Nutritional modulation of host physiology, behavior, and gut microbiome in the captive rodent Octodon degus

Abstract

Diet is a key determinant of health by affecting nutrient metabolism, energy balance, body weight regulation, and mental health. The gut-brain axis is a critical pathway through which dietary factors influence cognitive function and behavior via microbial metabolites. While this relationship has been extensively studied in traditional laboratory models, diet-microbiome-cognition interactions remain largely unexplored in Octodon degus, an emerging model for aging, neurodegeneration, and cognitive research. Here, we compared two widely used rodent diets—LabDiet and Champion—to evaluate their effects on digestive efficiency, behavior, and gut microbiome composition. We also examined the relationships between these variables using piecewise structural equation modeling (pSEM). Our results indicated that LabDiet-fed degus exhibited enhanced nutrient absorption, higher fecal acetic acid levels, and a higher abundance of Actinobacteria (particularly Bifidobacterium), likely driven by its vitamin C supplementation. These animals also showed improved working memory and social motivation, but they displayed increased anxiety-like behavior. In contrast, Champion-fed degus, which consumed a more fiber-diverse, plant-based diet, showed lower anxiety traits and significantly greater gut microbial richness, with higher abundance of Bacteroidota and Tenericutes. Innate behaviors, such as burrowing and nesting, remained unaffected by the diet. SEM analysis revealed that diet explained most of the variance in microbial activity and identified a positive association between acetic acid levels and cognitive performance. This emphasizes a strong relationship among diet, microbiome, and brain function. Overall, our results suggest that dietary composition is a key factor influencing experimental outcomes in degus, with important implications for physiology, cognition, and microbial ecology. Standardizing dietary inputs is essential to ensure reproducibility in behavioral and biomedical studies using this model. Additionally, our results reinforce the microbiome’s role as a mediator of diet-driven brain function via SCFAs, underscoring degus as a powerful system for investigating diet–microbiome–neurobehavioral interactions relevant to aging and mental health.

Introduction

“We are what we eat,” or at least that is how we think diet modulates and influences our physiology. Diet is a fundamental determinant of health and well-being, exerting profound effects on various physiological processes, including nutrient metabolism, energy balance, body weight regulation, and mental health^{1, 2, 3–4}. Across different geographical regions, a wide variety of dietary patterns can be observed, each exerting distinct positive or negative impacts on health, depending on their nutritional composition^{5, 6–7}. For example, Western diets, typically characterized by high levels of saturated fats and refined carbohydrates, have been linked to an increased risk of metabolic disorders such as obesity, hypertension, and insulin resistance^8,9. In contrast, Mediterranean diets are rich in long-chain omega-3 fatty acids (LCFAs), polyphenols, lean proteins, dietary fiber, probiotics, and beneficial phytoactive compounds, such as curcumin, isoflavones, capsaicin, and mushroom-derived polysaccharides^{10, 11–12}. These bioactive components exhibit antioxidant and anti-inflammatory properties, and have been associated with reduced anxiety and mood disorders, a reduced risk of developing intestinal diseases, cardiovascular conditions, and cancer, as well as improved control of type 2 diabetes mellitus among others^{10,12, 13–14}.

Diet-derived fatty acids (FAs), including saturated, monounsaturated, and polyunsaturated FAs, play essential roles in modulating lipid metabolism, membrane fluidity, and signaling pathways that influence inflammation, insulin sensitivity, and support brain function^{14, 15, 16, 17–18}. For example, diets high in fiber, polyphenols, and healthy fats, including monounsaturated and polyunsaturated fatty acids are linked to enhanced mood, reduced depressive symptoms, and improved cognitive function¹⁴. Similarly, short-chain fatty acids (SCFAs)—principally acetate, propionate, and butyrate—are produced through bacterial fermentation of fibers (e.g., resistant starch and polysaccharides) and simple sugars, and exert profound effects on gut barrier function, immune modulation, and host energy homeostasis^{19, 20, 21–22}. Administering SCFAs to mice exposed to psychosocial stress mitigates persistent changes in anhedonia and heightened stress responsiveness, while also preventing stress-induced increases in intestinal permeability²³.

Diet is also a major driver of the gut microbiome (GM)²⁴. Specific nutrients, such as dietary fats, proteins, and insoluble fiber, have significant effects on the structure and function of the gut microbiome, as well as on the secretion of microbial metabolites that modulate immune responses and influence various metabolic and inflammatory pathways^10,25,26. For instance, a high intake of dietary fiber alters gut microbiome composition in both humans and non-human animals, resulting in a decrease in Firmicutes and an increase in Bacteroidetes–particularly Bacteroides acidifaciens, which produces high levels of SCFAs, and is associated with regulatory functions in lipids, cholesterol, and glucose metabolism, anti-inflammatory and immune responses, and gut barrier integrity^10,20,27,28.

Increasing evidence links the GM to brain function through the gut–brain axis^{29, 30, 31–32}. Dietary factors that influence the composition and activity of the GM can have profound effects on brain physiology and emotional well-being^33,34. SCFAs, key microbial metabolites, alleviate anxiety-like behavior by modulating the gut-brain axis, enhancing neurotransmitter production (e.g., serotonin), and reducing stress-induced inflammation and neuronal damage^{14,35, 36–37}. Moreover, both the quantity and quality of dietary FAs influence the production of SCFAs and other microbial metabolites, establishing a critical dietary–microbiome–brain interface^38,39. Therefore, diet not only shapes the gut microbial ecosystem but also indirectly affects central nervous system function and mental health^33,40. Moreover, gastrointestinal disorders (e.g., irritable bowel syndrome and functional dyspepsia) have been linked to neuropsychiatric conditions including autism, anxiety, depression^{40, 41, 42–43}, schizophrenia⁴⁴, and neurodegenerative diseases such as Alzheimer’s disease (AD)^45,46 and Parkinson’s disease⁴⁷.

