Features of the gut microbiome in mice with polygenic disorders of energy metabolism

Full Text

Introduction

Metabolic syndrome (MetS) is a cluster of interrelated metabolic disorders—visceral obesity, insulin resistance, hypertension, and dyslipidemia — that significantly increase the risk of developing cardiovascular disease and type 2 diabetes (T2D) [1, 2]. Researchers are paying increasing attention to the role of the gut microbiota in the pathogenesis of MetS. Numerous clinical and experimental data indicate that dysbiosis — a disruption in the composition and function of the gut microbiota — may contribute to the development of systemic inflammation, impairment of the intestinal barrier function, and changes in the metabolism of short-chain fatty acids and other metabolites, which, in turn, contribute to the development of insulin resistance and metabolic disorders [3].

The gut microbiota plays an important role in the metabolism, absorption, storage, and expenditure of energy derived from food [4–6], including by influencing hormones and brain regions associated with eating behavior and digestion [7]. The gut microbiota–brain axis is a bidirectional signaling system that regulates changes in body weight by balancing appetite, energy storage, and energy expenditure [8]. Despite species-specific differences in the human and animal microbiota, common trends in its changes have been described in cases of obesity and disorders of carbohydrate and lipid metabolism. For example, in humans with type 2 diabetes and obesity, alpha diversity is typically reduced, the relative abundance of opportunistic enterobacteria and staphylococci increases, and the relative abundance of Akkermansia and Bifidobacteria decreases [9]. However, the data are sometimes contradictory and difficult to compare due to individual differences in the manifestation of MetS, comorbidity, the use of various medications, and limitations on the use of invasive research methods.

Experimental biological models are widely used to study the pathophysiological mechanisms of MetS and the role of the microbiota. Mice and rats are considered the most suitable mammals for these purposes, as they are characterized by small body size, rapid reproduction, and share many anatomical, physiological, and genetic similarities with humans. Despite differences in composition, the microbiota of mice and rats includes key taxa and functional groups similar to those found in humans, which allows these rodents to be used as models for studying host-microbiome interactions in a medical context [10].

To induce MetS in rodents, researchers use high-calorie diets, pharmacological agents, and surgical interventions. They also use lineages of recombinant animals with single or multiple genetic mutations in the genome. Genetically modified laboratory animal strains proposed as models for obesity and diabetes are increasingly used by researchers, despite the fact that the mutations responsible for the MetS phenotype in experimental models are rarely found in humans [11]. Each rodent strain has specific characteristics, which allows for a comprehensive study of the mechanisms underlying the development of these pathologies, as well as potential drugs for the treatment of these diseases.

Mice with recombinant genomes vary in appearance and exhibit complex phenotypes. The C57Bl/6J (B6) mouse strain is frequently used to study endocrine disorders, cardiovascular diseases, as well as in neurobiology, hematology, and immunology. They have no genetic predisposition to developing MetS; phenotypically, they show no signs of energy metabolism disorders, and their adrenal lipid concentration is low. Mice of this strain are most often used as control animals that do not exhibit the pathology under study [12].

Mice of the KK.Cg-a/a (KK) strain, bred in Japan in 1944, carry several genes that induce type 2 diabetes, particularly due to the characteristics of their hepatic and renal esterases. When fed a normal-calorie diet, these mice exhibit “mild” obesity, hyperglycemia, insulin resistance, and hyperinsulinemia. The manifestations of MetS were exacerbated in 1969 by М. Nishimura et al. through the transfer of a mutant allele from the gene locus responsible for coat color¹. The Ay subline differs from KK only in the dominant lethal allele Agouti lethal yellow, which ensures continuous synthesis of the agouti signaling protein (ASIP) in all tissues of the body. Animals of this sublineage, designated KK.Cg-Ay/a (Ay), are characterized by a brighter yellow coat color, pronounced obesity, hyperleptinemia, and increased lipogenesis in the liver accompanied by suppression of basal lipolysis [13]. ASIP is encoded by a gene responsible for controlling pigmentation and is expressed primarily in follicular melanocytes. It induces the production of the yellow and red pigment pheomelanin while simultaneously inhibiting the brown/black pigment; pheomelanin production is regulated through the antagonism of α-melanocyte-stimulating hormone at the melanocortin-1 receptor. In mice of Ay sublineage, obesity is caused by a combination of a 10–36% increase in food intake and an increase in its caloric efficiency due to an enhanced ability to conserve calories by storing them as fat. Agouti protein is known as a competitive antagonist of melanocortin receptors 1, 3, and 4. Furthermore, ASIP is an antagonist of the adrenocorticotropic hormone receptor [13].

