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. 2026 Jun 15;17:1859419. doi: 10.3389/fmicb.2026.1859419

Exposure to 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine causes cecal microbiota dysbiosis in mice

Nesreen Aljahdali 1,2,*
PMCID: PMC13312906  PMID: 42376569

Abstract

2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) is a heterocyclic amine (HCA) that is formed during high-temperature meat cooking, especially grilling, and is classified as a carcinogenic food-borne compound that can impact multiple physiological systems. The aim of this study was to evaluate how exposure to PhIP affects the composition of the cecal microbiota in mice. A total of 32 mice were randomly divided into four groups: A negative control group, a positive control group, and two groups treated orally with either 10 or 20 mg/kg PhIP for 2 months. The V3-V4 regions of the bacterial 16S rRNA gene were sequenced to characterize the cecal microbial communities. Microbial richness and diversity decreased in the PhIP-treated mice compared to the negative control group. At the phylum level, the relative abundance of Bacillota and Actinomycetota decreased significantly after PhIP exposure, while the abundance of Campylobacterota and Bacteroidota increased. At the genus level, the abundance of Roseburia, Lactobacillus, and Bifidobacterium decreased, while the abundance of Blautia, Lachnospiraceae-NK4A136 group, Bacteroides, Alistipes, Helicobacter, and Escherichia–Shigella increased. The results highlight that bacterial community composition and its relative abundance in the cecum change with PhIP exposure and offer further evidence of microbiota dysbiosis that occurs with PhIP exposure.

Keywords: cecal microbiota, dietary carcinogens, heterocyclic amines, Maillard reaction products, PhIP and gut microbiota

1. Introduction

The non-enzymatic browning reaction between reducing sugars and free amino groups, which leads to the formation of Maillard reaction products (MRPs), is one of the key chemical transformations in foods. This reaction was first described by Louis Camille Maillard in 1912 during studies on heat-induced changes in amino acid–sugar systems (Gupta et al., 2018). MRPs are widely present in Western dietary patterns, occurring in thermally processed foods such as bread, baked potato, cake, roast pork, breakfast cereal, and pastry. These compounds play a pivotal role in the formation and modulation of the flavor, color, and aroma characteristics of foods during heat treatment (Tuohy et al., 2006). The formation and progression of MRPs can be modulated by multiple physicochemical parameters, including temperature, pH, and water activity (Ames, 1990). These factors can lead to changes in the reaction paths, rate, and end products (Sumaya-Martinez et al., 2005). The progression of RPs can be divided into three principal phases: The initial phase, the intermediate phase, and the final phase (Hodge, 1953). Each stage of food preparation, including the application of various cooking techniques, induces a series of complex chemical reactions that lead to the formation of numerous process-induced compounds, such as Nϵ-fructosyllysine (furosine), 5-hydroxymethylfurfural (HMF), acrylamide, heterocyclic amines (HCAs), advanced glycation end products (AGEs), and melanoidins (ALjahdali and Carbonero, 2019). HCAs, which are formed through reactions among reducing sugars, amino acids, and their precursor creatine, are produced during the cooking of skeletal muscle meats, including beef, pork, poultry, and fish (Jägerstad et al., 1991; Tuohy et al., 2006). Evidence has shown that the formation of HCAs in meat occurs due to increases in temperature above 200 °C or when meat is cooked for prolonged periods at lower temperatures (Jägerstad et al., 1991, Tuohy et al., 2006). The formation of HCAs in thermally processed meat is influenced by both the type of meat and the specific cooking conditions employed. For example, beef cooked to a well-done degree exhibits substantially higher concentrations of HCAs compared to beef cooked to a medium degree of doneness (Puangsombat et al., 2012). The levels of HCAs in different types of meat have been quantified. Specifically, it has been reported that the concentrations of HCAs in fried bacon, fried pork, fried beef, and fried chicken are 17.59 ng/g, 13.91 ng/g, 8.92 ng/g, and 7.06 ng/g, respectively (Puangsombat et al., 2012). Several types of HCAs have been identified and investigated in food matrices, including 2-amino-3-methylimidazo [4,5-f]quinoline (IQ), 2-amino-3,4-dimethylimidazo [4,5-f]quinoline (MeIQ), 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx), and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP). These compounds are classified as carcinogenic by the International Agency for Research on Cancer (IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, International Agency for Research on Cancer, and World Health Organization, 1993).

PhIP was first discovered in fried ground beef cooked at 300 °C (Felton et al., 1986). It has been reported that the concentration of PhIP in cooked beef reached 182 ng/g, whereas in cooked chicken it was 49 ng/g (Augustsson et al., 1997; Felton et al., 2002; Sugimura et al., 2004). Furthermore, it was estimated that the amount of PhIP was 5.22 ng/g in fried camel meat (Khan et al., 2017; Rizwan Khan et al., 2017). Several studies have demonstrated that PhIP is absorbed through the intestinal tract and subsequently excreted in urine as metabolites within 24 h following the ingestion of barbecued meat (Kulp et al., 2004; Lynch et al., 1992). Numerous epidemiological investigations have demonstrated an association between dietary exposure to PhIP and adverse health outcomes. For example, previous studies have reported that PhIP induces intestinal carcinomas and lymphomas in both male and female rats (Ochiai et al., 2002), as well as mammary carcinomas in female rats (Imaida et al., 1996). In addition, it has been reported that PhIP is detectable in human breast milk and can be transmitted from the mother to the offspring via lactational exposure (Jägerstad et al., 1994). Moreover, studies have found that PhIP could cause prostate and colon carcinoma in rodents (Li et al., 2012; Nicken et al., 2013). Acute exposure to PhIP within the colonic environment may induce oxidative stress, as evidenced by elevated nitrotyrosine concentrations and a concomitant reduction in the enzymatic activities of glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) (Chen et al., 2017; Li et al., 2013). Moreover, short-term exposure to PhIP was found to disrupt colonic energy metabolism, characterized by an upregulation of tricarboxylic acid cycle activity and a concomitant inhibition of glycolysis (Zhang et al., 2024).

