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Article

Dietary Fibre Modulates Gut Microbiota Responses to Copper Nanoparticles

by
Bartosz Fotschki
1,*,
Dorota Napiórkowska
1,
Joanna Fotschki
1,
Kamil Myszczyński
1,
Ewelina Cholewińska
2,
Katarzyna Ognik
2 and
Jerzy Juśkiewicz
1
1
Division of Food Science, InLife Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, Trylińskiego 18, 10-683 Olsztyn, Poland
2
Department of Biochemistry and Toxicology, Faculty of Animal Sciences and Bioeconomy, University of Life Sciences in Lublin, Akademicka 13, 20-950 Lublin, Poland
*
Author to whom correspondence should be addressed.
Nutrients 2026, 18(5), 828; https://doi.org/10.3390/nu18050828
Submission received: 26 January 2026 / Revised: 20 February 2026 / Accepted: 2 March 2026 / Published: 3 March 2026
(This article belongs to the Special Issue The Gut Microbiota at the Crossroads of Chronic Disease: Biological Functions, Disruptions, and Innovations in Therapy)
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Abstract

Background/Objectives: Although copper nanoparticles (Cu-NPs) are increasingly explored as food and feed additives, there is still limited evidence on how the commonly consumed dietary fibre matrix modulates their effects on the gut microbiota. This study evaluated whether different dietary fibres (cellulose, pectin, inulin, psyllium) modulate Cu-NP–driven changes in caecal microbiota activity, composition, and bile acid metabolism in rats in a multifactorial design accounting for fibre type, copper dose, and copper form. Methods: Wistar male rats (n = 10 per group, 10 groups) were fed semi-purified diets for 6 weeks. Cu-NPs were provided at 6.5 or 13 mg Cu/kg diet and combined with cellulose (control fibre) or with pectin, inulin, or psyllium. Caecal digesta parameters, microbial enzyme activities, short-chain fatty acids (SCFAs), bile acids, and 16S rRNA sequencing were used to assess microbial diversity. Results: Final body weight did not differ among groups, whereas feed intake decreased most consistently with inulin and psyllium. Inulin and psyllium increased caecal digesta and tissue mass, while pectin increased caecal ammonia. Higher Cu-NPs dose reduced several microbial enzyme activities and lowered major SCFAs across most treatments; pectin most strongly preserved/enhanced glycosidase activities and was associated with increased SCFA levels vs. control, with a 32% rise in acetate, a 47% rise in propionate, and a 61% rise in butyrate. Fibre type dominated bile acid outcomes: psyllium reduced total bile acids by 11.8% vs. control, while inulin increased muricholic acids by 216% vs. control. Microbiota alpha and beta diversity separated primarily by fibre type, with distinct clustering particularly in pectin-fed groups. Across comparisons, Mucispirillum was consistently reduced in fibre-supplemented groups vs. cellulose, alongside recurrent changes in selected genera; functional profiling highlighted shared shifts in carbohydrate, fermentation, transport, and stress-response features under Cu-NPs exposure. Conclusions: The gastrointestinal and microbiota responses to Cu-NPs are strongly fibre-dependent; thus, Cu-NP safety and functionality should be evaluated together with the accompanying dietary fibre matrix, not as a standalone exposure. Implications for humans remain indirect and require confirmation in human-relevant models and clinical settings.

1. Introduction

There is growing interest in applying nanotechnology to develop new food ingredients. Recent research indicates that consuming nanocompounds in the diet offers potential health benefits and risks. On the positive side, nanotechnology in food can greatly improve the bioavailability and absorption of essential minerals and vitamins, which are vital for tackling global nutritional deficiencies [1]. On the other hand, concerns are rising about the possible toxicity of nanoparticles (NPs) used in food, which may cause systemic toxicity and intestinal dysfunction [2,3]. Beyond nutrient delivery, nanocompounds can improve food quality and functionality by enhancing the solubility of poorly water-soluble ingredients, protecting sensitive compounds from degradation, and enabling texture and shelf-life improvements; however, these benefits must be weighed against uncertainties regarding how nanoparticle properties and transformations during digestion may affect biological responses after ingestion [3].
In recent years, particular attention has been paid to the health benefits of nano copper. Studies have shown that these nanoparticles may promote beneficial effects on the organism by improving antioxidant capacity [4], supporting immune responses [5] and exhibiting antibacterial activity [6]. Nevertheless, there is little information on the impact of nano copper as a dietary component on the gut microbiome’s activity and profile. The interaction of NPs with the intestinal microbiome is particularly concerning because it could contribute to many severe disorders [2]; therefore, there is a need to accurately assess the potential impact of nano copper on the gut microbiome. On the other hand, there is also a need to investigate how the complex gastrointestinal milieu, including common dietary components such as fibres naturally present in foods, can modify nanoparticle behaviour and thereby modulate the impact of nano copper on the gut microbiome.
Most of the research on how copper in the diet affects the gastrointestinal microbiome focuses on traditional forms of these molecules. However, reducing the size to the nanoscale greatly increases the surface area of copper, which may significantly influence the functioning of the gastrointestinal tract, including the microbiome. In particular, nanoscale copper may differ from conventional copper salts in dissolution kinetics and local ionic Cu release, and it may interact more readily with microbial cell surfaces, which together can shape microbial growth and metabolic outputs such as SCFA production. A nutritional in vivo study indicated that copper-loaded chitosan nanoparticles might positively affect gut microbiota composition, promoting beneficial microbial growth while suppressing harmful bacteria [7].
Other studies showed that dietary exposure to the traditional form of copper may have different effects on the gastrointestinal tract. It was shown that higher levels of this compound can influence gut bacteria balance by exerting toxic effects, disrupting the gut microbiota’s composition and function [8]. The study indicates that high dietary copper levels significantly alter microbial richness and diversity in the faeces of Sprague-Dawley rats. This change in gut microbiota composition correlated with increased serum levels of inflammatory cytokines, suggesting that excessive copper exposure may induce an inflammatory response [9]. Moreover, high copper levels have been shown to reduce the abundance of butyrate-producing bacteria, which are very important for the proper functioning of colonocytes [10].
Beyond compositional changes, diet-induced modulation of the gut microbiota also affects its metabolic activity, particularly the production of short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate, which are key microbial metabolites derived from dietary fibre fermentation. SCFAs play a crucial role in maintaining intestinal homeostasis by serving as an energy source for colonocytes, strengthening the gut barrier, and regulating inflammatory and immune responses [11]. Importantly, copper-induced alterations in gut microbiota composition, including a reduced abundance of butyrate-producing bacteria, may lead to impaired SCFA production and disturbed intestinal function, thereby linking microbial metabolic activity with host metabolic and inflammatory pathways [12,13]. One of the main ways copper affects the gut microbiota is associated with modification of metabolic functions, including energy metabolism and amino acid biosynthesis [8]. This bidirectional relationship between copper and gut microbiota highlights the complex interactions that can influence health outcomes. Nevertheless, to avoid undesirable effects on the consumer’s body, attention should be paid to the dose of copper nanoparticles in the diet. Additionally, a greater surface area of nanoparticles interacting with the body can lead to potential adverse effects. As a result, regulating their activity alongside other dietary factors that might influence their bioavailability and bioactivity might be essential.
Dietary fibres are a diet component that impacts gut microbiota activity and may influence copper compounds’ interaction in the gastrointestinal tract. Mechanistically, fibres differ in properties that can shape Cu-NP bioactivity in the gut. Fermentable fibres (e.g., inulin, pectin) modulate microbial carbohydrate metabolism and SCFA production, while viscous, gel-forming fibres (e.g., psyllium) can alter digesta rheology and transit, affecting nanoparticle dispersion and microbial exposure [14]. Moreover, fibre-specific ion-binding/chelating capacity may reduce luminal copper availability, and bile acid binding may modify bile acid pools and microbial bile acid transformations, linking fibre properties to the endpoints measured in this study [14]. Studies in Wistar rats have shown that the biological effects of dietary copper nanoparticles can be modified by the type of dietary fibre, as reflected by fibre-dependent differences in small-intestinal responses (apoptosis and DNA repair/redox pathways) and hepatic metabolic outcomes [15,16]. Furthermore, studies on rats fed a diet containing nano copper and different sources of fibre showed a significant reduction in the enzymatic activity of the microbiota in faeces [17]. These changes in the enzymatic activity of the gut microbiota may indirectly affect many important mechanisms responsible for the proper functioning of the digestive system. One of the key mechanisms is the metabolism of bile acids by the gut microbiota. Bile acids represent one of the most important interfaces between gut microbiota and host metabolism, serving dual functions as digestive detergents and potent signalling molecules that regulate whole-body energy homeostasis [18,19]. These cholesterol-derived molecules undergo extensive transformation by gut microorganisms, creating a diverse pool of primary and secondary bile acids with distinct receptor affinities and physiological effects [20]. The intricate relationship between bile acid metabolism and gut microbiota has emerged as a critical determinant of metabolic health, immune function, and digestive physiology.
Observing the growing interest in the use of copper nanoparticles as a novel food additive and their significant impact on the gastrointestinal microbiota, there is a need to analyse not only the dosage of the nanoparticles but also the potential food matrix that may modulate the activity of these particles. We hypothesised that fibre type determines the gut response to copper nanoparticles by modulating microbial enzymatic activity, SCFA production, microbiota composition, and bile acid pools and transformations. Therefore, the present study was designed to investigate the effects of combining various dietary fibres (inulin, psyllium, pectin, and cellulose) with copper nanoparticles on the composition and activity of the gut microbiota in Wistar rats. The results aim to provide a better understanding of how the interaction between fibres and copper at the nanoscale may influence gut microbial functions relevant to host metabolic health.