KK.Cg-a/a (KK) and KK.Cg-Ay/a (Ay) mice are increasingly recognized as the best model organisms for studying diabetic nephropathy and hepatocellular damage [13]. However, despite the progress made, many aspects of the physiology of these strains remain understudied. Furthermore, their microbiota—whose properties may be closely linked to disturbances in energy metabolism—has not yet been sufficiently characterized.

In a preliminary phase of the study, we had already identified increased body weight and extractable fat, hyperglycemia, and—using real-time polymerase chain reaction (qPCR) — we analyzed the content of a limited number of bacterial taxa in fecal samples and the presence of gut dysbiosis [14].

The aim of the study was to identify the characteristics of the gut microbiome by sequencing the 16S rRNA-coding regions in inbred mice of KK strain and Ay sublineage with experimental MetS caused by various genetic disorders.

Materials and methods

Animals and their care

The study was conducted on intact male mice from the inbred lineages C57Bl6/J (B6; n = 31), KK (n = 32), Ay (n = 35), aged 100–140 days, which were obtained at the Institute of Physiology of the Russian Academy of Sciences from breeding stock acquired in 2021 from the Jackson Laboratory.

The animals were housed in a single room, 3–5 per cage, at a temperature of 23 ± 1°C and under an artificial photoperiod (light/dark — 12/12 h). Mice from all lineages were fed LbK-120 S-19 pelleted diet, containing 58% carbohydrates, 6% fat, and 19% protein, with an energy content of 2.50 kcal/g (BioPro). Filtered tap water was available ad libitum.

The experimental protocol was approved by the Bioethics Committee of the Institute of Physics, Russian Academy of Sciences (Animal Welfare Assurance No. A5952-01; No. 03/15 dated March 15, 2022). All experimental procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85-23, revised in 1996) and the European Convention for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes. Every effort was made to protect the laboratory animals and minimize their suffering throughout the study. The experiments were conducted in accordance with the ARRIVE 2.0 guidelines [15].

Morphometry

At the end of the experiment, following sedation with a CO₂/O₂ gas mixture (50/50 vol. %), the animals were euthanized by decapitation. Blood samples were then collected, and a necropsy was performed, during which the mass of the liver, pancreas, and fat depots was measured to an accuracy of 0.001 g: interscapular, subcutaneous dorsolumbar, inguinal, gluteal, renal, mesenteric, and bilateral gonadal fat deposits. The brain was removed, and the hypothalamus was dissected; it was placed in IntactRNA storage solution for 24 hours, then frozen and stored at –80°C until PCR analysis. Serum was isolated from the blood and also frozen and stored at –80°C until analysis on the VitaLine 200 biochemical analyzer (VitaLine Development Corporation). Glycogen content was determined spectrophotometrically in fresh liver tissue [16].

Biochemical indicators

Blood samples were obtained from an incision in the tail, and plasma glucose concentrations were measured using a Contour Plus One glucose meter (Ascensia Diabetes Care Holdings AG). Leptin concentrations were determined by enzyme-linked immunosorbent assay (ELISA) using the CEA448Mu kit (CloudClone Corp.). Serum cholesterol and triglyceride levels were measured using the corresponding test kits for the VitaLine 200 analyzer (all manufactured by Vital Development Corporation).

