The Influence of Multi-generational High-Fiber Diet on the Gut Microbiota of Root Voles (Microtus oeconomus)

The Influence of Multi-generational High-Fiber Diet on the Gut Microbiota of Root Voles (Microtus oeconomus) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Influence of Multi-generational High-Fiber Diet on the Gut Microbiota of Root Voles (Microtus oeconomus) yan zhang, Yihong Wang, Ruijun Wanyan, Baohui Yao, Zhaoxian Tan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4858686/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fiber influences animal metabolism by affecting the gut microbiota, and high-fiber diets are often considered beneficial. However, existing research primarily focuses on the short-term effects of high-fiber diets, with limited studies on their long-term and trans-generational impacts on gut microbiota. This study investigated the long-term high-fiber diets and trans-generational effects on root voles ( Microtus oeconomus )’ gut microbiota over five generations (F 0 to F 4 ) using 16S rRNA gene sequencing. Results showed that high-fiber diet significantly increased the diversity and complexity of gut microbiota and upregulated genes related to metabolism and immunity. The proportion of non-cellulose-degrading bacteria such as Proteobacteria and Spirochaetes decreased, while cellulose-degrading Firmicutes increased, raising the Firmicutes/Bacteroidetes ratio. Generational factors significantly influenced microbial community structure, reducing similarity. Over generations, both diets led to a reduction in beneficial bacteria such as Lactobacillus , Sporanaerobacter , and Clostridium , impairing the breakdown of proteins and starches. Meanwhile, potentially harmful bacteria like Desulfovibrio and Oscillospira increased, and the Firmicutes/Bacteroidetes ratio decreased, suggesting that a long-term, trans-generational uniform high-fiber diet may cause unfavorable shifts in gut microbiota. In summary, a high-fiber diet can increase gut microbiota abundance and diversity, promote cellulose-degrading bacteria, and upregulate certain metabolic genes, but long-term, uniform diets may cause gut microbiota imbalance, reducing beneficial bacteria and increasing potentially harmful ones. trans-generational effect high-fiber diet gut microbiota root voles Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION In recent years, gut microbiota has received increasing attention because of their key roles in host digestion and absorption, immune metabolism, psychological and behavioral health (Fan & Pedersen 2021). The various factors involved, such as dietary [ 1 ], genetic [ 2 ], environmental [ 3 ] and etc., can significantly alter the composition and function of gut microbiota, thereby affecting the host’s health, reproduction and survival. Among these influencing factors, dietary is very crucial in shaping an individual’s gut microbiome [ 4 ]. Given that food determines the nutrients available to gut microbiota, its type and quantity can determine gut microbial composition and activity. For instance, diets high in fat and sugar can encourage the growth of pathogenic bacteria, reduce the number and diversity of beneficial bacteria [ 1 ]. Meanwhile, the richness of a plant-based diet, particularly certain types, can enhance the abundance of beneficial microbes within the gut microbiota [ 5 ], hereby improving animal metabolism and health [ 6 ] . A diverse plant-based diet rich in fiber plays a crucial role in shaping the gut microbiome. One role of fiber intake is its ability to alter the gut microbiota, generating substantial short-chain fatty acids (SCFAs), indirectly benefiting cardiovascular metabolism and digestive health [ 7 ]. It might comprise various mechanisms and processes, including lipid and sugar absorption, fecal elimination, and the stimulation of intestinal alterations, thereby impacting its functional properties within the gastrointestinal tract [ 8 ]. Moreover, it contributes to enhancing glucose metabolism and glycogen storage in mice [ 9 ]. Additionally, fiber exhibits disease-improving properties, and it is associated with reduced cardiovascular disease, coronary heart disease and cancer mortality rates [ 10 , 11 ]. Fiber promotes beneficial gut bacterial growth and reduces carcinogenic bacterial population [ 12 ]. Because it possesses strong water absorption capabilities, fiber increases fecal volume, facilitating the formation of well-formed stools, promoting excretion, and shortening the time stools remain in the intestine [ 13 ]. This characteristic decreases the concentration of carcinogenic substances in the intestines and reduces the risk of disease. Finally, fiber intake improves gut health and influences hormonal balance and its metabolites through the gut-liver and gut-brain axes, significantly enhancing the reproductive performance and lactation of sows [ 14 ]. In summary, fiber can promote animal health by transforming into SCFAs, fostering the growth of beneficial bacteria, and regulating hormone metabolism, it plays a pivotal role in shaping the gut microbiota and maintaining overall health. Thus, it is necessary for investigating the influence of varying fiber content on the intestinal microbiota of animals. However, most studies on the impact of dietary fiber on gut microbiota were based on short-term or single-generation experimental designs. Long-term or transgenerational studies are rare, primarily because long-term research requires more time, more complex experimental designs and resources. However, multigenerational treatments can lead to changes in animal behavior or physiological traits, and the outcomes of multigenerational treatments may differ from those of single-generation treatments. For instance, environmental factors may affect offspring differently across generations [ 15 ], suggesting that single-generation studies might overlook the importance of transgenerational transmission. Environmental conditions can lead to different changes in individual physiological and behavioral traits across multiple generations [ 16 ], indicating that single-generation studies might not fully capture the impact of long-term environmental exposure. In microbiome research, modern industrialized diets change the relationship between gut microbiota and human health, and dietary habits can have long-term impacts on gut microbiota diversity that may be passed from one generation to the next [ 17 ]. With increasing generations of transmission, the gut microbial diversity of ICR mice gradually decreases, while basic metabolic pathways such as amino acid synthesis and carbohydrate metabolism are inhibited [ 18 ]. Long-term low-fiber diets may lead to the permanent extinction of fiber-dependent microbial communities, making recovery difficult [ 19 ]. Lack of fiber in lactating mother mice alters the microbiome of their offspring, resulting in mild inflammation and a predisposition to obesity [ 20 ]. Therefore, transgenerational studies are imperative for understanding how dietary fiber affects gut microbiota and how this impact is transmitted between parent and offspring generations. Research in animal models and humans indicates that a high-fiber diet in mothers can protect their offspring from the effects of fat accumulation, whereas a low-fiber diet in Brandt’s vole ( Lasiopodomys brandtii ) mothers may accelerate the growth of lean tissue in their offspring [ 21 ]. Therefore, given the lack of research on the multi-generational effects of high-fiber diets on animal gut microbiota and health, multi-generational experiments focusing on high-fiber and normal-fiber diets are needed to complement the understanding of the transgenerational factors affecting the gut microbiota. In this study, we chose the fast-reproducing root vole as our study subject to investigate the impact of high-fiber diets on the gut microbiota composition and overall health across different generations of this species (root voles have short generational cycles, so they are suitable for cross-generational exploratory experiments). We conducted a five-generation fiber-controlled transgenerational experiment on root voles, the aims of the study were: 1) explore the impact of a high-fiber diet on gut microbiota community structure and abundance in different generations; 2) test whether high-fiber diet leads to similar microbial community extinction or colonization phenomena. Based on these research objectives, we hypothesize that a high-fiber diet will enhance the diversity and stability of gut microbiota across different generations, predominantly promoting cellulose-degrading bacteria. This work will help address the research gap in fiber diet aspects in previous transgenerational experiments and provide a comprehensive understanding, revealing the long-term effects of a high-fiber diet on gut microbiota’s community structure. MATERIALS AND METHODS Animal rearing The root vole ( Microtus oeconomus ) population used in this study was captured from near the Qinghai Haibei National Field Research Station of Alpine Grassland Ecosystem in Menyuan County, Qinghai Province (N37°37', E101°19', elevation 3200 m). They were bred indoors for 3 years in the Laboratory of Northwest Institute of Plateau Biology, Chinese Academy of Sciences, in a group-housed manner. Each cage housed 3–4 voles, provided with sawdust as bedding, water dispensers, and allowed continuous access to feed to ensure ad libitum feeding and drinking. Root vole feed was custom-made by Beijing Keao Company. The normal-fiber diet contained approximately 5% crude fiber and 8% crude ash. The high-fiber diet replaced all crude ash with crude fiber, achieving a crude fiber content of approximately 13% (Table S1 ), while maintaining other nutritional constituents unchanged. Prior to experimentation, animals were individually moved to separate cages for a 2-week acclimation period. Table S1 Nutritional composition table of high and normal fiber diets for mouse feed Nutritional Composition of Mouse Growth Maintenance Feed Group C H Crude Fiber ≤ 5% 13% Crude Ash≤ 8% 0 Moisture and Other Volatile Substances≤ 10% 10% Crude Protein≥ 18% 18% Crude Fat ≥ 4% 4% Total Phosphorus 0.6–1.2% 0.6–1.2% Calcium 1.0-1.8% 1.0-1.8% This experiment adhered to the animal care and use guidelines established by the Chinese Academy of Sciences Animal Research Committee. All experimental procedures involving animals were reviewed and approved by the Institutional Animal Ethics Committee. The experiment received animal ethical approval number NIWPB-2015-031. We rigorously implemented various measures to ensure the welfare and ethical treatment of the experimental animals. Multi-generational reproduction experiment The experiment comprised two main groups: the high-fiber group (H group) and the control group (C group), with the former receiving an excessive amount of dietary fiber, whereas the latter was provided with a standard fiber intake. At the beginning of the F 0 generation, 8 pairs of male and female parental individuals were housed separately and provided with either high or normal-fiber feed. During reproduction, offspring birth time, gender and litter size were recorded. Pups cohabitated with dams for 30 days (as root voles typically wean at 28–30 days, during which their digestive systems mature sufficiently to transition from liquid milk to solid food. By around 30 days, the pups usually have developed enough independence to feed themselves). On the 31st day, pups were separated from their parents and marked as the next generation. Some individuals were randomly selected and dissected under anesthesia to collect cecal contents, while the remaining individuals were paired for subsequent breeding. During the experiment, record the number of offspring for each generation. This process was iterated for four generations, totaling five generations (Fig. 1 ), denoted as F 0 to F 4 . After offspring separation, a portion of the parents were dissected (select individuals for dissection with a nearly 1:1 sex ratio and record it in detail), the entire cecum was excised and stored in sterile tubes at -80°C. Figure 1 . Two groups were established: the experimental group, fed a high-fiber diet and the control group, fed a normal-fiber diet (low-fiber group). Each group consisted of 8 pairs of F0 parental male and female individuals, housed separately in 8 cages. They were provided either a high-fiber or normal-fiber diet. Upon reproduction, a portion of the individuals underwent dissection to collect cecal contents, while the remaining individuals were paired off for successive breeding of the next generation for five consecutive generations until F4." 16S rRNA gene sequencing and data processing Microbial DNA was extracted from cecal contents, followed by the amplification of the V3-V4 hypervariable regions of the 16S rRNA gene using specific primers (Robin et al. , 2016). The obtained PCR products were then sequenced using the Illumina MiSeq sequencing platform. The sequencing results were processed through filtering, assembly, and denoising to obtain OTU species abundance profiles. For detailed procedures, refer to Appendix 2. Data analysis Statistical analysis of community structure was conducted at various taxonomic levels, α and β diversity values were calculated. We applied the null model to investigate the assembly mechanisms of gut microbiota communities and the formation of diversity, assessing whether the observed outcomes align with random expectations. Two null model methods were employed: the β-nearest taxon index (β-NTI) [ 22 ] and the modified stochasticity ratio (MST) [ 23 ], a specific form of the normalized stochasticity ratio (NST) [ 23 ]. The “picante” and “ecodist” packages were employed to compute βNTI and RC-bray values, respectively. βNTI results and α and β diversity values were organized by sample grouping, and visual representations were generated using Origin software (version 2018, Origin Lab Corporation, USA). Species distribution stack plots and Venn diagrams were also created using Origin software. Using PERMANOVA with Bray-Curtis distance, we evaluated how high- and normal-fiber diets and transgenerational factors affect gut microbiota’s composition and generational similarity [ 24 ]. The distance index can be converted to similarity using the formula “Similarity = 1 - distance index.” Permutation tests assessed the significance of findings. PERMANOVA (Permutational Multivariate Analysis of Variance) was conducted to test for significant differences between the high-fiber and normal-fiber diet groups across different generations. To assess the extent to which transgenerational factors and fiber factors explain the variation, we particularly focused on the potential impact of “transgenerational factors” (i.e., through transgenerational experiments) on community composition, conducting 999 random permutation tests. Additionally, Origin software was used for regression curve analysis of specific microbial groups, the R “psych” package was employed to calculate correlation matrices. Network stability was assessed using the “igraph” package, and Gephi software was utilized for visualizing network graphs [ 25 ]. All analyses were conducted in R 4.3.2. RESULT The results showed significant differences in microbial diversity and composition between the high-fiber group (H group) and the control group (C group), with the H group exhibiting higher diversity, more complex and stable network structures, and upregulation of metabolic and immune pathways. Both the H group and the C group were characterized by deterministic factors governing community assembly processes. Transgenerational factors led to a decrease in community similarity between generations and resulted in differences in the composition of the gut microbiota, affecting the abundance of key microbial groups. Transmission experiment