This evidence underscores the importance of diet as a key factor influencing physiological, metabolic, and behavioral outcomes in humans. It also highlights the value of using animal models, where diet, genetics, and environment can be precisely controlled^{48, 49–50}. Among the emerging models, the degu (Octodon degus) - an endemic Chilean rodent that develops metabolic, sleep, social-affective, and age-related disorders, including neurodegeneration similar to that seen in humans - has gained significant attention across various disciplines, from molecular biology to ecology^{51, 52–53}. Degus are diurnal, long-lived, and highly social rodents that serve as an excellent model for studying complex cognitive functions^54,55. Like humans, degus exhibit age-related physiological decline, including reductions in cognitive performance and synaptic activity, which begin around three years of age and progressively worsen over time⁵⁶. Among various environmental factors, diet has been recognized as a key modulator of cognitive function and disease progression in this species^52,53. Homan et al. (2010) induced atherosclerosis in degus by using cholesterol-rich diets and found that these animals exhibit lipoprotein metabolism similar to that of humans⁵⁷. Additionally, Rivera et al. (2018) demonstrated that prolonged consumption of fructose from early life into adulthood led to features of metabolic syndrome, non-alcoholic fatty liver disease, and cognitive decline⁵⁸. Although it is expected that nutritional regimes may significantly shape experimental outcomes, no studies have yet directly examined how diet influences the gut microbiome, physiology, and cognitive performance in O. degus. To address this, we evaluated the effects of two commonly used laboratory diets, differing primarily in fiber content and formulated for different species (Champion for rabbits and LabDiet for guinea pigs, Table 1), on physiological traits (total fatty acid profiles, SCFA production, body mass, and digestive efficiency) and behavioral measures (innate behaviors, anxiety-like behavior, and cognitive performance). Additionally, we analyzed gut microbiome dynamics to explore how these two diets drive shifts in microbial communities. By integrating these variables using piecewise structural equation modeling (pSEM), we assessed how differences in dietary lipid composition shape microbial metabolic activity and whether these microbial outputs are associated with cognitive and behavioral outcomes. This systems-level approach provides a comprehensive understanding of how diet modulates the microbiota–gut–brain axis, helping to clarify whether microbial changes translate into measurable effects on host physiology and behavior. Moreover, by situating our study within the extensive evidence linking diet and health in humans, we aimed to highlight the need for systematic evaluation of diet in degus, thereby reinforcing the translational relevance of this species in biomedical research.

Results

Physiological parameters

Total fatty acid profile of the Champion and LabDiet diets

Both diets contained key fatty acids relevant for metabolic and physiological functions, including C16:0 (palmitic acid), C18:1n9c (oleic acid), C18:2n6c (linoleic acid, an omega-6), and C18:3n3 (α-linolenic acid, an omega-3) (Table Supplementary Material, Table S1). Additionally, both diets provided total n-6 and n-3 fatty acids (Table S1).

Male degus fed the Champion diet had a significantly higher proportion of n-6 polyunsaturated fatty acids (PUFAs) compared to the LabDiet [t-test = 20.95, p < 0.01; Fig. 1]. The same diet induced higher concentrations of palmitic acid (C16:0) and linoleic acid (C18:2n6c) [t-test = 25.94, p < 0.01 and t-test = 20.95, p < 0.01, respectively; Fig. 1], whereas oleic acid (C18:1n9) was more abundant in the LabDiet [t-test = -4.62, p < 0.01]. Saturated fatty acids (SFA) were significantly elevated in the LabDiet [t-test = -46.04, p < 0.01; Fig. 1], while total PUFAs were higher in the Champion diet [t-test = 18.39, p < 0.01; Fig. 1]. These findings align with the expected fatty acid profile based on the ingredient composition, particularly the inclusion of sunflower, rapeseed, and soybean meals—major sources of linoleic and oleic acids.

Fig. 1 [Images not available. See PDF.]

Total fatty acid concentrations in Octodon degus diets. Comparison of total fatty acid profiles between the Champion rabbit diet and the LabDiet guinea pig diet. Saturated fatty acids (SFA) include C13:0, C14:0, C16:0, C17:0, C18:0, C20:0, and C22:0. Monounsaturated fatty acids (MUFA) include C14:1, C15:1, C16:1, C17:1, C18:1n-9, and C22:1n-9. Polyunsaturated fatty acids (PUFA) include C18:2n-6, C18:3n-6, C18:3n-3, C20:3n-3, C20:4n-6, and C22:6n-3. Statistical significance was determined using Student’s t-tests or the Mann–Whitney U test when normality assumptions were not met. Different letters above the bars indicate significant differences between diets (p < 0.05).

Fig. 2 [Images not available. See PDF.]

Total fatty acid concentrations in fecal samples from Octodon degus. Saturated fatty acids (SFAs) include C13:0, C14:0, C16:0, C17:0, C18:0, C20:0, and C22:0. Monounsaturated fatty acids (MUFAs) include C14:1, C15:1, C16:1, C17:1, C18:1n-9, and C22:1n-9. Polyunsaturated fatty acids (PUFAs) include C18:2n-6, C18:3n-6, C18:3n-3, C20:3n-3, C20:4n-6, and C22:6n-3. Statistical significance was determined using Student’s t-tests or the Mann–Whitney U test when normality assumptions were not met. Different letters above the bars indicate significant differences between diets (p < 0.05).

Fig. 3 [Images not available. See PDF.]

Short-chain fatty acid concentrations in fecal samples from Octodon degus. (A) Concentrations of SCFAs obtained by gas–liquid chromatography, expressed in µM/g of fecal samples. (B) Total SCFA concentrations in fecal samples from animals under each dietary condition. Statistical significance was determined using Student’s t-tests or the Mann–Whitney U test when normality assumptions were not met. Different letters above the bars indicate significant differences between diets (p < 0.05).