It is now widely recognized that the gastrointestinal tract (GIT) harbors the majority of the gut-associated microbial community (Ley et al., 2006). The bacterial population densities in the small and large intestines are approximately 105–106 cells/mL and 108–109 cells/mL, respectively. These bacteria produce short-chain fatty acids (SCFAs) through the fermentation and degradation of dietary substrates. In addition to bacteria, other components of the gut microbiota are present in the large intestine, including Archaea, protozoa, viruses, and fungi; however, the gut microbiota is predominantly composed of anaerobic bacterial species (Walter and Ley, 2011). The interaction between dietary factors and the gut microbiota has been implicated in both the pathogenesis and prevention of various diseases (Louis et al., 2007). The possibility that the intestinal microbiota convert PhIP into additional toxic metabolites must be considered. A previous study demonstrated that, upon incubation with human stool samples, PhIP was biotransformed into 7-hydroxy-5-methyl-3-phenyl-6,7,8,9-tetrahydropyrido[3′,2′:4,5]-imidazo[1,2-a]pyrimidin-5-ium chloride (PhIP-M1) by strains belonging to the species Enterococcus faecium, Enterococcus durans, Enterococcus avium, and Lactobacillus reuteri (Vanhaecke et al., 2006; Vanhaecke et al., 2007). With respect to PhIP-M1 toxicity, it has been demonstrated that exposure to 100–200 μM PhIP-M1 induces DNA damage, apoptosis, and cell cycle arrest in Caco-2 cells (Vanhaecke et al., 2008). However, an additional study indicated that this concentration of PhIP-M1 is unlikely to exert cytotoxic or carcinogenic effects on the colorectal mucosa (Nicken et al., 2015). Previous studies have demonstrated that exposure of rat fecal samples to PhIP resulted in an increased relative abundance of the bacterial family Muribaculaceae, concomitant with decreased relative abundances of the families Ruminococcaceae and Lactobacillaceae (Zhao et al., 2021). A recent study demonstrated that co-administration of PhIP and dextran sulfate sodium (DSS) resulted in an increased relative abundance of the bacterial taxon Clostridia UCG014 in rat fecal samples (Zapico et al., 2024). The impact of PhIP on biological systems, and in particular on the composition of the cecal microbiota, remains poorly characterized. Consequently, the objective of this study was to elucidate the effects of PhIP exposure on cecal microbiota composition in mice.

2. Materials and methods

2.1. Chemicals

PhIP (purity: 98.9%) and corn oil were obtained from Aladdin Scientific (California, United States).

2.2. Experimental animals

The animal experiment was conducted at the Animal House Facility, Faculty of Pharmacy, King Abdulaziz University. A total of 32 male BALB/c mice, 8 weeks of age and with a body weight of 20 g, were randomly allocated to four experimental groups (n = 8 per group). The animals were housed in groups per cage and maintained under a 12 h light/12 h dark photoperiod at standard room temperature (20 ± 2 °C) and relative humidity (50 ± 5%), with ad libitum access to standard chow and water.

2.3. Experimental design

A total of 32 mice were randomly assigned to four experimental groups and housed in separate cages, with eight animals per group. Group 1 served as the negative control (N) and received distilled water; this group was further subdivided into N1 and N2, corresponding to animals sacrificed after the first and second months, respectively. Group 2 served as the positive control (P) and received 1.5 mL of corn oil; this group was similarly subdivided into P1 and P2 for the first and second months, respectively. Group 3 received PhIP at a dose of 10 mg/kg body weight and was designated as group D, with subgroups M1D and M2D representing animals sampled at the end of the first and second months, respectively. Group 4 received PhIP at a dose of 20 mg/kg body weight and was designated as group T, with subgroups M1T and M2T corresponding to the first and second months, respectively (Li et al., 2013, Kimura et al., 2003). Mice in the treatment groups were administered PhIP by oral gavage for a total duration of 2 months. For oral administration, PhIP was dissolved in corn oil at a dosing volume of 10 mL/kg body weight, in accordance with previously described protocols (Diehl et al., 2001). At the end of the experimental period, mice were euthanized and subjected to necropsy. Cecal contents were collected immediately under anaerobic conditions for downstream sequencing and microbiota analyses (Figure 1).

Figure 1.

Experimental workflow diagram for a mouse study outlining five stages: animal groups assignment, eight weeks of treatment and monitoring with sampling at weeks four and eight, DNA extraction, sequencing analysis using Illumina, bioinformatics analysis via QIIME, and statistical analysis using PAST 4.

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Overview of the study design and procedures for data collection and analysis.