feeding We conducted a five-generation dietary fiber intervention experiment (F 0 -F 4 ) from April 2015 to December 2016. Throughout these five generations, a total of 111 root voles were dissected, and their cecal contents were extracted for 16S rRNA gene sequencing to analyze the gut microbial composition. These samples were labeled as S 1 -S 71 and S 78 -S 118 , in which S 1 -S 77 represented samples from the C group, and S 78 -S 118 represented individuals from the H group, respectively. Finally, a total of 96 samples were successfully sequenced, obtaining corresponding operational taxonomic unit (OTU) data, and their specific sample numbering is detailed in Table S2. Gut microbial composition and dynamics We generated stacked bar charts for the top 10 phyla and genera in the H and C groups to compare the effects of dietary fiber on gut microbial abundance at these levels. The results indicated that the phylum levels of Firmicutes , Bacteroidetes , Proteobacteria and Actinobacteria were the most abundant in the gut microbiota of root voles. Notably, the proportion of Bacteroidetes and Spirochaetes was lower in the H group compared with the C group (Figure S1 ). Throughout the generational changes, Bacteroidetes displayed an increasing trend in both H and C groups, whereas Actinobacteria and TM7 decreased. At the genus level, prominent genera in the gut of root voles included Lactobacillus , Desulfovibrio , Clostridium , Coprococcus and Ruminococcus . Specifically, genera like Lactobacillus and Sporanaerobacter decreased with generational progression in both H and H groups, whereas genera such as Desulfovibrio and Oscillospira significantly increased with generations (Figure S2). Our research findings indicate that the gut microbiome diversity in the H group is significantly greater than that in the C group. In generations F1-F4, the Simpson’s index in the H group remained consistently lower than that in the C group, indicating that the gut microbiota diversity was higher in the H group (Fig. 2 a). As observed in Fig. 2 c, the overall gut microbiota diversity was higher in the H group compared to the C group, with F 0 -F 3 generations showing consistently higher Shannon’s index. The minor difference in α-diversity in the F 0 generation resulted from the initial feeding of different fiber diets, where microbial divergence was yet to be significant. The insignificance of α-diversity in the F4 generation resulted from a limited number of samples in the H group, contributing to experimental errors. The Chao1 index in the H group consistently exceeded that in the C group (Fig. 2 e), confirming the higher gut microbiota diversity in the H group. However, when comparing the overall α-diversity indices between the H and C groups, no statistically significant differences were observed (Figs. 2 b, 2 d and 2 f, p-values > 0.05) due to within-group variability and limited sample sizes. NMDS analysis exhibited substantial differences in gut microbiota composition between H and C groups (Fig. 3 a-b). The gut microbiota of each generation in the H and C groups showed significant differences (Fig. 3 c-d), forming four distinct modules. Lower stress values indicate better representation of similarities or differences among samples in the NMDS plot. Stress values were all below 0.2 in the NMDS plots of each generation (Fig. 3 e-i), with P < 0.05, indicating high feasibility of classification and significant differences among gut microbiota compositions across groups. Notably, gut microbiota from each generation in the H and C groups exhibited distinct clustering, demonstrating that the dietary fiber content significantly altered gut microbiota composition and abundance between the two groups.The significant inter-group differences observed through NMDS analysis provided a basis for us to use PERMANOVA for pairwise comparisons, which allowed us to quantify these differences' statistical significance. Table S3 shows that some generational comparisons yielded significant P values (< 0.05), but there was no consistent trend of increasing significance over time. The R² values did not show a clear increasing trend either. Although significant differences existed between certain generations, they did not systematically increase, suggesting that dietary fiber content had a relatively stable impact on the gut microbiome throughout the study. PERMANOVA analysis also revealed that transgenerational factors significantly affected community composition (Degrees of Freedom = 1; Sum of Squares = 0.601; F = 1.5133; p = 0.042*), as indicated by the significant R² value and P < 0.05. Figure 2 . (a) Box plots of Simpson index for each generation in the high and control groups (P 0.05). (c) Box plots of Shannon index for each generation in the high and control groups (P 0.05). (e) Box plots of Chao1 index for each generation in the high and control groups (P 0.05). Figure 3 . (a) Bray-Curtis NMDS plot for all samples in the high and control groups. (b) Bray-Curtis NMDS plot for ten generations (F0-F4) in the high and control groups. (c) Bray-Curtis NMDS plot for four generations in the control group. (d) Bray-Curtis NMDS plot for four generations in the high-fiber group. (e-i) Bray-Curtis NMDS plots for the five generations (F0-F4) in the high and control groups. Heatmaps illustrated the relative abundance of the top 50 OTUs in the H and C groups, highlighting significant variances in microbial distribution across fiber diets (Figure S3). Clustering analysis identified stable patterns over generations, with distinct groupings in both H and C groups that underscored the influence of dietary fiber on microbial community dynamics. Venn diagrams further demonstrated the shared OTUs between groups, clarifying similarities and differences in microbial communities across generations (Figure S4). Gut microbial community similarity and stability As presented in Table S4, our results showed that similarity gradually decreased from F 0 -F 1 to F 2 -F 3 in the C group. With the progression of generations, the composition of the gut microbial community in the C group changed, and the magnitude of these changes increased, leading to a gradual decline in community similarity. The lowest similarity during the F 2 -F 3 period suggested the most significant difference in microbial community composition between these two generations. Conversely, an increase in similarity from F 3 -F 4 may imply a trend toward recovery or periodic changes in the community after significant alterations. However, in the H group, the similarity between generations F 0 and F 1 was extremely low, reflecting significant changes in community structure and indicating a substantial difference in gut microbiota composition between the F 1 generation and the F 0 generation. From F 1 to F 2 , the community similarity significantly increased indicating a close community composition between these two generations. The similarity decreased from F 2 to F 3 and further decreased from F 3 to F 4 , showing ongoing changes in community composition, with the similarity of the gut microbial community decreasing over generations. The data from both fiber groups, revealed the similarity of the community changes with the progression of generations, showing that the community composition diversified and variability increased. Compared with the C group, the H group shows greater community stability, especially in facing environmental or biological stresses, where its ability to recover and adapt was stronger. This ability was exemplified by the significant increase in similarity from F 1 to F 2 . These findings highlighted the important impact of fiber intake on the dynamics of the gut microbial community. Subsequently, we calculated the Firmicutes/Bacteroidetes (F/B) ratio within the gut microbiota of the H and C groups to explore the impact of dietary fiber content. Significant differences were observed in the effect of high- and normal-fiber diets on the F/B ratio across different generations of root voles. In the C group, the F/B ratio increased from F 0 generation to F 1 generation, followed by a significant decline in subsequent generations. Conversely, in the H group, the F/B ratio started at an extremely high value in the F 0 generation and then showed a marked decrease, dropping by the F 4 generation (Table S5). On the basis of data fitting results, the F/B ratio in both groups exhibited a significant pattern of decreasing across generations (Fig. 4 ). Overall, the F/B ratio in the H group was significantly higher than that in the C group for most generations, suggesting that dietary fiber content significantly influenced the abundance of microbial populations within the phyla Firmicutes and Bacteroidetes . Additionally, transgenerational factors had a significant impact on the F/B ratio, with both H and C groups showing a trend of decreasing F/B ratio across generations. Figure 4 . Regression analysis chart of the Firmicutes/Bacteroidetes (F/B) ratio across generations. The horizontal axis represents generations from F0 to F4, labeled as 0, 1, 2, 3 and 4, respectively. Blue dots represent the high fiber group (H group), purple dots represent the control group (C group), the green area represents the 95% confidence interval for the high fiber group, and the pink area represents the 95% confidence interval for the control group. Mechanisms of Community Assembly and Network Analysis MST analysis revealed that the overall values for both H and C groups mostly fell below 50% (Fig. 5 a-b). This result indicated that community composition and diversity were mainly influenced by deterministic processes [ 26 ]. Nonetheless, individual MST values for each generation displayed a mix of values above and below 50%, suggesting an interplay between deterministic and stochastic factors across different generations. β-NTI analysis, aimed at assessing the influences of deterministic and stochastic factors on community assembly, showed that the average β-NTI values within the H and C groups exceeded 2, pointing to a significant influence by deterministic factors, specifically heterogeneous selection [ 27 ]. This pattern was consistent within both groups, as illustrated in Fig. 5 c. Further analysis for different generations within these groups (Figs. 5 d-f) indicated that the average β-NTI values between the H and C groups were 2 in the F 1 -F 4 generations. This result suggested the influence of stochastic and deterministic factors in the F 0 generation. While most community changes were driven by deterministic factors, some were influenced by stochastic processes. Analysis using the Raup-Crick distance categorized random processes into heterogeneous selection, dispersal limitation, homogenizing dispersal and undominated. The C group showed approximately 72% heterogeneous selection, 23% dispersal limitation and 5% undominated. In contrast, the H group had about 59% heterogeneous selection, 18% undominated and 23% dispersal limitation (Figs. 5 g-h).To further understand the impact of these stochastic and deterministic processes on microbial communities, we analyzed the co-occurrence patterns and network robustness of the top 100 operational taxonomic units (OTUs) in the H and C groups. In the H group, the number of nodes, edges, and modules were all higher than those in the C group, indicating that the co-occurrence network complexity was greater in the H group than in the C group (Fig. 6 a-b). The H group showed significantly higher average and natural connectivity compared with the C group (Fig. 6 c), suggesting a tighter and more complex microbial network. Additionally, the higher natural connectivity in the H group indicates better maintenance of functionality and structure when nodes are removed (Fig. 6 d). Overall, a high-fiber diet enhances the complexity and robustness of the gut microbiota network. Figure 5 . (a) Represents the overall MST values for the high and control groups. (b) Depicts the MST values for generations F0 to F4 in both high and control groups. (The red dashed line represents the 50% MST value line used to differentiate deterministic and stochastic processes) (c) βNTI values within and between high-fiber and control groups; (d) βNTI values between F0-F4 generations within high-fiber and control groups; (e) βNTI values within and between generations in the control group; (f) βNTI values within and between generations in the high-fiber group. (The red dashed line represents the threshold at βNTI absolute value equals 2, used to discern between stochastic and deterministic community assembly processes) (g-h) Bray-Curtis-based Raup-Crick distance values (RC-bray). Panel a illustrates the overall RC values for the high fiber and control groups, while panel b shows the RC values for each generation separately. Figure 6 . Co-occurrence network diagram. (a) shows the situation of the control group, while (b) depicts the situation of the high-fiber group. (c) Network Robustness Chart. The 'Average degree' chart in panel a illustrates the average connectivity of nodes within the network, indicating the average number of connections each node has to other nodes. Panel b's 'Natural connectivity' chart is commonly used to assess the stability and robustness of networks, describing the network's ability to maintain connectivity and overall structure when nodes are removed or damaged. The horizontal axis 'Remove nodes' simulates the quantity of nodes removed from the network, while the vertical axis represents the level of stability. Predictive functional profiling and pathway analysis of gut microbiota Figure 7 shows the top 15 enriched pathways, including those related to the immune system, digestion, metabolism, cellular signaling and regulation, cellular biology, biochemical processes, and pathways linked to carcinogens and infections. Functions within the immune system, such as antigen processing and presentation, showed an upregulated trend in the H group, suggesting efficient recognition and processing of exogenous antigens. Digestive system functions, like glycolysis/gluconeogenesis, were also upregulated, indicating high energy metabolism or regulation of cellular pathways. Retrograde endocannabinoid signaling in cellular signaling and metabolic regulation were upregulated, potentially affecting neural regulation or signaling. Additionally, cellular biology and biochemical processes, such as phagosome and ribosome activities, showed upregulation, possibly reflecting enhanced cellular metabolism and protein synthesis. Pathways related to carcinogenic agents and infections such as chemical carcinogenesis reactive oxygen species, legionellosis, and salmonella infection also displayed an upregulated characteristic in the H group. Figure 7 . Bar chart displaying the top 15 enriched functions based on KEGG pathway analysis using GSEA. DISCUSSION Impact of high-fiber diet and transgenerational factors on gut microbiota abundance and diversity The gut bacteria play a crucial role in maintaining individual health, bolstering the host's intestinal defense system and sustaining normal intestinal function, with their composition influenced by dietary factors [ 28 ]. Individuals consuming fiber-rich dietary formulas exhibit milder negative symptoms like diarrhea and abdominal discomfort compared with those on a fiber-free formula. Fiber-rich diets contribute to maintaining the stability of essential bacteria in the gut, enhancing gut microbiota diversity [ 29 ]. A high-fiber diet scheme elevates α-diversity of the human gut microbiome and stimulates bacteria producing SCFAs [ 30 ]. Fiber interventions, particularly with fructans and galacto-oligosaccharides, lead to an increase in the abundance of Bifidobacterium and Lactobacillus in feces [ 31 ]. Similarly, our study demonstrated that the gut microbiota diversity in the H group was richer, consistent with the