Semi-quantitative assessment of gene expression

Total RNA was isolated from the hypothalamus using a TRIzol analog (ExtractRNA reagent, Eurogen), after which the concentration and purity were assessed using a NanoPhotometer NP80 spectrophotometer (Implen). To remove residual genomic DNA, 2 μg of the isolated RNA was treated with DNase E (Eurogen); the treated RNA was precipitated with 80% ethanol in the presence of 0.1 M sodium acetate. Complementary DNA was synthesized from the RNA using MMLV reverse transcriptase with an oligo(dT)15 primer, which was then used in PCR. The PCR reaction contained 10 ng of cDNA, 400 pkm of forward and reverse primers, Taq DNA polymerase (Biolabmix), and SYBR-Green I dye (Eurogen); the reaction and data acquisition were performed on an Applied Biosystems Quant Studio 5 instrument (Thermo Fisher Scientific Inc.). The following amplification protocol was used:

• initial denaturation at 95°C for 5 min;
• 40 cycles of denaturation–annealing–extension (95–60–72°C for 15 s each; data collection took place during the extension phase);
• construction of a melting curve to monitor the formation of primer dimers and nonspecific reaction products. The sequences of the primers used were selected using the Primer-BLAST tool and synthesized by Eurogen (Table 1).

 

Table 1. Genes studied and their primers

Gene	Forward primer	Reverse primer	Product length, bp
Control genes
Beta-2 microglobulin — B2m	ACAGTTCCACCCGCCTCACATT	TAGAAAGACCAGTCCTTGCTGAAG	105
Genes of interest
Agouti-related protein — Agrp	GCCTCAAGAAGACAACTGCAGAC	AAGCAGGACTCGTGCAGCCTTA	136
Neuropeptide-Y — Npy	CCGCTCTGCGACACTACAT	TGTCTCAGGGCTGGATCTCT	68
Proopio-melanocortin — Pomc	CCATAGATGTGTGGAGCTGGTG	CATCTCCGTTGCCAGGAAACAC	138
Taste receptor, type 1, subunit 3 — Tas1r3	AGATGAGAACAGTGGCGGTG	TCTGGTAGGGCTAGGTTCCC	94

 

The expression levels of the genes of interest were calculated using the 2^–∆∆Ct method relative to the B2m control gene. RT-qPCR (real-time reverse transcription polymerase chain reaction) data are presented as a fold change in expression relative to the control group — the B6 (C57BL/6J) strain.

Fecal sample collection and analysis of the gut microbiota

To collect feces, the animals were isolated for 1 hour in cages lined with sterile filter paper. The samples were stored at –80°C until DNA extraction for determining the composition of the gut microbiota.

The HiPure Soil DNA Kit (Magen Biotechnology) was used to isolate DNA from the feces.

Metagenomic analysis was performed by sequencing the hypervariable V3 and V4 regions of the 16S rRNA gene using the MiSeq (Illumina), as previously described [17]. Bioinformatic processing of the sequencing results was performed using QIIME 2. The analysis included demultiplexing of reads, quality control, trimming of low-quality regions, denoising, assembly of paired-end reads, and removal of chimeric sequences using the q2-dada2 plugin, resulting in the generation of a table of amplicon sequence variants (ASVs). Taxonomic annotation was performed in QIIME 2 using the q2-feature-classifier plugin based on the SILVA 16S rRNA reference database, release 138.