Total fatty acids in fecal samples from animals fed the Champion and LabDiet diets

The analysis of fecal content (Table S2) revealed significant differences between the groups in total saturated fatty acid (SFA) and polyunsaturated fatty acid (PUFA) content [t-test = -3.03, p < 0.01 and t-test = 4.12, p < 0.01, respectively; Fig. 2]. An opposing pattern was evident, with SFA levels being higher in animals fed the LabDiet, while PUFA levels were elevated in those fed the Champion diet. Several individual fatty acids commonly found in other rodent feces also differed significantly between diets (Table S2). Notably, palmitic acid (C16:0) [t-test = -5.19, p < 0.01] and stearic acid (C18:0) [t-test = -3.38, p < 0.01] were more abundant in LabDiet-fed animals, whereas linoleic acid (C18:2n-6) was significantly higher in Champion-fed animals [t-test = 3.82, p < 0.01; Fig. 2]. These findings demonstrate that diet composition distinctly shapes the fecal fatty acid profile in degus and can be used to monitor nutrition.

Short-chain fatty acids in fecal samples from animals fed Champion and LabDiet diets

Fecal samples of animals (Table S3) fed the LabDiet diet exhibited a significantly higher concentration of acetic acid [t-test = − 2.71, p = 0.015; Fig. 3A]. No significant differences were detected in the concentrations of the other SCFAs between dietary groups (Table S3). Although total SCFA content was higher in the LabDiet group compared to the Champion-fed animals, this difference was not statistically significant (p = 0.63; Fig. 3B).

Body mass and digestive efficiency changed with the administration of both types of diet

At the start of the treatment, no statistically significant differences were observed in the body mass of animals fed the Champion diet (220.97 ± 3.81 g) compared to those fed the LabDiet (212.21 ± 3.54 g) (p = 0.11). During the diet administration period, the animal’s body mass was recorded monthly. The repeated measures ANOVA revealed a significant effect of time on the body mass across the 15-months period [F_(14,252) = 5.74, p < 0.01; Fig. 4A], indicating that body weight changed significantly over time in both diet groups. There was also a significant effect of diet [F_(1,18) = 4.83, p = 0.04] and significant time x diet interaction was observed [F_(14,252) = 5.74, p < 0.01]. Further analysis showed that degus fed the Champion diet maintained higher body weights compared to those in the LabDiet group (Fig. 4B).

For the analysis of the apparent digestibility coefficient, the repeated measures ANOVA revealed a significant effect of time on digestibility across the evaluated period [F_(3,54) = 2.96, p  = 0.040; Fig. 4C]. There was a significant effect of diet on digestibility [F_(1,18) = 54.62, p < 0.01], indicating that the type of diet had a substantial impact on the animals’ digestive efficiency. Additionally, a significant interaction between factors was detected [F_{(3, 54)} = 4.02, p = 0.01). Further analysis showed that degus fed the LabDiet exhibited higher digestibility than those fed the Champion diet (Fig. 4D). These results indicated that digestive efficiency varied over time and that the type of diet influenced and modulated how digestibility efficiency evolved throughout the experimental period.

Fig. 4 [Images not available. See PDF.]

Body mass and apparent digestibility (defined as the percentage of ingested matter, energy, or nutrients absorbed by the body) in male Octodon degus fed either a commercial rabbit diet (Champion) or a guinea pig diet (LabDiet). The t value corresponds to the Student’s t-test.

Innate behavior is unaffected by diet administration

For the burrowing test, the repeated measures ANOVA revealed no significant effect of time (p = 0.21), diet (p = 0.30), or the interaction between these factors (p = 0.52) on the amount of substrate not removed from the tube. Although animals fed the Champion diet appeared to remove more substrate than those in the other group, no significant differences were found between the two diets (Table 2). These results suggest that the type of diet did not significantly influence burrowing activity. Similarly, for the nesting test, the repeated measures ANOVA revealed no significant effects of time (p = 0.62), diet (p = 0.71), or the interaction between factors (p = 0.51). Nestlet weights were then averaged across time points, and no statistically significant differences were observed between the two diet groups (p = 0.71; Table 2). Taken together, these analyses indicate that dietary treatment had no measurable impact on the innate behaviors evaluated in this study.

Table 1.  Nutritional composition of two commercially available laboratory rodent diets: the rabbit pellet diet (Champion®, Santiago, Chile) and the guinea pig pellet diet (LabDiet® 5025, LabDiet, USA). The chemical composition of the Champion® rabbit pellet diet was taken from Veloso & Bozinovic (1993)⁶⁷, which reports proximate analyses for this formulation. For the LabDiet® treatment, composition was obtained from the manufacturer’s product datasheet (LabDiet® 5025, Guinea Pig Diet; LabDiet, St. Louis, MO, USA; technical sheet).

Nutritional composition	Diet treatment
	Champion	LabDiet
Protein, %	20.0	19.3
Fat (ether extract), %	3.0	4.3
Fiber (Crude), %	16.5	14.5
Neutral detergent fiber (NDF), %	37.8	27.9
Acid detergent fiber (ADF), %	10.8	17.5
Moisture, %	13.0	12
Ash, %	10.8	7.4
Carbohydrates, %	40.3	60.2
Total energy content, kcal/g	4.6	3.54

 Because the datasheet provides typical ingredients and guaranteed analyses but not proprietary ingredient proportions, the table summarizes the comparable proximate metrics across diets; for the specific batches used in this study, we additionally quantified the fatty-acid profile (Table S1).

Diet can be a modulator of anxious behaviour and cognitive performance

Anxiety-like behavior under diet administration

In the open field test, three variables were analyzed. First, the percentage of time spent in the corners differed significantly between diets [t-test = 3.179, p < 0.01; Table 2], with degus fed the Champion diet spending less time in the corners of the arena. For the percentage of time spent in the center, a significant difference was also observed between diets [Mann–Whitney U = 9.00, z = − 3.067, p < 0.01], with Champion-fed degus spending more time in the center (Table 2). However, no significant difference was found between the diets for the number of diagonal crossings (p = 0.70; Table 2).