2. Materials and Methods

2.1. Dietary Fibres and Copper Nanoparticles

Copper nanoparticles (Cu-NPs; 99.9% purity powder, 40–60 nm size, 12 m2/g specific surface area (SSA), spherical morphology, 0.19 g/cm3 bulk density, 8.9 g/cm3 true density) were obtained from Sky Spring Nanomaterials Inc. (Houston, TX, USA). The physicochemical characteristics were provided by the manufacturer and are consistent with those reported previously [15]. To minimise particle agglomeration and oxidative degradation during diet preparation, Cu-NPs were handled as a dry powder only briefly during weighing and were immediately dispersed in rapeseed oil and incorporated into the diet as an emulsion. Copper carbonate (CuCO3) used in the control diets was purchased from Merck KGaA (Darmstadt, Germany), and α-cellulose was obtained from Sigma (Poznań, Poland). Dietary fibre sources used for formulation of the experimental diets included pectin (Pectin E440(i); Brouwland, Beverlo, Belgium), inulin (Frutafit Tex; Sensus, Roosendaal, The Netherlands), and psyllium husk powder (NaturaleBio, Rome, Italy).

2.2. In Vivo Experiment

The experimental protocol, including the research objectives, in vivo study design, and analytical plan, was submitted to the National Science Centre (Kraków, Poland) and subsequently approved and funded.
The study was conducted using 100 nine-week-old outbred male Wistar rats (Cmdb:Wi) obtained from the Medical University of Bialystok (Bialystok, Poland). Following a two-week acclimatization period, animals were randomly allocated into 10 experimental groups (n = 10 per group) using a random number generator in Microsoft Excel (version 15.0.5589.1000). The sample size (n = 10 per group) was determined a priori based on power analysis. Specifically, an a priori power calculation was performed for the primary outcomes (e.g., total SCFAs/total bile acids/selected microbial endpoints) using G*Power (version 3.1.9.7), assuming α = 0.05 and power (1 − β) = 0.80. The expected effect size was estimated from our previous work using the same model and endpoints [15,16] (effect size parameter: Cohen’s f = 0.30), which indicated that at least 9 animals per group were required; therefore, n = 10 was used to ensure adequate power while adhering to the 3R principles. Rats were housed individually in metabolic cages (Tecniplast, Cat. No. 3700M081; Tecniplast S.p.A., Buguggiate, Italy) with a transparent polycarbonate upper chamber and stainless-steel components; cage dimensions were 31.75 × 30.48 × 33.02 cm (length × width × height) and maintained under controlled environmental conditions, including a 12 h light–dark cycle, ambient temperature of 21 ± 1 °C, relative humidity of 50–70%, and 20 air exchanges per hour. Throughout the six-week experimental period, animals had ad libitum access to tap water and modified semipurified casein-based diets formulated according to the recommendations of the American Institute of Nutrition for laboratory rodents. The intervention period was chosen because it is sufficient to detect measurable changes in gastrointestinal microbiota activity and caecal parameters in this rat model. Diets were weighed daily and provided fresh in the morning (between 08:00 and 12:00) throughout the intervention. Feed intake was assessed based on the daily difference between the offered and remaining diet. The experimental design included two dietary levels of copper nanoparticles (Cu-NPs), corresponding to the recommended dose (6.5 mg/kg diet) and a double dose (13 mg/kg diet), combined with different sources of dietary fibre. Control diets contained a mineral mixture with CuCO3 at standard and elevated levels (6.5 and 13 mg/kg diet), whereas diets supplemented with Cu-NPs were prepared using a mineral mixture without CuCO3. α-Cellulose was used as the control fibre source at 8% of the diet, while experimental fibre sources, including inulin with a prebiotic effect, psyllium with a bulking effect, and pectin with a viscous effect, were incorporated at 6% of the diet by partial replacement of cellulose. To limit confounding by total fibre level, the overall added fibre fraction was kept constant at 8% (w/w) across diets. In the fibre intervention diets, 6% of cellulose was replaced with the test fibre while 2% cellulose was retained (Table 1), so that differences primarily reflect fibre type–specific physicochemical properties rather than differences in total fibre content. This level is commonly used in rodent nutritional studies to elicit measurable microbiota and caecal fermentation effects without compromising diet formulation or palatability. Copper nanoparticles were dispersed in oil prior to diet preparation. Briefly, the required amount of Cu-NPs was first pre-dispersed in a small aliquot of rapeseed oil and then mixed thoroughly with the remaining oil fraction to obtain a uniform emulsion prior to incorporation into the diet. This approach was used to minimise particle agglomeration and oxidative degradation during diet preparation. Diets were stored at −70 °C until use and provided fresh daily; no visible phase separation or sedimentation of the oil fraction was observed under routine preparation and handling conditions. The applied Cu-NP doses (6.5 and 13 mg Cu/kg diet) corresponded to the recommended dietary copper level for laboratory rodents and its two-fold value, enabling evaluation of both nutritionally relevant exposure and a higher ‘worst-case’ scenario while maintaining the same copper amount across copper sources. The detailed composition of the experimental diets is provided in Table 1. All procedures involving animals were performed in accordance with the European Union Directive 2010/63/EU for the protection of animals used for scientific purposes and were approved by the Local Institutional Animal Care and Use Committee in Olsztyn, Poland (approval no. 19/2021, 17 March 2021). The study was conducted in compliance with the ARRIVE guidelines. Humane endpoints were predefined, and in cases of severe adverse symptoms, including prolonged cessation of food intake, signs of pain or neurological impairment, or persistent gastrointestinal bleeding, humane euthanasia was performed by a trained veterinarian using carbon dioxide inhalation or cervical dislocation under prior sedation. Based on current evidence from the literature and our prior experimental experience, the occurrence of these symptoms is unlikely to be associated with the applied dietary fibres or copper nanoparticle supplementation, and the overall risk was considered low.

2.3. Collection of Biological Material

No exclusions occurred, as none of the predefined humane endpoints were met during the experiment. Allocation to experimental groups was known only to the project coordinator, while personnel involved in sample analysis were blinded to the treatment assignments. Throughout the experimental period, feed intake and body weight were recorded regularly. After six weeks of dietary intervention, animals were anaesthetised using a ketamine and xylazine mixture administered at doses of 100 mg/kg and 10 mg/kg body weight, respectively. To minimise circadian variability, euthanasia and sample collection were performed during the light phase, in the morning hours (between 08:00 and 12:00). Animals were fasted for 8 h prior to sacrifice, with free access to water. The caecum was excised, and caecal digesta was collected, thoroughly mixed, and aliquoted (approximately 0.2 g per tube). Aliquots intended for microbiota sequencing were placed into sterile microcentrifuge tubes and stored at −80 °C. Additional aliquots for SCFA and bile acid analyses were snap-frozen in liquid nitrogen and stored at −80 °C until extraction.