Statistical analysis

For the analysis of microbiome data and the calculation of ecological metrics, statistical processing was performed in R v. 4.3.2 using the vegan, phyloseq, and microbiome packages; ggplot2 was used to visualize the results. Intergroup differences in alpha diversity indices were assessed using the Shannon and Simpson metrics. Differences in microbial community structure between groups were analyzed using PERMANOVA on a Bray–Curtis matrix (vegan, adonis2 function). PCoA and NMDS were used to visualize differences between samples. A heat map was constructed based on the relative abundance values of taxa at the genus level, which were subjected to a base-10 logarithmic transformation with the addition of a small pseudo-count to ensure proper handling of zero values. Differences between groups in the relative abundance of taxa were identified using the Mann–Whitney U test. For morphometric and biochemical data, the Student’s t-test was used. Differences were considered statistically significant at p <  0.05; for multiple comparisons, the Benjamini–Hochberg correction was applied.

Results

The gut microbiota of mice with impaired energy metabolism was compared with that of B6 mice that did not exhibit the pathology in question. Initially, we compared morphometric and biochemical parameters and assessed neurotransmitter expression in all groups of mice studied.

Morphophysiological and biochemical parameters

Mice from the KK and Ay groups significantly exceeded the B6 group in body weight and relative fat mass. Ay mice also differed from KK mice in having greater body and liver weights (Table 2).

 

Table 2. Morphometric parameters of mice from various groups

Morphometric parameters	B6	KK	Ay
Body weight, g	26.86 ± 0.95	31.91 ± 1.60*	37.83 ± 1.06*#
Body fat, %	3.03 ± 0.12	6.86 ± 1.63*	9.74 ± 0.55*
Liver weight, %	4.08 ± 0.14	4.66 ± 0.17*	6.99 ± 0.13*#

Note. Data are presented as the mean and standard error of the mean; *p <  0.05 compared with B6; ^#p <  0.05 compared with KK.

 

An analysis of blood biochemical parameters revealed that mice from the KK and Ay groups significantly exceeded those from the B6 control group in terms of basal glucose levels, cholesterol content, and leptin concentration (Table 3). At the same time, mice from the Ay sublineage, compared to the parent KK lineage, exhibited elevated glucose and leptin concentrations and higher serum triglyceride levels. However, KK and Ay mice had lower liver glycogen content than B6 mice and did not differ from each other in this parameter.

 

Table 3. Biochemical parameters of animals from various groups

Biochemical parameters	B6	KK	Ay
Plasma glucose, mmol/L	8.75 ± 0.24	12.12 ± 2.6*	31.73 ± 1.5*#
Leptin, ng/mL	0.99 ± 0.23	4.41 ± 0.48**	6.68 ± 1.08*#
Cholesterol, mmol/L	1.88 ± 0.16	2.95 ± 0.18**	2.91 ± 0.38*
Triglycerides, mmol/L	2.83 ± 0.44	3.70 ± 0.43	4.83 ± 0.63*
Liver glycogen, mg/g	60.34 ± 5.04	26.04 ± 2.86*	32.35 ± 2.08*

Note. Data are presented as the mean and standard error of the mean. *p <  0.05, **p <  0.01–0.001 compared with B6; ^#p <  0.05, ^##p <  0.01–0.001 compared with KK.

 

Expression of neuropeptide and taste receptor mRNAs in the hypothalamus

Significant interlineage differences in the expression levels of key neuropeptides regulating feeding behavior and metabolism, as well as the T1R3 taste receptor gene, were detected in the hypothalamus using real-time RT-PCR. Compared with the B6 strain, KK and Ay mice exhibited lower expression of the gene encoding Agouti-related peptide (AgRP); at the same time, POMC and NPY expression was significantly higher in KK and, especially, Ay mice (Fig. 1). The level of tas1r3 expression in Ay exceeded that in both B6 and KK, while it was lowest in KK.

 

Fig. 1. mRNA expression in the hypothalamus as determined by quantitative PCR.

The figures show the mRNA expression levels of genes in the hypothalamus relative to those in the control group, which is set as 1. *p <  0.05, **p <  0.01 compared to B6; ^#p <  0.05, ^##p <  0.05 compared to KK.