findings of Koecher K J [ 29 ]. These findings collectively suggest that a H diet may help preserve gut microbiota diversity and maintain a relatively stable state, promoting intestinal health. In the H group, the gut microbial community displayed lower proportions of Bacteroidetes and Spirochaetes . Both these microbial phyla play essential metabolic roles in the colonic environment [ 32 ]. Bacteroidetes participate in carbohydrate and nitrogen compound fermentation, along with the biotransformation of bile acids and other sterols [ 33 ], which are critical in obesity and metabolic diseases [ 34 ]. Spirochaetes are often associated with health issues. The reduced proportions of Bacteroidetes and Spirochaetes in the H group might induce changes in digestive capabilities, affecting energy absorption and utilization and potentially leading to digestive issues or altered energy supply needs. Then, regression analysis was conducted on the top six phyla of total abundance (Fig. 8 a). In the C group, Proteobacteria levels increased with generations, whereas a declining trend was observed in the H group. Consistent with previous research, increased fiber intake might cause changes in the gut microbiota composition in patients with inflammatory bowel disease, resulting in decreased Proteobacteria [ 35 ]. Thus, this decline in Proteobacteria in the H group may suggest enhanced microbial balance and improved gut health. Conversely, Spirochaetes increased with generations in the C group and decreased in the H group. Although Spirochaetes are commonly associated with diseases, these findings lack significance and are provided as reference. A similar generational replacement regression analysis was performed on the top ten genera (Fig. 8 b), revealing an upward trend in Desulfovibrio in the C group and a downward trend in the H group. An increase in Desulfovibrio might lead to an imbalance in the intestinal ecosystem, with its hydrogen sulfide production potentially exerting toxic effects on intestinal epithelial cells, impacting gut health [ 36 ]. Moreover, this imbalance may linked to metabolic disorders and obesity [ 37 ]. Therefore, it might unfavorably impact the gut health of the H group; however, further research is needed to ascertain its specific effects. Both H and C groups demonstrated a significant decrease in the proportions of Lactobacillus , Sporanaerobacter , Clostridium and Sporanaerobacter with generational progression. Conversely, several unclassified genera from the family S24-7, order Clostridiales (UG), and families Lachnospiraceae (UG), Desulfovibrio , Oscillospira showed a noticeable increase in proportions. This shift might be attributed to dietary differences, suggesting that the gut microbiota gradually adapted to distinct high- and normal-fiber diets with generational progression. Figure 8 . (a) Regression analysis chart of the abundance of the top 6 phyla of gut microbiota. (The upper graph represents the control group, while the lower graph represents the high-fiber group. The pink shaded area represents the 95% confidence interval. The horizontal axis denotes generations from F0 to F4, labeled as 0, 1, 2, 3 and 4, respectively.)(b) Regression analysis of the top 10 genera at the phylum and genus level of gut microbiota. The upper panel illustrates the situation in the control group, while the lower panel illustrates the high-fiber group. The pink shadow represents the 95% confidence interval. The x-axis indicates 0, 1, 2, 3, 4, representing generations F0 to F4, respectively. Firmicutes and Bacteroidetes are pivotal bacteria involved in the catabolism of food remnants. Through intricate metabolic mechanisms, they aid in the digestion of fibers and polyphenolic compounds, employing cooperative and shared metabolic pathways [ 38 ]. Bacteroidetes dominate the intestinal microbiota, encompassing various Gram-negative bacteria that release energy by breaking down fibers and starches, contributing to the catabolism of bile acids and proteins [ 39 ]. Firmicutes comprise mostly Gram-positive bacteria, synthesizing SCFAS like butyrate and playing a vital role in the host’s nutrition and metabolism [ 40 ]. The ratio between Firmicutes and Bacteroidetes, often abbreviated as the F/B ratio, is considered an indicator of gut microbial imbalance; the former is often linked to obesity [ 41 ], whereas the latter is associated with inflammatory bowel disease [ 42 ]. In this study, the H and C groups showed a decreasing F/B ratio across generations. Firmicutes decreased, whereas Bacteroidetes increased. This shift may result from transgenerational transmission gradually altering the microbiome. The microbiome composition is influenced by the current diet and maternal microbiota passed to the offspring. Research showed that maternal gut dysbiosis can affect placental function and fetal development, increasing health risks in the offspring. As the population of beneficial bacteria decrease across generations, gut dysbiosis may worsen, further increasing health risks [ 43 ]. During long-term experiments, microbiota drift can occur, where some bacteria disappear and are replaced by others, also affecting the F/B ratio. However, across nearly all generations, the F/B ratio in the H group consistently exceeded that of the C group, indicating that high-fiber feeding may promote the relative growth of Firmicutes. This result could be due to the different components in high-fiber feed, such as soluble and insoluble fibers, and their utilization by gut microbiota. When diets are high in fat and protein, Bacteroidetes became more prevalent, whereas they are less so when carbohydrate consumption is high [ 39 ]. A comparative study of the fecal microbiota of American children on an animal-based diet versus Bangladeshi children on a plant-based diet found that the F/B ratio in the feces of American children was significantly lower than that of Bangladeshi children [ 44 ]. Another experiment involving increased dietary fiber intake while avoiding a Western diet found an increase in the F/B ratio within the gut microbiota [ 45 ]. These findings suggested that increasing dietary fiber may raise the F/B ratio in the gut microbiota, with an elevated proportion of Firmicutes, consistent with our results. Some Firmicutes species can efficiently decompose polysaccharides like fiber by using them as an energy source. Thus, a high-fiber diet promotes Firmicutes growth. However, this effect weakened over generations, possibly due to adaptive changes in the gut microbiome under long-term high-fiber pressure. Microbial community drift during the feeding experiment might also influence the long-term F/B ratio trend. The stability and adaptability of microbial communities are complex, involving many interacting factors, including the host’s physiological state, environmental conditions and gut microbiota interactions. Long-term fiber feeding experiments allow us to observe gut microbiota changes and adaptations across generations. Future research should explore the mechanisms behind gut microbiota stability and how they adapt to long-term dietary fiber changes. Impact of transgenerational experiments on gut microbiota similarity Transgenerational factors have significantly impacted the composition of root vole’s gut microbiota, leading to differences in community similarity across generations. By comparing the community similarity across generations under diets with varying fiber contents, we observed that from generation F 0 to F 1 , the community changes (decrease in similarity) in the H diet group were significantly greater than those in the L diet group, indicating a more pronounced impact of high-fiber diets on the microbial community. However, between generations F 1 and F 2 , the similarity in the H group increased, while it continued to decline in the C group. This result suggested that the community changes induced by the high-fiber diet might stabilize or adapt after one generation, whereas the community structure in the C group continues to evolve, with a persistent decrease in similarity. From generation F 2 to F 4 , both H and C groups exhibited fluctuations in community similarity, but the fluctuations were larger in the H group, suggesting that high-fiber diets might lead to a more dynamic and unstable community composition. Overall, high-fiber diets had a more intense and direct impact on the microbial community at the initial stage of the experiment, but the microbial community might gradually adapt to this diet over time, resulting in an increase in similarity. By contrast, the impact of a normal fiber diet on the community was more gradual and sustained. This difference may stem from the varying types and amounts of nutrients provided by diets with different fiber contents, thereby affecting the structure and function of the community. To explore if different fiber contents affect community similarity within the same generation, we calculated the community similarity for both H and L diet groups within the same generation. At generation F 0 , the community similarity between the two diet conditions was relatively high, indicating that the microbial community structures were quite similar at the beginning of the experiment. From generation F 0 to F 3 , the similarity gradually decreased, reflecting that the structural differences in the microbial communities under different fiber diet conditions became increasingly significant over time. By generation F 4 , although the similarity between the two groups slightly increased, it remained lower than that in the initial generation, possibly indicating that the microbial communities became stable after undergoing significant initial changes. Analysis of the community similarity between different fiber diet groups within the same generation revealed that the dietary factor of fiber content had a significant impact on the structure of the gut microbial communities, causing notable changes in community composition and similarity. Mechanisms of Gut Microbiota Community Assembly and Network Stability In our study, most taxa had MST values below 50% and β-NTI values above 2, suggesting that deterministic factors particularly heterogeneous selection govern the microbial community [ 26 , 27 ]. Thus, the presence and relative abundance of species are driven by specific, predictable ecological processes rather than random events. The results of Raup-Crick distance analysis showed that heterogeneous selection predominated in both H and C groups, indicating that deterministic factors play a crucial role in shaping the microbial community structure. Homogeneous selection and heterogeneous selection are both deterministic processes. Homogeneous selection refers to a situation where selection pressures are uniformly distributed across a population or environment, meaning all individuals face the same pressures [ 46 ]. By contrast, heterogeneous selection involves unevenly distributed pressures [ 27 ], with different individuals experiencing varying selective forces. The influencing factors of heterogeneous selection pressures may include environmental heterogeneity [ 47 ] and uneven resource distribution [ 48 ]. In our study, environmental heterogeneity was reflected in the local environments within the mouse cages, and uneven resource distribution was likely due to the difference in fiber content between the two groups. Moreover, the proportion of heterogeneous selection was higher in the C group than in the H group, suggesting a greater influence from these factors. Although the community structure is primarily influenced by deterministic factors, there is also a proportion of random factors at play. The artificial feeding environment provides stable food and water supplies, resulting in high resource abundance and weak selection pressures. Studies have shown that when selection pressures are weak, environmental conditions have less impact on animals survival and reproduction [ 49 ]. Under such conditions, ecological drift plays a significant role in community assembly. Microbiota with low abundance are more susceptible to drift than their counterparts, as slight negative changes in their abundance may lead to local extinction [ 50 ]. In this study, the C group had lower overall abundance, making it more susceptible to drift, compared with the H group. This result may explain the influence of some stochastic factors on community assembly observed in our experiment. Overall, our research highlighted the complexity of community assembly processes by demonstrating the interplay between deterministic and stochastic factors in gut microbiota assembly. The findings revealed that the H group had more nodes and modules in its network indicating a more complex network structure, compared with the C group [ 51 ]. The H group also showed higher natural and average connectivity, indicating better network robustness. Network robustness refers to the ability of a network to continue functioning and preserve its overall structural characteristics when confronted with perturbations such as node deletions, intrusions, or other adverse conditions [ 52 ]. Several studies have assessed the level of network robustness by dismantling nodes and testing the natural and average connectivity of the network, and they reported that dismantling nodes results in a decrease in natural and average connectivity [ 53 ]. Therefore, trends in natural and average connectivity reflect changes in network robustness. Natural connectivity measures the overall cohesiveness of the network, whereas average connectivity indicates the density or tightness by showing the number of connections per node. High values of both metrics are often associated with improved stability when the network is attacked or nodes are removed. Thus, a high-fiber diet promotes the diversity and complexity of the gut microbiota, stabilizing the network. High-fiber diets likely provide rich nutrients for beneficial bacteria, fostering growth and creating a complex, stable microbial community. Increased diversity and microbial interactions enhance the community’s resilience and functionality. Additionally, a high-fiber diet boosts SCFA production, fermenting indigestible fibers and producing beneficial metabolites [ 54 ]. These metabolites support host health and help stabilize and enhance the microbial network’s complexity. Regulatory effects of a high-fiber fiet on gut microbiota KEGG pathways In the immune system, the H group shows an upregulation in functions like antigen processing and presentation because fiber promotes the growth of beneficial bacteria such as Bifidobacterium and Lactobacillus , which secrete SCFAs. SCFAs act as ligands for G protein-coupled receptors involved in metabolic and immune regulation. SCFAs, especially butyrate, exhibit anti-inflammatory activity by affecting immune cell migration, adhesion, cytokine expression, and processes such as cell proliferation, activation and apoptosis [ 55 ], which may explain the upregulation of immune-related pathways. In the digestive system, processes like glycolysis/gluconeogenesis were also upregulated in the H group, suggesting high regulation of energy or cellular metabolic pathways. Gut bacteria produce fiber-degrading enzymes that break down fiber, generating SCFAs [ 56 ]. Among these, acetate is an important energy source, providing about 10% of the total energy for the body. Butyrate, a major metabolite of gut microbiota, provides about 60% − 70% of the energy for intestinal epithelial cells; Propionate, after entering the bloodstream, can participate in the conversion of pyruvate to glucose in the liver [ 57 ]. The H group likely showed upregulation of metabolic pathways due to increased SCFA levels. In the H group, retrograde endocannabinoid signaling was upregulated in cell signaling and metabolic regulation. Additionally, cellular biology