2.4. DNA extraction and sequencing

Genomic DNA was extracted from the cecal samples using the commercial QIAamp DNA Stool and Tissue Mini Kit (Qiagen, Germany) according to the manufacturer’s instructions. Target regions were amplified by PCR using specific primers conjugated to sample-specific barcodes, followed by agarose gel electrophoresis to select amplicons of the desired size. The PCR amplification targeted the V3-4 hypervariable regions of the bacterial 16S rRNA gene. Sequencing libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina) and sequenced on an Illumina MiSeq platform in a 2 × 250 bp paired-end configuration, following the manufacturer’s protocol.

2.5. Bioinformatics and statistical analyses

From 32 samples, a total of 12,877,243 raw sequence reads were obtained, of which 10,089,340 high-quality reads were retained for downstream analyses. FASTQ files were processed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline, which enables comprehensive microbial community analysis (Kuczynski et al., 2012). Briefly, raw reads were merged and quality filtered to generate clean sequences. Representative sequences of each amplicon sequence variant (ASV) were subsequently taxonomically annotated to determine the corresponding species identities and their relative abundances. ASV tables were used to assess species richness, calculate α-diversity indices, and construct Venn and flower diagrams. To evaluate differences in microbial community composition and structure between the groups, Student’s t-tests and MetagenomeSeq analyses were performed. In addition, nonparametric comparisons were conducted using the Kruskal–Wallis test followed by Dunn’s post hoc tests in PAST software (version 4.03). Microbiota data were ordinated using non-metric multidimensional scaling (NMDS) based on Bray–Curtis similarity. The statistical significance of subgroup clustering was assessed using permutational multivariate analysis of variance (PERMANOVA), with a significance threshold set at a p-value of < 0.05.

3. Results

3.1. Operational taxonomic unit analysis

3.1.1. Venn diagram

A Venn diagram was used to depict the shared core taxa among the different microbial community groups. In the first month, a total of 7,458 OTUs were identified, of which 1,081 OTUs were shared across all groups. In the second month, a total of 6,993 OTUs were detected, with 1,365 OTUs shared by all groups. In the first month, the number of OTUs in the negative control group (N1) was 864, whereas the positive control group (P1) harbored 1,268 OTUs. The treated groups M1D and M1T contained 1,626 and 1,038 OTUs, respectively. In the second month, the negative control group (N2) comprised 700 OTUs, while the positive control group (P2) comprised 1,253 OTUs. The treated groups M2D and M2T contained 1,169 and 1,226 OTUs, respectively (Figures 2A,B).

Figure 2.

Two Venn diagrams labeled A and B compare overlaps between four sets each, indicated by colored ovals with set names (N1, M1D, MIT, P1 for A; N2, M2D, M2T, P2 for B). Numbers represent the size of overlapping regions.

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Venn diagram representing the total number of OTUs across all groups. (A) Different colors indicate different groups in the first month. (B) Different colors indicate different groups in the second month. The overlapping circles represent shared OTUs between groups.

3.1.2. Alpha diversity analysis and rarefaction curves

The diversity within a microbial community is called alpha diversity. Within-sample diversity was assessed using the Chao1, observed species, and Shannon indices in the present study. These indices are used to estimate the number and diversity of species, including rare species that are not always observed in one sample. Alpha diversity was not significantly different among the four experimental groups in the first month. However, the negative control group (N1) showed a tendency toward greater richness and diversity than the other groups. The same situation was seen in the second month, with no overall significant differences between the four groups; however, the negative control group (N2) again showed higher richness and diversity than the treated groups. Notably, there was a statistically significant difference between the negative control group (N2) and the positive control group (P2) (Kruskal–Wallis, p < 0.05; Figures 3A,C,E). Rarefaction curves based on the Chao1, observed species, and Shannon indices were created to assess sequencing depth and to illustrate alpha diversity for all samples. Species richness at various sequencing depths for each group is shown in these curves (Figures 3B,D,F), suggesting that the sequencing depth was sufficient to cover most of the bacterial diversity in each group. The relative abundance of bacterial phyla at the sample level was also analyzed. The relative abundance of Bacillota (formerly Firmicutes)—a phylum comprising several well-characterized probiotic taxa—was higher in the negative control group (N) than in the treated groups (Figure 4).

Figure 3.

Panel A shows a boxplot comparing chao1 diversity estimates among different groups, while panel B presents corresponding rarefaction curves. Panel C displays observed features as boxplots for the same groups, with panel D providing related rarefaction curves. Panel E features boxplots for Shannon diversity index, and panel F depicts rarefaction curves by Shannon index across these groups. Asterisks highlight groups with significant differences. Colors differentiate groups throughout all panels.

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Alpha diversity metrics calculated from OTUs using the Chao1, observed species, and Shannon indices. (A,C,E) Boxplots comparing species richness and community distribution among all groups. The lines within the boxes represent the median, and the box boundaries represent the 25th and 75th percentiles, respectively. (B,D,F) Rarefaction curves showing the observed number of species across all groups. The abscissa indicates the number of sequences, and the ordinate indicates the average number of OTUs per sample in each group.

Figure 4.

Stacked bar chart showing relative abundance of multiple bacterial phyla across various samples, with Bacillota and Bacteroidota as dominant groups, and others present in smaller proportions based on the color-coded legend.

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Relative abundance within a single sample. Higher diversity and richness of phyla were observed in the negative control groups (N1C and N2C) compared to the positive control (P1C and P2C) and the treated groups (M1D, M2D, M1T, and M2T).