2.4. Caecal Microbial Enzymatic Activity, SCFAs and Bile Acids

Caecal fermentation activity was further evaluated by measuring the activity of selected bacterial enzymes, including α-galactosidase, α-glucosidase, β-galactosidase, β-glucosidase, and β-glucuronidase. The selected enzymatic markers represent complementary aspects of microbial metabolism relevant to dietary fibre-driven fermentation and host–microbiota interactions. α-/β-glycosidases reflect saccharolytic capacity and the microbial ability to degrade complex carbohydrates released from dietary fibres, whereas β-glucuronidase is commonly used as an indicator of microbial deconjugation activity and potential generation of bioactive/toxic aglycones. Enzyme activities were normalised to caecal digesta mass (µmol product·h−1·g−1 wet digesta) to reflect functional activity in the luminal ecosystem and to enable comparison across fibre treatments that alter caecal bulk. Enzyme activities were determined in caecal supernatants based on the release of p- and o-nitrophenols from the corresponding nitrophenyl glycoside substrates, according to a previously described method [17]. Caecal digesta samples were centrifuged at 7211× g for 15 min, and the resulting supernatants were diluted 1:10 (v/v) with 100 mM phosphate buffer (pH 7.0). The reaction mixture consisted of 0.3 mL of substrate solution (5 mM) and 0.2 mL of the diluted sample. Incubation was carried out at 37 °C, and the enzymatic reaction was terminated by the addition of 2.5 mL of ice-cold 0.25 M sodium carbonate. Absorbance was measured at 400 nm for p-nitrophenol and at 420 nm for o-nitrophenol. Enzyme activities were expressed as micromoles of product formed per hour per gram of wet caecal digesta.
SCFAs, including acetic, propionic, butyric, isobutyric, valeric, and isovaleric acids, were quantified in caecal digesta using a gas chromatography–mass spectrometry (GC-MS) method based on that described by Yao et al. (2022) [21], with minor modifications. Briefly, 0.1 g of caecal digesta was homogenised with 24 µL of 2 N HCl and 976 µL of deionized water. After vortexing, samples were centrifuged at 10,000× g for 10 min at 4 °C. Subsequently, 200 µL of the supernatant was collected and mixed with 360 µL of cold methyl tert-butyl ether (MTBE), vortexed for 15 s, and centrifuged again under the same conditions. Approximately 60 µL of the upper organic phase was collected and stored at −20 °C until analysis. GC–MS analysis was performed using a Shimadzu Nexis GC-2030 system coupled to a GCMS-QP2020NX mass spectrometer (Shimadzu, Kyoto, Japan) equipped with an SH-Polar WAX MS column (30 m × 0.25 mm × 0.25 µm). Samples were injected at a volume of 1 µL using a split ratio of 5:1, with the injector temperature set at 240 °C. The oven temperature programme was as follows: initial temperature of 100 °C held for 0.5 min, increased to 175 °C at 10 °C/min, then raised to 240 °C at 40 °C/min and held for 3 min, resulting in a total run time of 12.7 min. Helium was used as the carrier gas at a flow rate of 1.2 mL/min. Detection was carried out in single-ion monitoring (SIM) mode under electron-impact ionisation. Pure SCFA standards were obtained from Sigma (Poznań, Poland), and calibration curves were prepared using standard mixtures of the individual acids. The concentration of putrefactive SCFAs (PSCFAs) was calculated as the sum of isobutyric, isovaleric, and valeric acids. SCFA concentrations were expressed as micromoles per gram of wet caecal digesta.
Bile acids (BAs) in wet caecal digesta were analysed using the liquid chromatography–mass spectrometry (LC–MS) method based on that described by Fotschki et al. (2017) [22]. Separation was performed on a Shimadzu liquid chromatography system coupled to an LCMS-2020 mass spectrometer (Shimadzu, Kyoto, Japan) using an ACE C18-amide column (75 mm × 2.1 mm, 2.7 µm; Avantor, Radnor, PA, USA). The mobile phases consisted of 0.01% (v/v) formic acid in water (phase A) and a methanol–acetonitrile mixture (1:9, v/v; phase B). The column temperature was maintained at 45 °C, and the flow rate was set to 0.35 mL/min. The gradient elution program was as follows: 40% B from 0 to 8 min, increased to 50% B from 8 to 14 min, ramped from 50% to 100% B between 14 and 15.3 min, and returned to 40% B from 15.3 to 17.4 min. The injection volume was 1 µL. Data acquisition was performed using LabSolutions software (version 5.109; Shimadzu). Quantification was achieved using external calibration curves constructed with bile acid standards in the concentration range of 0.01–1 µM, yielding correlation coefficients between 0.994 and 0.997. All analyses were performed in triplicate for each sample.
Internal standards for SCFAs and BAs were not applied because they were analysed by GC-MS and LC-MS, where matrix effects are typically limited and retention time/ion fragmentation patterns provide robust selectivity, and all samples were processed using the same extraction protocol, analysed within the same analytical batches, and quantified against multipoint calibration curves with frequent re-calibration and QC checks, ensuring adequate accuracy and repeatability for between-groups comparisons in a uniform matrix.
Method performance was verified using standard solutions and matrix-spiked caecal digesta extracts. For SCFAs (GC–MS, SIM), the typical LOD and LOQ were 0.01 and 0.03 µmol/g wet digesta, respectively, with mean recoveries of 90–110% and precision expressed as CV of ≤7% (intraday) and ≤10% (interday). For bile acids (LC–MS), the typical LOD and LOQ were 0.001 and 0.003 µmol/g wet digesta, respectively, with mean recoveries of 85–115% and precision of ≤10%. Calibration curves were linear over the applied ranges (R = 0.994–0.997).

2.5. Caecal Bacterial Sequencing

The composition and diversity of the caecal microbiota were analysed using next-generation sequencing (NGS) targeting the bacterial 16S rRNA gene based on the methodology described in our previous work [23]. Caecal digesta samples were collected into sterile microcentrifuge tubes and stored at −80 °C until further processing. Genomic DNA was isolated using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany) in accordance with the manufacturer’s protocol. Microbial community profiling was performed by amplification of the V3–V4 hypervariable regions of the 16S rRNA gene using primers and procedures recommended in the Illumina 16S Metagenomic Sequencing Library Preparation guidelines. Sequencing was carried out on an Illumina MiSeq platform (San Diego, CA, USA) using a paired-end approach (2 × 250 bp). To facilitate biological interpretation and reduce redundancy, downstream taxonomic and functional analyses were restricted to bacterial genera and predicted genes that showed consistent and recurrent changes across the majority of fibre- and copper nanoparticle-related comparisons. This targeted approach was applied to minimise stochastic variation inherent to 16S rRNA-based datasets and to highlight reproducible microbiota features associated with fibre–nanoparticle interactions. Details of the bioinformatics and statistical analyses are provided in Supplementary File S1, which also reports sequencing quality-control criteria (negative controls, minimum read thresholds, and contaminant filtering), as well as the details and limitations of 16S-based functional prediction.

2.6. Statistical Analysis

Statistical analyses were performed using Statistica software (version 12.0; StatSoft Corp., Kraków, Poland). Data were evaluated using two-way analysis of variance (ANOVA) and Student’s t-test, as appropriate. Individual experimental groups (CN, PN, JN, and SN) were compared with the corresponding control group (C) using the t-test. Likewise, the CNH, PNH, JNH, and SNH groups were analysed in comparison with the CH control group. To evaluate the effects of copper nanoparticle supplementation, two-way ANOVA was applied to all Cu-NP-treated groups (CN, CNH, PN, PNH, JN, JNH, SN, and SNH). The model included copper nanoparticle dose (D; low, 6.5 mg/kg diet, and high, 13 mg/kg diet) and dietary fibre type (F; cellulose, pectin, inulin, and psyllium) as main factors, as well as their interaction (D × F). When significant effects were detected, differences between group means were identified using Duncan’s multiple range test. This post hoc procedure was selected to maintain sensitivity for detecting diet-related differences across multiple intervention groups, particularly in the presence of interactions. Prior to statistical analysis, data were assessed for normality of distribution. The normality of residuals was assessed using the Shapiro–Wilk test, and homogeneity of variance was verified using Brown–Forsythe test. A p-value of less than 0.05 was considered statistically significant. For analyses involving multiple simultaneous tests (e.g., microbiome taxa and predicted functions), p-values were adjusted using the Benjamini–Hochberg false discovery rate (FDR), with q < 0.05 considered significant. Sample size was determined a priori by power analysis (α = 0.05; power = 0.80) for predefined primary outcomes (see Section 2.2). For microbiome data, β-diversity was evaluated using distance matrices and tested by PERMANOVA, with pairwise post hoc comparisons where applicable; detailed procedures are provided in the Supplementary File S1.

