads were truncated at 270 bp and 210 bp, respectively, to remove low-quality regions, and chimeric sequences were filtered out subsequently. Measurement of enzyme activities Microbial enzyme activities were determined by spectrophotometric assays conducted under standardized conditions using a Jasco V-600 UV/Vis spectrophotometer (Jasco, Groß-Umstadt, Germany). All reactions were carried out in 100 mM potassium phosphate buffer (pH 7.0) supplemented with 0.5 mM CaCl 2 and MgCl 2 . To measure intracellular dehydrogenase activities, bacterial cells were lysed by bead beating. Therefore, 1 mL of the bacterial suspension in modified SHIME® saline was supplemented with 0.1 mM EDTA, 0.1% (v/v) Triton X-100, and a 1× protease inhibitor cocktail (Sigma-Aldrich, Darmstadt, Germany). Bead beating was performed on a vortex mixer equipped with a horizontal adapter for 15 min at 4°C, followed by centrifugation at 17,000 × g for 5 min at 4°C. The supernatant containing the crude enzyme extract was collected, and used directly for enzyme assays. Lactate dehydrogenase (lactate DH) and malate dehydrogenase (malate DH) activities were determined by monitoring nicotinamide adenine dinucleotide (NADH) consumption at 340 nm (ε = 6.22 mM⁻¹ cm⁻¹) at 37°C. For lactate DH, 10–50 µL of cell lysate were added to 950–990 µL of 50 mM Tris-HCl buffer (pH 7.4) containing 0.5 mM pyruvate and 0.25 mM NADH. Malate DH activity was measured under identical conditions using 0.5 mM oxaloacetate and 0.25 mM NADH as substrates. Extracellular enzyme activities were determined using colorimetric and dye-linked assays based on the hydrolysis of p-nitrophenyl (pNp), Remazol Brilliant Blue (RBB)-, and Azo-conjugated substrates. Esterase and glycosidase activities were measured by the enzymatic cleavage of pNp substrates (acetate, butyrate, palmitate, α-glucopyranoside, β-galactopyranoside). 10–75 µL of sample were incubated with 2–4 µL of the respective pNp substrate (125–250 mM in DMSO, Ethanol or CH₂Cl₂) in buffer to a final reaction volume of 500 µL. After 10–15 min at 37°C, reactions were stopped with 500 µL ethanol and centrifuged (18,000 x g, 5 min). Absorbance was measured at 410 nm (ε = 18.3 mM -1 cm -1 ). Xylanase and cellulase activities were determined using RBB-xylan (2.5 mg mL -1 ), Azo-xylan (6.25 mg mL -1 ), and Azo-cellulose (6.25 mg mL -1 ). Samples (50–100 µL) were incubated with 100 µL substrate in a final volume of 500 µL at 37°C for 2 h. Reactions were stopped with 1 mL precipitation buffer (10 g sodium acetate x 3 H 2 O, 1 g zinc acetate in 50 mL H 2 O, pH 5.0, containing 200 mL ethanol) and centrifuged (18,000 x g, 5 min). Absorbance was measured at 595 nm (ε = 6.17 mM -1 cm -1 ). Amylase activity was determined using 100 µL Red-starch (20 mg mL -1 in 0.5 M KCl) incubated with 10–20 µL sample and 180–200 µL buffer for 20 min at 37°C. Reactions were stopped with 500 µL ethanol, centrifuged (18,000 x g, 5 min), and absorbance was measured at 510 nm (ε = 6.17 mM -1 cm -1 ). Protease activity was assayed using 100 µL Azo-casein (20 mg mL -1 ) incubated with 10–20 µL sample and 80–100 µL buffer for 10 min at 37°C. Reactions were terminated with 600 µL 5% (w/v) trichloroacetic acid (TCA), centrifuged (18,000 x g, 5 min), and absorbance was recorded at 440 nm. Enzymatic activity was calculated using absorbance values to determine the reaction rate (∆E min -1 ), which was converted to µmol min -1 using the Beer-Lambert law and the appropriate molar extinction coefficient. Specific activity was expressed as nmol min -1 mg protein -1 . Protein concentrations were measured at 595 nm using the Bradford assay with bovine serum albumin (BSA) as standard (Bradford, 1976). SCFA quantification using HPLC The SCFAs were quantified using a high-performance liquid chromatography (HPLC) system equipped with refractive index (RI) and ultaviolet (UV) detectors. Separation was achieved on an Aminex HPX-87H column (300 mm × 7.8 mm, Bio-Rad, Munich, Germany) at 65°C with 5 mM sulfuric acid (H 2 SO 4 ) as the mobile phase at a flow rate of 0.6 mL min -1 . Prior to injection, fecal and colon model samples were clarified by mixing 100 µL of sample with 50 µL Carrez I solution (0.15 g mL -1 K 4 [Fe(CN) 6 ]) and 50 µL Carrez II solution (0.30 mg mL -1 MgSO 4 x H 2 O). The mixture was vortexed and centrifuged at 18,000 x g for 2 min. Subsequently, 70 µL of the supernatant were diluted with 140 µL of 5 mM H 2 SO 4 . An additional centrifugation step (18,000 x g, 2 min) was performed to remove any remaining particulates. Quantification was performed using external calibration curves. Xenobiotic assays quantified with HPLC and validated with LC-MS To evaluate microbial xenobiotic metabolism under defined conditions, incubations were performed with model substrates representing key biotransformation classes with 4-acetoxyacetanilide (ester hydrolysis), sulindac (sulfoxide reduction), sulfasalazine (azo bond reduction), and nitrendipine (nitro group reduction). Quantification of parent compounds and metabolites was carried out by HPLC, and results were validated by liquid chromatography-mass spectrometry (LC-MS). Detailed experimental conditions and analytical parameters are provided in Supplementary S1 and Tab. S1. Statistics All statistical analyses were performed using GraphPad Prism (version 8.0.2, GraphPad Software, San Diego, CA, USA). For taxonomic profiling, changes in microbial composition at the genus level were assessed by comparing relative abundances between baseline (T0) and post-incubation (T24) under four different substrate conditions. Dominant genera (≥ 1% mean relative abundance) and low-abundance genera (≤ 1%) were visualized using heatmaps and bar plots. For each genus, the condition showing the smallest deviation was identified. Differences across conditions were evaluated using Kruskal-Wallis tests, and conditions with p < 0.05 were considered statistically significant. Enzymatic activity measurements were expressed as specific activity (nmol min -1 mg protein -1 ), normalized to protein content. Comparisons between original fecal samples and colon model compartments were analyzed using paired two-tailed t-tests (n = 8–10). For functional dynamics during substrate incubation, enzymatic activities at T24 were compared to T0 values (baseline set to 100%) using paired t-tests (n = 8–10). The SCFA concentrations (acetate, propionate, butyrate) were quantified by HPLC. Data were presented as mean ± standard deviation (SD), with technical replicates (n = 8) for controls and pooled replicates (n = 17) for substrate-supplemented samples. Comparisons between fecal and colon model samples, with or without substrate supplementation, were performed using unpaired t-tests. Significance levels were defined as follows: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. More details are described in the supplementary material. Results Development of an in vitro model reflecting the natural microbiota in the human colon To establish a physiologically relevant in vitro model, particularly suited for investigating the contribution of the gut microbiota to xenobiotic transformations, a new method for the isolation and functional regeneration of native microbial communities from human fecal samples was developed and validated. Fecal samples from multiple donors were diluted 1:10 (w/v) and pooled to minimize interindividual variability. CTAB (0.001% w/v) was added as a mild detergent to detach bacteria from particle surfaces and biofilm structures (Hevia et al. 2015 ). This was followed by a Nycodenz® density-gradient centrifugation (Macfarlane et al. 1997 ), which separated the fecal microbiota from stool samples. Three fractions were obtained (Fig. 1A): (i) an upper aqueous layer enriched in extracellular components, including secreted enzymes and low-density non-cellular material such as mucosal residues, lipids, and proteins; (ii) an intermediate opaque layer containing intact bacterial cells representative of the phylogenetic diversity of the human colonic microbiota, hereafter referred to as the bacterial colon model; and (iii) a lower dense fraction containing undigested dietary fibers, host-derived debris, and cell fragments. To assess the efficiency of bacterial cell isolation, total protein concentrations were determined in the original fecal slurry, the purified colon model fraction, and the corresponding supernatant after centrifugation. The isolation procedure achieved an average recovery rate of 86 ± 17% relative to the initial protein content, indicating efficient preservation of microbial biomass. Hence, this protocol enabled a clean and targeted isolation of the intestinal microbial community, minimizing matrix-derived interference and ensuring high compatibility with downstream functional, biochemical, and analytical assays. Bright-field microscopy revealed a morphologically diverse and densely populated bacterial community exhibiting high levels of motility and structural heterogeneity (Fig. 1B). Bacterial cells showed distinct morphotypes, indicating a broad phylogenetic spectrum. Importantly, the preparation was free of visible particulate contamination or residual matrix components, which could otherwise interfere with subsequent analyses. Figure 1: Separation of the bacterial colon model from human fecal samples. [A] Schematic representation of the density-gradient centrifugation. The left panel shows diluted fecal samples layered on the density-gradient medium, and the right panel illustrates the separated fractions [B] Representative microscopic images of the isolated bacterial cell fraction (1,600× magnification). The bacterial cell fraction remained structurally intact, allowing long-term preservation at -70°C using glycerol as a cryoprotectant, followed by successful reactivation without compromising cellular integrity. For reactivation, samples were thawed and washed twice to remove glycerol, which would otherwise serve as a carbon source for certain bacteria and thereby alter both the metabolic output and microbial community composition (De Weirdt et al. 2010 ). The presence of glycerol indeed affected the microbial community structure, as reflected by a shift in the SCFA profile, characterized by reduced acetate formation and increased levels of propionate, butyrate, and additional, yet unidentified, SCFAs (data not shown). Substrate-induced microbial community changes during regeneration Processing of the bacterial colon model resulted in the loss of extracellular enzymes. These enzymes are not only involved in the degradation of structurally complex dietary polysaccharides such as pectin and xylan (Yüksel et al. 2025 ), but also play a key role in the microbial biotransformation of xenobiotics that are typically not internalized but undergo extracellular modification (Smacchi and Gobbetti 2000 ). Restoring this enzymatic activity while maintaining the native community structure was therefore essential for conducting physiologically relevant metabolic studies. To achieve enzymatic regeneration, colon model preparations were incubated anaerobically for 24 h under four defined conditions to evaluate the influence of nutrient composition on microbiota recovery: in the absence of substrates, in the presence of a fiber-rich medium consisting of 1 g L -1 each of mucin, pectin, and xylan, and with a Western-style diet formulation, either with or without the addition of bile salts. The fiber-rich substrates contained structurally diverse dietary fibers, designed to mimic fiber-rich nutritional input. In contrast, the Western-style substrate formulation incorporated starch, proteinaceous compounds, a mix of saturated and unsaturated fatty acids, as well as SCFAs. To assess the effect of host-derived components, bile salts were either included (0.5 g L-¹) or omitted from the formulation. Microbial composition was analyzed at baseline and after incubation using 16S rRNA gene amplicon sequencing. In total, 74 families, 203 genera, and 412 species were identified across all samples, revealing compositional shifts that reflected substrate-dependent selection pressures. For comparative visualization, only genera with a mean relative abundance of > 1% across all conditions (n = 34) were included in the heatmap analysis (Fig. 2 ). All remaining low-abundance genera were grouped into a collective category termed “others”. The complete list of detected organisms and their substrate-dependent abundances is provided in Supplementary Tab. S2. Several low-abundance genera (< 5%), such as Collinsella , Dorea , and members of the Christensenellaceae or Erysipelotrichaceae families, remained stable across all substrate conditions. In contrast, pronounced shifts were observed among dominant taxa, particularly within the genera Faecalibacterium and Bacteroides , which together represented more than 32% of the initial community (15.4 ± 0.5% and 17.1 ± 0.9%). Faecalibacterium showed the strongest decline under Western-style conditions with bile salts, decreasing from 11.5 ± 0.4% to 3.6 ± 0.2% after 24 h. A comparable reduction occurred under substrate-free conditions, with a final abundance of only 5.2 ± 0.2%, emphasizing the sensitivity of this genus to nutrient limitation and substrate composition. In contrast, the fiber-rich medium minimized these losses, preserving a post-incubation abundance of 8.5 ± 0.1%, representing the most effective recovery among all tested conditions. With an initial abundance of 17.1 ± 0.9%, the genus Bacteroides also underwent a significant decrease under substrate deprived conditions, reaching 9.1 ± 0.1%. Species of this genus were also strongly affected by the presence of bile salts under Western-style conditions (5.5 ± 1.5%), whereas the fiber-rich medium sustained an abundance of 15.6 ± 1.3% after 24 h. Additionally, the fiber-rich medium effectively mitigated the loss of key anaerobic commensals such as Agathobacter , Roseburia , and Fusicatenibacter , while supporting the maintenance of several health-associated taxa. These included Subdoligranulum (4.7 ± 0.3%), Bifidobacterium (3.4 ± 0.3%), and the Eubacterium hallii group (4.2 ± 0.1%), which are all known producers of SCFAs implicated in gut homeostasis. The Escherichia - Shigella group, typically detected at only 0.05 ± 0.01% at baseline, expanded nearly 100-fold to 4.6 ± 1.0% in the Western-style medium, representing the strongest increase among all taxa (Fig. 3 ). Under these conditions, Fusobacterium and Klebsiella , both associated with inflammation and epithelial barrier dysfunction, also showed marked increases to 2.1 ± 0.03% and 1.9 ± 0.3%, respectively (Fig. 3 ). These findings are consistent with previous reports linking Western dietary patterns to microbial imbalance and pro-inflammatory