3.2. Impact of PhIP on the composition of the cecal microbiota

3.2.1. Impact of PhIP on gut microbiota profiles and dynamics

In the 32 mice, the effects of PhIP on the composition of the cecal microbiota were evaluated. The pie chart (Figure 5A) shows the predominant bacterial phyla in the first month, including Bacillota (formerly Firmicutes), Bacteroidota, Campylobacterota, Actinomycetota (formerly Actinobacteria), and Pseudomonadota (formerly Proteobacteria). Bacillota was the most abundant phylum in the negative control group (N1), with a relative abundance of 0.69, while Bacteroidota, Campylobacterota, Actinomycetota, and Pseudomonadota accounted for 0.186, 0.033, 0.063, and 0.011, respectively. The relative abundance of Bacillota also significantly dropped in the positive control group (P1) to 0.38, while the abundances of Bacteroidota and Campylobacterota significantly rose to 0.28 and 0.22, respectively. The relative abundances of Actinomycetota and Pseudomonadota were relatively low at 0.054 and 0.013, respectively. The level of Bacillota further decreased in the group that was given PhIP at 10 mg/kg (M1D), while the levels of Bacteroidota and Campylobacterota moderately increased in the same group to 0.324 and 0.219, respectively. The abundance of Actinomycetota decreased to 0.027, and Pseudomonadota remained at a low abundance of 0.013. Bacillota accounted for 0.452 of the relative abundance in the 20 mg/kg PhIP-treated group (M1T), while Bacteroidota and Campylobacterota increased to 0.280 and 0.150, respectively. The relative abundances of Actinomycetota and Pseudomonadota were also low (0.043 and 0.019, respectively), as indicated in the bar chart (Figure 5B). Non-metric multidimensional scaling (NMDS) based on the Bray–Curtis similarity index showed that the microbial community structure was different among the four groups. All major phyla were found in all groups, but the samples from the negative control group (N1) were grouped more closely together than the others. The communities of N1 and M1D differed from each other significantly according to PERMANOVA (p < 0.05) (Figure 5C).

Figure 5.

Three-panel scientific graphic comparing bacterial taxa distribution. Panel A: Four colored pie charts for N1, P1, M1D, and M1T groups, showing proportions of Bacillota, Bacteroidota, Campylobacterota, Actinomycetota, and Pseudomonadota. Panel B: Bar graph of bacterial phyla abundance by group with error bars and statistical labels. Panel C: Ordination plot with labeled, overlapping polygons representing group clustering based on coordinates.

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Relative abundance of Bacillota, Bacteroidota, Campylobacterota, Actinomycetota, and Pseudomonadota among the four groups in the first month. (A) Pie chart; (B) bar chart showing significant differences indicated by different letters (Kruskal–Wallis test and Dunn’s post hoc test, p < 0.05); (C) NMDS (Bray–Curtis similarity index): N1 (blue color), P1 (purple color), M1D (red color), and M1T (orange color). A significant difference was observed between N1 and M1D (PERMANOVA, p < 0.05).

The second month also showed varying data in terms of phylum-level distribution among the groups, as shown in Figure 6A. In the negative control group (N2), the dominant phylum was still Bacillota, with a relative abundance of 0.47, while the other phyla were present at relative abundances of 0.131, 0.156, 0.137, and 0.0107, respectively. The relative abundance of Bacillota decreased in the positive control group (P2) to 0.322, and the abundance of Actinomycetota also slightly decreased to 0.118. In comparison, Bacteroidota, Campylobacterota, and Pseudomonadota had moderate growth values of 0.295, 0.174, and 0.0138, respectively. In the PhIP-treated groups, Bacillota exhibited a slight decrease in both M2D (0.435) and M2T (0.416). A decrease was also observed in Actinomycetota in M2D (0.050) and M2T (0.070). Bacteroidota increased in both M2D and M2T, with a value of 0.295 and 0.291, respectively, while Campylobacterota also increased to 0.165 and 0.185, respectively. As shown in Figure 6B, the relative abundance of Pseudomonadota was low, although slightly higher in both treated groups (M2D and M2T), at 0.012 and 0.013, respectively. Furthermore, NMDS analysis based on the Bray–Curtis similarity index revealed that there was no tight grouping of samples from the negative control group (N2) with those from the treated groups. In the second month, there were no significant differences in the overall community composition among the groups (Figure 6C).

Figure 6.

Panel A displays four pie charts comparing the relative abundance of five bacterial phyla in groups N2, P2, M2D, and M2T. Panel B presents a bar graph titled “Second Month” showing mean proportions and standard deviations for the same bacterial phyla across the four groups, with different letters indicating statistical differences. Panel C shows a principal coordinate analysis plot with colored polygons representing the distribution and clustering of groups N2, P2, M2D, and M2T based on microbial community similarity.

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Relative abundance of Bacillota, Bacteroidota, Campylobacterota, Actinomycetota, and Pseudomonadota among the four groups in the second month. (A) Pie chart; (B) bar chart showing significant differences indicated by different letters (Kruskal–Wallis test and Dunn’s post hoc test, p < 0.05); (C) NMDS (Bray–Curtis similarity index): N2 (blue color), P2 (purple color), M2D (red color), and M2T (orange color). No significant difference was observed among all groups (PERMANOVA, p < 0.05).