3. Results

The final body weight and body weight gain did not differ among groups (p > 0.05; Figure 1). Two-way ANOVA analysis showed that the highest daily feed intake was observed in the CN and CNH rats (p < 0.05 vs. all dietary groups with inulin and psyllium), while the lowest dietary daily intake was noted in both groups fed diets with inulin (p < 0.05 vs. CN, CNH, PN, PNH). Regardless of the Cu-NP dose, adding inulin and psyllium to the diet significantly increased the relative weight of caecal digesta and tissue compared to treatments with cellulose and pectin (p < 0.05; Figure 2). Additionally, the inulin treatment also exceeded the psyllium one concerning caecal digesta and tissue weights (p < 0.05).
The highest ammonia content in the caecal digesta was noted in rats fed diets with pectin, irrespective of Cu-NP dose (p < 0.05 vs. all other fibre treatments). The lowest ammonia level in the caecum was noted in the cellulose and inulin treatments (p < 0.05 vs. pectin and psyllium). Student’s t-test showed that the groups fed diets with inulin and psyllium had increased (p < 0.05) caecal digesta and tissue weights as compared to their control C and CH counterparts (rats fed diets with CuCO3 and cellulose). The caecal digesta pH value was higher in the CN than in the C rats (p < 0.05; t-test) and in the SNH than in the CH rats (p < 0.05; t-test). In the CN and JN groups, the caecal ammonia was lower than in the control C rats (p < 0.05; t-test), while both pectin groups (PN and PNH) were characterised by increased ammonia level in the caecum (p < 0.05 vs. C and CH, respectively).
The two-way ANOVA revealed that all bioactive fibres (pectin, inulin, psyllium) enhanced the extracellular activity of bacterial caecal α-glucosidase compared to cellulose, irrespective of Cu-NP dose (p < 0.05; Figure 3). The highest activity of that enzyme was noted in the inulin dietary treatment (p < 0.05 vs. all other fibre types). Regardless of Cu-NP dose, the highest and the lowest activity of the extracellular β-glucosidase was observed in the pectin and inulin treatments, respectively (in both cases, p < 0.05 vs. other treatments). The t-test showed enhanced caecal β-glucosidase activity in the PN rats (p < 0.05 vs. C). Both inulin groups (JN, JNH) had lowered β-glucosidase activity compared to their control counterparts (p < 0.05 vs. C and CH, respectively).
Regardless of Cu-NP dose, the pectin and psyllium dietary additions caused a significant increase in the extracellular activity of bacterial α-galactosidase in the caecal digesta as compared to the treatments with cellulose and inulin (p < 0.05). The CNH rats had lower α-galactosidase activity than the CH rats (p < 0.05; t-test). The t-test showed higher α-galactosidase activity in the PN and SN groups than in the control C group (p < 0.05). The highest caecal activity of extracellular β-galactosidase was noted in rats fed diets with pectin addition (p < 0.05 vs. remaining groups). A fibre-by-Cu-NP dose interaction showed that the PN addition group had higher β-galactosidase activity than the PNH group (p < 0.05). The t-test showed lowered β-galactosidase activity in the CN group (vs. C) and increased activity of that enzyme in both pectin groups (PN vs. C and PNH vs. CH).
The two-way ANOVA revealed that dietary addition of pectin enhanced, while inulin and psyllium diminished, the caecal activity of bacterial β-glucuronidase compared to cellulose (p < 0.05). The PN group had higher β-glucuronidase activity than the control C group, while the JN, JNH, and SNH groups were characterised by lower activity of β-glucuronidase vs. their control counterparts (p < 0.05). Regardless of the Cu-NP dose, the dietary addition of pectin and psyllium enhanced the activity of bacterial α-arabinopyranosidase and α-arabinofuranosidase in the caecal digesta compared to the treatments with cellulose and inulin (p < 0.05). It should be noted that the highest activity of those enzymes was observed in the pectin treatment (p < 0.05 vs. other fibre treatments). The t-test showed that both pectin groups exceeded their control counterparts with regard to α-arabinopyranosidase and α-arabinofuranosidase activities. Additionally, the SN and SNH groups had higher α-arabinopyranosidase activity than the groups C and CH, respectively (p < 0.05; t-test). In the case of JNH rats, they had lower α-arabinopyranosidase activity in comparison to the control C rats (p < 0.05; t-test).
The caecal activity of bacterial α-rhamnosidase was the highest in the pectin treatment, while it was the lowest in the cellulose treatment (in both cases, p < 0.05 vs. remaining treatments). The t-test showed that all three bioactive fibres enhanced α-rhamnosidase activity in the caecal digesta compared to their respective control counterparts (p < 0.05). The two-way ANOVA showed that irrespective of the nanoparticle dose, all fibre treatments differed statistically from each other regarding β-mannosidase activity, and the relationships were as follows: pectin > inulin > psyllium > cellulose (in all cases p < 0.05 vs. other treatments). The CN and CNH groups had lower β-mannosidase activity than the control groups C and CH, respectively. The addition of bioactive fibre caused a significant increase in β-mannosidase activity compared to their C or CH counterparts, except for in the SN group (t-test). Regardless of the Cu-NP dose, the psyllium treatment decreased the caecal activity of bacterial α-cellobiosidase vs. cellulose and pectin treatments (p < 0.05). The two-way ANOVA showed that the dietary application of a higher dose of Cu-NP caused a significant decrease in α-glucosidase, β-galactosidase, β-glucuronidase, α-rhamnosidase, and β-mannosidase activities in comparison to the treatment with a lower Cu-NP dose (p < 0.05).
Irrespective of the Cu-NP dose, the highest and the lowest acetic acid concentration in the caecal digesta was noted in the pectin and the inulin fibre treatments, respectively (in both cases, p < 0.05 vs. other treatments; Figure 4). The same differences were noted in the total SCFA concentration in the caecal digesta. The highest and the lowest caecal concentration of propionic acid followed the pectin and the cellulose treatments, regardless of the Cu-NP dose (p < 0.05 vs. other treatments). The pectin fibre treatments excelled other treatments with regard to iso-butyric and total PSCFA concentrations (p < 0.05). In comparison to the control cellulose fibre treatment, pectin and psyllium enhanced, whereas inulin diminished, the caecal concentration of butyric acid (p < 0.05). Moreover, the pectin treatment significantly exceeded the other ones in this respect. The highest iso-valeric acid concentration in the caecal digesta followed the pectin treatment (p < 0.05 vs. psyllium). The pectin fibre treatment significantly increased and the inulin and psyllium treatments significantly decreased the caecal concentration of valeric acid compared to the cellulose control.
The two-way ANOVA showed that, regardless of the fibre type, the doubled dietary Cu-NP dose caused a significant decrease in the caecal concentration of total and three major SCFAs, i.e., acetic, propionic and butyric acids (p < 0.05 vs. lower Cu-NP dose). The t-test showed that all groups fed diets with Cu-NP, except both pectin groups, exhibited a decreased caecal concentration of total SCFAs and acetic acid (p < 0.05 vs. respective controls without Cu-NP, i.e., C and CH groups). The CN, JN, and SN groups had significantly lower propionic acid level in the caecal digesta than the control C group (t-test). Similarly, the CNH treatment caused a decrease in propionic acid level as compared to the CH group (p < 0.05; t-test). The caecal level of butyric acid was diminished in all groups with dietary copper nanoparticles as compared to their respective controls with CuCO3 (in all cases p < 0.05; t-test). The total PSCFA concentration in the caecal digesta was decreased in the SNH rats vs. the CH group (p < 0.05; t-test).
Regardless of the Cu-NP dose, the highest caecal concentrations of total bile acids and αMCA acid were observed in the pectin fibre treatment (p < 0.05 vs. cellulose and psyllium; Figure 5). The lowest total bile acids and αMCA caecal levels were noted in the psyllium treatment (p < 0.05 vs. pectin and inulin). Irrespective of the Cu-NP dose, the inulin fibre treatment was characterised by the highest level of ωMCA and the lowest level of HDCA in the caecal digesta (in both cases, p < 0.05 vs. remaining fibre treatments). In the case of caecal CDCA level, a significant D × F interaction revealed its highest level in the CN rats (p < 0.05 vs. other groups), while its lowest level was noted in the SN animals (p < 0.05 vs. CN, JN, PNH groups). Regardless of the Cu-NP dose, the pectin treatment increased the caecal DCA concentration in comparison to the cellulose and inulin treatments (p < 0.05). The highest UDCA level was noted in the cellulose treatment (p < 0.05 vs. inulin and psyllium), while the lowest level of that bile acid in the caecal digesta was observed in the psyllium treatment (p < 0.05 vs. cellulose and pectin).
Irrespective of dietary fibre type, an increased Cu-NP dose caused elevated levels of caecal cholic acid (p < 0.05). The t-test showed that the SN rats had significantly decreased caecal levels of αMCA, CDCA, UDCA, and total bile acids compared to the C control group. The SNH group was characterised by a decreased caecal content of HDCA, LCA, and UDCA vs. the control CH group (p < 0.05; t-test). Feeding rats with a JNH diet caused elevated αMCA level in the caecum (p < 0.05 vs. control CH; t-test). Both inulin groups (JN, JNH) had increased ωMCA and decreased HDCA and DCA concentrations in the caecum compared to their control counterparts (p < 0.05 vs. C, CH; t-test).
Alpha diversity of the caecal microbiota, expressed as the Shannon index, was significantly affected by dietary fibre type in rats receiving copper nanoparticles (p < 0.05; Figure 6A). Pectin supplementation induced the most pronounced changes in alpha diversity relative to cellulose-based diets, particularly at the higher Cu-NP dose, whereas inulin caused moderate but less consistent effects. Psyllium supplementation resulted in comparatively smaller or more variable changes in alpha diversity. Consistently, beta diversity analysis based on Bray–Curtis distances revealed a clear separation of microbial community structures according to dietary fibre type (p < 0.05; Figure 6B), with pectin-fed groups forming distinct clusters and inulin-fed groups showing partial separation depending on Cu-NP dose. Differences between low and high Cu-NP doses were also evident within fibre types, indicating an additional modulatory effect of copper nanoparticle level on microbiota composition.
Global genus-level functional profiling identified a limited number of bacterial genera consistently associated with dietary fibre supplementation irrespective of Cu-NP dose (Figure 7A). Among them, Mucispirillum was the only genus uniformly reduced across all fibre-supplemented groups compared with cellulose controls, while Alistipes, Anaeroplasma, the Eubacterium xylanophilum group, Papillibacter, and Ruminiclostridium were shared across most comparisons, indicating fibre-sensitive but nonuniversal genus-level responses.
Comparative functional profiling further revealed a common set of microbial genes enriched in all bioactive fibre treatments relative to cellulose controls, regardless of Cu-NP dose (Figure 7B). These included genes involved in carbohydrate-responsive regulation (yydK), fermentative metabolism (alsD), amino acid transport (AAT family), oxidative stress response (npr), and central metabolic pathways (coaA, mvaK). Despite this shared functional signature, fibre-specific patterns were evident, with pectin showing the strongest enrichment of fermentation-related genes, inulin displaying moderate alterations in central metabolic functions, and psyllium primarily affecting genes related to transport and stress adaptation.
Overall, these compositional and functional profiles align with fibre-dependent differences observed in microbial enzymatic activity, fermentation patterns, bile acid metabolism, and microbial diversity under copper nanoparticle exposure.