 

Assessment of gut microbiota diversity

Upon analysis of the sequencing results for fragments of the gene encoding 16S rRNA, it was found that the alpha diversity (species diversity within individual samples, characterized by the number of different species and how evenly they are represented) of the gut microbiota did not show significant differences when comparing different groups of mice. However, a comparison of the groups based on beta diversity (a measure that reflects differences in the composition of the microbiota between different samples) revealed significant differences in their microbiota communities (Fig. 2). Principal coordinate analysis (PCoA) based on Braey–Curtis distances showed that samples from the three groups partially form clusters along Axis 1 and Axis 2, although some overlap between points is observed. Analysis of the data using the PERMANOVA method revealed pronounced and statistically significant differences in the mouse microbiota, where group affiliation explains approximately 40.2% of the total variance (R² = 0.40; F = 27.87; p = 0.001). NMDS (non-metric multidimensional scaling) yielded an optimal stress of ≈ 0.16, which, according to the generally accepted scale, is considered a satisfactory reproduction of the original distances in two-dimensional space (stress <  0.20) [18].

 

Fig. 2. Comparison of beta diversity in the gut microbiota of mice from the Ay, KK, and B6 strains.

a — principal coordinate analysis based on Braey–Curtis distances; b — NMDS plot.

 

In the heat map showing taxon representation (Fig. 3), samples are grouped according to their metabolic status. Samples from group B6 cluster separately from samples from groups of mice predisposed to developing MetS, which is consistent with the results of PERMANOVA and the ordinations.

 

Fig. 3. A heat map showing the relative abundance of taxa at the genus level in fecal samples from mice in various groups, based on logarithmic values, with a small pseudo-count added.

 

Composition of the gut microbiota

The phylum Bacillota accounts for less than one-third of the gut microbiota of the animal lineages studied; it has a similar composition of genera and their abundance, consisting mainly of the families Lachnospiraceae, Ruminococcaceae, and Oscillospiraceae, the latter of which is more abundant in the KK and Ay animal lineages (Fig. 4, а). The main differences in the gut microbiota between the B6 and KK groups with Ay were evident in the abundance of the phylum Bacteroidota. In the Ay and KK groups, the percentage of representatives of this phylum was higher than in the control group (Fig. 4, b). A comparison at the family level within the Bacteroidota phylum revealed that, in Group B6, it is represented primarily by the Muribaculaceae family (Fig. 4, c).

 

Fig. 4. Relative abundance of Oscillospiraceae (а), Bacteroidota (b), and Muribaculaceae (c) in the gut microbiota of animals in groups Ay, KK, and B6.

 

A comparison at the genus level (within the phylum under study) revealed that Odoribacter, Alistipes, and Bacteroides were the most abundant in fecal samples from all animals (Fig. 5). However, the abundance of these taxa was significantly lower in mice from group B6. We were unable to determine the species composition of Bacteroides representatives; however, we clearly demonstrated a higher abundance of Bacteroides in KK-lineage mice compared to other animals.

 

Fig. 5. Relative abundance of various bacterial genera in the gut microbiota of animals in groups Ay, KK, and B6.

 

It was also noteworthy that the Akkermansia genus (phylum Verrucomicrobiota) constituted a large proportion of the B6 microbiota.

Another interesting finding was the higher abundance of Enterobacteriaceae (phylum Pseudomonadota, class Gammaproteobacteria) (Fig. 6).

 

Fig. 6. Relative abundance of Enterobacteriaceae in the gut microbiota of animals in groups Ay, KK, and B6.

 

Discussion

In this study, we compared the microbiomes of three groups of mice — sexually mature males of the same age (4 months): genetically modified KK and Ay mice with a spontaneously occurring metabolic disorder, and B6 mice, a lineage of relatively healthy mice. These groups of animals were characterized through a comprehensive analysis of their morphometric and biochemical parameters, as well as the expression of neurotransmitters and key receptors that influence the manifestation of digestive and metabolic disorders affecting energy metabolism in general, and carbohydrate and lipid metabolism in particular.