host responses (Statovci et al. 2017 ; Christ et al. 2019 ). Megasphaera also expanded notably (1.9 ± 0.1%), potentially reflecting its ability to thrive under altered nutrient availability and reduced competition from strict anaerobes. Detailed statistical evaluations are provided in the Supplementary Material S3. Compared to the Western died, the changes in the frequency of the genera mentioned was much smaller in the fiber-rich medium or no significant changes in abundance were found. Overall the fiber rich medium consistently resulted in the lowest average deviations across genera and preserved the initial community structure the best. Analysis of enzymatic activity and fermentative capacity in the bacterial colon model Based on the primary objective of restoring metabolic functionality while preserving microbial community structure, subsequent analyses focused on key enzymatic and fermentative pathways involved in core microbial metabolism. As the fiber-rich substrate formulation was most effective in maintaining community composition of the bacterial colon model, this condition was selected to assess microbial enzymatic activity after 24 h. Enzymatic activities were compared to those of diluted fecal reference samples to determine the degree of functional similarity between the in vitro model and the native gut microbiota. The analyses encompassed a representative panel of metabolic enzymes, including intracellular oxidoreductases (lactate dehydrogenase, malate dehydrogenase), hydrolases such as esterases, lipases, and proteases, as well as carbohydrate-active enzymes (α-glucosidase, β-galactosidase, β-xylosidase, cellulase, and xylanase). In addition, the main microbial fermentation products acetate, propionate, and butyrate were quantified to assess overall fermentative capacity and metabolic output (Fig. 4 ). The activities of key intracellular redox enzymes, lactate - and malate - dehydrogenase, were maintained or slightly elevated relative to fecal reference values (lactate DH 106 % ; malate DH: 116 % ), indicating preserved intracellular metabolism. The same was observed for the esterases catalyzing the hydrolysis of acetate and butyrate esters, with pNp-acetate and pNp-butyrate activities slightly exceeding the fecal reference values (105% and 113%, respectively). In contrast, enzymes associated with lipid and protein degradation exhibited slightly reduced values. Lipolytic activity, measured by pNp-palmitate hydrolysis, was decreased to 77%, and proteolytic activity, assessed by azocasein hydrolysis, showed activity levels at 82% of the fecal control. These reductions likely reflect not a general metabolic impairment but rather substrate-induced regulatory shifts. The absence of complex dietary lipids and proteins in the incubation medium may have downregulated lipase and protease expression, thereby redirecting the microbial metabolism toward carbohydrate utilization. Among glycoside hydrolases, a particularly consistent recovery was evident for enzymes mediating glycosidic bond cleavage. α-Glucosidase and β-galactosidase activities even exceeded fecal reference levels (102% and 113%, respectively), highlighting an efficient re-establishment of saccharolytic functionality. Enzymes involved in the breakdown of structurally complex polysaccharides showed a largely preserved activity profile relative to fecal reference levels. The α-amylase activity, determined with red starch as substrate, remained close to baseline at 93%, while xylanolytic activity showed only marginal reductions, reaching 98% and 89% of reference values with RBB- and Azo-xylan, respectively. In contrast, cellulase activity was significantly lower at 72% of the fecal control (p ≤ 0.001), indicating a partial loss of cellulose-degrading capacity. Similar to lipases and proteases, this reduction likely reflects the absence of corresponding structural polysaccharides in the incubation medium, resulting in downregulation of the respective hydrolases. The SCFA production, as an indicator of microbial fermentation, remained stable across all three major end products (acetate, propionate and butyrate) with levels from around 100% in comparison to fecal samples. Taken together, these results demonstrate that the bacterial colon model supports a robust bacterial metabolic activity closely reflecting that of the original fecal microbiota. Moreover, the data highlight the critical influence of substrate composition in shaping not only the taxonomic structure but also the functional capacity of the gut community under in vitro conditions. The overall enzymatic potential remained high, indicating that key metabolic functions were largely preserved. This suggests that the model likewise retains the broad enzymatic repertoire required for more complex metabolic transformations, such as xenobiotic modification. Substrate supplementation is essential to preserve physiological enzyme activities in the in vitro colon model To assess the extent to which microbial functionality depends on substrate availability, the SCFAs concentrations were quantified in colon model samples following 24 h of incubation with and without supplementation of the fiber-rich substrate. The results clearly demonstrate that the presence of polysaccharides is critical for the production of the major fermentative end products (acetate, propionate and butyrate). SCFA levels increased by approximately one order of magnitude compared to the substrate-free control, and all differences were highly significant (**** p ≤ 0.0001). These findings indicate that physiologically relevant metabolic activity in the colon model can only be sustained in the presence of digestible substrates. (Fig. 5 ). Given that washing effectively removes extracellular enzymes, further experiments focused on the model’s capacity to regain functional activity in response to substrate availability. Representative hydrolytic enzyme activities were quantified at baseline (T0) and after 24 hours of incubation with the fiber-rich substrate (T24) (Fig. 6). This targeted approach provides a direct evaluation of substrate-dependent metabolic adaptability, revealing the extent to which key enzymatic functions can be re-established in vitro . After incubation, the colon model showed a significant reactivation of microbial enzymatic functions, indicating a shift from a metabolically suppressed to a physiologically active state. Compared to the baseline (T0), mean relative hydrolytic activities increased across all enzyme classes, ranging from marginal changes ( 100%). The most significant changes were observed in enzymes associated with polymer and lipid degradation. Lipase activity showed the strongest response, increasing more than twofold to 233% of baseline levels, while proteolytic activity also increased to 159%. Esterase activities also showed significant upregulation, with pNp-acetate and pNp-butyrate hydrolysis reaching 132% and 119% of baseline values, respectively.Within the glycoside hydrolase group, β-galactosidase activity increased by approximately 50%, whereas α-glucosidase activity remained unchanged, consistent with the notion that some substrates are also accessible to intracellular enzymes, resulting in a lower apparent regeneration requirement. Enzymes involved in the breakdown of complex polysaccharides responded particularly strongly: cellulase activity nearly doubled to 197%, while xylanase activities increased by 45–59% across both substrates tested. In contrast, α-amylase activity increased moderately to 136%. Taken together, these data demonstrate that the metabolic capacity of microbes in the colon model remained largely latent in the absence of environmental stimulation, but could be reactivated by providing the appropriate substrate. This emphasizes the vital role of nutrition in restoring physiologically relevant microbial activity. Functional validation of the bacterial colon model using microbial xenobiotic metabolism To evaluate the metabolic capabilities of the human colon model presented here, a panel of structurally diverse xenobiotics was selected based on characterized microbial biotransformation pathways reported in the literature (Fig. 7 , Supplementary Fig. S1 ). A total of six representative compounds covering four major reaction classes were included: ester hydrolysis, represented by 4-acetoxyacetanilide (Fig. 7 A) (Kamberi et al. 2004 ) and roxatidine acetate (Supplementary Fig. S1 A) (Zimmermann et al. 2019 ), sulfoxide reduction, using sulindac (Fig. 7 B) (Lemmens et al. 2021 ), azoreduction, exemplified by sulfasalazine (Fig. 7 C) (Lima et al. 2024 ), as well as nitroreduction using chloramphenicol (Supplementary Fig. S1 B) (Crofts et al. 2019 ) and nitrendipine (Fig. 7 D) (Vertzoni et al. 2018 ). These compounds were chosen as established model substrates with known microbial conversion, thereby enabling a robust proof-of-concept evaluation across distinct enzymatic processes within the colon model. All compounds were incubated anaerobically for 24 h with three biological matrices, pooled human fecal slurry, the bacterial colon model, and the sterile-filtered fecal exoenzyme fraction (fraction 1), derived from three independent donor pools (n = 3 each). The direct comparison with fecal incubations served to determine whether the colon model can reproduce physiologically relevant microbial biotransformation activities and thus be applied for toxicological testing. All investigated compounds remained chemically stable in buffer controls after 24 h (Fig. 7 ). Among the tested reactions, the ester hydrolysis of 4-acetoxyacetanilide proceeded most rapidly, resulting in complete compound depletion within 4 h in both the fecal slurry and the bacterial colon model. After 24 h, no residual parent compound (m/z 193.2, ESI⁺) was detectable, while the expected metabolite paracetamol (m/z 151.16, ESI⁺) was formed, confirming the predicted ester-cleavage pathway (Fig. 7 A). By contrast, substrates undergoing azo-, sulfoxide-, and nitroreduction showed slower conversion kinetics, with minor product formation detectable during the first 4 h of incubation. Significant degradation became apparent after 24 h, at which point the remaining parent compound concentrations in both fecal slurry and colon model had decreased to < 15 µM. Sulindac (m/z 356.41, ESI⁺) was reduced to its corresponding sulfide metabolite (m/z 340.41, ESI⁺), whereas sulfasalazine (m/z 398.41, ESI⁺) was cleaved into sulfapyridine (m/z 249.29, ESI⁺) and an additional metabolite at m/z 217.21, suggesting further reductive metabolism, possibly through desulfonation. Nitrendipine (m/z 360.36, ESI⁺) likewise underwent complete reduction, yielding the expected nitro-reduced product (m/z 330.14, ESI⁺, Cas. No. 138135-48-5). The data for the conversion of the other xenobiotics are shown in Supplementary Fig. S1 ) This close correlation in degradation rates and kinetic profiles between the fecal slurry and the colon model underscored the model’s robustness in reproducing the metabolic activity of native gut microbiota for toxicologically relevant biotransformation reactions. In contrast, the cell-free exoenzyme fraction showed activity only toward substrates undergoing ester hydrolysis (Fig. 7 A). As this fraction contains naturally released extracellular enzymes, primarily esterases and glycosidases, it provides complementary information on transformation processes that occur independently of intracellular microbial metabolism. Discussion The gastrointestinal tract is a dynamic interface between host and environment, colonized by a complex microbial community whose metabolic potential exceeds those of the human organism (Thursby and Juge 2017 ). Models for studying gut microbiota dynamics and metabolism can be broadly categorized into three main classes: (i) static batch fermentations, (ii) continuous bioreactor systems, and (iii) microfluidic or organ-on-a-chip platforms. Each model type addresses different experimental needs and provides distinct levels of physiological representation. Dynamic multistage bioreactor systems, such as the Simulator of the Human Intestinal Microbial Ecosystem (SHIME) (Van De Wiele et al. 2015 ), the TNO Intestinal Model (TIM) (Minekus et al. 1999 ), the SIMulator of the GastroIntestinal tract (SIMGI) (Barroso et al. 2015 ), and the EnteroMix (Mäkeläinen et al. 2007 ), are designed to closely mimic the physicochemical gradients of the human gut. Although they offer high physiological relevance and spatial resolution, their complexity, infrastructure requirements, long stabilization periods, and limited scalability make them less suitable for high-throughput applications or routine screening studies. Microfluidic and organ-on-a-chip platforms, such as the Human-Microbial Crosstalk (HuMiX) model (Shah et al. 2016 ), the Human Oxygen-Bacteria Anaerobic (HoxBan) system (Sadaghian Sadabad et al. 2015 ), and the Host-Microbiota Interaction (HMI) model (Marzorati et al. 2014 ), allow controlled co-culture of gut epithelial cells and microbial communities. These systems provide valuable insights into host-microbe interactions, barrier integrity, and immunological signaling. However, simultaneously maintaining the aerobic requirements of the host epithelium and the strict anaerobic conditions required by gut microbes remains a major technical challenge. Furthermore, their complexity and low throughput limit their applicability to screening-based studies. Static batch fermentation systems constitute the most scalable and experimentally accessible model format. They are widely used for evaluating microbial transformations under anaerobic conditions, especially for studying specific metabolic activities or substrate conversion (Zimmermann et al. 2019 ; Shetty et al. 2022 ). But a fundamental limitation shared by all current in vitro gut microbiota models is their reliance on either undefined fecal inocula (El Oufir et al. 2000 ; Marzorati et al. 2014 ) or simplified synthetic microbial communities composed of a limited number of strains (Goodman et al. 2009 ; van Leeuwen et al. 2023 ). Fecal samples represent a complex and undefined matrix containing not only a diverse bacterial population but also residual dietary material, host-derived cells, enzymes, and endogenous metabolites. This compositional complexity introduces substantial variability between preparations and complicates controlled experimental analyses, as test compounds may interact unpredictably with reactive components of the fecal matrix. The use of synthetic microbial consortia offers greater experimental standardization and reproducibility but tend to be limited in terms of taxonomic complexity. (Goodman et al. 2009 ; Perez et al. 2021 ). Moreover, the use of fecal