3.2.2. Impact of PhIP at the phylum level

The relative abundance of Bacillota varied significantly between the groups in the first month, with lower abundance observed in the PhIP-treated groups. There were no differences in the abundance of Bacteroidota between the groups, but relative abundance was higher in the treated groups. Similarly, Campylobacterota did not exhibit any significant differences among the groups, but higher abundances were also observed in the treated groups. The relative abundance of Actinomycetota in the treated groups, on the other hand, decreased, although the change was not significant. Similarly, Pseudomonadota tended to be more abundant in the treated groups, with no significant difference between the groups. In the second month, the relative abundance of Bacillota remained lower in the treated groups; however, no significant differences were detected among the groups. A reduction in abundance was also noted in the treated groups for Actinomycetota, although this was not statistically significant. There was no significant difference between the groups for either Bacteroidota or Campylobacterota, but both of these phyla had relatively higher percentages in the treated groups. The relatively higher abundance of Pseudomonadota in the treated groups (Figures 7A,B) did not result in significant differences between the groups.

Figure 7.

Bar graphs depict the relative abundances of five bacterial phyla Bacillota, Bacteroidota, Campylobacterota, Actinomycetota, and Pseudomonadota across four experimental groups during the first month (A: N1, P1, M1D, M1T) and the second month (B: N2, P2, M2D, M2T). Error bars represent standard deviations. Statistically significant differences in the relative abundance of Bacillota during the first month are indicated by the letters “A” and “B,” whereas all other comparisons are denoted as N.S. (not significant).

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Relative abundance at the phylum level during the study: (A) First month; (B) second month. Significant differences (p < 0.05) are indicated by different letters. N.S. indicates no significant difference.

3.2.3. Impact of PhIP at the genus level

The phylum Bacillota comprises several families, including Lachnospiraceae, Lactobacillaceae, Ruminococcaceae, Erysipelotrichaceae, and others. Several genera within Lachnospiraceae were altered following PhIP treatment. Notably, the relative abundance of Roseburia, a well-known butyrate-producing genus, slightly reduced in the M1D and M1T (PhIP-treated) groups compared to the N1 and P1 groups in the first month; however, no statistically significant differences were observed among the groups in the second month. In contrast, the relative abundance of Blautia showed a generally non-significant increase in the M1D and M1T groups compared to N1 and P1 in the first month but was significantly elevated in the M2D and M2T groups relative to N2 and P2 in the second month. Furthermore, the abundance of Lachnospiraceae-NK4A136 group was higher in the M1D and M1T groups than in N1 and P1 in the first month and remained elevated in the M2D and M2T groups compared to N2 and P2 in the second month (Figure 8). The relative abundance of the genus Lactobacillus significantly decreased in the M1D and M1T groups compared to the N1 and P1 groups in the first month, whereas no significant differences were observed among the groups in the second month (Figure 9). Within the family Ruminococcaceae, the genera affected were Ruminococcus and Eubacterium siraeum. The abundances of Ruminococcus and Eubacterium siraeum slightly increased in the M1D and M1T groups compared to N1 and P1 in the first month and further increased in the M2D and M2T groups compared to N2 and P2 in the second month (Figure 10). Within the family Erysipelotrichaceae, the genera Dubosiella and Allobaculum were influenced by the treatment. The M1D and M1T groups exhibited a pronounced increase in Dubosiella, which was maintained in the M2D and M2T groups. In addition, the abundance of Allobaculum increased in the M1D and M1T groups and remained elevated in the M2D and M2T groups (Figure 11).

Figure 8.

Six-panel grouped bar charts illustrate the relative abundance of Bacillota across three taxonomic comparisons: Roseburia versus other Lachnospiraceae (panel A), Blautia versus other Lachnospiraceae (panel B), and Lachnospiraceae_NK4A136 versus other Lachnospiraceae (panel C). Each panel displays four experimental groups, with separate analyses conducted for the first month (N1, P1, M1D, MIT) and the second month (N2, P2, M2D, M2T). Data are depicted as bar plots with associated error bars for each group. Statistical significance is denoted by annotations such as “N.S.” (not significant) and the lettering scheme “A,” “B,” and “AB,” which indicate groups that differ significantly according to the applied multiple-comparison procedure.

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Relative abundance of Lachnospiraceae’s genera during the study. (A) Roseburia; (B) Blautia; (C) Lachnospiraceae_NK4A136. Significant differences (p < 0.05) are indicated by different letters. N.S. indicates no significant difference.

Figure 9.

Two-panel bar graph illustrating the relative abundances of Lactobacillus and other members of the family Lactobacillaceae within the phylum Bacillota. The left panel presents four experimental groups (N1, P1, M1D, MIT), among which statistically significant differences are denoted by the letters A and B. The right panel depicts four distinct groups (N2, P2, M2D, M2T) in which no statistically significant differences are observed, as indicated by the label N.S. (not significant). Error bars are shown for all bars, representing the variability of the data (e.g., standard error or standard deviation).

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Relative abundance of the genus Lactobacillus during the study. Significant differences (p < 0.05) are indicated by different letters. N.S. indicates no significant difference.

Figure 10.

Four bar charts depict the relative abundance of members of the phylum Bacillota, focusing on the genus Ruminococcus, other taxa within the family Ruminococcaceae, Eubacterium_siraeum, and additional Ruminococcaceae taxa, across experimental groups designated N1, P1, M1D, and MI1 for the first month, and N2, P2, M2D, and M2T for the second month. Each chart presents group means with associated error bars, accompanied by annotations indicating non-significant differences (N.S.) in relative abundance among the compared groups.

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Relative abundance of Ruminococcaceae’s genera during the study: (A) Ruminococcus; (B) Eubacterium_siraeum. Significant differences (p < 0.05) are indicated by different letters. N.S. indicates no significant difference.

Figure 11.