functions such as phagosome and ribosome activity were upregulated. Pathways related to carcinogens and infections, like chemical carcinogenesis-reactive oxygen species, legionellosis and salmonella infection, also showed an upregulation trend. These changes may reflect regulation in cell metabolism and protein synthesis. High-fiber diets provide rich substrates that produce SCFAs through microbial fermentation. These metabolic products may enhance neural regulation and signal transmission between microbiota, further improving neural and cellular metabolic regulation. Long-term same diet may be detrimental to gut microbiota Changes in dietary components exert profound effects on organismal physiology. Studies indicated the pivotal role of diet in shaping the composition of gut microbiota [ 58 ]. Alterations in the nutritional components within rodent feed might significantly impact gut microbial composition and host metabolism [ 30 ]. Long-term dietary habits can significantly affect the diversity and function of the microbiome. Long-term consumption of high-saturated-fat or high-sugar diets can negatively impact the microbiome, potentially leading to metabolic disorders and health issues [ 59 ]. For example, when a person eats only McDonald's for 10 consecutive days, their gut bacterial diversity decreases by 40%, losing approximately 1,400 species. This indicates that long-term consumption of the same food can significantly affect the diversity and health of the gut microbiome [ 60 ]. Additionally, transgenerational factors also significantly impact the composition of the microbiome. Dietary habits not only affect the current generation but may also impact subsequent generations through transgenerational transmission (Sonnenburg & Sonnenburg, 2019). In this study, we examined the transgenerational effects of a high-fiber diet on root voles' gut microbiota. Results showed that a high-fiber diet increased gut microbial diversity and the F/B ratio, boosted cellulose-degrading bacteria, and upregulated genes related to metabolism and immunity. However, over multiple generations, this diet reduced the F/B ratio and beneficial bacteria such as Lactobacillus , Sporanaerobacter and Clostridium , potentially weakening gut barrier function and harming health. The experiment also increased harmful Desulfovibrio , which may cause gut inflammation and other issues. Therefore, moderate high-fiber intake can enhance microbial diversity and complexity, but long-term consumption of the same diet may be harmful. Our study is limited to microbiota effects, future research will explore long-term impacts on metabolism and physiology to better assess the risks and benefits of a high-fiber diet. Declarations Ethics approval and consent to participate All experimental procedures involving animals were reviewed and approved by the Institutional Animal Ethics Committee. The experiment received animal ethical approval number NIWPB-2015-031. We rigorously implemented various measures to ensure the welfare and ethical treatment of the experimental animals. Consent for publication This submission acknowledges that all authors have made significant contributions and agree with the content of the manuscript. Availability of data and materials The datasets generated and analyzed during the current study, including 16S rRNA sequencing data, are available from the corresponding author on reasonable request. The 16S rRNA sequencing data are currently being prepared for upload to a public repository, and the corresponding accession number will be provided once the upload is complete. Other data that support the findings of this study, such as raw and processed data files, detailed protocols, and any additional materials, can also be provided by the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This work was supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0501), National Natural Science Foundation of China (31770459), CAS "Light of West China" for Interdisciplinary Innovation Team, Qinghai Provincial Key R & D and Transformation Program (2023-SF-120) and leading talents of the Kunlun talents in Qinghai Province. Authors' contributions Y. Zhang made substantial contributions to the drafting of the manuscript text and the creation of the tables. Y.H. Wang, R.J. Wanyan, and B.H. Yao made substantial contributions to the preparation of figures 1-3. Z.X. Tan and R. Wang made substantial contributions to the preparation of figures 4-8. H. Li and J.P. Qu made substantial contributions to the revision and editing of the manuscript. All authors have approved the submitted version of the manuscript and agree to be personally accountable for their own contributions. All authors also ensure that questions related to the accuracy or integrity of any part of the work, even those in which the authors were not personally involved, are appropriately investigated and resolved. Acknowledgements The authors would like to express their sincere gratitude to Professor Huan Li and her research team from the Department of Occupational and Environmental Health, School of Public Health, Lanzhou University, for their guidance on data analysis. We also greatly appreciate the insightful suggestions provided by Professor Ming Liu from the Institute of Zoology, Chinese Academy of Sciences, which have significantly improved the quality of this manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4858686","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359626014,"identity":"7eb5ebbe-918e-4184-96e8-b71a9439680d","order_by":0,"name":"yan zhang","email":"","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"yan","middleName":"","lastName":"zhang","suffix":""},{"id":359626016,"identity":"5da97111-f447-4a96-81ad-ece5fd19cf5d","order_by":1,"name":"Yihong Wang","email":"","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yihong","middleName":"","lastName":"Wang","suffix":""},{"id":359626019,"identity":"224016ea-5437-43da-bfdc-d804d960595c","order_by":2,"name":"Ruijun Wanyan","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ruijun","middleName":"","lastName":"Wanyan","suffix":""},{"id":359626021,"identity":"54bbbbd2-ca38-4ec7-b70f-6f43f86180c5","order_by":3,"name":"Baohui Yao","email":"","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Baohui","middleName":"","lastName":"Yao","suffix":""},{"id":359626024,"identity":"83eb6dc2-fcd0-4294-a1dd-dc4ac9efdc87","order_by":4,"name":"Zhaoxian Tan","email":"","orcid":"","institution":"Qinghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoxian","middleName":"","lastName":"Tan","suffix":""},{"id":359626025,"identity":"89a433c2-81e6-4411-9f60-5ec250f0dff0","order_by":5,"name":"Rong Wang","email":"","orcid":"","institution":"Qinghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Wang","suffix":""},{"id":359626026,"identity":"642c0748-3457-4c78-a257-af08baf00840","order_by":6,"name":"Huan Li","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Li","suffix":""},{"id":359626027,"identity":"ee267848-24f0-4048-bce5-9e82ffb962d2","order_by":7,"name":"Jiapeng Qu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACxgYg8YGBgYdBghQtjDNI0gICzDwgkmgtzLObt0nb7jgsYy7dwPjhxx8GeXOCDptzrEw698xhHss5B5gle9sYDHc2ENIyI8dMOrftNo/BjQQ2Bt4GhgSDA8RosYRqYfzzh1gtjFAtzDxsxGiZc6zYsrftP9AvB5ulZdskDDcQ0mI4u3njjZ9tafbm0s0HP775YyNP0BbDGQwGYIYBJCEQETvyEnAto2AUjIJRMApwAADSzzyJuRPs8wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Jiapeng","middleName":"","lastName":"Qu","suffix":""}],"badges":[],"createdAt":"2024-08-05 02:09:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4858686/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4858686/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65722566,"identity":"b129bcb7-9edd-4823-900c-da4b186855c0","added_by":"auto","created_at":"2024-10-01 17:14:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":332537,"visible":true,"origin":"","legend":"\u003cp\u003eTwo groups were established: the experimental group, fed a high-fiber diet and the control group, fed a normal-fiber diet (low-fiber group). Each group consisted of 8 pairs of F0 parental male and female individuals, housed separately in 8 cages. They were provided either a high-fiber or normal-fiber diet. Upon reproduction, a portion of the individuals underwent dissection to collect cecal contents, while the remaining individuals were paired off for successive breeding of the next generation for five consecutive generations until F4.\"\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/faea9f763231604511c8f4a6.png"},{"id":65723849,"identity":"f6c570d1-a719-47bf-b2e3-75607e2ba160","added_by":"auto","created_at":"2024-10-01 17:30:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":263876,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Box plots of Simpson index for each generation in the high and control groups (P\u0026lt;0.001). (b) Box plots of Simpson index for all samples in the high and control groups (P\u0026gt;0.05). (c) Box plots of Shannon index for each generation in the high and control groups (P\u0026lt;0.001). (d) Box plots of Shannon index for all samples in the high and control groups (P\u0026gt;0.05). (e) Box plots of Chao1 index for each generation in the high and control groups (P\u0026lt;0.001). (f) Box plots of Chao1 index for all samples in the high and control groups (P\u0026gt;0.05).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/222282d7e4a5fd3ba166d0fa.png"},{"id":65722998,"identity":"7ffbb7f3-8c30-45a8-ad13-3e2c72ced63b","added_by":"auto","created_at":"2024-10-01 17:22:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1769908,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Bray-Curtis NMDS plot for all samples in the high and control groups. (b) Bray-Curtis NMDS plot for ten generations (F\u003csub\u003e0\u003c/sub\u003e-F\u003csub\u003e4\u003c/sub\u003e) in the high and control groups. (c) Bray-Curtis NMDS plot for four generations in the control group. (d) Bray-Curtis NMDS plot for four generations in the high-fiber group.\u003cstrong\u003e \u003c/strong\u003e(e-i) Bray-Curtis NMDS plots for the five generations (F\u003csub\u003e0\u003c/sub\u003e-F\u003csub\u003e4\u003c/sub\u003e) in the high and control groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/ad7c5b057a23b197b879f220.png"},{"id":65722565,"identity":"68df5b0e-7e2e-4973-b247-f80063f92345","added_by":"auto","created_at":"2024-10-01 17:14:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137452,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis chart of the Firmicutes/Bacteroidetes (F/B) ratio across generations. The horizontal axis represents generations from F\u003csub\u003e0\u003c/sub\u003e to F\u003csub\u003e4\u003c/sub\u003e, labeled as 0, 1, 2, 3 and 4, respectively. Blue dots represent the high fiber group (H group), purple dots represent the control group (C group), the green area represents the 95% confidence interval for the high fiber group, and the pink area represents the 95% confidence interval for the control group.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/3fe2c866e0e77829459e738e.png"},{"id":65722568,"identity":"ccac5919-bd53-445a-91b6-77cf2026ee4e","added_by":"auto","created_at":"2024-10-01 17:14:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1016785,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Represents the overall MST values for the high and control groups. (b) Depicts the MST values for generations F\u003csub\u003e0\u003c/sub\u003e to F\u003csub\u003e4\u003c/sub\u003e in both high and control groups. (The red dashed line represents the 50% MST value line used to differentiate deterministic and stochastic processes) (c) βNTI values within and between high-fiber and control groups; (d) βNTI values between F\u003csub\u003e0\u003c/sub\u003e-F\u003csub\u003e4\u003c/sub\u003e generations within high-fiber and control groups; (e) βNTI values within and between generations in the control group; (f) βNTI values within and between generations in the high-fiber group. (The red dashed line represents the threshold at βNTI absolute value equals 2, used to discern between stochastic and deterministic community assembly processes) (g-h) Bray-Curtis-based Raup-Crick distance values (RC-bray). Panel a illustrates the overall RC values for the high fiber and control groups, while panel b shows the RC values for each generation separately.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/9a6c309095dbdff07ad619dd.jpg"},{"id":65722999,"identity":"6a21a45e-646b-49c5-ac2b-8154dce9a33c","added_by":"auto","created_at":"2024-10-01 17:22:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1954725,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence network diagram. (a) shows the situation of the control group, while (b) depicts the situation of the high-fiber group. (c) Network Robustness Chart. The 'Average degree' chart in panel a illustrates the average connectivity of nodes within the network, indicating the average number of connections each node has to other nodes. Panel b's 'Natural connectivity' chart is commonly used to assess the stability and robustness of networks, describing the network's ability to maintain connectivity and overall structure when nodes are removed or damaged. The horizontal axis 'Remove nodes' simulates the quantity of nodes removed from the network, while the vertical axis represents the level of stability.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/036c7a2ea0eeea8cbc49c1e5.png"},{"id":65723850,"identity":"104b601e-0686-419e-853e-3b7f35eed9c3","added_by":"auto","created_at":"2024-10-01 17:30:14","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2426233,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart displaying the top 15 enriched functions based on KEGG pathway analysis using GSEA.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/263b61ad4202e665fa67ffe7.jpg"},{"id":65722573,"identity":"64a716bb-836f-43ff-a6a9-abcfe71045c3","added_by":"auto","created_at":"2024-10-01 17:14:14","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":487050,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Regression analysis chart of the abundance of the top 6 phyla of gut microbiota. （The upper graph represents the control group, while the lower graph represents the high-fiber group. The pink shaded area represents the 95% confidence interval. The horizontal axis denotes generations from F0 to F4, labeled as 0, 1, 2, 3 and 4, respectively.）(b) Regression analysis of the top 10 genera at the phylum and genus level of gut microbiota. The upper panel illustrates the situation in the control group, while the lower panel illustrates the high-fiber group. The pink shadow represents the 95% confidence interval. The x-axis indicates 0, 1, 2, 3, 4, representing generations F0 to F4, respectively.