inocula, while taxonomically broad, often results in a loss of microbial diversity during prolonged incubation. Although all existing gut microbiota models have their strengths and can be valuable tools in specific research contexts, their suitability must be critically evaluated in the context of the biological process under investigation. Especially in studies focusing on the microbial transformation of xenobiotics, it is essential that the models preserve both the full taxonomic and metabolic repertoire of the native gut microbiota while remaining compatible with high-throughput screening techniques. To overcome the limitations of existing in vitro approaches, we developed a novel bacterial colon model based on a purified suspension of native gut bacteria. Our system is built on a stable, high-density microbial community that closely reproduces the taxonomic diversity and metabolic functionality of the human colonic microbiota. Amplicon sequencing identified more than 400 bacterial species, highlighting the model’s ability to preserve a complex and representative microbial consortium over 24 h in a physiologically relevant dimension. Importantly, the preparation was free of fecal matrix components, including host cells, food residues and reactive metabolites. A further advantage of this model is its modularity: microbiota preparations from different individuals can be combined to minimize stochastic effects and inter-individual outliers. However, the model is also fully compatible with single-donor preparations. This flexibility enables researchers to investigate donor-specific metabolic phenotypes if required. In addition, this platform supports the defined processing of microbial dietary components, supplements, pharmaceuticals and xenobiotics, unaffected by substrate competition. This high level of analytical control makes the model particularly suitable for mechanistic studies at the interface of diet, xenobiotic metabolism, and microbial ecology. To ensure the physiological relevance of functional investigations, it is essential that microbial population densities approximate those found in the human colon. Native colonic communities reach approximately 8.8 × 10 10 cells mL -1 (Sender et al. 2016 ), whereas many in vitro gut models operate at substantially lower bacterial concentrations (Minnebo et al. 2023 ), which can limit their capacity to reproduce in vivo -like metabolic activity. The bacterial colon model developed in this study addresses this limitation by employing an inoculum standardized to 1.43 × 10 10 cells mL -1 , with scalability up to 4.3× 10 10 cells mL -1 . This corresponds to more than 50% of the in vivo bacterial density and therefore provides a realistic basis for analyzing microbial metabolism and community-driven biotransformations. At the same time, the system remains compatible with high-throughput formats, as assay volumes can be reduced to 100 µL, enabling efficient screening of large xenobiotic panels in multiwell plates. To ensure physiologically meaningful functionality, the model supports the regeneration of enzymatic activity, thereby maintaining the native community structure during incubation. Within 24 h, microbial functions including glycosidase, protease, and esterase activities were re-established to levels comparable to those measured in fecal reference samples. This allows short-term incubations (≤ 24 h) for the screening of bacterial transformations under stable compositional and biochemical conditions without the need for complex reactor systems or multi-day stabilization phases. In contrast, many established in vitro platforms require continuous cultivation and extended adaptation periods to achieve stable microbial community structures. The presented results also demonstrate that the metabolic activity of the gut microbiota can be functionally restored under defined in vitro conditions, provided that an appropriate substrate composition is selected. Comparative testing of different substrate formulations showed that fermentable carbohydrates are essential for restoring microbial metabolic functionality, yet the specific composition critically determines the extent of recovery. In particular, the fiber-rich formulation supported the most effective regeneration of enzymatic activities, preserved taxonomic stability, and enabled SCFA production at physiologically relevant levels. The recovered enzymatic activity in our bacterial colon model reflected those of fecal reference samples and fell within the physiologically range reported for native human feces. Literature values for key microbioal enzymes typically span approximately 60–190 nmol min -1 mg protein − 1 (Flores et al. 2012 ; Śliżewska et al. 2023 ), placing the activities observed in our system well within the expected biological variability. Similarly, the concentrations of SCFAs produced in the bacterial colon model with144.5 mmol kg feces -1 for acetate, 38.3 mmol kg feces -1 for propionate, and 30.7 mmol kg feces -1 for butyrate, were consistent with reported physiological ranges in human feces (acetate: 12.8-103.4 mmol kg⁻¹; propionate: 4.5–27.8 mmol kg⁻¹; butyrate: 4.0–53.0 mmol kg⁻¹) (Høverstad et al. 1984 ). Notably, the proportional distribution of SCFAs in the model also mirrored the in vivo profile, which is typically characterized by relative ratios of approximately 60:20:20 for acetate, propionate, and butyrate, respectively (Cummings et al. 1987 ; Hijova and Chmelarova 2007 ; Binder 2010 ). In contrast to many conventional systems, which must limit glycerol concentrations to 10–15% for cryopreservation (Bircher et al. 2020 ) to avoid introducing excess glycerol at inoculation, the presented model overcomes this limitation by incorporating an washing step that removes the cryoprotectant prior to experimental application. This allows the application of cryopreservation protocols using glycerol concentrations of 25% or higher, which are known to markedly improve cell viability and long-term stability (Tutrina and Zhurilov 2024 ). The removal of glycerol is particularly relevant because even small residual glycerol concentrations, as low as 1 mM, were found in preliminary experiments (data not shown) to substantially alter microbial fermentation profiles. Since glycerol serves as a metabolizable carbon source for fast-growing facultative anaerobes such as E. coli (Kopp et al. 2017 ), residual amounts can drive their selective overgrowth, thereby outcompeting slower-growing or more fastidious taxa. Such selective enrichment contributes to community imbalance and can fundamentally alter the system´s metabolic output (De Weirdt et al. 2010 ; Gao et al. 2021 ).The presented bacterial colon model offers considerable potential as a next-generation platform for translational microbiome research. It becomes feasible to targeted comparisons between healthy and perturbed microbial communities, facilitating investigations into microbiome-associated disorders and their metabolic signatures. Currently, in-depth examinations of diet-microbiome interactions rely on in vivo studies, which are time-consuming, costly, and limited experimental (Wu et al. 2011 ; David et al. 2014 ). In contrast, the colon model permits studies on the effects of dietary components and supplements on microbial composition and metabolic output within a short timeframe that reflects the physiological transit time of the human colon. While the current work intentionally isolates microbiota-driven processes to provide a clean mechanistic readout, the system can be incorporated into host-microbiome frameworks. Coupling the bacterial colon model with gut-on-chip devices or in vivo mouse models would allow these platforms to benefit from its extensive taxonomic and functional diversity, thereby improving physiological fidelity in studies of host-microbial crosstalk. Beyond its applicability in mechanistic microbiome research, the presented model offers significant opportunities for the development of New Approach Methodologies (NAMs) aimed at reducing reliance on animal testing in toxicology. The system is also well suited to generate quantitative kinetic data for integration into physiologically based kinetic (PBK) models(Stevanoska et al. 2024 ). Although recent PBK frameworks have begun to incorporate microbial metabolism as a dedicated compartment, a major limitation remains the lack of standardized tools to characterize gut microbial transformation rates. By enabling controlled, high-resolution assessments of xenobiotic conversion, the colon model may help fill this gap and improve the predictive accuracy of in silico toxicokinetic simulations. Taken together, this system addresses a critical methodological gap by combining physiological complexity with experimental standardization, thereby providing a scalable and analytically robust platform for studying microbiota-mediated processes at the interface of diet, xenobiotic metabolism and microbial ecology. Abbreviations ACN, acetonitrile; BS, bile salts; BSA, bovine serum albumin; CTAB, cetyltrimethylammonium bromide; DNA, deoxyribonucleic acid; EDTA, ethylenediaminetetraacetic acid; EtOH, ethanol; HMI, Host-Microbiota Interaction model; HoxBan, Human Oxygen-Bacteria Anaerobic system; HPLC, high-performance liquid chromatography; HuMiX, Human-Microbial Crosstalk model; LC-MS, liquid chromatography-mass spectrometry; LDH, lactate dehydrogenase; LOD, limit of detection; LOQ, limit of quantification; MDH, malate dehydrogenase; m/z, mass-to-charge ratio; NADH, nicotinamide adenine dinucleotide; NAMs, New Approach Methodologies; OD 600 , optical density at 600 nm; PBK, physiologically based kinetic; PBS, phosphate-buffered saline; PCR, polymerase chain reaction; pNp, p-nitrophenyl; RBB, Remazol Brilliant Blue; RI, refractive index; SCFA, short-chain fatty acids; SHIME®, Simulator of the Human Intestinal Microbial Ecosystem; SIMGI, SIMulator of the GastroIntestinal tract; T0, baseline at 0 h; T24, time point after 24 h incubation; TCA, trichloroacetic acid; TIM, TNO Intestinal Model; UV, ultraviolet. Declarations All participants provided informed consent to take part in the study. Ethical approval Human fecal samples were collected from healthy adult volunteers in accordance with the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of the University of Bonn (approval no. DRKS00036882). Data availability All sequencing data and processed datasets supporting the findings of this study are provided in the Supplementary Material. Raw datasets are available from the corresponding author upon reasonable request. Author contributions N.H. designed the study, performed the experiments, analyses, and data evaluation, and wrote the manuscript. J.-L.W. contributed to method development and experimental work. L.F. assisted with sample processing, LC-MS analysis and data interpretation. M.C.S. and W.S. performed 16S rRNA gene sequencing and scientific interpretation. J.-L.C.M.D. provided toxicological expertise and revised the manuscript. 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Nature 570:462–467. https://doi.org/10.1038/s41586-019-1291-3 Additional Declarations No competing interests reported. Supplementary Files Colonmodelpapersupplementaryfinal.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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19:25:37","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38880,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/a9b06b050e9f59b504e12e1f.png"},{"id":97558161,"identity":"0cc3895f-b1c3-432b-9801-53ed77b41867","added_by":"auto","created_at":"2025-12-05 19:25:37","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63584,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/cf80c2aaf08afef67609734f.png"},{"id":97558160,"identity":"d7c2ea64-83c0-433b-a971-49919106d54d","added_by":"auto","created_at":"2025-12-05 19:25:37","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14397,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/9b5f6a14d3a888aae42d9690.png"},{"id":97672233,"identity":"2a0e9cf7-a37a-49e8-ac9e-afcfc38f7612","added_by":"auto","created_at":"2025-12-08 09:34:55","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49733,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/248add8eea83420fa7cf608e.png"},{"id":97672146,"identity":"8b40c0e0-46af-4bc5-8065-df06b53568f5","added_by":"auto","created_at":"2025-12-08 09:34:24","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42567,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/167490533bff90e0a6657344.png"},{"id":97558153,"identity":"f6e570e2-0b25-446e-8aa3-dc053253854c","added_by":"auto","created_at":"2025-12-05 19:25:37","extension":"xml","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185207,"visible":true,"origin":"","legend":"","description":"","filename":"05ef98b7c74b4731bfbcb65f7f7b50a11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/656355065569bafa2e183525.xml"},{"id":97671487,"identity":"c79064e2-cd5f-43b3-8969-31fd6fa4c2f0","added_by":"auto","created_at":"2025-12-08 09:32:39","extension":"html","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":193922,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/d5c576686707fef72309313d.html"},{"id":97558126,"identity":"4718705e-b74a-494a-9e81-90b4be54acbe","added_by":"auto","created_at":"2025-12-05 19:25:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1581614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeparation of the bacterial colon model from human fecal samples.\u003c/strong\u003e [A] Schematic representation of thedensity-gradient centrifugation. The left panel shows diluted fecal samples layered on the density-gradient medium, and the right panel illustrates the separated fractions [B] Representative microscopic images of the isolated bacterial cell fraction (1,600× magnification).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/da42deba7bb63f02ec785e6e.png"},{"id":97672302,"identity":"b92741d2-7ad2-49f2-a0cd-6ab576ee689f","added_by":"auto","created_at":"2025-12-08 09:35:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxonomic shifts in the gut microbiota composition under different substrate conditions.\u003c/strong\u003e Heatmap showing changes in the relative abundance of dominant bacterial genera (≥ 1% mean abundance) following 24 h incubation under four substrate conditions: no substrate, fiber-rich medium, Western-style medium supplemented with bile salts (BS), and Western-style medium without bile salts (BS). Values were compared to the initial baseline (T0) as determined by 16S rRNA gene amplicon sequencing.