Four-panel figure depicting bar graphs of the relative abundances of Bacillota taxa Dubosiella, Allobaculum, and other members of the family Erysipelotrichaceae, stratified by experimental group and sampling time point. The y-axis represents relative abundance, and each panel illustrates group-specific distribution patterns accompanied by statistical significance annotations. Bars labeled “N.S.” indicate comparisons for which no statistically significant difference was detected. Statistically significant differences among groups are denoted by the superscript letters A and B.

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Relative abundance of Erysipelotrichaceae’s genera during the study. (A) Dubosiella; (B) Allobaculum. Significant differences (p < 0.05) are indicated by different letters. N.S. indicates no significant difference.

Several genera within the phylum Bacteroidota were significantly affected in this study. The relative abundance of the genus Bacteroides did not differ significantly among the experimental groups in the first month; however, a significant increase was observed in the M2D group compared to the other groups in the second month (Figure 12). The genus Alistipes exhibited increased relative abundance in the M1D and M1T groups compared to the N1 and P1 groups in the first month, and its abundance was likewise elevated in the M2D and M2T groups compared to the N2 and P2 groups in the second month (Figure 13). Although the abundances of Segatella and Prevotellaceae_UCG-003 did not differ significantly among the groups in either the first or second month, a trend toward increased abundance was observed in the treated groups over the course of the study. The relative abundance of Prevotellaceae_UCG-001 was slightly higher in the N1 group compared to the other groups in the first month, whereas an increase was detected in the treated M2D and M2T groups relative to N2 and P2 in the second month (Figures 14A–C). Within the phylum Actinomycetota, the dominant genus was Bifidobacterium, which showed higher relative abundance in the negative and positive control groups compared to the treated groups throughout the study, although these differences were not statistically significant in either the first or second month (Figure 15). In addition, the genera Adlercreutzia and Asaccharobacter exhibited increased relative abundance in the M1D and M1T groups in the first month and were significantly increased in the M2D and M2T groups in the second month (Figure 16). Within the phylum Campylobacterota, the genus Helicobacter showed a decrease in relative abundance in the N1 group compared to the treated M1D and M1T groups in the first month; however, no significant differences among the groups were detected in the second month (Figure 17). Among the Proteobacteria, the responsive genus was Escherichia–Shigella, which did not differ significantly among the groups in the first month but exhibited a marked increase in the M2T group in the second month (Figure 18).

Figure 12.

Two side-by-side bar charts illustrate the relative abundance of the genus Bacteroides within the phylum Bacteroidota. The left panel compares the N1, P1, M1D, and M1T groups for the first month, all of which exhibit similarly low relative abundances, with no statistically significant differences detected among them. In contrast, the right panel compares the N2, P2, M2D, and M2T groups for the second month and demonstrates a pronounced increase in Bacteroides abundance in the M2D group. Statistically significant differences among groups are denoted by the superscript letters A, B, and AB.

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Relative abundance of the genus Bacteroides during the study. Significant differences (p < 0.05) are indicated by different letters. N.S. indicates no significant difference.

Figure 13.

Two side-by-side bar charts labeled “Bacteroidota” depict the relative abundances of the bacterial taxa Alistipes and other members of the family Rikenellaceae across four experimental groups during the first month (N1, P1, M1D, M1T) and the second month (N2, P2, M2D, M2T). Error bars are included to represent standard deviation. No statistically significant differences (N.S.) are observed among the experimental groups for either bacterial taxon.

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Relative abundance of the genus Alistipes during the study. N.S. indicates no significant difference.

Figure 14.

Six-panel bar-plot figure illustrating the relative abundances of Bacteroidota genera and families across different experimental groups. Error bars denote inter-sample variability, and brackets labeled “N.S.” indicate that no statistically significant differences were detected between the compared groups in each panel. Panel A depicts the relative abundance of Segatella versus other members of the family Prevotellaceae. Panel B shows the relative abundance of Prevotellaceae_UCG-001 versus other Prevotellaceae taxa, and Panel C shows the relative abundance of Prevotellaceae_UCG-003 versus other Prevotellaceae taxa. For each row (A-C), the left and right bar charts represent distinct pairwise group comparisons. “N.S.” denotes comparisons in which no significant difference was observed.

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Relative abundance of Prevotellaceae’s genera during the study: (A) Segatella; (B) Prevotellaceae_UCG-001; (C) Prevotellaceae_UCG-003. N.S. indicates no significant difference.

Figure 15.

The bar graph panels depict the relative abundance of Bifidobacterium within the phylum Actinomycetota across the experimental groups N1, P1, M1D, and M1T (left panel) and N2, P2, M2D, and M2T (right panel). All groups exhibit comparably low mean values with overlapping error bars, and both panels are annotated as “N.S.”, indicating that no statistically significant differences were detected among the groups.

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Relative abundance of the genus Bifidobacterium during the study. N.S. indicates no significant difference.

Figure 16.

Side-by-side bar charts entitled “Actinomycetota” depict the relative abundances of Adlercreutzia, Asaccharobacter, and other members of the family Eggerthellaceae, with error bars representing data variability across distinct experimental groups. The left panel presents groups N1, P1, M1D, and M1T, for which no statistically significant differences are observed, as indicated by the label “N.S.” (not significant). The right panel displays groups N2, P2, M2D, and M2T, where statistically significant differences in relative abundance are denoted by distinct superscript letters (A, B, AB) above each bar, with different letters indicating pairwise statistical significance and shared letters indicating no significant difference.