B6 mice were used as the healthy control group; their biochemical and morphometric parameters are well-documented in the literature and were consistent with these values in our studies [19].

As previously shown, male mice with whole-genome disruptions in the KK lineage and the Ay sublineage develop obesity and alterations in carbohydrate and lipid metabolism on a normal-calorie diet, with these changes being more pronounced in the Ay group. The identified differences in morphometric parameters and blood biochemical parameters are consistent with the baseline characteristics of the KK mouse lineaage [13, 14].

Comparisons of morphometric and biochemical parameters conducted in this study revealed Ay-mutation-associated increases in body weight and liver weight, hyperglycemia, and hyperleptinemia, which is fully consistent with data on the disruption of central and peripheral regulatory processes of feeding behavior and energy metabolism observed in carriers of the Ay allele [13].

This study is the first to demonstrate that KK and Ay mice have lower liver glycogen content than B6 mice, which may be associated with both impaired glycogen storage and enhanced glycogenolysis. On the other hand, it has been shown that leptin, by acting on the hypothalamus, suppresses glycogenolysis in the liver but stimulates gluconeogenesis independently of melanocortin [20].

Furthermore, marked differences were identified for the first time among B6, KK, and Ay mouse strains in the expression of the hypothalamic neuropeptides NPY and AGRP — which are involved in the regulation of feeding behavior and energy balance — as well as the T1R3 taste receptor gene [21].

Elevated blood glucose levels in KK mice, marked hyperglycemia in the Ay sublineage, and hyperleptinemia observed in these lineages are likely to contribute to the activation of POMC neurons and the inhibition of NPY/AGRP neurons in the arcuate nucleus of the hypothalamus [21]. In this study, we indeed observed suppressed AGRP expression and increased POMC expression in KK and Ay mice, which apparently reflects the effects of metabolic signals and hormones on the activity of these hypothalamic neuronal populations.

The tas1r3 gene not only plays a key role in the perception of sweet taste but also participates in the regulation of carbohydrate and lipid metabolism at various levels [22, 23]. A recent study revealed a significant effect of hyperglycemia in Ay sublineage mice on taste sensitivity compared to the wild-type KK phenotype [24]. It can be hypothesized that the higher expression of the T1R3 taste receptor gene in Ay mice is an additional mechanism exacerbating energy metabolism disorders.

Changes in the genome of the KK line and the Ay sublineage, which led to significant changes in the animals’ physiology, also affected their gut microbiome. As expected, this was more pronounced in the Ay group, although the KK and Ay groups were generally comparable in terms of β-diversity. Common characteristics were also identified when comparing the Ay and KK groups with the B6 control group in terms of taxon abundance. Most notably, there was a sharp decrease in the relative abundance of the genus Akkermansia, which confirmed our previous findings when assessing Akkermansia levels using qPCR [14].

A. muciniphila regulates glucose metabolism and, by breaking down mucus in the intestine, creates the substrate necessary for a large number of beneficial bacteria (lactobacilli, bifidobacteria, and other obligate members of the gut microbiota), which provide colonization resistance, influence glucose absorption, and participate in glycolysis processes directly and indirectly linked to gluconeogenesis. During mucin fermentation, acetic and propionic acids are formed; these acids possess antimicrobial activity and contribute to the development of colonization resistance. Furthermore, under the influence of Akkermansia, sulfate is released, which promotes the elimination of toxins and hydrogen sulfide [25].

An increase in the relative abundance of Bacteroidota was pathognomonic for the manifestations of MetS.

Another important argument in favor of the conclusion that the microbiota was disrupted in Ay and KK mice was the difference in the taxonomic composition of the phylum in question. In the normocinosis group B6, Bacteroidota was primarily represented by the family Muribaculaceae, which is widespread in the mammalian gut microbiota. As in our previous studies, it was shown that in C57BL/6J mice, this family accounted for 54.99–83.44% of the other taxa in the phylum under consideration [26]. In 2019, the family Muribaculaceae was annotated, renamed, and 685 species were clustered within it. These bacteria use starch as their primary energy source [27]. Like Akkermansia, members of the Muribaculaceae break down mucin, and their abundance is negatively correlated with body weight, hip circumference, waist circumference, and other measures [28].