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/e791caecbb2cf4ba07e3b342.png"},{"id":97558118,"identity":"3ea3832b-abc3-4075-9a59-4626964b8f28","added_by":"auto","created_at":"2025-12-05 19:25:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative shifts among low-abundance bacterial genera under different substrate conditions. \u003c/strong\u003eBar charts illustrate the relative abundance of selected taxa after 24 h incubation under four substrate conditions: no substrate, fiber-rich medium, Western-style medium with bile salts (BS), and Western-style medium without bile salts (BS). Values are shown relative to the initial baseline (T0) as determined by 16S rRNA gene amplicon sequencing.\u003cstrong\u003e \u003c/strong\u003eStatistical significance was assessed using unpaired two-tailed t-tests (n = 3 per condition). Asterisks denote significance levels: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ***p ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/05c8bf38b99c044c6bb2ab84.png"},{"id":97673193,"identity":"32ab5fa8-1b19-4780-b08e-9294a4adfa0d","added_by":"auto","created_at":"2025-12-08 09:39:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative enzymatic activities and SCFA production [%] of the bacterial colon model compared to original fecal samples. \u003c/strong\u003eSpecific enzymatic activities (nmol min⁻¹ mg protein⁻¹) were normalized to the corresponding fecal reference values, which were set to 100 %. Bars represent the relative activity or concentration measured in the colon model after 24 h of incubation. DH = dehydrogenase, pNp = p-nitrophenyl, Azo = azo dye-coupled, RBB = Remazol Brilliant Blue-labelled, SCFA = short-chain fatty acids (acetate, propionate, butyrate). Statistical significance was determined using paired two-tailed t-tests (n = 8–10). Only Azo-cellulose showed a significant reduction (*** p ≤ 0.001).\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/95110a984ffcc723f85508fb.png"},{"id":97558136,"identity":"50abc0bd-d63e-45ab-abcc-c373c7ac1ae7","added_by":"auto","created_at":"2025-12-05 19:25:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSCFA production in the bacterial colon model with and without substrate supplementation.\u003c/strong\u003e Concentrations of acetate, propionate, and butyrate after 24 h anaerobic incubation of the colon model without substrate (light green) or with the fiber-rich formulation (dark green). SCFAs were quantified by HPLC and expressed as µmol mL⁻¹. Substrate addition markedly increased SCFA formation compared to the substrate-free control (**** p ≤ 0.0001, paired t-test).\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/f8175490c4bb6cbc5e6e33a1.png"},{"id":97558140,"identity":"2aa97cc5-33f6-46e3-a2af-ffbebfa2eacb","added_by":"auto","created_at":"2025-12-05 19:25:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnzymatic activity profile of the colon model at baseline (T0) and after incubation (T24) with fiber-rich substrates. \u003c/strong\u003eActivities are expressed as relative specific activities normalized to protein content (nmol min⁻¹ mg protein⁻¹), with T0 values set to 100%. Bars represent mean relative activities of key hydrolytic enzymes involved in microbial lipid, protein, and carbohydrate turnover. Significant increases were observed in several functional categories, including esterases, lipase, protease, glycoside hydrolases, and fibrolytic enzymes. Statistical evaluation was performed using paired t-tests (n =8-10). Significance coding: ns (p \u0026gt; 0.05), * (p ≤ 0.05), ** (p ≤ 0.01), *** (p ≤ 0.001), **** (p ≤ 0.0001).\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/1d50ca5f7f0d117f4b7a2aed.png"},{"id":97558155,"identity":"e6acac8a-60db-444a-ac17-07aa303357f7","added_by":"auto","created_at":"2025-12-05 19:25:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":278791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial biotransformation of xenobiotic model compounds in buffer, fecal slurry, colon model, and the corresponding exoenzyme fraction.\u003c/strong\u003e Representative substrates were selected to cover different microbial transformation reactions commonly observed in the human colon: 4-acetoxyacetanilide for ester hydrolysis (A), sulindac for sulfoxide reduction (B), sulfasalazine for azoreduction (C), and nitrendipine for nitroreduction (D). Parent-compound concentrations were quantified by HPLC at 0 h (dark grey), 0.1 h (blue), 4 h (orange), and 24 h (light grey) under anaerobic conditions (37°C). Data represent mean ± SD of three independent biological pools, each composed of three human donors\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/04b530b1d8eef88048ef2a9f.png"},{"id":97678793,"identity":"eceae10c-dc9c-4932-8252-c8746a180836","added_by":"auto","created_at":"2025-12-08 09:56:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3536781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/0cc227cb-75b2-4b32-93ad-2801d0cf3fb1.pdf"},{"id":97672509,"identity":"3e36d29a-4f25-4c20-83e6-f4c89e351040","added_by":"auto","created_at":"2025-12-08 09:38:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":108092,"visible":true,"origin":"","legend":"","description":"","filename":"Colonmodelpapersupplementaryfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8136690/v1/f8b6675c0cfd3ca76b9572d0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a gut microbiota model for the analysis of bacterial modifications of xenobiotics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe human body serves as a favored environment for a vast diversity of bacteria, archaea, and fungi (Schmidt et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Collectively referred to as the human microflora or microbiota, these microorganisms inhabit various body sites and typically coexist with their host in a mutualistic relationship. In an adult human, the body harbors up to 10\u003csup\u003e14\u003c/sup\u003e prokaryotic cells (Sender et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with the majority reside in the gastrointestinal tract. This complex microbial population interacts actively with the gut lymphoid tissues and the immune system of the host (Gensollen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, the gut microbiota plays a pivotal role in maintaining intestinal health, and influencing the development of both intestinal and non-intestinal diseases. A major class of non-host-derived substances are xenobiotics, a diverse group of chemicals including pharmaceuticals, environmental pollutants, food additives, and naturally occurring compounds that may exert toxic at elevated concentrations (Soucek \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Given their structural diversity and widespread presence, the estimated interaction of more than 25,000 different compounds with the gut microbiota underscores its critical function in shaping host responses (Lindell et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Unlike human metabolism, which primarily relies on hepatic oxidation and conjugation, the gut microbiome, harboring more than 3.3\u0026nbsp;million genes exceeding the human genome by over 150-fold (Qin et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Hence, the gut microbiome provides a remarkably broad enzymatic repertoire, including hydrolysis, reduction, and deconjugation reactions (Zimmermann et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These microbial transformations can significantly impact the half-life, bioavailability, and the pharmacokinetics of xenobiotics by altering their toxicity, metabolite formation, or pharmacological effects (Koppel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Prominent examples include the activation of the ulcerative colitis drug sulfasalazine through azo bond cleavage (Lima et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the deglucuronidation of non-steroidal anti-inflammatory drugs such as diclofenac (Boelsterli et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the acylation-mediated inactivation of 5-aminosalicylic acid (Delom\u0026eacute;nie et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), as well as the modulation of drug efficacy and toxicity through reduction, hydrolysis, and competition with host metabolic pathways, as observed for digoxin (Haiser et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), irinotecan (Takasuna et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), and acetaminophen (Clayton et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). By shaping the chemical landscape of the intestinal lumen, the gut microbiota affects not only systemic exposure but also presents both challenges and opportunities for contemporary chemical risk assessment. Traditional approaches, based on animal studies, clinical trials, and epidemiological data, often neglect the role of microbial metabolism in modulating xenobiotic fate (Stevanoska et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In response, emerging fields such as toxicomicrobiomics aim to elucidate the bidirectional interactions between gut microbes and toxicants (Abdelsalam et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Incorporating microbiome dynamics into physiologically based kinetic models (PBK models) has the potential to improve the accuracy of exposure and risk predictions. This underscores the need for advanced gut microbiome models that more accurately reflect human microbial diversity and functional capacity. Such standardized systems are not only critical for mechanistic understanding but also hold promise for improving preclinical screening and regulatory safety assessment of xenobiotics. Extensive efforts have been made to create experimental \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003eex vivo\u003c/em\u003e models of the human intestine, enabling the analysis of gastrointestinal pathophysiology and the intricate involvement of bacteria in these processes (Stevanoska et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, for certain applications, such as screening, standardization, and integration into higher-throughput pipelines, simpler and more accessible models are essential. To address this need, we developed a streamlined and defined \u003cem\u003ein vitro\u003c/em\u003e bacterial colon model optimized for reproducibility, ease of use, and compatibility with routine xenobiotic screening workflows. Together, these features make the system particularly suitable for rapid, high-throughput applications in toxicomicrobiomics, including studies on how xenobiotics are transformed by the gut microbiota.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eChemicals\u003c/h2\u003e\u003cp\u003eAll reagents, chemicals, and substrates were obtained from commercial suppliers including Carl Roth GmbH\u0026thinsp;+\u0026thinsp;Co. KG (Karlsruhe, Germany), Sigma-Aldrich (Darmstadt, Germany), as well as Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan), BLDpharm Co., Ltd. (Shanghai, China), Chemos GmbH \u0026amp; Co. KG (Regenstauf, Germany), and Cayman Chemical Company (Ann Arbor, MI, USA). The gases CO\u003csub\u003e2\u003c/sub\u003e (99.9%), H\u003csub\u003e2\u003c/sub\u003e (99.9%), and N\u003csub\u003e2\u003c/sub\u003e (99.9%) were obtained from Air Liquide (D\u0026uuml;sseldorf, Germany). All substances were of analytical grade or higher and were used without further purification.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eThe objective of this study was to develop and validate a new and standardized \u003cem\u003ein vitro\u003c/em\u003e colon model based on bacterial communities isolated from human feces. The primary objective was to preserve the taxonomic diversity and metabolic functionality of the gut microbiota while enabling controlled and reproducible functional assays. Diluted fecal samples from healthy individuals were used to obtain both untreated reference inocula and standardized bacterial preparations for the colon model. To minimize interindividual variability, donors were randomly assigned to groups of three, and their samples were pooled under anaerobic conditions, allowing for randomization at both the collection and application stages.\u003c/p\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eFecal samples were obtained from 20 healthy male and female volunteers aged 20 to 60 years who had not received antibiotic treatment within the six months prior to recruitment. The study protocol was approved by the Ethics Committee of the University of Bonn and is registered in the German Clinical Trials Register (registration number DRKS00036882, registered on 26 September 2025; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://drks.de/search/de/trial/DRKS00036882\u003c/span\u003e\u003cspan address=\"https://drks.de/search/de/trial/DRKS00036882\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and all procedures were conducted in accordance with the relevant guidelines and regulations. Each participant collected two fecal samples using a standardized collection kit. Immediately after collection, 5 g of fresh stool were diluted in 50 mL of pre-anaerobized modified SHIME\u0026reg; saline buffer (29.8 mM NaHCO\u003csub\u003e3\u003c/sub\u003e, 34.2 mM NaCl, 18.7 mM NH\u003csub\u003e4\u003c/sub\u003eCl, 49.5 mM K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, 49.8 mM KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 0.07 mM CaCl\u003csub\u003e2\u003c/sub\u003e x 2H\u003csub\u003e2\u003c/sub\u003eO, 0.03 mM MgSO\u003csub\u003e4\u003c/sub\u003e x 7H\u003csub\u003e2\u003c/sub\u003eO, pH 7.0), which had been flushed with N\u003csub\u003e2\u003c/sub\u003e/CO\u003csub\u003e2\u003c/sub\u003e (80%/20%) and sealed in serum flasks with butyl rubber stoppers (Van De Wiele et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Following sample addition, the flasks were flushed twice with 50 mL of 80% N\u003csub\u003e2\u003c/sub\u003e and 20% CO\u003csub\u003e2\u003c/sub\u003e, to remove the air in the headspace. The samples were stored at 4\u0026deg;C until further processing (\u0026lt;\u0026thinsp;2 h).\u003c/p\u003e\n\u003ch3\u003eSample processing- for the in vitro bacterial colon model\u003c/h3\u003e\n\u003cp\u003eOur model was based on the isolation of intact bacterial communities from human feces. Bacterial isolation was performed using a modified density gradient centrifugation protocol first described by Hevia et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To detach bacteria adhering to particulate matter and embedded within fecal biofilms, 0.001% (w/v) cetyltrimethylammonium bromide (CTAB) was added to each diluted fecal sample 30 min prior to further processing (Macfarlane et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). All steps involving open sample handling were conducted under strictly anaerobic conditions (79% N\u003csub\u003e2\u003c/sub\u003e, 19% CO\u003csub\u003e2\u003c/sub\u003e, 2% H\u003csub\u003e2\u003c/sub\u003e) within a vinyl anaerobic chamber (Coy Laboratory Products, Grass Lake, Michigan, USA). Samples from three donors were randomly pooled for each preparation to reduce variability among individuals. From each pooled and diluted fecal suspension, 15 mL were layered onto 3.5 mL of 55% (w/v) Nycodenz\u0026reg; (Serumwerk Bernburg AG, Bernburg, Germany) in anaerobic centrifuge tubes. The samples were then centrifuged at 4,000 x g for 30 min at room temperature. Following centrifugation, three distinct layers were obtained: an upper layer containing extracellular enzymes and soluble components, an intermediate opaque layer composed of bacterial cells, and a lower layer consisting of insoluble debris.