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Relative abundance of Eggerthellaceae’s genera during the study. Significant differences (p < 0.05) are indicated by different letters. N.S. indicates no significant difference.

Figure 17.

Bar chart depicting the relative abundance of the genus Helicobacter within the phylum Campylobacterota across four experimental groups in each of two panels. Error bars represent the variability of the measurement’s standard deviation, and “N.S.” denotes that no statistically significant differences were detected between groups in either panel.

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Relative abundance of the genus Helicobacter during the study. N.S. indicates no significant difference.

Figure 18.

Two side-by-side bar charts depict the relative abundance of the genus Escherichia-Shigella within the phylum Pseudomonadota across distinct sample groups, labeled N1, P1, M1D, and M1T on the left panel, and N2, P2, M2D, and M2T on the right panel. In both panels, no statistically significant differences are observed among the compared groups, as indicated by the notation “N.S.” above the brackets spanning each set of bars along the x-axis. Error bars are shown for each bar, representing the variability of the standard deviation.

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Relative abundance of the genus Escherichia–Shigella during the study. N.S. indicates no significant difference.

4. Discussion

PhIP is formed at elevated temperatures during the cooking of meat, particularly through high-heat methods such as grilling, pan-frying, and barbecuing, and its formation is especially pronounced when cooking temperatures reach approximately 150–300 °C (Felton et al., 1986). It has been documented that PhIP is initially metabolized in the liver by two enzymes, specifically cytochrome P450 and CYP1A2 (Nakagama et al., 2005). In addition, a previous study demonstrated that exposure to PhIP induced perturbations in the composition of the intestinal microbiota (Zhao et al., 2020). Previous studies have demonstrated that the human gut microbiota has the capacity to biotransform PhIP into a conjugated metabolite, designated PhIP-M1. Specifically, it has been shown that the intestinal bacterium Eubacterium hallii can efficiently catalyze the conversion of PhIP to PhIP-M1 (Fekry et al., 2016). Accordingly, the objective of this study was to determine whether a two-month exposure to PhIP induces alterations in the composition of the cecal microbiota in mice. In this study, it was found that the distribution of OTUs varied between the negative control group and the treated group (PhIP), which is consistent with an earlier study that reported that the Venn diagram showed differences in OTU distribution between the CK (control) and PhIP groups. A total of 1,070 OTUs were shared by the two groups, while 447 and 514 unique OTUs were identified in the CK and PhIP groups, respectively (Zhao et al., 2020).

Richness and evenness constitute the principal components of bacterial α-diversity and serve as indicators of ecological resilience and stability. In the present study, the Chao1, observed species, and Shannon indices revealed reduced species richness, evenness, and distribution in the PhIP-treated groups relative to the negative control group. These findings align with previous reports documenting decreases in species richness and evenness following PhIP exposure (Zhao et al., 2021). Furthermore, a previous study reported that the CK (control) group exhibited greater microbial richness and evenness than the PhIP-treated group, as reflected by the Shannon, Simpson, Sobs, ACE, and Chao1 diversity indices (Zhao et al., 2020). In this study, the predominant bacterial phyla were Bacillota, Bacteroidota, Campylobacterota, Actinomycetota, and Pseudomonadota. Relative to the negative control group, the PhIP-treated group showed a reduced relative abundance of Bacillota and increased relative abundances of Bacteroidota and Campylobacterota. These findings are consistent with a previous report indicating that PhIP exposure disrupts gut microbiota composition and decreases the relative abundance of Firmicutes (Zhao et al., 2021). Moreover, a previous study found that PhIP caused a significant reduction in members of the Ruminococcaceae and Lactobacillaceae families (Zhao et al., 2021). In this study, it was found that the genera Roseburia and Lactobacillus, which are considered probiotic bacteria that support the GIT and immune system, slightly decreased in the PhIP-treated group compared to the negative control group. In addition, PhIP exposure has been reported to induce shifts in the composition of the gut microbiota at the genus level, characterized by reduced relative abundances of Lactobacillus, Ruminococcus_2, and Ruminococcus_1 (Zhao et al., 2021).

It was also reported that multiple bacterial taxa previously linked to proinflammatory responses, including Bacteroides massiliensis, Lachnospira, Ruminococcus, and Eubacterium fissicatena, were associated with intestinal inflammation (Liu et al., 2025). In addition, elevated relative abundance of Eubacterium siraeum may be indicative of intestinal dysbiosis, a condition that can adversely affect overall health status and gastrointestinal motility (Lai et al., 2024). Moreover, Ruminococcus gnavus has been reported to be associated with a growing range of intestinal and extraintestinal diseases, from inflammatory bowel disease (IBD) to neurological disorders (Crost et al., 2023). In addition, a previous study reported that certain species of Allobaculum mucolyticum are associated with inflammatory bowel disease (IBD) (van Muijlwijk et al., 2021). Moreover, Blautia and Lachnospiraceae_NK4A136 have been reported to play a role in reducing the risk of metabolic diseases (Pecyna et al., 2025; Wu et al., 2020). In this study, we observed increased relative abundances of Blautia, Lachnospiraceae_NK4A136, Ruminococcus, Eubacterium siraeum, Dubosiella, and Allobaculum in the PhIP-treated groups compared to the negative control group.