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/5db79c3dc0e91d7d333be7ed.png"},{"id":91300224,"identity":"d40a4b29-f4c3-4917-8f30-44b85bd04786","added_by":"auto","created_at":"2025-09-15 05:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9341575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/d0f091e5-630d-4d2a-a1b6-f05507513350.pdf"},{"id":65722996,"identity":"7198c856-ba3d-4438-9fd0-9e1dd7c259d7","added_by":"auto","created_at":"2024-10-01 17:22:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1037730,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/30ca759653ad2736ad29a3f5.docx"},{"id":65722571,"identity":"2b05d13f-0c37-4119-8fca-a1e8323acaf7","added_by":"auto","created_at":"2024-10-01 17:14:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1048412,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4858686/v1/fcb1bb643bdb5ba34e5c7df6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Influence of Multi-generational High-Fiber Diet on the Gut Microbiota of Root Voles (Microtus oeconomus)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn recent years, gut microbiota has received increasing attention because of their key roles in host digestion and absorption, immune metabolism, psychological and behavioral health (Fan \u0026amp; Pedersen 2021). The various factors involved, such as dietary [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], genetic [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], environmental [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and etc., can significantly alter the composition and function of gut microbiota, thereby affecting the host\u0026rsquo;s health, reproduction and survival. Among these influencing factors, dietary is very crucial in shaping an individual\u0026rsquo;s gut microbiome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given that food determines the nutrients available to gut microbiota, its type and quantity can determine gut microbial composition and activity. For instance, diets high in fat and sugar can encourage the growth of pathogenic bacteria, reduce the number and diversity of beneficial bacteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Meanwhile, the richness of a plant-based diet, particularly certain types, can enhance the abundance of beneficial microbes within the gut microbiota [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], hereby improving animal metabolism and health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eA diverse plant-based diet rich in fiber plays a crucial role in shaping the gut microbiome. One role of fiber intake is its ability to alter the gut microbiota, generating substantial short-chain fatty acids (SCFAs), indirectly benefiting cardiovascular metabolism and digestive health [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It might comprise various mechanisms and processes, including lipid and sugar absorption, fecal elimination, and the stimulation of intestinal alterations, thereby impacting its functional properties within the gastrointestinal tract [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, it contributes to enhancing glucose metabolism and glycogen storage in mice [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, fiber exhibits disease-improving properties, and it is associated with reduced cardiovascular disease, coronary heart disease and cancer mortality rates [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Fiber promotes beneficial gut bacterial growth and reduces carcinogenic bacterial population [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Because it possesses strong water absorption capabilities, fiber increases fecal volume, facilitating the formation of well-formed stools, promoting excretion, and shortening the time stools remain in the intestine [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This characteristic decreases the concentration of carcinogenic substances in the intestines and reduces the risk of disease. Finally, fiber intake improves gut health and influences hormonal balance and its metabolites through the gut-liver and gut-brain axes, significantly enhancing the reproductive performance and lactation of sows [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, fiber can promote animal health by transforming into SCFAs, fostering the growth of beneficial bacteria, and regulating hormone metabolism, it plays a pivotal role in shaping the gut microbiota and maintaining overall health. Thus, it is necessary for investigating the influence of varying fiber content on the intestinal microbiota of animals. However, most studies on the impact of dietary fiber on gut microbiota were based on short-term or single-generation experimental designs. Long-term or transgenerational studies are rare, primarily because long-term research requires more time, more complex experimental designs and resources. However, multigenerational treatments can lead to changes in animal behavior or physiological traits, and the outcomes of multigenerational treatments may differ from those of single-generation treatments. For instance, environmental factors may affect offspring differently across generations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], suggesting that single-generation studies might overlook the importance of transgenerational transmission. Environmental conditions can lead to different changes in individual physiological and behavioral traits across multiple generations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], indicating that single-generation studies might not fully capture the impact of long-term environmental exposure. In microbiome research, modern industrialized diets change the relationship between gut microbiota and human health, and dietary habits can have long-term impacts on gut microbiota diversity that may be passed from one generation to the next [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. With increasing generations of transmission, the gut microbial diversity of ICR mice gradually decreases, while basic metabolic pathways such as amino acid synthesis and carbohydrate metabolism are inhibited [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Long-term low-fiber diets may lead to the permanent extinction of fiber-dependent microbial communities, making recovery difficult [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Lack of fiber in lactating mother mice alters the microbiome of their offspring, resulting in mild inflammation and a predisposition to obesity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, transgenerational studies are imperative for understanding how dietary fiber affects gut microbiota and how this impact is transmitted between parent and offspring generations.\u003c/p\u003e \u003cp\u003eResearch in animal models and humans indicates that a high-fiber diet in mothers can protect their offspring from the effects of fat accumulation, whereas a low-fiber diet in Brandt\u0026rsquo;s vole (\u003cem\u003eLasiopodomys brandtii\u003c/em\u003e) mothers may accelerate the growth of lean tissue in their offspring [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, given the lack of research on the multi-generational effects of high-fiber diets on animal gut microbiota and health, multi-generational experiments focusing on high-fiber and normal-fiber diets are needed to complement the understanding of the transgenerational factors affecting the gut microbiota. In this study, we chose the fast-reproducing root vole as our study subject to investigate the impact of high-fiber diets on the gut microbiota composition and overall health across different generations of this species (root voles have short generational cycles, so they are suitable for cross-generational exploratory experiments). We conducted a five-generation fiber-controlled transgenerational experiment on root voles, the aims of the study were: 1) explore the impact of a high-fiber diet on gut microbiota community structure and abundance in different generations; 2) test whether high-fiber diet leads to similar microbial community extinction or colonization phenomena. Based on these research objectives, we hypothesize that a high-fiber diet will enhance the diversity and stability of gut microbiota across different generations, predominantly promoting cellulose-degrading bacteria. This work will help address the research gap in fiber diet aspects in previous transgenerational experiments and provide a comprehensive understanding, revealing the long-term effects of a high-fiber diet on gut microbiota\u0026rsquo;s community structure.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimal rearing\u003c/h2\u003e \u003cp\u003eThe root vole (\u003cem\u003eMicrotus oeconomus\u003c/em\u003e) population used in this study was captured from near the Qinghai Haibei National Field Research Station of Alpine Grassland Ecosystem in Menyuan County, Qinghai Province (N37\u0026deg;37', E101\u0026deg;19', elevation 3200 m). They were bred indoors for 3 years in the Laboratory of Northwest Institute of Plateau Biology, Chinese Academy of Sciences, in a group-housed manner. Each cage housed 3\u0026ndash;4 voles, provided with sawdust as bedding, water dispensers, and allowed continuous access to feed to ensure \u003cem\u003ead libitum\u003c/em\u003e feeding and drinking. Root vole feed was custom-made by Beijing Keao Company. The normal-fiber diet contained approximately 5% crude fiber and 8% crude ash. The high-fiber diet replaced all crude ash with crude fiber, achieving a crude fiber content of approximately 13% (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), while maintaining other nutritional constituents unchanged. Prior to experimentation, animals were individually moved to separate cages for a 2-week acclimation period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutritional composition table of high and normal fiber diets for mouse feed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNutritional Composition of Mouse Growth Maintenance Feed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Fiber \u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Ash\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture and Other Volatile Substances\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Protein\u0026ge;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Fat \u0026ge;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Phosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u0026ndash;1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u0026ndash;1.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0-1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0-1.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e This experiment adhered to the animal care and use guidelines established by the Chinese Academy of Sciences Animal Research Committee. All experimental procedures involving animals were reviewed and approved by the Institutional Animal Ethics Committee. The experiment received animal ethical approval number NIWPB-2015-031. We rigorously implemented various measures to ensure the welfare and ethical treatment of the experimental animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMulti-generational reproduction experiment\u003c/h2\u003e \u003cp\u003eThe experiment comprised two main groups: the high-fiber group (H group) and the control group (C group), with the former receiving an excessive amount of dietary fiber, whereas the latter was provided with a standard fiber intake. At the beginning of the F\u003csub\u003e0\u003c/sub\u003e generation, 8 pairs of male and female parental individuals were housed separately and provided with either high or normal-fiber feed. During reproduction, offspring birth time, gender and litter size were recorded. Pups cohabitated with dams for 30 days (as root voles typically wean at 28\u0026ndash;30 days, during which their digestive systems mature sufficiently to transition from liquid milk to solid food. By around 30 days, the pups usually have developed enough independence to feed themselves). On the 31st day, pups were separated from their parents and marked as the next generation. Some individuals were randomly selected and dissected under anesthesia to collect cecal contents, while the remaining individuals were paired for subsequent breeding. During the experiment, record the number of offspring for each generation. This process was iterated for four generations, totaling five generations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), denoted as F\u003csub\u003e0\u003c/sub\u003e to F\u003csub\u003e4\u003c/sub\u003e. After offspring separation, a portion of the parents were dissected (select individuals for dissection with a nearly 1:1 sex ratio and record it in detail), the entire cecum was excised and stored in sterile tubes at -80\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Two groups were established: the experimental group, fed a high-fiber diet and the control group, fed a normal-fiber diet (low-fiber group). Each group consisted of 8 pairs of F0 parental male and female individuals, housed separately in 8 cages. They were provided either a high-fiber or normal-fiber diet. Upon reproduction, a portion of the individuals underwent dissection to collect cecal contents, while the remaining individuals were paired off for successive breeding of the next generation for five consecutive generations until F4.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA gene sequencing and data processing\u003c/h2\u003e \u003cp\u003eMicrobial DNA was extracted from cecal contents, followed by the amplification of the V3-V4 hypervariable regions of the 16S rRNA gene using specific primers (Robin \u003cem\u003eet al.\u003c/em\u003e, 2016). The obtained PCR products were then sequenced using the Illumina MiSeq sequencing platform. The sequencing results were processed through filtering, assembly, and denoising to obtain OTU species abundance profiles. For detailed procedures, refer to Appendix 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis of community structure was conducted at various taxonomic levels, α and β diversity values were calculated. We applied the null model to investigate the assembly mechanisms of gut microbiota communities and the formation of diversity, assessing whether the observed outcomes align with random expectations. Two null model methods were employed: the β-nearest taxon index (β-NTI) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and the modified stochasticity ratio (MST) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], a specific form of the normalized stochasticity ratio (NST) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The \u0026ldquo;picante\u0026rdquo; and \u0026ldquo;ecodist\u0026rdquo; packages were employed to compute βNTI and RC-bray values, respectively. βNTI results and α and β diversity values were organized by sample grouping, and visual representations were generated using Origin software (version 2018, Origin Lab Corporation, USA). Species distribution stack plots and Venn diagrams were also created using Origin software. Using PERMANOVA with Bray-Curtis distance, we evaluated how high- and normal-fiber diets and transgenerational factors affect gut microbiota\u0026rsquo;s composition and generational similarity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The distance index can be converted to similarity using the formula \u0026ldquo;Similarity\u0026thinsp;=\u0026thinsp;1 - distance index.\u0026rdquo; Permutation tests assessed the significance of findings. PERMANOVA (Permutational Multivariate Analysis of Variance) was conducted to test for significant differences between the high-fiber and normal-fiber diet groups across different generations. To assess the extent to which transgenerational factors and fiber factors explain the variation, we particularly focused on the potential impact of \u0026ldquo;transgenerational factors\u0026rdquo; (i.e., through transgenerational experiments) on community composition, conducting 999 random permutation tests. Additionally, Origin software was used for regression curve analysis of specific microbial groups, the R \u0026ldquo;psych\u0026rdquo; package was employed to calculate correlation matrices. Network stability was assessed using the \u0026ldquo;igraph\u0026rdquo; package, and Gephi software was utilized for visualizing network graphs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. All analyses were conducted in R 4.3.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULT","content":"\u003cp\u003eThe results showed significant differences in microbial diversity and composition between the high-fiber group (H group) and the control group (C group), with the H group exhibiting higher diversity, more complex and stable network structures, and upregulation of metabolic and immune pathways. Both the H group and the C group were characterized by deterministic factors governing community assembly processes. Transgenerational factors led to a decrease in community similarity between generations and resulted in differences in the composition of the gut microbiota, affecting the abundance of key microbial groups.