\u003c/p\u003e\u003cp\u003eTo further purify the extracellular enzyme (exoenzyme) fraction, the supernatant was centrifuged at 18,000 x g for 3 min at 4\u0026deg;C. The resulting preparation was then sterile-filtered three times through 0.22 \u0026micro;m syringe filters to ensure complete removal of remaining microbial cells. The sterile exoenzyme fraction was stored anaerobically at -70\u0026deg;C until further use. The middle layer, which contained the gut bacteria, was collected and referred to as the bacterial colon model. The isolated microbiota was then diluted in sterile 50% (v/v) glycerol and stored at -70\u0026deg;C under anaerobic conditions. Prior to use in experiments, the cryopreserved cells were washed three times with modified SHIME\u0026reg; saline (18,000 x g, 3 min, 4\u0026deg;C) to remove any remaining Nykodenz\u0026reg;, glycerol, and particulate material. The final cell suspension was adjusted to an optical density at 600 nm (OD\u003csub\u003e600\u003c/sub\u003e) of 10, corresponding to approximately 1.43 \u0026times; 10\u003csup\u003e10\u003c/sup\u003e cells mL\u003csup\u003e-1\u003c/sup\u003e, as determined microscopically using a Neubauer counting chamber (Hager et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This microbial density was about 16% of the mean \u003cem\u003ein vivo\u003c/em\u003e bacterial concentration in the human colon, estimated at 8.8 \u0026times; 10\u003csup\u003e10\u003c/sup\u003e cells mL\u003csup\u003e-1\u003c/sup\u003e (assuming a fecal density of 1.04 g mL\u003csup\u003e-1\u003c/sup\u003e (Sender et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)). The system can be upscaled to achieve cell numbers corresponding to up to 50% of the \u003cem\u003ein vivo\u003c/em\u003e abundance of colonic bacteria.\u003c/p\u003e\u003cp\u003eSince this washing procedure eliminates all extracellular enzymes, a targeted enzymatic regeneration step was necessary to restore the microbial metabolic functionality. Therefore, distinct substrate formulations were tested for their ability to support functional recovery while preserving the taxonomic integrity and metabolic activity. Substrate conditions included (i) a no-substrate control, (ii) a complex fiber-rich formulation composed of mucin, pectin, and xylan (each at 1 g L\u003csup\u003e-1\u003c/sup\u003e), (iii) a Western-style formulation containing all fiber components plus starch (1 g L\u003csup\u003e-1\u003c/sup\u003e), protein sources (casein and peptone, 0.5 g L\u003csup\u003e-1\u003c/sup\u003e each), saturated and unsaturated fatty acids (palmitic, stearic, oleic, and linoleic acid, 0.25 g L\u003csup\u003e-1\u003c/sup\u003e each), short-chain fatty acids (SCFAs; acetate 10 mM; propionate and butyrate 2.5 mM each), and bile salts (0.5 g l\u003csup\u003e-1\u003c/sup\u003e) as well as (iv) the same Western-style mixture without bile salts. Due to the limited purity of commercially available polysaccharides, xylan, mucin, and pectin were purified by triple ethanol precipitation (100 mL of a 10% solution in 900 mL 99.9% ethanol), followed by lyophilization and purity verification using HPLC analysis. All regeneration incubations were carried out for 24 h at 37\u0026deg;C under strictly anaerobic conditions.\u003c/p\u003e\n\u003ch3\u003eMicroscopic imaging\u003c/h3\u003e\n\u003cp\u003eTo evaluate the morphological characteristics of the isolated bacterial colon model, the samples were examined using bright-field microscopy. To immobilize the cells, 1.5% (w/v) low-melting-point agarose was mixed with SHIME\u0026reg; saline and melted at 70\u0026deg;C. The melted agarose was dispensed onto concave microscope slides (Thermo Fisher Scientific, Walthman, MA, USA) and covered with a coverslip to allow solidification. Washed bacterial suspensions (2\u0026ndash;5 \u0026micro;L) were then applied to the agarose surface and covered with a coverslip. Images were acquired using a Nikon Ti2-E inverted microscope equipped with a 160x oil-immersion objective and a Prime BSI sCMOS camera (Teledyne Photometrics, Tucson, AZ, USA; pixel size 107 nm).\u003c/p\u003e\u003cp\u003e\u003cb\u003e16S rRNA amplicon sequencing for microbial community profiling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMicrobial community profiling was performed by 16S rRNA gene amplicon sequencing using the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA). Total genomic DNA was extracted from 150 mg fecal material or 100 \u0026micro;L of microbial cell pellet (1 x 10\u003csup\u003e9\u003c/sup\u003e cells) using the Quick-DNA\u0026trade; Fecal/Soil Microbe Microprep Kit (Zymo Research, Irvine, CA, USA), following the manufacturer\u0026rsquo;s instructions. Mechanical lysis was performed via bead beating on a vortex mixer equipped with a horizontal adapter. DNA was eluted in a final volume of 50 \u0026micro;L. Concentrations were measured using a NanoDrop\u0026trade; 2000c Spectrophotometer (Eppendorf, Hamburg, Germany), and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until further analysis.\u003c/p\u003e\u003cp\u003eAmplicon libraries targeting the V3-V4 region of the 16S rRNA gene were generated using the primer pair Bakt_341F (5\u0026prime;-CCTACGGGNGGCWGCAG-3\u0026prime;) and Bakt_805R (5\u0026prime;-GACTACHVGGGTATCTAATCC-3\u0026prime;). PCR amplification was carried out using 2 \u0026times; KAPA HiFi HotStart ReadyMix polymerase (Roche, Mannheim, Germany) under the following cycling conditions: 22 cycles of 95\u0026deg;C (denaturation), 55\u0026deg;C (annealing), and 72\u0026deg;C (extension), followed by a final elongation step.\u003c/p\u003e\u003cp\u003eTo attach Illumina Nextera XT adapter sequences, a second PCR was performed using indexed primers. Amplicons were purified using AMPure XP beads (Beckman Coulter, Krefeld, Germany), and quantified by the Qubit\u0026trade; dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA), as well as normalized and pooled manually. The pooled libraries were denatured in 0.2 N NaOH, diluted to 10 pM, and spiked with 20% PhiX control DNA (Illumina, San Diego, CA, USA) prior to sequencing. Sequencing was performed on an Illumina MiSeq platform using the MiSeq Reagent Kit v3 with 2 \u0026times; 300 bp paired-end chemistry. Raw sequence reads were demultiplexed using MiSeq Reporter v2.5 and further processed with the QIIME2 pipeline (version 2021.4). Quality control and denoising were performed using the DADA2 plugin (Callahan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), applying a minimum quality threshold of Q30. Forward and reverse reads were truncated at 270 bp and 210 bp, respectively, to remove low-quality regions, and chimeric sequences were filtered out subsequently.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMeasurement of enzyme activities\u003c/h2\u003e\u003cp\u003eMicrobial enzyme activities were determined by spectrophotometric assays conducted under standardized conditions using a Jasco V-600 UV/Vis spectrophotometer (Jasco, Gro\u0026szlig;-Umstadt, Germany). All reactions were carried out in 100 mM potassium phosphate buffer (pH 7.0) supplemented with 0.5 mM CaCl\u003csub\u003e2\u003c/sub\u003e and MgCl\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eTo measure intracellular dehydrogenase activities, bacterial cells were lysed by bead beating. Therefore, 1 mL of the bacterial suspension in modified SHIME\u0026reg; saline was supplemented with 0.1 mM EDTA, 0.1% (v/v) Triton X-100, and a 1\u0026times; protease inhibitor cocktail (Sigma-Aldrich, Darmstadt, Germany). Bead beating was performed on a vortex mixer equipped with a horizontal adapter for 15 min at 4\u0026deg;C, followed by centrifugation at 17,000 \u0026times; g for 5 min at 4\u0026deg;C. The supernatant containing the crude enzyme extract was collected, and used directly for enzyme assays. Lactate dehydrogenase (lactate DH) and malate dehydrogenase (malate DH) activities were determined by monitoring nicotinamide adenine dinucleotide (NADH) consumption at 340 nm (ε\u0026thinsp;=\u0026thinsp;6.22 mM⁻\u0026sup1; cm⁻\u0026sup1;) at 37\u0026deg;C. For lactate DH, 10\u0026ndash;50 \u0026micro;L of cell lysate were added to 950\u0026ndash;990 \u0026micro;L of 50 mM Tris-HCl buffer (pH 7.4) containing 0.5 mM pyruvate and 0.25 mM NADH. Malate DH activity was measured under identical conditions using 0.5 mM oxaloacetate and 0.25 mM NADH as substrates.\u003c/p\u003e\u003cp\u003eExtracellular enzyme activities were determined using colorimetric and dye-linked assays based on the hydrolysis of p-nitrophenyl (pNp), Remazol Brilliant Blue (RBB)-, and Azo-conjugated substrates. Esterase and glycosidase activities were measured by the enzymatic cleavage of pNp substrates (acetate, butyrate, palmitate, α-glucopyranoside, β-galactopyranoside). 10\u0026ndash;75 \u0026micro;L of sample were incubated with 2\u0026ndash;4 \u0026micro;L of the respective pNp substrate (125\u0026ndash;250 mM in DMSO, Ethanol or CH₂Cl₂) in buffer to a final reaction volume of 500 \u0026micro;L. After 10\u0026ndash;15 min at 37\u0026deg;C, reactions were stopped with 500 \u0026micro;L ethanol and centrifuged (18,000 x g, 5 min). Absorbance was measured at 410 nm (ε\u0026thinsp;=\u0026thinsp;18.3 mM\u003csup\u003e-1\u003c/sup\u003e cm\u003csup\u003e-1\u003c/sup\u003e). Xylanase and cellulase activities were determined using RBB-xylan (2.5 mg mL\u003csup\u003e-1\u003c/sup\u003e), Azo-xylan (6.25 mg mL\u003csup\u003e-1\u003c/sup\u003e), and Azo-cellulose (6.25 mg mL\u003csup\u003e-1\u003c/sup\u003e). Samples (50\u0026ndash;100 \u0026micro;L) were incubated with 100 \u0026micro;L substrate in a final volume of 500 \u0026micro;L at 37\u0026deg;C for 2 h. Reactions were stopped with 1 mL precipitation buffer (10 g sodium acetate x 3 H\u003csub\u003e2\u003c/sub\u003eO, 1 g zinc acetate in 50 mL H\u003csub\u003e2\u003c/sub\u003eO, pH 5.0, containing 200 mL ethanol) and centrifuged (18,000 x g, 5 min). Absorbance was measured at 595 nm (ε\u0026thinsp;=\u0026thinsp;6.17 mM\u003csup\u003e-1\u003c/sup\u003e cm\u003csup\u003e-1\u003c/sup\u003e). Amylase activity was determined using 100 \u0026micro;L Red-starch (20 mg mL\u003csup\u003e-1\u003c/sup\u003e in 0.5 M KCl) incubated with 10\u0026ndash;20 \u0026micro;L sample and 180\u0026ndash;200 \u0026micro;L buffer for 20 min at 37\u0026deg;C. Reactions were stopped with 500 \u0026micro;L ethanol, centrifuged (18,000 x g, 5 min), and absorbance was measured at 510 nm (ε\u0026thinsp;=\u0026thinsp;6.17 mM\u003csup\u003e-1\u003c/sup\u003e cm\u003csup\u003e-1\u003c/sup\u003e). Protease activity was assayed using 100 \u0026micro;L Azo-casein (20 mg mL\u003csup\u003e-1\u003c/sup\u003e) incubated with 10\u0026ndash;20 \u0026micro;L sample and 80\u0026ndash;100 \u0026micro;L buffer for 10 min at 37\u0026deg;C. Reactions were terminated with 600 \u0026micro;L 5% (w/v) trichloroacetic acid (TCA), centrifuged (18,000 x g, 5 min), and absorbance was recorded at 440 nm. Enzymatic activity was calculated using absorbance values to determine the reaction rate (∆E min\u003csup\u003e-1\u003c/sup\u003e), which was converted to \u0026micro;mol min\u003csup\u003e-1\u003c/sup\u003e using the Beer-Lambert law and the appropriate molar extinction coefficient. Specific activity was expressed as nmol min\u003csup\u003e-1\u003c/sup\u003e mg protein\u003csup\u003e-1\u003c/sup\u003e. Protein concentrations were measured at 595 nm using the Bradford assay with bovine serum albumin (BSA) as standard (Bradford, 1976).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSCFA quantification using HPLC\u003c/h3\u003e\n\u003cp\u003eThe SCFAs were quantified using a high-performance liquid chromatography (HPLC) system equipped with refractive index (RI) and ultaviolet (UV) detectors. Separation was achieved on an Aminex HPX-87H column (300 mm \u0026times; 7.8 mm, Bio-Rad, Munich, Germany) at 65\u0026deg;C with 5 mM sulfuric acid (H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) as the mobile phase at a flow rate of 0.6 mL min\u003csup\u003e-1\u003c/sup\u003e. Prior to injection, fecal and colon model samples were clarified by mixing 100 \u0026micro;L of sample with 50 \u0026micro;L Carrez I solution (0.15 g mL\u003csup\u003e-1\u003c/sup\u003e K\u003csub\u003e4\u003c/sub\u003e[Fe(CN)\u003csub\u003e6\u003c/sub\u003e]) and 50 \u0026micro;L Carrez II solution (0.30 mg mL\u003csup\u003e-1\u003c/sup\u003e MgSO\u003csub\u003e4\u003c/sub\u003e x H\u003csub\u003e2\u003c/sub\u003eO). The mixture was vortexed and centrifuged at 18,000 x g for 2 min. Subsequently, 70 \u0026micro;L of the supernatant were diluted with 140 \u0026micro;L of 5 mM H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e. An additional centrifugation step (18,000 x g, 2 min) was performed to remove any remaining particulates. Quantification was performed using external calibration curves.\u003c/p\u003e\n\u003ch3\u003eXenobiotic assays quantified with HPLC and validated with LC-MS\u003c/h3\u003e\n\u003cp\u003eTo evaluate microbial xenobiotic metabolism under defined conditions, incubations were performed with model substrates representing key biotransformation classes with 4-acetoxyacetanilide (ester hydrolysis), sulindac (sulfoxide reduction), sulfasalazine (azo bond reduction), and nitrendipine (nitro group reduction). Quantification of parent compounds and metabolites was carried out by HPLC, and results were validated by liquid chromatography-mass spectrometry (LC-MS). Detailed experimental conditions and analytical parameters are provided in Supplementary S1 and Tab. S1.