In contrast to the control group, inbred mice showed an increase in the abundance of the genera Odoribacter, Alistipes, and Bacteroides, which are frequently associated with the development of colorectal cancer, inflammatory bowel disease, obesity, and type 2 diabetes [29]. Common risk factors for colorectal cancer include a high-fat diet, obesity, and age [30–32].

At the same time, members of these genera are obligate components of the mammalian gut microbiota, and their role may be beneficial. An increase in their abundance may indicate a compensatory response to the development of pathological processes, primarily those affecting the digestive tract. For example, bacteria of these genera produce short-chain fatty acids, which have anti-inflammatory and immunomodulatory effects [33]. Several studies have noted a protective effect of Alistipes in diseases such as colitis [34], liver fibrosis [35, 35], and cardiovascular diseases [36, 37]. Odoribacter splanchnicus is a candidate for the development of a new-generation probiotic [34] capable of producing secondary bile acids with antimicrobial properties, such as isoallo-lithocholic acid [39].

Bacteria of the genus Bacteroides express capsular polysaccharide A, which maintains immune system homeostasis, protects against experimental colitis caused by Helicobacter hepaticus and herpes simplex virus type 1, and prevents damage caused by Bartonella henselae [40].

It should be emphasized, however, that the presence of members of this genus in particular is often considered a marker of carbohydrate metabolism disorders and obesity in humans and animals. However, the specific species within this taxon are decisive. For example, in prediabetes, the populations of B. caccae and B. finegoldii typically increase [41]. In type 2 diabetes (T2D), the abundance of B. vulgatus [42] and B. plebeius [43] increases.

In a study of patients with obesity and impaired glucose tolerance, we detected an increase in the B. fragilis group using qPCR (the Colonoflor-16 Premium test system, AlfaLab) [44]. The unexpectedly greater increase in the relative abundance of Bacteroides in the KK group compared to the Ay group is evidently associated with the species balance of Bacteroides spp. and the dual nature of the functions of representatives of this genus, which include, for example, butyrate-producing and toxin-producing strains.

It is known that dysbiosis—often referred to as “low-grade inflammation” in MetS and most commonly caused by Gram-negative enterobacteria — can affect metabolic activity, leading to metabolic disorders, obesity, and type 2 diabetes [45].

In the present study, the presence of intestinal dysbiosis confirms our previous findings [14]. Indirect evidence of more pronounced intestinal dysbiosis caused by members of the family Enterobacteriaceae in Ay mice compared KK was previously identified using qPCR. The number of opportunistic Gram-negative enterobacteria (Enterobacter spp.) significantly exceeded the normal range in the Ay group and may have contributed not only to dysbiosis but also to low-grade inflammation due to their lipopolysaccharide-based endotoxin [46]. This endotoxin could have triggered changes in liver morphology and function in animals of this strain.

Currently, there is virtually no information on the effect of the Ay phenotype on the gut microbiota. In the only study available to us, carriers of the Agouti yellow gene exhibited gut microbiota dysbiosis after prolonged feeding on a high-calorie diet [47].

The mechanisms by which the Ay phenotype influences the gut microbiome are unknown. It can be assumed that this interaction is based on metabolic changes in the animals’ bodies, which are determined by the activity of central [48] and peripheral neuronal melanocortin receptors involved in the regulation of feeding behavior and energy balance. Thus, at the periphery (in enteroendocrine cells), MC4R receptors can activate the secretion of incretins, inhibit ion transport and intestinal motility, and stimulate afferent vagal nerve fibers [49]. It is also worth noting that melanocortin receptors are involved in maintaining the body’s immune status, as they inhibit inflammatory and immune responses, particularly hypersensitivity, neutrophil migration, and the local effects of irritants and inflammatory mediators [50].