4. Discussion

The present study demonstrates that the gastrointestinal effects of dietary copper nanoparticles are not determined by copper exposure alone but instead are strongly shaped by the dietary fibre matrix in which nanoparticles are consumed. Although copper nano-particles have been widely investigated as food and feed additives due to their high bioavailability and antimicrobial properties [3,24], increasing evidence indicates that their biological impact is highly context-dependent and modulated by interactions with dietary components, particularly fibre [7,15,16,17]. In line with this concept, the applied Cu-NP doses did not affect final body weight or body weight gain, indicating that copper nanoparticles alone were insufficient to induce overt systemic toxicity, but clear physiological and microbial responses emerged when Cu-NPs were combined with specific fibre fractions.
Differences in feed intake and caecal morphology observed in the present study emphasise the importance of the physical properties of dietary fibres. Our data show that inulin- and psyllium-containing diets reduced feed intake and markedly increased caecal digesta mass and tissue weight, consistent with the high water-binding capacity and viscosity of these fibres, which are known to enhance satiety signalling and delay gastrointestinal transit [25,26]. These effects appear to be driven primarily by fibre-induced changes in luminal volume and fermentation dynamics rather than by copper exposure itself, supporting previous observations that functional fibres can dominate host responses even in the presence of bioactive nanoparticles [16,17].
At the microbial level, copper nanoparticles exerted their strongest effects through modulation of microbial enzymatic activity and fermentation capacity. The observed reductions in the activities of several glycosidases and β-glucuronidase at higher Cu-NP doses are consistent with known mechanisms of copper-induced microbial toxicity, including oxidative stress generation, binding of copper ions to thiol-containing enzymes, and disruption of bacterial membrane integrity [6,27,28]. These mechanisms impair polysaccharide degradation and limit substrate availability for short-chain fatty acid production, which was reflected by the dose-dependent reduction in total SCFA concentrations across most Cu-NP-supplemented groups. Importantly, dietary fibre markedly modified these copper-driven effects. Pectin exerted the most pronounced protective influence, restoring microbial hydrolase activity and sustaining high levels of acetate, propionate, and butyrate. This effect can be attributed to the unique structural properties of pectin, including its high galacturonic acid content, which enables chelation of copper ions and reduces their bioactive toxicity [29,30]. In addition, the formation of a viscous gel matrix by pectin may physically limit direct contact between Cu-NPs and microbial cells, stabilising the gut environment and supporting fermentative metabolism [31]. The elevated SCFA concentrations observed in pectin-fed rats are of particular relevance, as SCFAs are key mediators of epithelial energy supply, barrier integrity, and immune regulation [32,33,34]; however, these mechanisms were not directly assessed in the present study. At the same time, increased ammonia and putrefactive SCFA levels in pectin-supplemented groups indicate that enhanced carbohydrate fermentation was accompanied by intensified amino acid metabolism. This observation is consistent with established links between active fermentation, increased microbial growth, and parallel stimulation of protein degradation pathways [35,36]. Functional metagenomic profiling further supported this interpretation, revealing enrichment of genes associated with amino acid transport and decarboxylation alongside fermentative pathways in fibre-supplemented diets.
Bile acid metabolism provided additional insight into the fibre-dependent modulation of Cu-NP effects. In contrast to fermentation-related outcomes, copper nanoparticles exerted only modest direct effects on total bile acid concentrations, while dietary fibre type emerged as the dominant determinant. Psyllium markedly reduced total bile acids and secondary bile acid levels, consistent with its high viscosity and strong bile acid-binding capacity, which limits enterohepatic circulation and microbial bile acid transformations [37,38]. Inulin induced a distinct bile acid profile characterised by increased muricholic acids and reduced UDCA, suggesting altered microbial epimerase and dehydroxylation activity, in line with previous reports linking fermentable fibres to shifts in FXR-related bile acid pathways [39,40]. Pectin, by contrast, preserved a bile acid profile similar to cellulose controls, reflecting its moderate bile acid-binding properties and balanced fermentation pattern [41,42]. The fibre-dependent patterns observed across microbial enzyme activities, SCFAs, and bile acids suggest that fibre physicochemical properties (fermentability, viscosity, and binding capacity) shape luminal conditions and microbial substrate availability, which may buffer or amplify Cu-NP-related effects. Notably, the consistency of fibre-driven shifts across independent readouts (fermentation metabolites, enzymatic markers, and bile acid pools) supports the interpretation that the dietary fibre matrix is a higher-order driver relative to nanoparticle dose in this model.
Integration of genus-level microbiota profiling with functional analyses revealed that dietary fibre also determined the compositional restructuring of the caecal microbiota under Cu-NP exposure. The consistent reduction in Mucispirillum across all fibre-supplemented groups suggests a common response to fibre-driven changes in the mucus-associated niche, potentially reflecting reduced reliance on host-derived substrates in favour of dietary polysaccharides [43]. Additional fibre-sensitive genera, including Alistipes, Anaeroplasma, the Eubacterium xylanophilum group, Papillibacter, and Ruminiclostridium, showed variable but recurring responses, highlighting that fibre modulates not only microbial function but also ecological organisation. These compositional shifts were most pronounced in inulin-fed rats, yet, contrary to the expected prebiotic effect of inulin [44], its capacity to promote beneficial microbial profiles appeared attenuated in the presence of copper nanoparticles. This suggests that Cu-NPs may interfere with classical prebiotic responses, likely by altering microbial redox balance and enzymatic capacity, thereby reshaping fibre–microbiota interactions.

5. Conclusions

Collectively, the present findings indicate that, in this Wistar rat model, the gastrointestinal impact of copper nanoparticles cannot be evaluated independently of the dietary context. Dietary fibre emerged as a dominant modulator of microbial activity, fermentation patterns, bile acid metabolism, and microbiota composition, often outweighing the direct effects of Cu-NPs under the applied exposure conditions. Pectin provided the most robust protection against copper-associated microbial disturbances, psyllium primarily affected bile acid availability, while inulin-related prebiotic signatures were substantially modified under nanoparticle exposure. Overall, the observed Cu-NP effects in the gut ecosystem depended not only on dose and particle properties but also on the structural and fermentative characteristics of the accompanying dietary fibre matrix. These results support the view that evaluations of nanostructured minerals in food and feed systems should explicitly consider dietary composition when interpreting microbiota-related outcomes.
At the same time, several limitations should be acknowledged. The study was conducted in a rat model over a 6-week intervention, which enables controlled dietary manipulation and mechanistic insight, but translation to humans and long-term effects remain indirect. Food intake differed between some groups and, although monitored daily, may have contributed to variability in microbial and metabolic endpoints. In addition, functional microbiota results were derived from 16S rRNA-based predictions that infer functional potential rather than directly measured metagenomic or transcriptomic activity; therefore, these outputs should be considered hypothesis-generating until confirmed by shotgun metagenomics, metatranscriptomics, or targeted functional assays. While these findings highlight the fibre matrix as a major modulator of Cu-NP gut effects in rats, extrapolation to human dietary exposure requires confirmation in human-relevant models.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/nu18050828/s1; File S1: Caecal bacterial sequencing (References [45,46,47,48,49,50,51,52,53,54,55] are cited in the Supplementary Materials).

Author Contributions

Conceptualization, J.J., B.F. and K.O.; methodology, J.J., B.F., D.N. and J.F.; software, K.M.; validation, K.O., B.F. and E.C.; formal analysis, B.F.; investigation, B.F., J.J. and E.C.; resources, J.J. and K.O.; data curation, B.F. and K.M.; writing—original draft preparation, B.F., J.J. and J.F.; writing—review and editing, B.F. and J.J.; visualisation, B.F., J.F. and K.M.; supervision, K.O. and J.J.; project administration, J.J.; funding acquisition, K.O. and J.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Science Center, Poland (Grant No. 2021/41/B/NZ9/01104).