Previous reports have indicated that certain species, such as Bacteroides fragilis, function as opportunistic human pathogens and are capable of inducing infections within the peritoneal cavity (Sears, 2001). A previous study reported that Alistipes spp. are strongly associated with dysbiosis and have been implicated in the pathogenesis of colorectal cancer (Parker et al., 2020). In addition, a previous study found that the highest abundance of Alistipes was present in stool samples from a Crohn’s disease-like ileitis mouse model, indicating a positive association between Alistipes and colitis (Rodriguez-Palacios et al., 2018). In this study, a marked increase in the relative abundance of the genus Bacteroides spp. was observed in the M2D (PhIP)-treated group. In addition, the genus Alistipes exhibited a modest increase in relative abundance in the PhIP-treated group. These observations are consistent with previous reports indicating elevated proportions of Alistipes spp. and Bacteroides spp. in the PhIP + DSS treatment group, as well as in groups receiving dietary fiber supplementation (Zapico et al., 2024). Elevated abundances of Alistipes spp. and Bacteroides spp. have been documented in the context of high-fat dietary regimens, which are linked to increased production of lipopolysaccharides and reactive oxygen species—molecular mediators that may contribute to the promotion of tumorigenesis (Liu et al., 2025). These findings contrast with those of a previous study, which reported that the relative abundances of Alistipes spp. and Bacteroides spp. decreased in response to the consumption of processed fermented meat (Lee et al., 2024).

In this study, we observed that the relative abundances of several genera within the family Prevotellaceae, including Segatella, Prevotellaceae_UCG-001, and Prevotellaceae_UCG-003, increased in the PhIP-treated groups compared to the negative control group. Consistent with these findings, a previous study reported an elevated relative abundance of Prevotellaceae_UCG-001 in the PhIP-exposed group in an analysis of murine fecal samples (Zhao et al., 2021). Prevotellaceae_UCG-001 is a key commensal bacterium of the gastrointestinal tract that contributes to the degradation of dietary substrates but can function as an opportunistic pathogen under specific conditions (Yang et al., 2024). It has also been reported that Segatella species, formerly known as Prevotella copri, have been linked to several health problems, including rheumatoid arthritis, low-grade systemic inflammation in HIV infection, and glucose intolerance (Xiao et al., 2024). Bifidobacterium spp., a genus of bacteria commonly recognized for its probiotic properties, exhibited a slight decrease in relative abundance in the PhIP-treated groups compared to the control group. This observation is consistent with previous findings indicating that PhIP intake reduces the relative abundance of Bifidobacterium spp. (Zhao et al., 2021). Furthermore, it has been demonstrated that members of the genera Adlercreutzia and Asaccharobacter possess the metabolic capacity to biotransform dietary isoflavones, abundant in numerous plant species, particularly soybeans, into equol (4′,7-isoflavandiol) (Singh et al., 2023; Wang et al., 2005; Vázquez et al., 2017). In this study, PhIP administration led to a progressive increase in the relative abundance of Adlercreutzia and Asaccharobacter compared to the control group, indicating that these taxa may contribute substantially to the metabolic processing of dietary components. Helicobacter spp., which are implicated in gastric carcinogenesis, are capable of colonizing the stomach owing to their ability to withstand highly acidic conditions. It has been demonstrated that DNA damage induced by the heterocyclic amines MeIQx and PhIP is greater in H. pylori-infected groups than in non-infected groups (Lochhead and El-Omar, 2007). In the present study, an increased abundance of Helicobacter spp. was observed in the PhIP-treated group compared to the control group. Escherichia–Shigella encompasses a cluster of closely related bacterial species within the family Enterobacteriaceae, among which Shigella represents a pathogenic subgroup of Escherichia coli responsible for shigellosis. Consistent with these findings, a previous investigation reported that the relative abundance of Escherichia–Shigella was elevated in mice fed a diet containing high levels of fried soybean oil (Hu et al., 2024). In this study, the relative abundance of the Escherichia–Shigella genus was elevated in the PhIP-treated group compared to the control group.

5. Conclusion

Overall, PhIP exposure could affect the cecal microbiota by decreasing the relative abundance of Bacillota and Actinomycetota and increasing the relative abundance of Bacteroidota and Campylobacterota. These findings suggest that treatment with PhIP changes the relative abundance and overall composition of cecal bacterial communities, leading to dysbiosis of the cecal microbiota after short-term exposure. However, it should be noted that there are several limitations, including a relatively small sample size, which might have limited the ability to produce strong statistical evidence for subtle differences, and a short exposure period, which may not have been sufficient to reveal long-term effects. Further research is thus warranted to examine the effects of chronic exposure to PhIP and to utilize a functional approach, such as combining 16S rRNA-based microbiota profiling with metagenomic sequencing.

Acknowledgments

The author is grateful to King Abdulaziz University for its generous support and encouragement during the preparation of this manuscript.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. This study was funded by a grant from deanship of scientific research, King Abdulaziz University. (No.IPP: 626-247-2025)

Footnotes

Edited by: Charalampia Amerikanou, Harokopio University, Greece

Reviewed by: Francisco Denilson Rodrigues Gomes, Faculdade Ateneu Fortaleza, Brazil

Tülay Kandemir, Çukurova University, Türkiye

Data availability statement

The raw data generated in this study can be found in the NCBI BioProject repository under accession PRJNA1468591.

Ethics statement

The animal study was approved by the Animal House Facility of the Faculty of Pharmacy. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

NA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2026.1859419/full#supplementary-material

Data_Sheet_1.pdf (19.4KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data_Sheet_1.pdf (19.4KB, pdf)

Data Availability Statement

The raw data generated in this study can be found in the NCBI BioProject repository under accession PRJNA1468591.

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