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistics\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using GraphPad Prism (version 8.0.2, GraphPad Software, San Diego, CA, USA). For taxonomic profiling, changes in microbial composition at the genus level were assessed by comparing relative abundances between baseline (T0) and post-incubation (T24) under four different substrate conditions. Dominant genera (\u0026ge;\u0026thinsp;1% mean relative abundance) and low-abundance genera (\u0026le;\u0026thinsp;1%) were visualized using heatmaps and bar plots. For each genus, the condition showing the smallest deviation was identified. Differences across conditions were evaluated using Kruskal-Wallis tests, and conditions with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Enzymatic activity measurements were expressed as specific activity (nmol min\u003csup\u003e-1\u003c/sup\u003e mg protein\u003csup\u003e-1\u003c/sup\u003e), normalized to protein content. Comparisons between original fecal samples and colon model compartments were analyzed using paired two-tailed t-tests (n\u0026thinsp;=\u0026thinsp;8\u0026ndash;10). For functional dynamics during substrate incubation, enzymatic activities at T24 were compared to T0 values (baseline set to 100%) using paired t-tests (n\u0026thinsp;=\u0026thinsp;8\u0026ndash;10). The SCFA concentrations (acetate, propionate, butyrate) were quantified by HPLC. Data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), with technical replicates (n\u0026thinsp;=\u0026thinsp;8) for controls and pooled replicates (n\u0026thinsp;=\u0026thinsp;17) for substrate-supplemented samples. Comparisons between fecal and colon model samples, with or without substrate supplementation, were performed using unpaired t-tests. Significance levels were defined as follows: *p\u0026thinsp;\u0026le;\u0026thinsp;0.05, **p\u0026thinsp;\u0026le;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026le;\u0026thinsp;0.001, ****p\u0026thinsp;\u0026le;\u0026thinsp;0.0001. More details are described in the supplementary material.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDevelopment of an in vitro model reflecting the natural microbiota in the human colon\u003c/h2\u003e\u003cp\u003eTo establish a physiologically relevant \u003cem\u003ein vitro\u003c/em\u003e model, particularly suited for investigating the contribution of the gut microbiota to xenobiotic transformations, a new method for the isolation and functional regeneration of native microbial communities from human fecal samples was developed and validated. Fecal samples from multiple donors were diluted 1:10 (w/v) and pooled to minimize interindividual variability. CTAB (0.001% w/v) was added as a mild detergent to detach bacteria from particle surfaces and biofilm structures (Hevia et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This was followed by a Nycodenz\u0026reg; density-gradient centrifugation (Macfarlane et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), which separated the fecal microbiota from stool samples. Three fractions were obtained (Fig.\u0026nbsp;1A): (i) an upper aqueous layer enriched in extracellular components, including secreted enzymes and low-density non-cellular material such as mucosal residues, lipids, and proteins; (ii) an intermediate opaque layer containing intact bacterial cells representative of the phylogenetic diversity of the human colonic microbiota, hereafter referred to as the bacterial colon model; and (iii) a lower dense fraction containing undigested dietary fibers, host-derived debris, and cell fragments. To assess the efficiency of bacterial cell isolation, total protein concentrations were determined in the original fecal slurry, the purified colon model fraction, and the corresponding supernatant after centrifugation. The isolation procedure achieved an average recovery rate of 86\u0026thinsp;\u0026plusmn;\u0026thinsp;17% relative to the initial protein content, indicating efficient preservation of microbial biomass. Hence, this protocol enabled a clean and targeted isolation of the intestinal microbial community, minimizing matrix-derived interference and ensuring high compatibility with downstream functional, biochemical, and analytical assays. Bright-field microscopy revealed a morphologically diverse and densely populated bacterial community exhibiting high levels of motility and structural heterogeneity (Fig.\u0026nbsp;1B). Bacterial cells showed distinct morphotypes, indicating a broad phylogenetic spectrum. Importantly, the preparation was free of visible particulate contamination or residual matrix components, which could otherwise interfere with subsequent analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;1: Separation of the bacterial colon model from human fecal samples.\u003c/b\u003e [A] Schematic representation of the density-gradient centrifugation. The left panel shows diluted fecal samples layered on the density-gradient medium, and the right panel illustrates the separated fractions [B] Representative microscopic images of the isolated bacterial cell fraction (1,600\u0026times; magnification).\u003c/p\u003e\u003cp\u003eThe bacterial cell fraction remained structurally intact, allowing long-term preservation at -70\u0026deg;C using glycerol as a cryoprotectant, followed by successful reactivation without compromising cellular integrity. For reactivation, samples were thawed and washed twice to remove glycerol, which would otherwise serve as a carbon source for certain bacteria and thereby alter both the metabolic output and microbial community composition (De Weirdt et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The presence of glycerol indeed affected the microbial community structure, as reflected by a shift in the SCFA profile, characterized by reduced acetate formation and increased levels of propionate, butyrate, and additional, yet unidentified, SCFAs (data not shown).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSubstrate-induced microbial community changes during regeneration\u003c/h2\u003e\u003cp\u003eProcessing of the bacterial colon model resulted in the loss of extracellular enzymes. These enzymes are not only involved in the degradation of structurally complex dietary polysaccharides such as pectin and xylan (Y\u0026uuml;ksel et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), but also play a key role in the microbial biotransformation of xenobiotics that are typically not internalized but undergo extracellular modification (Smacchi and Gobbetti \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Restoring this enzymatic activity while maintaining the native community structure was therefore essential for conducting physiologically relevant metabolic studies. To achieve enzymatic regeneration, colon model preparations were incubated anaerobically for 24 h under four defined conditions to evaluate the influence of nutrient composition on microbiota recovery: in the absence of substrates, in the presence of a fiber-rich medium consisting of 1 g L\u003csup\u003e-1\u003c/sup\u003e each of mucin, pectin, and xylan, and with a Western-style diet formulation, either with or without the addition of bile salts. The fiber-rich substrates contained structurally diverse dietary fibers, designed to mimic fiber-rich nutritional input. In contrast, the Western-style substrate formulation incorporated starch, proteinaceous compounds, a mix of saturated and unsaturated fatty acids, as well as SCFAs. To assess the effect of host-derived components, bile salts were either included (0.5 g L-\u0026sup1;) or omitted from the formulation.\u003c/p\u003e\u003cp\u003eMicrobial composition was analyzed at baseline and after incubation using 16S rRNA gene amplicon sequencing. In total, 74 families, 203 genera, and 412 species were identified across all samples, revealing compositional shifts that reflected substrate-dependent selection pressures. For comparative visualization, only genera with a mean relative abundance of \u0026gt;\u0026thinsp;1% across all conditions (n\u0026thinsp;=\u0026thinsp;34) were included in the heatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All remaining low-abundance genera were grouped into a collective category termed \u0026ldquo;others\u0026rdquo;. The complete list of detected organisms and their substrate-dependent abundances is provided in Supplementary Tab. S2.\u003c/p\u003e\u003cp\u003eSeveral low-abundance genera (\u0026lt;\u0026thinsp;5%), such as \u003cem\u003eCollinsella\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, and members of the Christensenellaceae or Erysipelotrichaceae families, remained stable across all substrate conditions. In contrast, pronounced shifts were observed among dominant taxa, particularly within the genera \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e, which together represented more than 32% of the initial community (15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5% and 17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9%). \u003cem\u003eFaecalibacterium\u003c/em\u003e showed the strongest decline under Western-style conditions with bile salts, decreasing from 11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4% to 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2% after 24 h. A comparable reduction occurred under substrate-free conditions, with a final abundance of only 5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2%, emphasizing the sensitivity of this genus to nutrient limitation and substrate composition. In contrast, the fiber-rich medium minimized these losses, preserving a post-incubation abundance of 8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1%, representing the most effective recovery among all tested conditions. With an initial abundance of 17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9%, the genus \u003cem\u003eBacteroides\u003c/em\u003e also underwent a significant decrease under substrate deprived conditions, reaching 9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1%. Species of this genus were also strongly affected by the presence of bile salts under Western-style conditions (5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5%), whereas the fiber-rich medium sustained an abundance of 15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3% after 24 h.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, the fiber-rich medium effectively mitigated the loss of key anaerobic commensals such as \u003cem\u003eAgathobacter\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, and \u003cem\u003eFusicatenibacter\u003c/em\u003e, while supporting the maintenance of several health-associated taxa. These included \u003cem\u003eSubdoligranulum\u003c/em\u003e (4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%), \u003cem\u003eBifidobacterium\u003c/em\u003e (3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%), and the \u003cem\u003eEubacterium hallii\u003c/em\u003e group (4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1%), which are all known producers of SCFAs implicated in gut homeostasis. The \u003cem\u003eEscherichia\u003c/em\u003e-\u003cem\u003eShigella\u003c/em\u003e group, typically detected at only 0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01% at baseline, expanded nearly 100-fold to 4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0% in the Western-style medium, representing the strongest increase among all taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Under these conditions, \u003cem\u003eFusobacterium\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e, both associated with inflammation and epithelial barrier dysfunction, also showed marked increases to 2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03% and 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings are consistent with previous reports linking Western dietary patterns to microbial imbalance and pro-inflammatory host responses (Statovci et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Christ et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eMegasphaera\u003c/em\u003e also expanded notably (1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1%), potentially reflecting its ability to thrive under altered nutrient availability and reduced competition from strict anaerobes. Detailed statistical evaluations are provided in the Supplementary Material S3. Compared to the Western died, the changes in the frequency of the genera mentioned was much smaller in the fiber-rich medium or no significant changes in abundance were found. Overall the fiber rich medium consistently resulted in the lowest average deviations across genera and preserved the initial community structure the best.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of enzymatic activity and fermentative capacity in the bacterial colon model\u003c/h2\u003e\u003cp\u003eBased on the primary objective of restoring metabolic functionality while preserving microbial community structure, subsequent analyses focused on key enzymatic and fermentative pathways involved in core microbial metabolism. As the fiber-rich substrate formulation was most effective in maintaining community composition of the bacterial colon model, this condition was selected to assess microbial enzymatic activity after 24 h. Enzymatic activities were compared to those of diluted fecal reference samples to determine the degree of functional similarity between the \u003cem\u003ein vitro\u003c/em\u003e model and the native gut microbiota. The analyses encompassed a representative panel of metabolic enzymes, including intracellular oxidoreductases (lactate dehydrogenase, malate dehydrogenase), hydrolases such as esterases, lipases, and proteases, as well as carbohydrate-active enzymes (α-glucosidase, β-galactosidase, β-xylosidase, cellulase, and xylanase). In addition, the main microbial fermentation products acetate, propionate, and butyrate were quantified to assess overall fermentative capacity and metabolic output (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe activities of key intracellular redox enzymes, lactate - and malate - dehydrogenase, were maintained or slightly elevated relative to fecal reference values (lactate DH 106\u003cb\u003e%\u003c/b\u003e; malate DH: 116\u003cb\u003e%\u003c/b\u003e), indicating preserved intracellular metabolism. The same was observed for the esterases catalyzing the hydrolysis of acetate and butyrate esters, with pNp-acetate and pNp-butyrate activities slightly exceeding the fecal reference values (105% and 113%, respectively). In contrast, enzymes associated with lipid and protein degradation exhibited slightly reduced values. Lipolytic activity, measured by pNp-palmitate hydrolysis, was decreased to 77%, and proteolytic activity, assessed by azocasein hydrolysis, showed activity levels at 82% of the fecal control. These reductions likely reflect not a general metabolic impairment but rather substrate-induced regulatory shifts. The absence of complex dietary lipids and proteins in the incubation medium may have downregulated lipase and protease expression, thereby redirecting the microbial metabolism toward carbohydrate utilization.