Institutional Review Board Statement

The animal study protocol was approved by the National Ethics Committee for Animal Experiments (Permission No. 19/2021; Olsztyn, Poland, 17 March 2021). The study was conducted in accordance with the ARRIVE guidelines, and every attempt was made to minimise the animals’ suffering during the experiment.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Knijnenburg, J.T.N.; Posavec, L.; Teleki, A. Nanostructured Minerals and Vitamins for Food Fortification and Food Supple-mentation. In Nanomaterials for Food Applications; Elsevier: Amsterdam, The Netherlands, 2019; pp. 63–98. [Google Scholar] [CrossRef]
  2. Jiang, B.; Zhao, Y.; Cao, Y.; Sun, C.; Lu, W.; Fang, Y. Advances in the Interaction between Food-Derived Nanoparticles and the Intestinal Barrier. J. Agric. Food Chem. 2024, 72, 3291–3301. [Google Scholar] [CrossRef]
  3. McClements, D.J.; Xiao, H. Is Nano Safe in Foods? Establishing the Factors Impacting the Gastrointestinal Fate and Toxicity of Organic and Inorganic Food-Grade Nanoparticles. npj Sci. Food 2017, 1, 6. [Google Scholar] [CrossRef] [PubMed]
  4. Zhou, Y.-H.; Liu, X.-P.; Gu, X.-M.; Lv, H.-X.; Yang, Y.; Cai, Z.-X.; Di, B.; Wang, C.-K.; Gao, Y.-Y.; Jin, L. Effects of Dietary Nano-Composite of Copper and Carbon on Antioxidant Capacity, Immunity, and Cecal Microbiota of Weaned Ira White Rabbits. Animals 2025, 15, 184. [Google Scholar] [CrossRef] [PubMed]
  5. Kushwaha, R.; Kumar, V.; Kumar, M.; Vaswani, S.; Kumar, A.; Choudhury, S. Nano Copper Supplementation Increases Superoxide Dismutase and Catalase Gene Expression Profiles and Concentration of Antioxidants and Immune Variables in Sahiwal heifers. Biol. Trace Elem. Res. 2023, 201, 2319–2330. [Google Scholar] [CrossRef] [PubMed]
  6. Ramos-Zúñiga, J.; Bruna, N.; Pérez-Donoso, J.M. Toxicity Mechanisms of Copper Nanoparticles and Copper Surfaces on Bacterial Cells and Viruses. Int. J. Mol. Sci. 2023, 24, 10503. [Google Scholar] [CrossRef]
  7. Ma, Y.; Zhang, J.; Yu, N.; Shi, J.; Zhang, Y.; Chen, Z.; Jia, G. Effect of Nanomaterials on Gut Microbiota. Toxics 2023, 11, 384. [Google Scholar] [CrossRef]
  8. Zhu, Q.; Chen, B.; Zhang, F.; Zhang, B.; Guo, Y.; Pang, M.; Huang, L.; Wang, T. Toxic and Essential Metals: Metabolic Inter-actions with the Gut Microbiota and Health Implications. Front. Nutr. 2024, 11, 1448388. [Google Scholar] [CrossRef]
  9. Zhang, F.; Zheng, W.; Guo, R.; Yao, W. Effect of Dietary Copper Level on the Gut Microbiota and Its Correlation with Serum Inflammatory Cytokines in Sprague-Dawley Rats. J. Microbiol. 2017, 55, 694–702. [Google Scholar] [CrossRef]
  10. Zhang, F.; Zheng, W.; Xue, Y.; Yao, W. Suhuai Suckling Piglet Hindgut Microbiome-Metabolome Responses to Different Dietary Copper Levels. Appl. Microbiol. Biotechnol. 2019, 103, 853–868. [Google Scholar] [CrossRef]
  11. Hays, K.E.; Pfaffinger, J.M.; Ryznar, R. The Interplay between Gut Microbiota, Short-Chain Fatty Acids, and Implications for Host Health and Disease. Gut Microbes 2024, 16, 2393270. [Google Scholar] [CrossRef]
  12. Wen, Y.; Yang, L.; Wang, Z.; Xiao, D.; Tan, Z.; Yin, Y. Blocked Conversion of Lactobacillus johnsonii Derived Acetate to Butyrate Mediates Copper-Induced Epithelial Barrier Damage in a Pig Model. Microbiome 2023, 11, 218. [Google Scholar] [CrossRef] [PubMed]
  13. Du, Y.; Tu, Y.; Zhou, Z.; Hong, R.; Yan, J.; Zhang, G.-W. Effects of Organic and Inorganic Copper on Cecal Microbiota and Short-Chain Fatty Acids in Growing Rabbits. Front. Vet. Sci. 2023, 10, 1179374. [Google Scholar] [CrossRef]
  14. Liu, H.; Liao, C.; Wu, L.; Tang, J.; Chen, J.; Lei, C.; Zheng, L.; Zhang, C.; Liu, Y.Y.; Xavier, J.; et al. Ecological dynamics of the gut microbiome in response to dietary fiber. ISME J. 2022, 16, 2040–2055. [Google Scholar] [CrossRef]
  15. Marzec, A.; Fotschki, B.; Napiórkowska, D.; Fotschki, J.; Cholewińska, E.; Listos, P.; Juśkiewicz, J.; Ognik, K. The Effect of Copper Nanoparticles on Liver Metabolism Depends on the Type of Dietary Fibre. Nutrients 2024, 16, 3645. [Google Scholar] [CrossRef]
  16. Cholewińska, E.; Marzec, A.; Sołek, P.; Fotschki, B.; Listos, P.; Ognik, K.; Juśkiewicz, J. The Effect of Copper Nanoparticles and a Different Source of Dietary Fibre in the Diet on the Integrity of the Small Intestine in the Rat. Nutrients 2023, 15, 1588. [Google Scholar] [CrossRef]
  17. Juśkiewicz, J.; Fotschki, B.; Stępniowska, A.; Cholewińska, E.; Napiórkowska, D.; Marzec, A.; Brzuzan, Ł.; Fotschki, J.; Żary-Sikorska, E.; Ognik, K. Dietary Fibre with Functional Properties Counteracts the Thwarting Effects of Copper Nano-particles on the Microbial Enzymatic Activity and Short-Chain Fatty Acid Production in the Feces of Rats. Pol. J. Food Nutr. Sci. 2024, 74, 363–375. [Google Scholar] [CrossRef]
  18. Xie, C.; Huang, W.; Young, R.L.; Jones, K.L.; Horowitz, M.; Rayner, C.K.; Wu, T. Role of Bile Acids in the Regulation of Food Intake, and Their Dysregulation in Metabolic Disease. Nutrients 2021, 13, 1104. [Google Scholar] [CrossRef] [PubMed]
  19. Chiang, J.Y.L.; Ferrell, J.M. Bile Acid Receptors FXR and TGR5 Signaling in Fatty Liver Diseases and Therapy. Am. J. Phys-iol.-Gastrointest. Liver Physiol. 2020, 318, G554–G573. [Google Scholar] [CrossRef] [PubMed]
  20. Wahlström, A.; Sayin, S.I.; Marschall, H.-U.; Bäckhed, F. Intestinal Crosstalk between Bile Acids and Microbiota and Its Impact on Host Metabolism. Cell Metab. 2016, 24, 41–50. [Google Scholar] [CrossRef]
  21. Yao, L.; Davidson, E.A.; Shaikh, M.W.; Forsyth, C.B.; Prenni, J.E.; Broeckling, C.D. Quantitative Analysis of Short-Chain Fatty Acids in Human Plasma and Serum by GC–MS. Anal. Bioanal. Chem. 2022, 414, 4391–4399. [Google Scholar] [CrossRef]
  22. Fotschki, B.; Juśkiewicz, J.; Jurgoński, A.; Rigby, N.; Sójka, M.; Kołodziejczyk, K.; Mackie, A.; Zduńczyk, Z. Raspberry pomace alters cecal microbial activity and reduces secondary bile acids in rats fed a high-fat diet. J. Nutr. Biochem. 2017, 46, 13–20. [Google Scholar] [CrossRef]
  23. Fotschki, B.; Wiczkowski, W.; Sawicki, T.; Sójka, M.; Myszczyński, K.; Ognik, K.; Juśkiewicz, J. Stimulation of the intestinal microbiota with prebiotics enhances hepatic levels of dietary polyphenolic compounds, lipid metabolism and antioxidant status in healthy rats. Food Res. Int. 2022, 160, 111754. [Google Scholar] [CrossRef]
  24. Jiang, H.; Li, L.; Li, Z.; Chu, X. Metal-Based Nanoparticles in Antibacterial Application in Biomedical Field: Current Devel-opment and Potential Mechanisms. Biomed. Microdevices 2024, 26, 12. [Google Scholar] [CrossRef] [PubMed]
  25. Wanders, A.J.; van den Borne, J.J.G.C.; de Graaf, C.; Hulshof, T.; Jonathan, M.C.; Kristensen, M.; Mars, M.; Schols, H.A.; Feskens, E.J.M. Effects of Dietary Fibre on Subjective Appetite, Energy Intake and Body Weight: A Systematic Review of Randomized Controlled Trials. Obes. Rev. 2011, 12, 724–739. [Google Scholar] [CrossRef] [PubMed]
  26. Slavin, J.; Green, H. Dietary Fibre and Satiety. Nutr. Bull. 2007, 32, 32–42. [Google Scholar] [CrossRef]
  27. Baeg, E.; Choi, J.; Pham, H.D.; Kim, E.; Kim, J.; Lee, J. Copper Oxide Nanoparticles Cause a Dose-Dependent Toxicity via Inducing Reactive Oxygen Species in Drosophila. Nanomaterials 2018, 8, 824. [Google Scholar] [CrossRef]
  28. Vincent, M.; Duval, R.E.; Hartemann, P.; Engels-Deutsch, M. Contact Killing and Antimicrobial Properties of Copper. J. Appl. Microbiol. 2018, 124, 1032–1046. [Google Scholar] [CrossRef] [PubMed]
  29. Sriamornsak, P. Chemistry of Pectin and Its Pharmaceutical Uses: A Review. Silpakorn Univ. Int. J. 2003, 3, 206–228. [Google Scholar]
  30. Dongowski, G.; Lorenz, A.; Proll, J. The Degree of Methylation Influences the Degradation of Pectin in the Intestinal Tract of Rats and in Vitro. J. Nutr. 2002, 132, 1935–1944. [Google Scholar] [CrossRef]
  31. Mackie, A.; Bajka, B.; Rigby, N. Roles for Dietary Fibre in the Upper GI Tract: The Importance of Viscosity. Food Res. Int. 2016, 88, 234–238. [Google Scholar] [CrossRef]
  32. Koh, A.; De Vadder, F.; Kovatcheva-Datchary, P.; Bäckhed, F. From Dietary Fibre to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell 2016, 165, 1332–1345. [Google Scholar] [CrossRef] [PubMed]
  33. Tan, J.; McKenzie, C.; Potamitis, M.; Thorburn, A.N.; Mackay, C.R.; Macia, L. The Role of Short-Chain Fatty Acids in Health and Disease. Adv. Immunol. 2014, 121, 91–119. [Google Scholar] [CrossRef] [PubMed]
  34. Parada Venegas, D.; De la Fuente, M.K.; Landskron, G.; González, M.J.; Quera, R.; Dijkstra, G.; Harmsen, H.J.M.; Faber, K.N.; Hermoso, M.A. Short Chain Fatty Acids (SCFAs)-Mediated Gut Epithelial and Immune Regulation and Its Relevance for In-flammatory Bowel Diseases. Front. Immunol. 2019, 10, 277. [Google Scholar] [CrossRef]
  35. Russell, W.R.; Gratz, S.W.; Duncan, S.H.; Holtrop, G.; Ince, J.; Scobbie, L.; Duncan, G.; Johnstone, A.M.; Lobley, G.E.; Wallace, R.J.; et al. High-Protein, Reduced-Carbohydrate Weight-Loss Diets Promote Metabolite Profiles Likely to Be Detrimental to Colonic Health. Am. J. Clin. Nutr. 2011, 93, 1062–1072. [Google Scholar] [CrossRef]
  36. Smith, E.A.; Macfarlane, G.T. Dissimilatory Amino Acid Metabolism in Human Colonic Bacteria. Anaerobe 1997, 3, 327–337. [Google Scholar] [CrossRef] [PubMed]
  37. Marlett, J.A.; Kajs, T.M.; Fischer, M.H. An Unfermented Gel Component of Psyllium Seed Husk Promotes Laxation as a Lubricant in Humans. Am. J. Clin. Nutr. 2000, 72, 784–789. [Google Scholar] [CrossRef]