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eTransmission experiment feeding\u003c/h2\u003e\n \u003cp\u003eWe conducted a five-generation dietary fiber intervention experiment (F\u003csub\u003e0\u003c/sub\u003e-F\u003csub\u003e4\u003c/sub\u003e) from April 2015 to December 2016. Throughout these five generations, a total of 111 root voles were dissected, and their cecal contents were extracted for 16S rRNA gene sequencing to analyze the gut microbial composition. These samples were labeled as S\u003csub\u003e1\u003c/sub\u003e-S\u003csub\u003e71\u003c/sub\u003e and S\u003csub\u003e78\u003c/sub\u003e-S\u003csub\u003e118\u003c/sub\u003e, in which S\u003csub\u003e1\u003c/sub\u003e-S\u003csub\u003e77\u003c/sub\u003e represented samples from the C group, and S\u003csub\u003e78\u003c/sub\u003e-S\u003csub\u003e118\u003c/sub\u003e represented individuals from the H group, respectively. Finally, a total of 96 samples were successfully sequenced, obtaining corresponding operational taxonomic unit (OTU) data, and their specific sample numbering is detailed in Table S2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eGut microbial composition and dynamics\u003c/h2\u003e\n \u003cp\u003eWe generated stacked bar charts for the top 10 phyla and genera in the H and C groups to compare the effects of dietary fiber on gut microbial abundance at these levels. The results indicated that the phylum levels of \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e were the most abundant in the gut microbiota of root voles. Notably, the proportion of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eSpirochaetes\u003c/em\u003e was lower in the H group compared with the C group (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Throughout the generational changes, \u003cem\u003eBacteroidetes\u003c/em\u003e displayed an increasing trend in both H and C groups, whereas \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eTM7\u003c/em\u003e decreased. At the genus level, prominent genera in the gut of root voles included \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e. Specifically, genera like \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eSporanaerobacter\u003c/em\u003e decreased with generational progression in both H and H groups, whereas genera such as \u003cem\u003eDesulfovibrio\u003c/em\u003e and \u003cem\u003eOscillospira\u003c/em\u003e significantly increased with generations (Figure S2).\u003c/p\u003e\n \u003cp\u003eOur research findings indicate that the gut microbiome diversity in the H group is significantly greater than that in the C group. In generations F1-F4, the Simpson\u0026rsquo;s index in the H group remained consistently lower than that in the C group, indicating that the gut microbiota diversity was higher in the H group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). As observed in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec, the overall gut microbiota diversity was higher in the H group compared to the C group, with F\u003csub\u003e0\u003c/sub\u003e-F\u003csub\u003e3\u003c/sub\u003e generations showing consistently higher Shannon\u0026rsquo;s index. The minor difference in \u0026alpha;-diversity in the F\u003csub\u003e0\u003c/sub\u003e generation resulted from the initial feeding of different fiber diets, where microbial divergence was yet to be significant. The insignificance of \u0026alpha;-diversity in the F4 generation resulted from a limited number of samples in the H group, contributing to experimental errors. The Chao1 index in the H group consistently exceeded that in the C group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee), confirming the higher gut microbiota diversity in the H group. However, when comparing the overall \u0026alpha;-diversity indices between the H and C groups, no statistically significant differences were observed (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef, p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05) due to within-group variability and limited sample sizes. NMDS analysis exhibited substantial differences in gut microbiota composition between H and C groups (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). The gut microbiota of each generation in the H and C groups showed significant differences (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec-d), forming four distinct modules. Lower stress values indicate better representation of similarities or differences among samples in the NMDS plot. Stress values were all below 0.2 in the NMDS plots of each generation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee-i), with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, indicating high feasibility of classification and significant differences among gut microbiota compositions across groups. Notably, gut microbiota from each generation in the H and C groups exhibited distinct clustering, demonstrating that the dietary fiber content significantly altered gut microbiota composition and abundance between the two groups.The significant inter-group differences observed through NMDS analysis provided a basis for us to use PERMANOVA for pairwise comparisons, which allowed us to quantify these differences\u0026apos; statistical significance. Table S3 shows that some generational comparisons yielded significant P values (\u0026lt;\u0026thinsp;0.05), but there was no consistent trend of increasing significance over time. The R\u0026sup2; values did not show a clear increasing trend either. Although significant differences existed between certain generations, they did not systematically increase, suggesting that dietary fiber content had a relatively stable impact on the gut microbiome throughout the study. PERMANOVA analysis also revealed that transgenerational factors significantly affected community composition (Degrees of Freedom\u0026thinsp;=\u0026thinsp;1; Sum of Squares\u0026thinsp;=\u0026thinsp;0.601; F\u0026thinsp;=\u0026thinsp;1.5133; p\u0026thinsp;=\u0026thinsp;0.042*), as indicated by the significant R\u0026sup2; value and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. (a) Box plots of Simpson index for each generation in the high and control groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (b) Box plots of Simpson index for all samples in the high and control groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (c) Box plots of Shannon index for each generation in the high and control groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (d) Box plots of Shannon index for all samples in the high and control groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (e) Box plots of Chao1 index for each generation in the high and control groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (f) Box plots of Chao1 index for all samples in the high and control groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. (a) Bray-Curtis NMDS plot for all samples in the high and control groups. (b) Bray-Curtis NMDS plot for ten generations (F0-F4) in the high and control groups. (c) Bray-Curtis NMDS plot for four generations in the control group. (d) Bray-Curtis NMDS plot for four generations in the high-fiber group. (e-i) Bray-Curtis NMDS plots for the five generations (F0-F4) in the high and control groups.\u003c/p\u003e\n \u003cp\u003eHeatmaps illustrated the relative abundance of the top 50 OTUs in the H and C groups, highlighting significant variances in microbial distribution across fiber diets (Figure S3). Clustering analysis identified stable patterns over generations, with distinct groupings in both H and C groups that underscored the influence of dietary fiber on microbial community dynamics. Venn diagrams further demonstrated the shared OTUs between groups, clarifying similarities and differences in microbial communities across generations (Figure S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eGut microbial community similarity and stability\u003c/h2\u003e\n \u003cp\u003eAs presented in Table S4, our results showed that similarity gradually decreased from F\u003csub\u003e0\u003c/sub\u003e-F\u003csub\u003e1\u003c/sub\u003e to F\u003csub\u003e2\u003c/sub\u003e-F\u003csub\u003e3\u003c/sub\u003e in the C group. With the progression of generations, the composition of the gut microbial community in the C group changed, and the magnitude of these changes increased, leading to a gradual decline in community similarity. The lowest similarity during the F\u003csub\u003e2\u003c/sub\u003e-F\u003csub\u003e3\u003c/sub\u003e period suggested the most significant difference in microbial community composition between these two generations. Conversely, an increase in similarity from F\u003csub\u003e3\u003c/sub\u003e-F\u003csub\u003e4\u003c/sub\u003e may imply a trend toward recovery or periodic changes in the community after significant alterations. However, in the H group, the similarity between generations F\u003csub\u003e0\u003c/sub\u003e and F\u003csub\u003e1\u003c/sub\u003e was extremely low, reflecting significant changes in community structure and indicating a substantial difference in gut microbiota composition between the F\u003csub\u003e1\u003c/sub\u003e generation and the F\u003csub\u003e0\u003c/sub\u003e generation. From F\u003csub\u003e1\u003c/sub\u003e to F\u003csub\u003e2\u003c/sub\u003e, the community similarity significantly increased indicating a close community composition between these two generations. The similarity decreased from F\u003csub\u003e2\u003c/sub\u003e to F\u003csub\u003e3\u003c/sub\u003e and further decreased from F\u003csub\u003e3\u003c/sub\u003e to F\u003csub\u003e4\u003c/sub\u003e, showing ongoing changes in community composition, with the similarity of the gut microbial community decreasing over generations. The data from both fiber groups, revealed the similarity of the community changes with the progression of generations, showing that the community composition diversified and variability increased. Compared with the C group, the H group shows greater community stability, especially in facing environmental or biological stresses, where its ability to recover and adapt was stronger. This ability was exemplified by the significant increase in similarity from F\u003csub\u003e1\u003c/sub\u003e to F\u003csub\u003e2\u003c/sub\u003e. These findings highlighted the important impact of fiber intake on the dynamics of the gut microbial community.\u003c/p\u003e\n \u003cp\u003eSubsequently, we calculated the Firmicutes/Bacteroidetes (F/B) ratio within the gut microbiota of the H and C groups to explore the impact of dietary fiber content. Significant differences were observed in the effect of high- and normal-fiber diets on the F/B ratio across different generations of root voles. In the C group, the F/B ratio increased from F\u003csub\u003e0\u003c/sub\u003e generation to F\u003csub\u003e1\u003c/sub\u003e generation, followed by a significant decline in subsequent generations. Conversely, in the H group, the F/B ratio started at an extremely high value in the F\u003csub\u003e0\u003c/sub\u003e generation and then showed a marked decrease, dropping by the F\u003csub\u003e4\u003c/sub\u003e generation (Table S5). On the basis of data fitting results, the F/B ratio in both groups exhibited a significant pattern of decreasing across generations (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Overall, the F/B ratio in the H group was significantly higher than that in the C group for most generations, suggesting that dietary fiber content significantly influenced the abundance of microbial populations within the phyla \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e. Additionally, transgenerational factors had a significant impact on the F/B ratio, with both H and C groups showing a trend of decreasing F/B ratio across generations.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Regression analysis chart of the Firmicutes/Bacteroidetes (F/B) ratio across generations. The horizontal axis represents generations from F0 to F4, labeled as 0, 1, 2, 3 and 4, respectively. Blue dots represent the high fiber group (H group), purple dots represent the control group (C group), the green area represents the 95% confidence interval for the high fiber group, and the pink area represents the 95% confidence interval for the control group.\u003c/p\u003e\n \u003cp\u003eMechanisms of Community Assembly and Network Analysis\u003c/p\u003e\n \u003cp\u003eMST analysis revealed that the overall values for both H and C groups mostly fell below 50% (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-b). This result indicated that community composition and diversity were mainly influenced by deterministic processes [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Nonetheless, individual MST values for each generation displayed a mix of values above and below 50%, suggesting an interplay between deterministic and stochastic factors across different generations. \u0026beta;-NTI analysis, aimed at assessing the influences of deterministic and stochastic factors on community assembly, showed that the average \u0026beta;-NTI values within the H and C groups exceeded 2, pointing to a significant influence by deterministic factors, specifically heterogeneous selection [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. This pattern was consistent within both groups, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec. Further analysis for different generations within these groups (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed-f) indicated that the average \u0026beta;-NTI values between the H and C groups were \u0026lt;\u0026thinsp;2 in the F\u003csub\u003e0\u003c/sub\u003e generation, but they were all \u0026gt;\u0026thinsp;2 in the F\u003csub\u003e1\u003c/sub\u003e-F\u003csub\u003e4\u003c/sub\u003e generations. This result suggested the influence of stochastic and deterministic factors in the F\u003csub\u003e0\u003c/sub\u003e generation.\u003c/p\u003e\n \u003cp\u003eWhile most community changes were driven by deterministic factors, some were influenced by stochastic processes. Analysis using the Raup-Crick distance categorized random processes into heterogeneous selection, dispersal limitation, homogenizing dispersal and undominated. The C group showed approximately 72% heterogeneous selection, 23% dispersal limitation and 5% undominated. In contrast, the H group had about 59% heterogeneous selection, 18% undominated and 23% dispersal limitation (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eg-h).To further understand the impact of these stochastic and deterministic processes on microbial communities, we analyzed the co-occurrence patterns and network robustness of the top 100 operational taxonomic units (OTUs) in the H and C groups. In the H group, the number of nodes, edges, and modules were all higher than those in the C group, indicating that the co-occurrence network complexity was greater in the H group than in the C group (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea-b). The H group showed significantly higher average and natural connectivity compared with the C group (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec), suggesting a tighter and more complex microbial network. Additionally, the higher natural connectivity in the H group indicates better maintenance of functionality and structure when nodes are removed (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed). Overall, a high-fiber diet enhances the complexity and robustness of the gut microbiota network.