\u003c/p\u003e\u003cp\u003eAmong glycoside hydrolases, a particularly consistent recovery was evident for enzymes mediating glycosidic bond cleavage. α-Glucosidase and β-galactosidase activities even exceeded fecal reference levels (102% and 113%, respectively), highlighting an efficient re-establishment of saccharolytic functionality. Enzymes involved in the breakdown of structurally complex polysaccharides showed a largely preserved activity profile relative to fecal reference levels. The α-amylase activity, determined with red starch as substrate, remained close to baseline at 93%, while xylanolytic activity showed only marginal reductions, reaching 98% and 89% of reference values with RBB- and Azo-xylan, respectively. In contrast, cellulase activity was significantly lower at 72% of the fecal control (p\u0026thinsp;\u0026le;\u0026thinsp;0.001), indicating a partial loss of cellulose-degrading capacity. Similar to lipases and proteases, this reduction likely reflects the absence of corresponding structural polysaccharides in the incubation medium, resulting in downregulation of the respective hydrolases. The SCFA production, as an indicator of microbial fermentation, remained stable across all three major end products (acetate, propionate and butyrate) with levels from around 100% in comparison to fecal samples.\u003c/p\u003e\u003cp\u003eTaken together, these results demonstrate that the bacterial colon model supports a robust bacterial metabolic activity closely reflecting that of the original fecal microbiota. Moreover, the data highlight the critical influence of substrate composition in shaping not only the taxonomic structure but also the functional capacity of the gut community under \u003cem\u003ein vitro\u003c/em\u003e conditions. The overall enzymatic potential remained high, indicating that key metabolic functions were largely preserved. This suggests that the model likewise retains the broad enzymatic repertoire required for more complex metabolic transformations, such as xenobiotic modification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSubstrate supplementation is essential to preserve physiological enzyme activities in the in vitro colon model\u003c/h2\u003e\u003cp\u003eTo assess the extent to which microbial functionality depends on substrate availability, the SCFAs concentrations were quantified in colon model samples following 24 h of incubation with and without supplementation of the fiber-rich substrate. The results clearly demonstrate that the presence of polysaccharides is critical for the production of the major fermentative end products (acetate, propionate and butyrate). SCFA levels increased by approximately one order of magnitude compared to the substrate-free control, and all differences were highly significant (**** p\u0026thinsp;\u0026le;\u0026thinsp;0.0001). These findings indicate that physiologically relevant metabolic activity in the colon model can only be sustained in the presence of digestible substrates. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven that washing effectively removes extracellular enzymes, further experiments focused on the model\u0026rsquo;s capacity to regain functional activity in response to substrate availability. Representative hydrolytic enzyme activities were quantified at baseline (T0) and after 24 hours of incubation with the fiber-rich substrate (T24) (Fig.\u0026nbsp;6). This targeted approach provides a direct evaluation of substrate-dependent metabolic adaptability, revealing the extent to which key enzymatic functions can be re-established \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter incubation, the colon model showed a significant reactivation of microbial enzymatic functions, indicating a shift from a metabolically suppressed to a physiologically active state. Compared to the baseline (T0), mean relative hydrolytic activities increased across all enzyme classes, ranging from marginal changes (\u0026lt;\u0026thinsp;1%) to more than a twofold enhancement (\u0026gt;\u0026thinsp;100%). The most significant changes were observed in enzymes associated with polymer and lipid degradation. Lipase activity showed the strongest response, increasing more than twofold to 233% of baseline levels, while proteolytic activity also increased to 159%. Esterase activities also showed significant upregulation, with pNp-acetate and pNp-butyrate hydrolysis reaching 132% and 119% of baseline values, respectively.Within the glycoside hydrolase group, β-galactosidase activity increased by approximately 50%, whereas α-glucosidase activity remained unchanged, consistent with the notion that some substrates are also accessible to intracellular enzymes, resulting in a lower apparent regeneration requirement. Enzymes involved in the breakdown of complex polysaccharides responded particularly strongly: cellulase activity nearly doubled to 197%, while xylanase activities increased by 45\u0026ndash;59% across both substrates tested. In contrast, α-amylase activity increased moderately to 136%. Taken together, these data demonstrate that the metabolic capacity of microbes in the colon model remained largely latent in the absence of environmental stimulation, but could be reactivated by providing the appropriate substrate. This emphasizes the vital role of nutrition in restoring physiologically relevant microbial activity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eFunctional validation of the bacterial colon model using microbial xenobiotic metabolism\u003c/h2\u003e\u003cp\u003eTo evaluate the metabolic capabilities of the human colon model presented here, a panel of structurally diverse xenobiotics was selected based on characterized microbial biotransformation pathways reported in the literature (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of six representative compounds covering four major reaction classes were included: ester hydrolysis, represented by 4-acetoxyacetanilide (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) (Kamberi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and roxatidine acetate (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA) (Zimmermann et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), sulfoxide reduction, using sulindac (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) (Lemmens et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), azoreduction, exemplified by sulfasalazine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) (Lima et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as well as nitroreduction using chloramphenicol (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB) (Crofts et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and nitrendipine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eD) (Vertzoni et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These compounds were chosen as established model substrates with known microbial conversion, thereby enabling a robust proof-of-concept evaluation across distinct enzymatic processes within the colon model. All compounds were incubated anaerobically for 24 h with three biological matrices, pooled human fecal slurry, the bacterial colon model, and the sterile-filtered fecal exoenzyme fraction (fraction 1), derived from three independent donor pools (n\u0026thinsp;=\u0026thinsp;3 each). The direct comparison with fecal incubations served to determine whether the colon model can reproduce physiologically relevant microbial biotransformation activities and thus be applied for toxicological testing.\u003c/p\u003e\u003cp\u003eAll investigated compounds remained chemically stable in buffer controls after 24 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Among the tested reactions, the ester hydrolysis of 4-acetoxyacetanilide proceeded most rapidly, resulting in complete compound depletion within 4 h in both the fecal slurry and the bacterial colon model. After 24 h, no residual parent compound (m/z 193.2, ESI⁺) was detectable, while the expected metabolite paracetamol (m/z 151.16, ESI⁺) was formed, confirming the predicted ester-cleavage pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBy contrast, substrates undergoing azo-, sulfoxide-, and nitroreduction showed slower conversion kinetics, with minor product formation detectable during the first 4 h of incubation. Significant degradation became apparent after 24 h, at which point the remaining parent compound concentrations in both fecal slurry and colon model had decreased to \u0026lt;\u0026thinsp;15 \u0026micro;M. Sulindac (m/z 356.41, ESI⁺) was reduced to its corresponding sulfide metabolite (m/z 340.41, ESI⁺), whereas sulfasalazine (m/z 398.41, ESI⁺) was cleaved into sulfapyridine (m/z 249.29, ESI⁺) and an additional metabolite at m/z 217.21, suggesting further reductive metabolism, possibly through desulfonation. Nitrendipine (m/z 360.36, ESI⁺) likewise underwent complete reduction, yielding the expected nitro-reduced product (m/z 330.14, ESI⁺, Cas. No. 138135-48-5). The data for the conversion of the other xenobiotics are shown in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThis close correlation in degradation rates and kinetic profiles between the fecal slurry and the colon model underscored the model\u0026rsquo;s robustness in reproducing the metabolic activity of native gut microbiota for toxicologically relevant biotransformation reactions. In contrast, the cell-free exoenzyme fraction showed activity only toward substrates undergoing ester hydrolysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). As this fraction contains naturally released extracellular enzymes, primarily esterases and glycosidases, it provides complementary information on transformation processes that occur independently of intracellular microbial metabolism.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe gastrointestinal tract is a dynamic interface between host and environment, colonized by a complex microbial community whose metabolic potential exceeds those of the human organism (Thursby and Juge \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Models for studying gut microbiota dynamics and metabolism can be broadly categorized into three main classes: (i) static batch fermentations, (ii) continuous bioreactor systems, and (iii) microfluidic or organ-on-a-chip platforms. Each model type addresses different experimental needs and provides distinct levels of physiological representation. Dynamic multistage bioreactor systems, such as the Simulator of the Human Intestinal Microbial Ecosystem (SHIME) (Van De Wiele et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the TNO Intestinal Model (TIM) (Minekus et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), the SIMulator of the GastroIntestinal tract (SIMGI) (Barroso et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the EnteroMix (M\u0026auml;kel\u0026auml;inen et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), are designed to closely mimic the physicochemical gradients of the human gut. Although they offer high physiological relevance and spatial resolution, their complexity, infrastructure requirements, long stabilization periods, and limited scalability make them less suitable for high-throughput applications or routine screening studies. Microfluidic and organ-on-a-chip platforms, such as the Human-Microbial Crosstalk (HuMiX) model (Shah et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the Human Oxygen-Bacteria Anaerobic (HoxBan) system (Sadaghian Sadabad et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the Host-Microbiota Interaction (HMI) model (Marzorati et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), allow controlled co-culture of gut epithelial cells and microbial communities. These systems provide valuable insights into host-microbe interactions, barrier integrity, and immunological signaling. However, simultaneously maintaining the aerobic requirements of the host epithelium and the strict anaerobic conditions required by gut microbes remains a major technical challenge. Furthermore, their complexity and low throughput limit their applicability to screening-based studies. Static batch fermentation systems constitute the most scalable and experimentally accessible model format. They are widely used for evaluating microbial transformations under anaerobic conditions, especially for studying specific metabolic activities or substrate conversion (Zimmermann et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shetty et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBut a fundamental limitation shared by all current \u003cem\u003ein vitro\u003c/em\u003e gut microbiota models is their reliance on either undefined fecal inocula (El Oufir et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Marzorati et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or simplified synthetic microbial communities composed of a limited number of strains (Goodman et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; van Leeuwen et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fecal samples represent a complex and undefined matrix containing not only a diverse bacterial population but also residual dietary material, host-derived cells, enzymes, and endogenous metabolites. This compositional complexity introduces substantial variability between preparations and complicates controlled experimental analyses, as test compounds may interact unpredictably with reactive components of the fecal matrix. The use of synthetic microbial consortia offers greater experimental standardization and reproducibility but tend to be limited in terms of taxonomic complexity. (Goodman et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Perez et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the use of fecal inocula, while taxonomically broad, often results in a loss of microbial diversity during prolonged incubation.