To evaluate the animals’ response to a novel object placed in the center of the open field, three variables were measured: percentage of time spent in the corners, number of diagonal crossings, and time spent interacting with the object. No significant differences between diets were found for the percentage of time spent in the corners (p = 0.35; Table 2) or the number of diagonal crossings (p = 0.79; Table 2). However, for time spent interacting with the object, a significant difference was detected [t-test = − 2.92, p = 0.01; Table 2], with LabDiet-fed degus spending more time engaging with the object.

In the light/dark box test, no significant differences were observed between diet groups in the number of transitions between compartments (p = 0.24; Table 2) or in the time spent in the light compartment (p = 0.67; Table 2). However, the latency to enter the dark compartment was significantly longer in animals fed the Champion diet [Mann–Whitney U = 16.50, z = − 2.49, p < 0.01; Table 2]. Taken together, these results suggest that animals consuming LabDiet exhibited greater signs of anxiety compared to their counterparts.

Diet shapes cognitive performance

To assess social affiliation/motivation and social memory and preference, we used the three-chambered social interaction test. The Recognition Index (RI) revealed significant differences between diets during the first session [t-test = − 2.53, p = 0.02; Table 2], where LabDiet-fed animals displayed higher RI values than those fed the Champion diet. In the second session, no significant differences were found between the two diet groups (p = 0.08; Table 2). These results suggest that diet could influence social affiliation or motivation but did not affect social memory.

Table 2. Behavioral measurements were categorized as follows: innate behavior, assessed using the burrowing and nesting tests; anxiety-like behavior, evaluated through the open field test, the novel object open field test, and the light/dark box test; and cognitive performance, assessed using the three-chambered social interaction test and the novel location/object recognition (NLR/NOR) tests.

Behavioral tasks	Diet treatment
	Champion	LabDiet
Innate behaviour
Burrowing test (g)	309.41 ± 131.86	122.81 ± 70.78
Nesting test (g)
Anxiety-like behaviour	2.43 ± 0.98	2.97 ± 1.04
Open field
Number of central crossings
% time in the central zone	1.80 ± 0.33	2.00 ± 0.39
% time in corners	3.79 ± 0.47	1.96 ± 0.23**
“Novel object” open field test	35.03 ± 2.48	45.99 ± 2.39**
Time with the novel object (s)	19.65 ± 1.88	27.66 ± 2.34*
Number of entries into central zone	1.2 ± 0.29	1.1 ± 0.23
% time spend in the central zone	18.19 ± 3.87	17.04 ± 1.95
% time in the corners	29.05 ± 3.99	33.708 ± 2.80
Light-dark box test
Number of light-dark box transitions	17.94 ± 1.79	21.22 ± 1.97
Total duration in light box (s)	351.92 ± 33.34	364.25 ± 17.07
Latency to enter to the dark compartment (s)	8.45 ± 2.24	0.94 ± 0.01*
Cognitive performance
Three-chambered social interaction test
RI session I	0.88 ± 0.02	3.32 ± 0.42**
RI session II	0.50 ± 0.03	0.62 ± 0.06
NLR/NOR Test
RI NLR	0.62 ± 0.06	0.65 ± 0.04
RI NOR	0.54 ± 0.05	0.77 ± 0.04**

 In the burrowing test, the remaining weight of the pellets in the tube was recorded. For the three-chamber social interaction and NLR/NOR tests, the Recognition Index (RI) is shown. Data are presented as means ± SEM. Each diet group included ten degus. *p < 0.05; **p < 0.001.

To evaluate working memory and preference, we used the Novel Location Recognition/Novel Object Recognition (NLR/NOR) tests. No significant differences in the RI were found between diets during the NLR session (p = 0.61; Table 2). However, in the NOR session, significant differences were detected [t-test = − 3.35, p < 0.01], where LabDiet-fed animals exhibited higher RI values (Table 2).

Visualization of anxiety-like behavior and cognitive performance via PCA revealed a clear separation among diet treatments (Fig. 5), and one-way PERMANOVA confirmed that the Champion diet clustered distinctly from LabDiet [F = 3.041, p < 0.01; Fig. 5]. These findings indicate a robust effect of diet on the behavior of male degus.

Fig. 5 [Images not available. See PDF.]

Principal component analysis (PCA) of anxiety-like behavior and cognitive performance in male Octodon degus (n = 10 per group) across diet treatments. Each point represents an individual degu, with colors indicating the diet group (olive circles: Champion diet; green circles: LabDiet).

Fig. 6 [Images not available. See PDF.]

Diversity and richness of the gut microbiome in male Octodon degus fed the Champion or LabDiet diets. Boxplots represent (A) the Shannon diversity index (H′) and (B) the Chao1 richness index. t-values correspond to results from Student’s t-test.

Diversity of gut bacterial communities differs significantly between diets

To assess the diversity of bacterial species present in fecal samples from male degus fed the Champion or LabDiet diets, we calculated the Shannon diversity index. Our results revealed significant differences in alpha diversity between diets [t-test = 2.10, p = 0.04; Fig. 6A]. Similarly, the Chao1 richness index also showed significant differences between diets [t-test = 3.01, p < 0.01; Fig. 6B]. Using both indices, we found that animals fed the Champion diet exhibited greater gut bacterial diversity compared to those fed the LabDiet.