Research into the mechanisms by which the microbiota affects the body in cases of energy metabolism disorders will continue. Already, high hopes are pinned on identifying correlations between specific taxa of the microbiota in the inbred mice used in the study and the animals’ physiological parameters associated with the manifestations of MetS.

Conclusion

Characteristics of the gut microbiome specific to inbred mice of the KK.Cg-a/a (KK) lineage and the KK.Cg-Ay/a (Ay) sublineage, which exhibit varying degrees of MetS severity, were identified. Their microbiome differed from that of the control group in that it had a lower relative abundance of the family Muribaculaceae and the genus Akkermansia, accompanied by an increase in the percentage of the phylum Bacteroidota (genera Alistipes, Odoribacter, and Bacteroides). Microbiome changes were more pronounced in mice from the Ay sublineage, which had more significant genomic defects. A distinctive feature of the microbiome of the KK.Cg-Ay/a Ay sublineage was an increase in the percentage of the families Enterobacteriaceae and Oscillospiraceae (Ruminococcaceae). The models used can be recommended for studying the pathogenesis of energy metabolism disorders, taking into account the role of the microbiota, as well as for developing methods to predict disease severity and correct dysbiotic disorders arising in the context of MetS.

 

¹ Nishimura M. Breeding of mice strains for diabetes mellitus. Experimental Animals. 1969;18(4):147–157.

About the authors

Nadezhda S. Novikova

Institute of Experimental Medicine

Author for correspondence.
Email: nadezhda.lavrenova.vrn@gmail.com
ORCID iD: 0000-0003-0029-0741

researcher, Laboratory of Biomedical Microecology

Russian Federation, St. Peterburg

Vladimir O. Murovets

Pavlov Institute of Physiology

Email: murovetsvo@infran.ru
ORCID iD: 0000-0001-5741-1562

Cand. Sci. (Biol.), senior researcher, Laboratory of physiology of digestion

Russian Federation, St. Peterburg

Anastasia L. Sepp

Pavlov Institute of Physiology

Email: anastasiya.sepp@bk.ru
ORCID iD: 0000-0002-0711-0224

Cand. Sci. (Veterinary), researcher, Laboratory of nutritional physiology

Russian Federation, St. Peterburg

Nikita S. Gladyshev

Petrovsky National Research Center of Surgery

Email: krinege@mail.ru
ORCID iD: 0000-0003-2732-5676

researcher, Departmrnt of histology

Russian Federation, Moscow

Egor A. Sozontov

Pavlov Institute of Physiology

Email: sozontovea@infran.ru
ORCID iD: 0009-0000-9779-9557

junior researcher, Laboratory of physiology of digestion

Russian Federation, St. Peterburg

Lyubov S. Alferova

Institute of Experimental Medicine

Email: lu_bashka@mail.ru
ORCID iD: 0000-0002-0052-0896

researcher, Department of molecular microbiology

Russian Federation, St. Peterburg

Alena B. Zalicheva

Institute of Experimental Medicine

Email: tarno@list.ru
ORCID iD: 0000-0002-9570-4769

researcher, Laboratory of molecular genetics of pathogenic microorganisms

Russian Federation, St. Peterburg

Vasily A. Zolotarev

Pavlov Institute of Physiology

Email: zolotarevva@infran.ru
ORCID iD: 0000-0001-8784-2435

Dr. Sci. (Biol.), Head, Laboratory of physiology of digestion

Russian Federation, St. Peterburg

Elena I. Ermolenko

Institute of Experimental Medicine

Email: lermolenko1@yandex.ru
ORCID iD: 0000-0002-2569-6660

Dr. Sci. (Med.), Associate Professor, Head, Laboratory of Personalized Microbial Therapy

Russian Federation, St. Peterburg

References

Supplementary files