\u003c/p\u003e\u003cp\u003eAlthough all existing gut microbiota models have their strengths and can be valuable tools in specific research contexts, their suitability must be critically evaluated in the context of the biological process under investigation. Especially in studies focusing on the microbial transformation of xenobiotics, it is essential that the models preserve both the full taxonomic and metabolic repertoire of the native gut microbiota while remaining compatible with high-throughput screening techniques. To overcome the limitations of existing \u003cem\u003ein vitro\u003c/em\u003e approaches, we developed a novel bacterial colon model based on a purified suspension of native gut bacteria. Our system is built on a stable, high-density microbial community that closely reproduces the taxonomic diversity and metabolic functionality of the human colonic microbiota. Amplicon sequencing identified more than 400 bacterial species, highlighting the model\u0026rsquo;s ability to preserve a complex and representative microbial consortium over 24 h in a physiologically relevant dimension. Importantly, the preparation was free of fecal matrix components, including host cells, food residues and reactive metabolites. A further advantage of this model is its modularity: microbiota preparations from different individuals can be combined to minimize stochastic effects and inter-individual outliers. However, the model is also fully compatible with single-donor preparations. This flexibility enables researchers to investigate donor-specific metabolic phenotypes if required. In addition, this platform supports the defined processing of microbial dietary components, supplements, pharmaceuticals and xenobiotics, unaffected by substrate competition. This high level of analytical control makes the model particularly suitable for mechanistic studies at the interface of diet, xenobiotic metabolism, and microbial ecology.\u003c/p\u003e\u003cp\u003eTo ensure the physiological relevance of functional investigations, it is essential that microbial population densities approximate those found in the human colon. Native colonic communities reach approximately 8.8 \u0026times; 10\u003csup\u003e10\u003c/sup\u003e cells mL\u003csup\u003e-1\u003c/sup\u003e (Sender et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), whereas many \u003cem\u003ein vitro\u003c/em\u003e gut models operate at substantially lower bacterial concentrations (Minnebo et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which can limit their capacity to reproduce \u003cem\u003ein vivo\u003c/em\u003e-like metabolic activity. The bacterial colon model developed in this study addresses this limitation by employing an inoculum standardized to 1.43 \u0026times; 10\u003csup\u003e10\u003c/sup\u003e cells mL\u003csup\u003e-1\u003c/sup\u003e, with scalability up to 4.3\u0026times; 10\u003csup\u003e10\u003c/sup\u003e cells mL\u003csup\u003e-1\u003c/sup\u003e. This corresponds to more than 50% of the \u003cem\u003ein vivo\u003c/em\u003e bacterial density and therefore provides a realistic basis for analyzing microbial metabolism and community-driven biotransformations. At the same time, the system remains compatible with high-throughput formats, as assay volumes can be reduced to 100 \u0026micro;L, enabling efficient screening of large xenobiotic panels in multiwell plates. To ensure physiologically meaningful functionality, the model supports the regeneration of enzymatic activity, thereby maintaining the native community structure during incubation. Within 24 h, microbial functions including glycosidase, protease, and esterase activities were re-established to levels comparable to those measured in fecal reference samples. This allows short-term incubations (\u0026le;\u0026thinsp;24 h) for the screening of bacterial transformations under stable compositional and biochemical conditions without the need for complex reactor systems or multi-day stabilization phases. In contrast, many established \u003cem\u003ein vitro\u003c/em\u003e platforms require continuous cultivation and extended adaptation periods to achieve stable microbial community structures. The presented results also demonstrate that the metabolic activity of the gut microbiota can be functionally restored under defined \u003cem\u003ein vitro\u003c/em\u003e conditions, provided that an appropriate substrate composition is selected. Comparative testing of different substrate formulations showed that fermentable carbohydrates are essential for restoring microbial metabolic functionality, yet the specific composition critically determines the extent of recovery. In particular, the fiber-rich formulation supported the most effective regeneration of enzymatic activities, preserved taxonomic stability, and enabled SCFA production at physiologically relevant levels. The recovered enzymatic activity in our bacterial colon model reflected those of fecal reference samples and fell within the physiologically range reported for native human feces. Literature values for key microbioal enzymes typically span approximately 60\u0026ndash;190 nmol min\u003csup\u003e-1\u003c/sup\u003e mg protein \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Flores et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Śliżewska et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), placing the activities observed in our system well within the expected biological variability. Similarly, the concentrations of SCFAs produced in the bacterial colon model with144.5 mmol kg feces\u003csup\u003e-1\u003c/sup\u003e for acetate, 38.3 mmol kg feces\u003csup\u003e-1\u003c/sup\u003e for propionate, and 30.7 mmol kg feces\u003csup\u003e-1\u003c/sup\u003e for butyrate, were consistent with reported physiological ranges in human feces (acetate: 12.8-103.4 mmol kg⁻\u0026sup1;; propionate: 4.5\u0026ndash;27.8 mmol kg⁻\u0026sup1;; butyrate: 4.0\u0026ndash;53.0 mmol kg⁻\u0026sup1;) (H\u0026oslash;verstad et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Notably, the proportional distribution of SCFAs in the model also mirrored the \u003cem\u003ein vivo\u003c/em\u003e profile, which is typically characterized by relative ratios of approximately 60:20:20 for acetate, propionate, and butyrate, respectively (Cummings et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Hijova and Chmelarova \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Binder \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to many conventional systems, which must limit glycerol concentrations to 10\u0026ndash;15% for cryopreservation (Bircher et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to avoid introducing excess glycerol at inoculation, the presented model overcomes this limitation by incorporating an washing step that removes the cryoprotectant prior to experimental application. This allows the application of cryopreservation protocols using glycerol concentrations of 25% or higher, which are known to markedly improve cell viability and long-term stability (Tutrina and Zhurilov \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The removal of glycerol is particularly relevant because even small residual glycerol concentrations, as low as 1 mM, were found in preliminary experiments (data not shown) to substantially alter microbial fermentation profiles. Since glycerol serves as a metabolizable carbon source for fast-growing facultative anaerobes such as \u003cem\u003eE. coli\u003c/em\u003e (Kopp et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), residual amounts can drive their selective overgrowth, thereby outcompeting slower-growing or more fastidious taxa. Such selective enrichment contributes to community imbalance and can fundamentally alter the system\u0026acute;s metabolic output (De Weirdt et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The presented bacterial colon model offers considerable potential as a next-generation platform for translational microbiome research. It becomes feasible to targeted comparisons between healthy and perturbed microbial communities, facilitating investigations into microbiome-associated disorders and their metabolic signatures. Currently, in-depth examinations of diet-microbiome interactions rely on \u003cem\u003ein vivo\u003c/em\u003e studies, which are time-consuming, costly, and limited experimental (Wu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; David et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, the colon model permits studies on the effects of dietary components and supplements on microbial composition and metabolic output within a short timeframe that reflects the physiological transit time of the human colon. While the current work intentionally isolates microbiota-driven processes to provide a clean mechanistic readout, the system can be incorporated into host-microbiome frameworks. Coupling the bacterial colon model with gut-on-chip devices or \u003cem\u003ein vivo\u003c/em\u003e mouse models would allow these platforms to benefit from its extensive taxonomic and functional diversity, thereby improving physiological fidelity in studies of host-microbial crosstalk. Beyond its applicability in mechanistic microbiome research, the presented model offers significant opportunities for the development of New Approach Methodologies (NAMs) aimed at reducing reliance on animal testing in toxicology. The system is also well suited to generate quantitative kinetic data for integration into physiologically based kinetic (PBK) models(Stevanoska et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although recent PBK frameworks have begun to incorporate microbial metabolism as a dedicated compartment, a major limitation remains the lack of standardized tools to characterize gut microbial transformation rates. By enabling controlled, high-resolution assessments of xenobiotic conversion, the colon model may help fill this gap and improve the predictive accuracy of \u003cem\u003ein silico\u003c/em\u003e toxicokinetic simulations. Taken together, this system addresses a critical methodological gap by combining physiological complexity with experimental standardization, thereby providing a scalable and analytically robust platform for studying microbiota-mediated processes at the interface of diet, xenobiotic metabolism and microbial ecology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACN, acetonitrile; BS, bile salts; BSA, bovine serum albumin; CTAB, cetyltrimethylammonium bromide; DNA, deoxyribonucleic acid; EDTA, ethylenediaminetetraacetic acid; EtOH, ethanol; HMI, Host-Microbiota Interaction model; HoxBan, Human Oxygen-Bacteria Anaerobic system; HPLC, high-performance liquid chromatography; HuMiX, Human-Microbial Crosstalk model; LC-MS, liquid chromatography-mass spectrometry; LDH, lactate dehydrogenase; LOD, limit of detection; LOQ, limit of quantification; MDH, malate dehydrogenase; m/z, mass-to-charge ratio; NADH, nicotinamide adenine dinucleotide; NAMs, New Approach Methodologies; OD\u003csub\u003e600\u003c/sub\u003e, optical density at 600 nm; PBK, physiologically based kinetic; PBS, phosphate-buffered saline; PCR, polymerase chain reaction; pNp, p-nitrophenyl; RBB, Remazol Brilliant Blue; RI, refractive index; SCFA, short-chain fatty acids; SHIME\u0026reg;, Simulator of the Human Intestinal Microbial Ecosystem; SIMGI, SIMulator of the GastroIntestinal tract; T0, baseline at 0 h; T24, time point after 24 h incubation; TCA, trichloroacetic acid; TIM, TNO Intestinal Model; UV, ultraviolet.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll participants provided informed consent to take part in the study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman fecal samples were collected from healthy adult volunteers in accordance with the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of the University of Bonn (approval no. DRKS00036882).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sequencing data and processed datasets supporting the findings of this study are provided in the Supplementary Material. Raw datasets are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.H. designed the study, performed the experiments, analyses, and data evaluation, and wrote the manuscript. J.-L.W. contributed to method development and experimental work. L.F. assisted with sample processing, LC-MS analysis and data interpretation. M.C.S. and W.S. performed 16S rRNA gene sequencing and scientific interpretation. J.-L.C.M.D. provided toxicological expertise and revised the manuscript. U.D. supervised the project, contributed to study design and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Natalie Thum-Schmitz for technical assistance and Dr. André Neff for support with experimental planning and manuscript proofreading. We are also grateful to Paula Ricarda Schrage for assistance with microscopic image acquisition. This study was supported by funding from the EFSA project ADME4NGRA (OC/EFSA/MESE/2022/04).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdelsalam NA, Ramadan AT, ElRakaiby MT, Aziz RK (2020) Toxicomicrobiomics: The Human Microbiome vs. Pharmaceutical, Dietary, and Environmental Xenobiotics. 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To enable standardized assessment of microbial xenobiotic metabolism, we established a defined bacterial colon model from pooled human fecal samples, by preparing clean bacterial cell suspensions using density gradient centrifugation. The bacterial cell fraction remained structurally intact, allowing long-term preservation at -70\u0026deg;C with glycerol as a cryoprotectant. The bacteria were fully reactivated without compromising cellular integrity. Glycerol as a disturbing substrate was removed by washing steps. The system preserved microbial diversity, enzymatic activity, and metabolic functionality over 24 hours under anaerobic conditions. Unlike fecal-based models, the model was free from non-cell contaminants, which could cause unpredictable interactions between test compounds and reactive components within the fecal matrix. Enzymatic assays demonstrated hydrolytic, reductive, and proteolytic activities comparable to native feces. 16S rRNA gene sequencing confirmed taxonomic stability and compositional shifts in response to different substrates. A fiber-rich substrate proved to be optimal for maintaining the bacterial composition over 24 hours. We further applied the model to investigate the microbial biotransformation of selected model xenobiotics using high-performance liquid chromatography and mass spectrometry, revealing substrate-specific metabolite formation. A versatile addition of food ingredients, dietary supplements, pharmaceuticals, and environmental chemicals is possible to analyze their effects on the composition of the microbiota, its enzymatic activity, and the excretion of metabolites and end products. Together, this bacterial colon model provides a reproducible, high-throughput capable and ethically viable platform for pharmacomicrobiomic studies and next-generation risk assessment.\u003c/p\u003e","manuscriptTitle":"Development of a gut microbiota model for the analysis of bacterial modifications of xenobiotics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 19:25:30","doi":"10.21203/rs.3.rs-8136690/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6466a44a-1523-4a6e-9d69-64dc6a6c8738","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T08:55:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 19:25:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8136690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8136690","identity":"rs-8136690","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}