Taxonomic characterization of the gut bacterial communities in male degus fed the Champion and LabDiet diets

A total of 922 ASVs (Amplicon Sequence Variants) were identified across the samples from degus fed either the Champion or LabDiet diets. Of these, 206 ASVs were found in animals fed the Champion diet, and 188 ASVs in those fed the LabDiet . Sequence analysis classified all ASVs into ten bacterial phyla. The most dominant phyla in fecal samples were Bacteroidota (Champion: 56.39%, LabDiet: 49.40%) and Firmicutes (Champion: 33.78%, LabDiet: 38.69%), followed by Saccharibacteria in smaller proportions (Champion: 5.12%, LabDiet: 5.19%) (Fig. 7). Other minor phyla included Actinobacteria, Cyanobacteria, Elusimicrobia, Proteobacteria, Spirochaetae, Tenericutes, and Verrucomicrobia. Statistical analysis revealed significant differences in the relative abundances of Actinobacteria [t-test = -2.22, p = 0.03], Bacteroidota [t-test = 2.12, p = 0.04], and Tenericutes [t-test = 2.33, p = 0.03; Fig. 7]. Among them, Bacteroidota and Tenericutes showed higher abundance in degus fed the Champion diet, whereas Actinobacteria was more abundant in those fed the LabDiet.

At the class level, comparisons between diets showed a significantly higher abundance of Bacteroidia in Champion-fed degus [t-test = 2.12, p = 0.04; Fig. S2]. Although Clostridia also appeared more abundant in the Champion group, this difference was not statistically significant. Significant differences were also observed for the classes Actinobacteria [t-test = -2.19, p = 0.04] and Mollicutes [t-test = 2.33, p = 0.03], despite their relatively low abundance in both dietary groups (Fig. S2).

At the order level, members of Anaeroplasmatales were exclusively detected in LabDiet-fed degus and were absent in those fed the Champion diet [Mann–Whitney U = 25.00, z = − 2.434, p = 0.01; Fig. 8]. Additionally, Bacteroidales and Bifidobacteriales were significantly more abundant in the Champion-fed group [t-test = 2.12, p = 0.04 and t-test = − 2.19, p < 0.01, respectively; Fig. 8]. A significant difference was also observed for the order Rickettsiales (Mann–Whitney U = 9.50, z = − 3.064, p < 0.01; Fig. 8), with animals on the LabDiet displaying twice the amount of this order as those on the Champion diet.

At the family level, Anaeroplasmataceae was uniquely found in the LabDiet group [Mann–Whitney U = 25.00, z = − 2.434, p = 0.01; Fig. S3]. Bifidobacteriaceae was significantly more abundant in LabDiet-fed animals [t-test = − 2.19, p = 0.04; Fig. S3], whereas Family XIII showed higher abundance in the Champion group [t-test = 2.35, p = 0.03; Fig. S3]. Interestingly, Lachnospiraceae exhibited the opposite trend, being nearly twice as abundant in the LabDiet group compared to the Champion group [Mann–Whitney U = 12.00, z = − 2.83, p < 0.01; Fig. S3].

Fig. 7 [Images not available. See PDF.]

Analysis of the gut microbiome of male Octodon degus fed the Champion or LabDiet diets. Total relative abundance (%) of fecal bacterial communities at the Phyla level across both diets. Different letters above the boxplots indicate significant differences between diets (p < 0.05).

Fig. 8 [Images not available. See PDF.]

Analysis of the gut microbiome of male Octodon degus fed the Champion or LabDiet diets. Total relative abundance (%) of fecal bacterial communities at the Order level across both diets. Different letters above the boxplots indicate significant differences between diets (p < 0.05).

Comparable gut bacterial composition in Champion and LabDiet

One-way PERMANOVA analysis revealed significant differences in bacterial community composition between both diets (F = 2.44; p < 0.01). Of the bacterial sequences analyzed, 533 ASVs were shared between both diets, representing 57.81% of the total ASVs identified. Additionally, 206 ASVs (22.34%) were unique to the Champion diet, while 183 ASVs (19.84%) were unique to LabDiet (Fig. S4). Among these exclusive taxa, the same seven bacterial phyla were detected in both groups. The most abundant phylum in both diets was Firmicutes, with Clostridia being the dominant class. At the family level, Lachnospiraceae and Ruminococcaceae were the most representative taxa across both diets. However, an opposite pattern was observed: while LabDiet-fed animals exhibited nearly four times more Lachnospiraceae than those fed the Champion diet, Ruminococcaceae was more abundant in the Champion-fed group. In addition, Bacteroidota (class Bacteroidia), Actinobacteria (class Coriobacteriia), and Proteobacteria (Deltaproteobacteria) were more prevalent in LabDiet-fed animals. The phylum Tenericutes (class Mollicutes) showed higher representation in the Champion diet.

LEfSe analysis did not reveal any differentially abundant taxa at the phylum or class level. However, at the order level, Bifidobacteriales were enriched in LabDiet-fed animals, while Legionellales were more abundant in those fed the Champion diet (Fig. 9A). At the family level, two discriminative biomarkers - Lachnospiraceae and Bifidobacteriaceae – were associated with the LabDiet group. In contrast, three families – Bacteroidales, Christensenellaceae, and Coxiellaceae – were significantly enriched in the Champion-fed animals (Fig. 9B).

The core microbiome, defined as taxa present at a minimum relative abundance of 0.1% in at least 95% of the samples, was identified across all samples. Based on this criterion, a total of 11 families, belonging to 8 different phyla were classified as core members. These taxa represent a substantial portion of the microbial community in both diet groups, with a cumulative relative abundance of 74% in Champion-fed animals and 76% in those fed LabDiet. This finding suggests the existence of a stable and consistent core microbiome regardless of dietary treatment.