  38. Gunness, P.; Gidley, M.J. Mechanisms Underlying the Cholesterol-Lowering Properties of Soluble Dietary Fibre Polysaccha-rides. Food Funct. 2010, 1, 149–155. [Google Scholar] [CrossRef]
  39. Sayin, S.I.; Wahlström, A.; Felin, J.; Jäntti, S.; Marschall, H.-U.; Bamberg, K.; Angelin, B.; Hyötyläinen, T.; Orešič, M.; Bäckhed, F. Gut Microbiota Regulates Bile Acid Metabolism by Reducing the Levels of Tauro-Beta-Muricholic Acid, a Naturally Oc-curring FXR Antagonist. Cell Metab. 2013, 17, 225–235. [Google Scholar] [CrossRef]
  40. Ridlon, J.M.; Harris, S.C.; Bhowmik, S.; Kang, D.-J.; Hylemon, P.B. Consequences of Bile Salt Biotransformations by Intestinal Bacteria. Gut Microbes 2016, 7, 22–39. [Google Scholar] [CrossRef]
  41. Gunness, P.; Michiels, J.; Vanhaecke, L.; De Smet, S.; Kravchuk, O.; Van de Meene, A.; Gidley, M.J. Reduction in Circulating Bile Acid and Restricted Diffusion across the Intestinal Epithelium Are Associated with a Decrease in Blood Cholesterol in the Presence of Oat β-Glucan. FASEB J. 2016, 30, 4227–4238. [Google Scholar] [CrossRef]
  42. Chau, C.-F.; Huang, Y.-L.; Lin, C.-Y. Investigation of the Cholesterol-Lowering Action of Insoluble Fibre Derived from the Peel of Citrus sinensis L. Cv. Liucheng. Food Chem. 2004, 87, 361–366. [Google Scholar] [CrossRef]
  43. Kuffa, P.; Pickard, J.M.; Campbell, A.; Yamashita, M.; Schaus, S.R.; Martens, E.C.; Schmidt, T.M.; Inohara, N.; Núñez, G.; Caruso, R. Fibre-Deficient Diet Inhibits Colitis through the Regulation of the Niche and Metabolism of a Gut Pathobiont. Cell Host Microbe 2023, 31, 2007–2022.e12. [Google Scholar] [CrossRef]
  44. Makki, K.; Deehan, E.C.; Walter, J.; Bäckhed, F. The Impact of Dietary Fibre on Gut Microbiota in Host Health and Disease. Cell Host Microbe 2018, 23, 705–715. [Google Scholar] [CrossRef]
  45. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011, 17, 10–12. [Google Scholar] [CrossRef]
  46. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. 2010. Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 17 July 2025).
  47. Magoč, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [CrossRef]
  48. Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019, 37, 852–857. [Google Scholar] [CrossRef] [PubMed]
  49. Amir, A.; McDonald, D.; Navas-Molina, J.A.; Kopylova, E.; Morton, J.T.; Xu, Z.Z.; Kightley, E.P.; Thompson, L.R.; Hyde, E.R.; Gonzalez, A.; et al. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2017, 2, e00191-16. [Google Scholar] [CrossRef] [PubMed]
  50. McMurdie, P.J.; Holmes, S. Shiny-phyloseq: Web Application for Interactive Microbiome Analysis with Provenance Tracking. Bioinformatics 2014, 31, 282–283. [Google Scholar] [CrossRef] [PubMed]
  51. Langille, M.G.I.; Zaneveld, J.; Caporaso, J.G.; McDonald, D.; Knights, D.; Reyes, J.A.; Clemente, J.C.; Burkepile, D.E.; Vega Thurber, R.L.; Knight, R.; et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 2013, 31, 814–821. [Google Scholar] [CrossRef]
  52. Parks, D.H.; Tyson, G.W.; Hugenholtz, P.; Beiko, R.G. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics 2014, 30, 3123–3124. [Google Scholar] [CrossRef]
  53. McMurdie, P.J.; Paulson, J.N. Biomformat: An Interface Package for the BIOM File Format. R/Bioconductor Package. 2015. Available online: https://github.com/joey711/biomformat/ (accessed on 20 July 2025).
  54. Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer-Verlag: New York, NY, USA, 2016. [Google Scholar] [CrossRef]
  55. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Effects of dietary copper source and fibre composition on the growth parameters of experimental rats (n = 10 per group). Control groups (C and CH) received CuCO3 providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Letters are shown only when a significant diet × fibre interaction was detected (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters.
Figure 1. Effects of dietary copper source and fibre composition on the growth parameters of experimental rats (n = 10 per group). Control groups (C and CH) received CuCO3 providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Letters are shown only when a significant diet × fibre interaction was detected (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters.
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Figure 2. Effects of dietary copper source and fibre composition on the caecal parameters of experimental rats (n = 10 per group). Control groups (C and CH) received CuCO3 providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Letters are shown only when a significant diet × fibre interaction was detected (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C), and those fed 13 mg/kg Cu-NP (CNH, PNH, JNH, SNH) were compared with the CH group (& indicates a significant difference vs. CH). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters.
Figure 2. Effects of dietary copper source and fibre composition on the caecal parameters of experimental rats (n = 10 per group). Control groups (C and CH) received CuCO3 providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Letters are shown only when a significant diet × fibre interaction was detected (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C), and those fed 13 mg/kg Cu-NP (CNH, PNH, JNH, SNH) were compared with the CH group (& indicates a significant difference vs. CH). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters.
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Figure 3. Effects of dietary copper source and fibre composition on the caecal activity of bacterial enzymes in rats (n = 10 per group). Control groups (C and CH) received CuCO3, providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Black letters are shown only when a significant diet × fibre interaction was detected (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C), and those fed 13 mg/kg Cu-NP (CNH, PNH, JNH, SNH) were compared with the CH group (& indicates a significant difference vs. CH). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters. The red arrow marks a statistically significant (p < 0.05) increase in the measured parameter, demonstrating the dose-dependent effect of Cu-NP supplementation.
Figure 3. Effects of dietary copper source and fibre composition on the caecal activity of bacterial enzymes in rats (n = 10 per group). Control groups (C and CH) received CuCO3, providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Black letters are shown only when a significant diet × fibre interaction was detected (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C), and those fed 13 mg/kg Cu-NP (CNH, PNH, JNH, SNH) were compared with the CH group (& indicates a significant difference vs. CH). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters. The red arrow marks a statistically significant (p < 0.05) increase in the measured parameter, demonstrating the dose-dependent effect of Cu-NP supplementation.
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Figure 4. Effects of dietary copper source and fibre composition on SCFA levels in the caecum of experimental rats (n = 10 per group). Control groups (C and CH) received CuCO3 providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C), and those fed 13 mg/kg Cu-NP (CNH, PNH, JNH, SNH) were compared with the CH group (& indicates a significant difference vs. CH). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters. The red arrow marks a statistically significant (p < 0.05) increase in the measured parameter, demonstrating the dose-dependent effect of Cu-NP supplementation. C2, acetic acid; C3, propionic acid; C4, butyric acid; C4i, isobutyric acid; C5, valeric acid; C5i, isovaleric acid; SCFAs, short-chain fatty acids; PSCFAs, protein-derived short-chain fatty acids.
Figure 4. Effects of dietary copper source and fibre composition on SCFA levels in the caecum of experimental rats (n = 10 per group). Control groups (C and CH) received CuCO3 providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C), and those fed 13 mg/kg Cu-NP (CNH, PNH, JNH, SNH) were compared with the CH group (& indicates a significant difference vs. CH). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters. The red arrow marks a statistically significant (p < 0.05) increase in the measured parameter, demonstrating the dose-dependent effect of Cu-NP supplementation. C2, acetic acid; C3, propionic acid; C4, butyric acid; C4i, isobutyric acid; C5, valeric acid; C5i, isovaleric acid; SCFAs, short-chain fatty acids; PSCFAs, protein-derived short-chain fatty acids.
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Figure 5. Effects of dietary copper source and fibre composition on bile acid levels in the caecum of experimental rats (n = 10 per group). Control groups (C and CH) received CuCO3 providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Black letters are shown only when a significant diet × fibre interaction was detected (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C), and those fed 13 mg/kg Cu-NP (CNH, PNH, JNH, SNH) were compared with the CH group (& indicates a significant difference vs. CH). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters. The red arrow marks a statistically significant (p < 0.05) increase in the measured parameter, demonstrating the dose-dependent effect of Cu-NP supplementation. CA, cholic acid; DCA, deoxycholic acid; CDCA, chenodeoxycholic acid; LCA, lithocholic acid; α-, β-, ω-MCA, α-, β-, ω-muricholic acid; UDCA, ursodeoxycholic acid; HDCA, hyodeoxycholic acid.