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. (a) Represents the overall MST values for the high and control groups. (b) Depicts the MST values for generations F0 to F4 in both high and control groups. (The red dashed line represents the 50% MST value line used to differentiate deterministic and stochastic processes) (c) \u0026beta;NTI values within and between high-fiber and control groups; (d) \u0026beta;NTI values between F0-F4 generations within high-fiber and control groups; (e) \u0026beta;NTI values within and between generations in the control group; (f) \u0026beta;NTI values within and between generations in the high-fiber group. (The red dashed line represents the threshold at \u0026beta;NTI absolute value equals 2, used to discern between stochastic and deterministic community assembly processes) (g-h) Bray-Curtis-based Raup-Crick distance values (RC-bray). Panel a illustrates the overall RC values for the high fiber and control groups, while panel b shows the RC values for each generation separately.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Co-occurrence network diagram. (a) shows the situation of the control group, while (b) depicts the situation of the high-fiber group. (c) Network Robustness Chart. The \u0026apos;Average degree\u0026apos; chart in panel a illustrates the average connectivity of nodes within the network, indicating the average number of connections each node has to other nodes. Panel b\u0026apos;s \u0026apos;Natural connectivity\u0026apos; chart is commonly used to assess the stability and robustness of networks, describing the network\u0026apos;s ability to maintain connectivity and overall structure when nodes are removed or damaged. The horizontal axis \u0026apos;Remove nodes\u0026apos; simulates the quantity of nodes removed from the network, while the vertical axis represents the level of stability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictive functional profiling and pathway analysis of gut microbiota\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the top 15 enriched pathways, including those related to the immune system, digestion, metabolism, cellular signaling and regulation, cellular biology, biochemical processes, and pathways linked to carcinogens and infections. Functions within the immune system, such as antigen processing and presentation, showed an upregulated trend in the H group, suggesting efficient recognition and processing of exogenous antigens. Digestive system functions, like glycolysis/gluconeogenesis, were also upregulated, indicating high energy metabolism or regulation of cellular pathways. Retrograde endocannabinoid signaling in cellular signaling and metabolic regulation were upregulated, potentially affecting neural regulation or signaling. Additionally, cellular biology and biochemical processes, such as phagosome and ribosome activities, showed upregulation, possibly reflecting enhanced cellular metabolism and protein synthesis. Pathways related to carcinogenic agents and infections such as chemical carcinogenesis reactive oxygen species, legionellosis, and salmonella infection also displayed an upregulated characteristic in the H group.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Bar chart displaying the top 15 enriched functions based on KEGG pathway analysis using GSEA.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImpact of high-fiber diet and transgenerational factors on gut microbiota abundance and diversity\u003c/h2\u003e \u003cp\u003eThe gut bacteria play a crucial role in maintaining individual health, bolstering the host's intestinal defense system and sustaining normal intestinal function, with their composition influenced by dietary factors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Individuals consuming fiber-rich dietary formulas exhibit milder negative symptoms like diarrhea and abdominal discomfort compared with those on a fiber-free formula. Fiber-rich diets contribute to maintaining the stability of essential bacteria in the gut, enhancing gut microbiota diversity [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A high-fiber diet scheme elevates α-diversity of the human gut microbiome and stimulates bacteria producing SCFAs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Fiber interventions, particularly with fructans and galacto-oligosaccharides, lead to an increase in the abundance of \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e in feces [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similarly, our study demonstrated that the gut microbiota diversity in the H group was richer, consistent with the findings of Koecher K J [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These findings collectively suggest that a H diet may help preserve gut microbiota diversity and maintain a relatively stable state, promoting intestinal health.\u003c/p\u003e \u003cp\u003eIn the H group, the gut microbial community displayed lower proportions of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eSpirochaetes\u003c/em\u003e. Both these microbial phyla play essential metabolic roles in the colonic environment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003eBacteroidetes\u003c/em\u003e participate in carbohydrate and nitrogen compound fermentation, along with the biotransformation of bile acids and other sterols [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which are critical in obesity and metabolic diseases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. \u003cem\u003eSpirochaetes\u003c/em\u003e are often associated with health issues. The reduced proportions of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eSpirochaetes\u003c/em\u003e in the H group might induce changes in digestive capabilities, affecting energy absorption and utilization and potentially leading to digestive issues or altered energy supply needs. Then, regression analysis was conducted on the top six phyla of total abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). In the C group, \u003cem\u003eProteobacteria\u003c/em\u003e levels increased with generations, whereas a declining trend was observed in the H group. Consistent with previous research, increased fiber intake might cause changes in the gut microbiota composition in patients with inflammatory bowel disease, resulting in decreased \u003cem\u003eProteobacteria\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Thus, this decline in Proteobacteria in the H group may suggest enhanced microbial balance and improved gut health. Conversely, \u003cem\u003eSpirochaetes\u003c/em\u003e increased with generations in the C group and decreased in the H group. Although \u003cem\u003eSpirochaetes\u003c/em\u003e are commonly associated with diseases, these findings lack significance and are provided as reference. A similar generational replacement regression analysis was performed on the top ten genera (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e8\u003c/span\u003eb), revealing an upward trend in \u003cem\u003eDesulfovibrio\u003c/em\u003e in the C group and a downward trend in the H group. An increase in \u003cem\u003eDesulfovibrio\u003c/em\u003e might lead to an imbalance in the intestinal ecosystem, with its hydrogen sulfide production potentially exerting toxic effects on intestinal epithelial cells, impacting gut health [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, this imbalance may linked to metabolic disorders and obesity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, it might unfavorably impact the gut health of the H group; however, further research is needed to ascertain its specific effects. Both H and C groups demonstrated a significant decrease in the proportions of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eSporanaerobacter\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eSporanaerobacter\u003c/em\u003e with generational progression. Conversely, several unclassified genera from the family S24-7, order Clostridiales (UG), and families Lachnospiraceae (UG), \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eOscillospira\u003c/em\u003e showed a noticeable increase in proportions. This shift might be attributed to dietary differences, suggesting that the gut microbiota gradually adapted to distinct high- and normal-fiber diets with generational progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e8\u003c/span\u003e. (a) Regression analysis chart of the abundance of the top 6 phyla of gut microbiota. (The upper graph represents the control group, while the lower graph represents the high-fiber group. The pink shaded area represents the 95% confidence interval. The horizontal axis denotes generations from F0 to F4, labeled as 0, 1, 2, 3 and 4, respectively.)(b) Regression analysis of the top 10 genera at the phylum and genus level of gut microbiota. The upper panel illustrates the situation in the control group, while the lower panel illustrates the high-fiber group. The pink shadow represents the 95% confidence interval. The x-axis indicates 0, 1, 2, 3, 4, representing generations F0 to F4, respectively.\u003c/p\u003e \u003cp\u003eFirmicutes and Bacteroidetes are pivotal bacteria involved in the catabolism of food remnants. Through intricate metabolic mechanisms, they aid in the digestion of fibers and polyphenolic compounds, employing cooperative and shared metabolic pathways [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Bacteroidetes dominate the intestinal microbiota, encompassing various Gram-negative bacteria that release energy by breaking down fibers and starches, contributing to the catabolism of bile acids and proteins [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Firmicutes comprise mostly Gram-positive bacteria, synthesizing SCFAS like butyrate and playing a vital role in the host\u0026rsquo;s nutrition and metabolism [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The ratio between Firmicutes and Bacteroidetes, often abbreviated as the F/B ratio, is considered an indicator of gut microbial imbalance; the former is often linked to obesity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], whereas the latter is associated with inflammatory bowel disease [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the H and C groups showed a decreasing F/B ratio across generations. Firmicutes decreased, whereas Bacteroidetes increased. This shift may result from transgenerational transmission gradually altering the microbiome. The microbiome composition is influenced by the current diet and maternal microbiota passed to the offspring. Research showed that maternal gut dysbiosis can affect placental function and fetal development, increasing health risks in the offspring. As the population of beneficial bacteria decrease across generations, gut dysbiosis may worsen, further increasing health risks [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. During long-term experiments, microbiota drift can occur, where some bacteria disappear and are replaced by others, also affecting the F/B ratio. However, across nearly all generations, the F/B ratio in the H group consistently exceeded that of the C group, indicating that high-fiber feeding may promote the relative growth of Firmicutes. This result could be due to the different components in high-fiber feed, such as soluble and insoluble fibers, and their utilization by gut microbiota. When diets are high in fat and protein, Bacteroidetes became more prevalent, whereas they are less so when carbohydrate consumption is high [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A comparative study of the fecal microbiota of American children on an animal-based diet versus Bangladeshi children on a plant-based diet found that the F/B ratio in the feces of American children was significantly lower than that of Bangladeshi children [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Another experiment involving increased dietary fiber intake while avoiding a Western diet found an increase in the F/B ratio within the gut microbiota [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These findings suggested that increasing dietary fiber may raise the F/B ratio in the gut microbiota, with an elevated proportion of Firmicutes, consistent with our results. Some Firmicutes species can efficiently decompose polysaccharides like fiber by using them as an energy source. Thus, a high-fiber diet promotes Firmicutes growth. However, this effect weakened over generations, possibly due to adaptive changes in the gut microbiome under long-term high-fiber pressure. Microbial community drift during the feeding experiment might also influence the long-term F/B ratio trend. The stability and adaptability of microbial communities are complex, involving many interacting factors, including the host\u0026rsquo;s physiological state, environmental conditions and gut microbiota interactions. Long-term fiber feeding experiments allow us to observe gut microbiota changes and adaptations across generations. Future research should explore the mechanisms behind gut microbiota stability and how they adapt to long-term dietary fiber changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImpact of transgenerational experiments on gut microbiota similarity\u003c/h2\u003e \u003cp\u003eTransgenerational factors have significantly impacted the composition of root vole\u0026rsquo;s gut microbiota, leading to differences in community similarity across generations. By comparing the community similarity across generations under diets with varying fiber contents, we observed that from generation F\u003csub\u003e0\u003c/sub\u003e to F\u003csub\u003e1\u003c/sub\u003e, the community changes (decrease in similarity) in the H diet group were significantly greater than those in the L diet group, indicating a more pronounced impact of high-fiber diets on the microbial community. However, between generations F\u003csub\u003e1\u003c/sub\u003e and F\u003csub\u003e2\u003c/sub\u003e, the similarity in the H group increased, while it continued to decline in the C group. This result suggested that the community changes induced by the high-fiber diet might stabilize or adapt after one generation, whereas the community structure in the C group continues to evolve, with a persistent decrease in similarity. From generation F\u003csub\u003e2\u003c/sub\u003e to F\u003csub\u003e4\u003c/sub\u003e, both H and C groups exhibited fluctuations in community similarity, but the fluctuations were larger in the H group, suggesting that high-fiber diets might lead to a more dynamic and unstable community composition. Overall, high-fiber diets had a more intense and direct impact on the microbial community at the initial stage of the experiment, but the microbial community might gradually adapt to this diet over time, resulting in an increase in similarity. By contrast, the impact of a normal fiber diet on the community was more gradual and sustained. This difference may stem from the varying types and amounts of nutrients provided by diets with different fiber contents, thereby affecting the structure and function of the community.