Predicted functions in Champion and LabDiet-fed animals

To explore functional shifts in the gut microbiome associated with both diets, we performed a functional inference analysis. Fig. 10 presents the standardized abundance of major functional categories across samples, highlighting relative differences in predicted metabolic potential. Notably, functions related to energy metabolism, lipid metabolism, cell envelope biosynthesis, lipopolysaccharide (LPS) biosynthesis, the Calvin-Benson cycle, and cell wall/membrane component synthesis were enriched in several LabDiet-fed animals. In contrast, pathways associated with amino acid, nucleotide, and carbohydrate metabolism, as well as the tricarboxylic acid (TCA) cycle, were relatively more prominent in Champion-fed animals. Some functional categories, such as anaerobic metabolism, translation, and cofactor biosynthesis exhibited high inter-individual variability, with enrichment observed in specific samples. Despite this variability, hierarchical clustering of predicted functional profiles showed a general tendency to segregate by diet, underscoring the influence of dietary composition on microbiome function. This pattern was further supported by LEfSe analysis, which identified two significantly enriched functional biomarkers in the LabDiet group: one associated with the Calvin-Benson cycle and another with lipid metabolism. No significantly enriched functional pathways were detected in the Champion-fed group.

Direct and indirect effects of dietary treatment on gut microbiome, physiological and behavioral outcomes

The pSEM analysis revealed significant effects of diet on physiological traits, microbiome diversity, and behavior variables (Table S5). The selected model retained all statistically significant paths and exhibited excellent goodness-of-fit (AIC = 307.936; Fisher’s C = 7.53, p = 0.86), indicating that the model accurately captures the observed data structure with no major unexplained associations. This model explained 49% of the variance in microbiome diversity, 13% in anxiety-like behavior, 7% in cognitive performance, and varying degrees of variance in physiological traits (SFA: 81%, SCFA: 51%, digestive efficiency: 37%, body mass: 22%; Table S6).

Dietary treatment had a significant positive effect on SCFA concentration (β = 0.72; Fig. 11), SFA levels (β = 0.58; Fig. 11), and digestive efficiency (β = 0.90; Fig. 11), while it was negatively associated with body mass (β = − 0.47; Fig. 11). In addition, acetic acid showed a positive path coefficient with cognitive performance (β = 0.53; Fig. 11), suggesting a potential mechanistic link between diet-induced metabolic changes and cognitive behavior. Microbial diversity was strongly influenced by diet (β = 0.81; Fig. 11) and negatively associated with SFA levels (β = − 0.46; Fig. 11), but it did not directly influence behavioral outcomes. None of the predictors significantly accounted for variation in anxiety-like behavior, and its explained variance remained low (R² = 0.07). Taken together, these results underscore that diet primarily modulates host physiology and microbiome diversity, with downstream effects on cognition, reinforcing the role of gut-derived metabolites in the diet–brain axis within this model system.

Fig. 9 [Images not available. See PDF.]

Histograms of linear discriminant analysis (LDA) with effect size (LEfSe) on gut microbiome composition of adult degus fed the Champion or LabDiet diets at the (A) Order level and (B) at the Family level.

Fig. 10 [Images not available. See PDF.]

Metabolic heat-map illustrating differential metabolite enrichment between adult degus fed the Champion or LabDiet diets. Columns indicate the dietary group; red color indicates higher abundance, whereas blue color indicates lower abundance.

Fig. 11 [Images not available. See PDF.]

Selected conceptual model illustrating the direction and strength of the observed relationships linking diet to physiological, microbial and behavioral variables, including body mass, digestive efficiency, saturated fatty acids (SFA), short-chain fatty acids (SCFA, specifically acetic acid), gut microbiome diversity, anxiety-like behavior, and cognitive performance. Arrows indicate the relationships between variables and are proportional to the strength of the standardized coefficients. Solid green lines indicate positive relationships, solid red lines indicate significant negative relationships, and solid gray lines represent non-significant positive or negative relationships. Numbers adjacent to the arrows denote standardized path coefficients, indicating the effect size of each relationship.

Discussion

To our knowledge, this study is the first to simultaneously evaluate the effect of diet on physiological parameters, behavior, and gut microbiome diversity, abundance, and composition in O. degus. Despite the widespread use of degus as a model organism in neuroscience, aging, and metabolic research, there remains no commercially available diet formulated specifically for this species. The study by Edwards (2009)⁵⁹, which details the nutritional composition of experimental diets fed to degus compiled from previous studies, mentioned that two commercially available rodent diets (Prolab RMH 2000, 5P06 and Laboratory Rodent Diet, 5001) have demonstrated the ability to sustain degus across life stages. These diets contain moderate levels of structural carbohydrates (12.3% NDF with 4.4% ADF, and 15.6% NDF with 6.7% ADF, respectively). However, no prior studies have investigated how diets formulated for the requirements of an omnivorous species, such as mice⁶⁰, may affect the physiological, behavioral, and microbial parameters of obligate herbivores such as degus.

The two diets evaluated in our study — Champion and LabDiet — are frequently used in degu colonies, despite being formulated for distinct target species. The Champion diet, designed to promote weight gain in rabbits, is widely used in Chilean facilities due to its high vegetable fiber content and affordability. In contrast, LabDiet is a globally standardized guinea pig feed specifically designed to support reproduction, lactation, growth, and maintenance. It is manufactured under strict quality control conditions that comply with Good Manufacturing Practices (GMP), ensuring high food safety standards and minimizing potential biological risks.

Although degus, rabbits, and guinea pigs are all herbivorous mammals^61,62, significant differences in their digestive physiology, including fiber digestion efficiency and digesta retention times, make cross-species dietary substitution suboptimal^63,64. From a phylogenetic perspective, degus are caviomorph rodents with a closer evolutionary relationship to guinea pigs than to rabbits^65,66. This evolutionary proximity is reflected in several physiological traits, including dentition patterns, the hindgut fermentation process, the requirement for dietary fiber to maintain gut motility, and the practice of coprophagy^59,63,67,68.