Figure 5. Effects of dietary copper source and fibre composition on bile acid levels in the caecum of experimental rats (n = 10 per group). Control groups (C and CH) received CuCO3 providing 6.5 or 13 mg Cu/kg with 8% cellulose as the fibre source. Experimental groups were supplemented with copper nanoparticles (Cu-NP) at corresponding levels (6.5 or 13 mg Cu/kg) combined with different dietary fibres: cellulose (CN, CNH), pectin (PN, PNH), inulin (JN, JNH), or psyllium (SN, SNH). Values are presented as means ± SEM. Mean values with different letters differ significantly (p < 0.05). Black letters are shown only when a significant diet × fibre interaction was detected (p < 0.05). Groups fed 6.5 mg/kg Cu-NP (CN, PN, JN, SN) were compared with the control C group using Student’s t-test (# indicates a significant difference vs. C), and those fed 13 mg/kg Cu-NP (CNH, PNH, JNH, SNH) were compared with the CH group (& indicates a significant difference vs. CH). Statistically significant differences (p < 0.05) observed between the types of dietary fibre are presented with red letters. The red arrow marks a statistically significant (p < 0.05) increase in the measured parameter, demonstrating the dose-dependent effect of Cu-NP supplementation. CA, cholic acid; DCA, deoxycholic acid; CDCA, chenodeoxycholic acid; LCA, lithocholic acid; α-, β-, ω-MCA, α-, β-, ω-muricholic acid; UDCA, ursodeoxycholic acid; HDCA, hyodeoxycholic acid.
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Figure 6. Alpha (A) and beta (B) diversity of the caecal microbiota. Alpha diversity was assessed using the Shannon index, with statistically significant differences indicated for comparisons with p < 0.05. The horizontal line within the box plots represents the median, the asterisk indicates the mean, and individual points correspond to Shannon index values for each sample. Beta diversity was assessed using PCoA based on Bray–Curtis distances, with significance set at p < 0.05. Cu-NP were administered to experimental groups at low and high dietary levels of 6.5 and 13 mg Cu/kg, respectively. CN, diet with low Cu-NP dose and cellulose; PN, diet with low Cu-NP dose and pectin; JN, diet with low Cu-NP dose and inulin; SN, diet with low Cu-NP dose and psyllium; CNH, diet with high Cu-NP dose and cellulose; PNH, diet with high Cu-NP dose and pectin; JNH, diet with high Cu-NP dose and inulin; SNH, diet with high Cu-NP dose and psyllium.
Figure 6. Alpha (A) and beta (B) diversity of the caecal microbiota. Alpha diversity was assessed using the Shannon index, with statistically significant differences indicated for comparisons with p < 0.05. The horizontal line within the box plots represents the median, the asterisk indicates the mean, and individual points correspond to Shannon index values for each sample. Beta diversity was assessed using PCoA based on Bray–Curtis distances, with significance set at p < 0.05. Cu-NP were administered to experimental groups at low and high dietary levels of 6.5 and 13 mg Cu/kg, respectively. CN, diet with low Cu-NP dose and cellulose; PN, diet with low Cu-NP dose and pectin; JN, diet with low Cu-NP dose and inulin; SN, diet with low Cu-NP dose and psyllium; CNH, diet with high Cu-NP dose and cellulose; PNH, diet with high Cu-NP dose and pectin; JNH, diet with high Cu-NP dose and inulin; SNH, diet with high Cu-NP dose and psyllium.
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Figure 7. Fibre-dependent modulation of the caecal microbiota under copper nanoparticle expo-sure. Heatmap illustrating log2 fold changes in the relative abundance of selected bacterial genera (A) and the representation of key KEGG functional genes (B) in fibre-supplemented diets relative to cellulose-based controls, at low and high dietary copper nanoparticle levels (6.5 and 13 mg Cu/kg, respectively). The analysis includes six pairwise comparisons (CN vs. PN, JN, SN and CNH vs. PNH, JNH, SNH). Only bacterial genera and KEGG genes that were consistently altered in the majority of comparisons were included to highlight shared and fibre-sensitive microbial responses. Empty fields (white) indicate taxa that were not detected or did not meet the predefined criteria for differential representation in a given comparison. Colour intensity reflects the magnitude and direction of the log2 fold change. The selected KEGG genes represent pathways related to carbohydrate-responsive regulation (yydK), fermentative metabolism (alsD), amino acid transport and nitrogen metabolism (AAT family), oxidative stress response (npr), central metabolic processes (coaA, mvaK), and microbial transport and environmental adaptation (ABC and MFS transporters). CN, diet with low Cu-NP dose and cellulose; PN, diet with low Cu-NP dose and pectin; JN, diet with low Cu-NP dose and inulin; SN, diet with low Cu-NP dose and psyllium; CNH, diet with high Cu-NP dose and cellulose; PNH, diet with high Cu-NP dose and pectin; JNH, diet with high Cu-NP dose and inulin; SNH, diet with high Cu-NP dose and psyllium.
Figure 7. Fibre-dependent modulation of the caecal microbiota under copper nanoparticle expo-sure. Heatmap illustrating log2 fold changes in the relative abundance of selected bacterial genera (A) and the representation of key KEGG functional genes (B) in fibre-supplemented diets relative to cellulose-based controls, at low and high dietary copper nanoparticle levels (6.5 and 13 mg Cu/kg, respectively). The analysis includes six pairwise comparisons (CN vs. PN, JN, SN and CNH vs. PNH, JNH, SNH). Only bacterial genera and KEGG genes that were consistently altered in the majority of comparisons were included to highlight shared and fibre-sensitive microbial responses. Empty fields (white) indicate taxa that were not detected or did not meet the predefined criteria for differential representation in a given comparison. Colour intensity reflects the magnitude and direction of the log2 fold change. The selected KEGG genes represent pathways related to carbohydrate-responsive regulation (yydK), fermentative metabolism (alsD), amino acid transport and nitrogen metabolism (AAT family), oxidative stress response (npr), central metabolic processes (coaA, mvaK), and microbial transport and environmental adaptation (ABC and MFS transporters). CN, diet with low Cu-NP dose and cellulose; PN, diet with low Cu-NP dose and pectin; JN, diet with low Cu-NP dose and inulin; SN, diet with low Cu-NP dose and psyllium; CNH, diet with high Cu-NP dose and cellulose; PNH, diet with high Cu-NP dose and pectin; JNH, diet with high Cu-NP dose and inulin; SNH, diet with high Cu-NP dose and psyllium.
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Table 1. The composition of experimental diets administered to rats for 6 weeks.
Table 1. The composition of experimental diets administered to rats for 6 weeks.
CCHCNCNHPNPNHJNJNHSNSNH
Casein 114.814.814.814.814.814.814.814.814.814.8
DL-methionine0.20.20.20.20.20.20.20.20.20.2
Cellulose 28.08.08.08.02.02.02.02.02.02.0
Pectin 6.06.0
Inulin 6.06.0
Psyllium 6.06.0
Choline chloride0.20.20.20.20.20.20.20.20.20.2
Rapeseed oil8.08.08.08.08.08.08.08.08.08.0
Cholesterol0.30.30.30.30.30.30.30.30.30.3
Vitamin mix 31.01.01.01.01.01.01.01.01.01.0
Mineral mix 43.53.53.53.53.53.53.53.53.53.5
Maize starch 564.064.064.064.064.064.064.064.064.064.0
Calculation:
Cu from, mg/kg
CuCO36.513.000000000
Cu-NP006.513.06.513.06.513.06.513.0
1 Casein preparation: crude protein 89.7%, crude fat 0.3%, ash 2.0%, and water 8.0%. 2 α-Cellulose (SIGMA, Poznan, Poland), main source of dietary fibre. 3 AIN-93G-VM g/kg mix: 3.0 nicotinic acid, 1.6 Ca pantothenate, 0.7 pyridoxine-HCl, 0.6 thiamin-HCl, 0.6 riboflavin, 0.2 folic acid, 0.02 biotin, 2.5 vitamin B-12 (cyanocobalamin, 0.1% in mannitol), 15.0 vitamin E (all-rac-α-tocopheryl acetate, 500 IU g−1), 0.8 vitamin A (all-trans-retinyl palmitate, 500,000 IU/g), 0.25 vitamin D-3 (cholecalciferol, 400,000 IU g−1), 0.075 vitamin K-1 (phylloquinone), 974.655 powdered sucrose. 4 In the experimental treatments with Cu-NP, the mineral mix was deprived of CuCO3, and to minimise exposure during diet preparation, the Cu-NP preparation was added as an emulsion along with dietary rapeseed oil. Such a procedure was successfully applied as used previously in our studies. 5 Maize starch preparation: crude protein 0.6%, crude fat 0.9%, ash 0.2%, total dietary fibre 0%, and water 8.8%.
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Fotschki, B.; Napiórkowska, D.; Fotschki, J.; Myszczyński, K.; Cholewińska, E.; Ognik, K.; Juśkiewicz, J. Dietary Fibre Modulates Gut Microbiota Responses to Copper Nanoparticles. Nutrients 2026, 18, 828. https://doi.org/10.3390/nu18050828

AMA Style

Fotschki B, Napiórkowska D, Fotschki J, Myszczyński K, Cholewińska E, Ognik K, Juśkiewicz J. Dietary Fibre Modulates Gut Microbiota Responses to Copper Nanoparticles. Nutrients. 2026; 18(5):828. https://doi.org/10.3390/nu18050828

Chicago/Turabian Style

Fotschki, Bartosz, Dorota Napiórkowska, Joanna Fotschki, Kamil Myszczyński, Ewelina Cholewińska, Katarzyna Ognik, and Jerzy Juśkiewicz. 2026. "Dietary Fibre Modulates Gut Microbiota Responses to Copper Nanoparticles" Nutrients 18, no. 5: 828. https://doi.org/10.3390/nu18050828

APA Style

Fotschki, B., Napiórkowska, D., Fotschki, J., Myszczyński, K., Cholewińska, E., Ognik, K., & Juśkiewicz, J. (2026). Dietary Fibre Modulates Gut Microbiota Responses to Copper Nanoparticles. Nutrients, 18(5), 828. https://doi.org/10.3390/nu18050828

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