\u003c/p\u003e \u003cp\u003eTo explore if different fiber contents affect community similarity within the same generation, we calculated the community similarity for both H and L diet groups within the same generation. At generation F\u003csub\u003e0\u003c/sub\u003e, the community similarity between the two diet conditions was relatively high, indicating that the microbial community structures were quite similar at the beginning of the experiment. From generation F\u003csub\u003e0\u003c/sub\u003e to F\u003csub\u003e3\u003c/sub\u003e, the similarity gradually decreased, reflecting that the structural differences in the microbial communities under different fiber diet conditions became increasingly significant over time. By generation F\u003csub\u003e4\u003c/sub\u003e, although the similarity between the two groups slightly increased, it remained lower than that in the initial generation, possibly indicating that the microbial communities became stable after undergoing significant initial changes. Analysis of the community similarity between different fiber diet groups within the same generation revealed that the dietary factor of fiber content had a significant impact on the structure of the gut microbial communities, causing notable changes in community composition and similarity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMechanisms of Gut Microbiota Community Assembly and Network Stability\u003c/h2\u003e \u003cp\u003eIn our study, most taxa had MST values below 50% and β-NTI values above 2, suggesting that deterministic factors particularly heterogeneous selection govern the microbial community [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, the presence and relative abundance of species are driven by specific, predictable ecological processes rather than random events. The results of Raup-Crick distance analysis showed that heterogeneous selection predominated in both H and C groups, indicating that deterministic factors play a crucial role in shaping the microbial community structure. Homogeneous selection and heterogeneous selection are both deterministic processes. Homogeneous selection refers to a situation where selection pressures are uniformly distributed across a population or environment, meaning all individuals face the same pressures [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. By contrast, heterogeneous selection involves unevenly distributed pressures [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], with different individuals experiencing varying selective forces. The influencing factors of heterogeneous selection pressures may include environmental heterogeneity [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and uneven resource distribution [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our study, environmental heterogeneity was reflected in the local environments within the mouse cages, and uneven resource distribution was likely due to the difference in fiber content between the two groups. Moreover, the proportion of heterogeneous selection was higher in the C group than in the H group, suggesting a greater influence from these factors.\u003c/p\u003e \u003cp\u003eAlthough the community structure is primarily influenced by deterministic factors, there is also a proportion of random factors at play. The artificial feeding environment provides stable food and water supplies, resulting in high resource abundance and weak selection pressures. Studies have shown that when selection pressures are weak, environmental conditions have less impact on animals survival and reproduction [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Under such conditions, ecological drift plays a significant role in community assembly. Microbiota with low abundance are more susceptible to drift than their counterparts, as slight negative changes in their abundance may lead to local extinction [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In this study, the C group had lower overall abundance, making it more susceptible to drift, compared with the H group. This result may explain the influence of some stochastic factors on community assembly observed in our experiment. Overall, our research highlighted the complexity of community assembly processes by demonstrating the interplay between deterministic and stochastic factors in gut microbiota assembly.\u003c/p\u003e \u003cp\u003eThe findings revealed that the H group had more nodes and modules in its network indicating a more complex network structure, compared with the C group [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The H group also showed higher natural and average connectivity, indicating better network robustness. Network robustness refers to the ability of a network to continue functioning and preserve its overall structural characteristics when confronted with perturbations such as node deletions, intrusions, or other adverse conditions [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Several studies have assessed the level of network robustness by dismantling nodes and testing the natural and average connectivity of the network, and they reported that dismantling nodes results in a decrease in natural and average connectivity [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, trends in natural and average connectivity reflect changes in network robustness. Natural connectivity measures the overall cohesiveness of the network, whereas average connectivity indicates the density or tightness by showing the number of connections per node. High values of both metrics are often associated with improved stability when the network is attacked or nodes are removed. Thus, a high-fiber diet promotes the diversity and complexity of the gut microbiota, stabilizing the network. High-fiber diets likely provide rich nutrients for beneficial bacteria, fostering growth and creating a complex, stable microbial community. Increased diversity and microbial interactions enhance the community\u0026rsquo;s resilience and functionality. Additionally, a high-fiber diet boosts SCFA production, fermenting indigestible fibers and producing beneficial metabolites [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These metabolites support host health and help stabilize and enhance the microbial network\u0026rsquo;s complexity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory effects of a high-fiber fiet on gut microbiota KEGG pathways\u003c/h2\u003e \u003cp\u003eIn the immune system, the H group shows an upregulation in functions like antigen processing and presentation because fiber promotes the growth of beneficial bacteria such as \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e, which secrete SCFAs. SCFAs act as ligands for G protein-coupled receptors involved in metabolic and immune regulation. SCFAs, especially butyrate, exhibit anti-inflammatory activity by affecting immune cell migration, adhesion, cytokine expression, and processes such as cell proliferation, activation and apoptosis [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], which may explain the upregulation of immune-related pathways.\u003c/p\u003e \u003cp\u003eIn the digestive system, processes like glycolysis/gluconeogenesis were also upregulated in the H group, suggesting high regulation of energy or cellular metabolic pathways. Gut bacteria produce fiber-degrading enzymes that break down fiber, generating SCFAs [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Among these, acetate is an important energy source, providing about 10% of the total energy for the body. Butyrate, a major metabolite of gut microbiota, provides about 60% \u0026minus;\u0026thinsp;70% of the energy for intestinal epithelial cells; Propionate, after entering the bloodstream, can participate in the conversion of pyruvate to glucose in the liver [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The H group likely showed upregulation of metabolic pathways due to increased SCFA levels.\u003c/p\u003e \u003cp\u003eIn the H group, retrograde endocannabinoid signaling was upregulated in cell signaling and metabolic regulation. Additionally, cellular biology functions such as phagosome and ribosome activity were upregulated. Pathways related to carcinogens and infections, like chemical carcinogenesis-reactive oxygen species, legionellosis and salmonella infection, also showed an upregulation trend. These changes may reflect regulation in cell metabolism and protein synthesis. High-fiber diets provide rich substrates that produce SCFAs through microbial fermentation. These metabolic products may enhance neural regulation and signal transmission between microbiota, further improving neural and cellular metabolic regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLong-term same diet may be detrimental to gut microbiota\u003c/h2\u003e \u003cp\u003eChanges in dietary components exert profound effects on organismal physiology. Studies indicated the pivotal role of diet in shaping the composition of gut microbiota [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Alterations in the nutritional components within rodent feed might significantly impact gut microbial composition and host metabolism [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Long-term dietary habits can significantly affect the diversity and function of the microbiome. Long-term consumption of high-saturated-fat or high-sugar diets can negatively impact the microbiome, potentially leading to metabolic disorders and health issues [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. For example, when a person eats only McDonald's for 10 consecutive days, their gut bacterial diversity decreases by 40%, losing approximately 1,400 species. This indicates that long-term consumption of the same food can significantly affect the diversity and health of the gut microbiome [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Additionally, transgenerational factors also significantly impact the composition of the microbiome. Dietary habits not only affect the current generation but may also impact subsequent generations through transgenerational transmission (Sonnenburg \u0026amp; Sonnenburg, 2019).\u003c/p\u003e \u003cp\u003eIn this study, we examined the transgenerational effects of a high-fiber diet on root voles' gut microbiota. Results showed that a high-fiber diet increased gut microbial diversity and the F/B ratio, boosted cellulose-degrading bacteria, and upregulated genes related to metabolism and immunity. However, over multiple generations, this diet reduced the F/B ratio and beneficial bacteria such as \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eSporanaerobacter\u003c/em\u003e and \u003cem\u003eClostridium\u003c/em\u003e, potentially weakening gut barrier function and harming health. The experiment also increased harmful \u003cem\u003eDesulfovibrio\u003c/em\u003e, which may cause gut inflammation and other issues. Therefore, moderate high-fiber intake can enhance microbial diversity and complexity, but long-term consumption of the same diet may be harmful. Our study is limited to microbiota effects, future research will explore long-term impacts on metabolism and physiology to better assess the risks and benefits of a high-fiber diet.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures involving animals were reviewed and approved by the Institutional Animal Ethics Committee. The experiment received animal ethical approval number NIWPB-2015-031. We rigorously implemented various measures to ensure the welfare and ethical treatment of the experimental animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis submission acknowledges that all authors have made significant contributions and agree with the content of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study, including 16S rRNA sequencing data, are available from the corresponding author on reasonable request. The 16S rRNA sequencing data are currently being prepared for upload to a public repository, and the corresponding accession number will be provided once the upload is complete. Other data that support the findings of this study, such as raw and processed data files, detailed protocols, and any additional materials, can also be provided by the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0501), National Natural Science Foundation of China (31770459), CAS \u0026quot;Light of West China\u0026quot; for Interdisciplinary Innovation Team, Qinghai Provincial Key R \u0026amp; D and Transformation Program (2023-SF-120) and leading talents of the Kunlun talents in Qinghai Province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY. Zhang made substantial contributions to the drafting of the manuscript text and the creation of the tables. Y.H. Wang, R.J. Wanyan, and B.H. Yao made substantial contributions to the preparation of figures 1-3. Z.X. Tan and R. Wang made substantial contributions to the preparation of figures 4-8. H. Li and J.P. Qu made substantial contributions to the revision and editing of the manuscript. All authors have approved the submitted version of the manuscript and agree to be personally accountable for their own contributions. All authors also ensure that questions related to the accuracy or integrity of any part of the work, even those in which the authors were not personally involved, are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to Professor Huan Li and her research team from the Department of Occupational and Environmental Health, School of Public Health, Lanzhou University, for their guidance on data analysis. We also greatly appreciate the insightful suggestions provided by Professor Ming Liu from the Institute of Zoology, Chinese Academy of Sciences, which have significantly improved the quality of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavid LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. 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Diet Myth, Spector Tim.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"trans-generational effect, high-fiber diet, gut microbiota, root voles","lastPublishedDoi":"10.21203/rs.3.rs-4858686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4858686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFiber influences animal metabolism by affecting the gut microbiota, and high-fiber diets are often considered beneficial. However, existing research primarily focuses on the short-term effects of high-fiber diets, with limited studies on their long-term and trans-generational impacts on gut microbiota. This study investigated the long-term high-fiber diets and trans-generational effects on root voles (\u003cem\u003eMicrotus oeconomus\u003c/em\u003e)\u0026rsquo; gut microbiota over five generations (F\u003csub\u003e0\u003c/sub\u003e to F\u003csub\u003e4\u003c/sub\u003e) using 16S rRNA gene sequencing. Results showed that high-fiber diet significantly increased the diversity and complexity of gut microbiota and upregulated genes related to metabolism and immunity. The proportion of non-cellulose-degrading bacteria such as Proteobacteria and Spirochaetes decreased, while cellulose-degrading Firmicutes increased, raising the Firmicutes/Bacteroidetes ratio. Generational factors significantly influenced microbial community structure, reducing similarity. Over generations, both diets led to a reduction in beneficial bacteria such as \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eSporanaerobacter\u003c/em\u003e, and \u003cem\u003eClostridium\u003c/em\u003e, impairing the breakdown of proteins and starches. Meanwhile, potentially harmful bacteria like \u003cem\u003eDesulfovibrio\u003c/em\u003e and \u003cem\u003eOscillospira\u003c/em\u003e increased, and the Firmicutes/Bacteroidetes ratio decreased, suggesting that a long-term, trans-generational uniform high-fiber diet may cause unfavorable shifts in gut microbiota. In summary, a high-fiber diet can increase gut microbiota abundance and diversity, promote cellulose-degrading bacteria, and upregulate certain metabolic genes, but long-term, uniform diets may cause gut microbiota imbalance, reducing beneficial bacteria and increasing potentially harmful ones.\u003c/p\u003e","manuscriptTitle":"The Influence of Multi-generational High-Fiber Diet on the Gut Microbiota of Root Voles (Microtus oeconomus)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-01 17:14:09","doi":"10.21203/rs.3.rs-4858686/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e17e24c-79e6-48aa-87b4-84142a87cbd6","owner":[],"postedDate":"October 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T04:53:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-01 17:14:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4858686","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4858686","identity":"rs-4858686","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}