One significant physiological difference between guinea pigs and degus is their ability to produce ascorbic acid (vitamin C). Guinea pigs do not have the enzyme L-gulonolactone oxidase, which means they cannot synthesize vitamin C and must obtain it from their diet. In contrast, degus can produce this important antioxidant on their own^{59,69, 70–71}. Vitamin C is a key antioxidant involved in processes such as DNA repair, reduction of oxidative stress, immune regulation, and telomere maintenance—mechanisms tightly linked to aging and cellular senescence^{72, 73, 74, 75–76}. Moreover, vitamin E, another powerful antioxidant, has been shown to improve cognitive performance in aged rodent models^{77, 78–79}. While both diets contain vitamin E in their formulation, Arzi et al. (2004) demonstrated that combined supplementation with vitamins C and E significantly enhanced cognition in aged mice, though no effects were observed in younger animals⁸⁰. Given that degus naturally exhibit age-associated cognitive decline and Alzheimer’s disease-like pathology, and are susceptible to metabolic disorders such as diabetes and obesity^51,57,58, our findings raise important questions regarding the potential for diet to modulate these phenotypes.

Future research should investigate whether diets enriched with antioxidants, particularly vitamins C, can mitigate neurodegenerative and metabolic dysfunctions in this model. In this context, the improved cognitive performance observed in degus fed LabDiet may be linked to the inclusion of specific micronutrients and other bioactive compounds that are either lacking or present at different levels in the Champion diet.

Diet impacts on the O. degus physiology

Our findings suggest that diet has a significant influence on physiological parameters in degus. In terms of body weight, animals fed the Champion diet had a higher body mass by the end of the experiment, likely due to the elevated omega-6 polyunsaturated fatty acid (PUFA) content, particularly linoleic acid. This result is supported by prior studies linking excessive dietary linoleic acid to increased adipogenesis and altered energy homeostasis via the PPARγ and endocannabinoid pathways^{81, 82–83}. Despite the lower digestibility index observed in the Champion-fed group, the greater body mass of these animals suggests that dietary fat composition, rather than total caloric absorption, drives fat accumulation. This finding is consistent with results from rodent models, in which omega-6 PUFA-rich diets promote fat storage independently of total energy intake^84,85. In contrast, LabDiet’s higher saturated fatty acid (SFA) content was associated with elevated levels of palmitic and stearic acids in animals fed this diet, which may account for the leaner phenotype. Although SFAs are often deemed detrimental in humans, their specific roles in membrane function and intracellular signaling cannot be overlooked, particularly in non-human models where metabolic responses may differ^86,87. In addition,  her bivorous rodents like degus and guinea pigs have evolved to process high-fiber diets using microbial fermentation in their hindguts⁶³. When the diet shifts to a high-SFA (often low-fiber) diet, the digestive burden is lowered, leading to greater digestibility⁸⁸. Previous studies have shown that lipids are digested more efficiently than fiber, even in herbivorous species. For example, in rabbits, lipase activity increased following two weeks of dietary lipid supplementation⁸⁹. More recently, Chillpa-Sencia (2024) compared nutrient digestibility in growing and adult guinea pigs and found that ether extract (i.e., fat) digestibility reached approximately 60% in growing animals and 87% in adults. In contrast, crude fiber digestibility ranged from only 50 to 61%, clearly indicating that lipids are more efficiently digested than fiber in herbivorous guinea pigs⁹⁰.

The elevated levels of palmitic and stearic acid that we found in the feces of degus fed LabDiet may be related to the fact that, unlike fiber and some unsaturated fatty acids, some saturated fatty acids (SFAs) are not efficiently fermented and largely remain intact during gut transit, unlike more fermentable lipids or fiber⁹¹. The elevated acetic acid levels observed in the feces of LabDiet-fed degus may be linked to the vitamin C content of this diet. Li et al. (2025) reported that vitamin C supplementation significantly increased SCFA levels⁹². This suggests that modulating the gut microbiome through dietary components such as vitamin C may enhance SCFA production.

From a digestive physiology standpoint, the discrepancy in digestibility indices between diets likely results from variations in fiber complexity. LabDiet’s higher acid detergent fiber (ADF) content correlates with improved digestibility in herbivorous rodents, which depend on efficient hindgut fermentation^68,93. In contrast, the Champion diet’s elevated neutral detergent fiber (NDF) may prolong transit time and reduce nutrient bioavailability, especially in species like the degu, which has a colonic fermentative system⁹⁴. These findings are consistent with previous work highlighting how specific fiber fractions influence nutrient assimilation in herbivores^95,96.

Together, these results emphasize that diet-induced physiological outcomes in degus are not merely a function of caloric intake but are critically shaped by the interplay between dietary fat composition, fiber complexity, microbial fermentation, and host metabolism. Future work should incorporate longitudinal microbiome profiling and lipidomic analyses to dissect the mechanistic links between diet, microbiota-derived metabolites, and host physiology, particularly given the relevance of degus as a model for metabolic and neurodegenerative disorders^97,98.

The impact of diet on the O. degus behavior

Our findings demonstrate that diet can selectively influence specific behavioral domains in degus, while largely leaving innate behaviors unaffected. Behaviors such as burrowing and nest-building, which are commonly used to assess baseline well-being and neurological integrity in rodents^{99, 100, 101–102}, showed no significant differences between the Champion and LabDiet groups. Given that degus are semi-fossorial rodents that construct burrows primarily through scratch-digging¹⁰³, the lack of dietary influence on these innate behaviors suggests that basic species-specific behaviors are robust to nutritional variations. The burrowing test has previously been used as a biomarker of Alzheimer’s disease (AD)-like pathology, with affected animals exhibiting reduced burrowing activity^104,105. However, despite the propensity of four-year-old degus to display early AD-like symptoms^52,53,97, our results suggest that this paradigm may lack sensitivity to detect subtle age-related cognitive decline under these dietary conditions. Future studies integrating neuropathological and molecular markers (e.g., amyloid-beta accumulation, tau phosphorylation) are needed to evaluate whether diet modulates the onset or progression of AD-like symptoms in this species.

Anxiety-like behavior showed differences across diet treatments. In the open field test, LabDiet-fed animals exhibited increased thigmotaxis, indicating a higher baseline anxiety level. In contrast, Champion-fed degus explored the center area more extensively. Interestingly, during the modified open field test, LabDiet-fed animals displayed enhanced curiosity toward a novel object, suggesting that initial anxiety may diminish with environmental habituation. In contrast, in the light/dark box test, Champion-fed animals showed longer latencies to enter the dark compartment, reflecting heightened caution in novel environments¹⁰⁶. These contrasting responses highlight the context-dependent nature of anxiety-like behavior, which is consistent with evidence that dietary composition, particularly variations in fiber diversity and lipid profiles, can modulate stress responses and anxiety levels in rodents^{107, 108, 109–110}. This complexity underscores that diet-behavior interactions are not linear, as even genetically identical animals (e.g., mutant mice) often exhibit divergent outcomes across commonly used anxiety paradigms^111,112.

Dietary effects were also evident in cognitive performance. LabDiet-fed degus exhibited superior object recognition memory in the novel object recognition (NOR) test, as well as greater motivation to engage with novel conspecifics during the initial phases of social interaction. This suggests enhanced short-term memory and novelty-seeking behavior. These improvements may be linked to the presence of vitamins C and E in LabDiet. Vitamin C has been used to alleviate oxidative damage, reduce neuroinflammation^113,114, and enhance synaptic plasticity¹¹⁵. Recent work by Li et al. (2025) has further demonstrated that vitamin C supplementation enhances spatial learning and memory, while reducing hippocampal neuronal damage⁹². These effects are accompanied by an increase in short-chain fatty acid (SCFA) levels, particularly acetic acid. SCFAs are known to modulate gut-brain communication, regulate neuroinflammation, and influence synaptic plasticity^116,117. These findings suggest that the cognitive benefits observed in LabDiet-fed animals may result from both direct neuroprotection by antioxidants and indirect modulation of the gut microbiome. Indeed, reduced SCFA levels have been associated with cognitive impairment in humans¹¹⁸, which supports the idea of a mechanistic link between dietary components, microbiome-derived metabolites, and cognitive function. Finally, PCA and PERMANOVA confirmed a clear behavioral divergence between diets, with Champion-fed animals showing greater consistency in anxiety measures and LabDiet-fed animals excelling in cognitive domains.

Effect of diet on the gut microbiome of O. degus

SFAs and SCFAs, produced by the gut microbiome through the fermentation of dietary fiber, play essential roles in supporting cognitive function via the gut-brain axis^38,119. In our study, apparent differences in alpha diversity and species richness were observed between dietary groups: LabDiet-fed animals exhibited lower Shannon diversity and Chao-1 richness compared to those fed the Champion. Both diets were dominated by Bacteroidota and Firmicutes, phyla commonly associated with a healthy gut microbiome. Bacteroidota and Tenericutes were significantly enriched in Champion-fed animals, whereas Actinobacteria (particularly Bifidobacterium) were more abundant in LabDiet-fed animals. These findings are consistent with reports that Bifidobacterium abundance is increased by vitamin C supplementation¹²⁰, which is present in LabDiet, and that this genus promotes immune resilience and reduces the risk of infection¹²¹.

The increased abundance of Tenericutes (class Mollicutes) in Champion- fed animals is noteworthy, as this taxon has been associated with high-fat or Western-type diets and altered energy metabolism^122,123. Conversely, Anaeroplasmatales (class Mollicutes), detected only in LabDiet-fed degus, is commonly reported in standard rodent chows with balanced macronutrient composition¹²⁴. Although both diets share herbivore-targeted ingredients, the degu’s strictly herbivorous physiology, including prolonged digesta retention and specialized fiber digestion, likely results in differential fermentation patterns compared to omnivorous rodents^63,67.

Fiber composition appears to be a primary driver of these microbial differences^125,126. Champion, with higher neutral detergent fiber (NDF), enriched Bacteroidales and Ruminococcaceae, taxa specialized in the degradation of complex polysaccharides, while LabDiet promoted Lachnospiraceae, another important SCFA-producing family^127,128. Such compositional shifts likely affect the SCFA profile, which in turn modulates host metabolic and inflammatory pathways²². Our data suggest that LabDiet-fed animals exhibited higher acetic acid levels and also a trend toward an increase in SCFA. This evidence aligns with previous studies that have demostrated that SCFAs can improve gut barrier integrity, regulate immune responses, and enhance cognitive function by modulating neuroinflammation and neurotransmitter systems^116,129.

In line with these compositional differences, functional inference revealed clear metabolic segregation between the two dietary groups. Animals fed the LabDiet showed enrichment in pathways associated with energy and lipid metabolism, including lipopolysaccharide (LPS) biosynthesis and the Calvin–Benson cycle. In contrast, degus fed the Champion showed higher activity in amino acid, nucleotide and carbohydrate metabolism, which is consistent with the latter’s macronutrient-rich but less fibrous composition. These results suggest that even minor variations in dietary formulation can alter microbial functions and affect host physiology. LEfSe analysis further identified the Calvin-Benson cycle and lipid biosynthesis as significant biomarkers enriched in the LabDiet group. No significantly enriched pathways were detected in Champion-fed animals, which may indicate a more heterogeneous or functionally redundant microbial consortium¹³⁰.

The SEM analysis confirmed that diet indirectly influenced host physiology via microbial metabolites, particularly SCFAs. A positive correlation was found between acetic acid levels and digestive efficiency, as well as cognitive performance. This supports the idea that modulation of the gut microbiome by diet contributes to the regulation of the gut-brain axis^{33,38, 39–40,131}. In contrast, SFAs showed a negative correlation with microbial diversity, consistent with prior findings that link high SFA consumption to dysbiosis and diminished microbial resilience^132,133. Interestingly, although SCFAs were strongly associated with metabolic traits, they only explained a small amount of variation in anxiety-like behavior. This suggests that diet-driven changes to the microbiome influence cognition more directly than emotional reactivity.

Overall, our findings highlight that dietary composition is not a neutral factor in studies employing O. degus as an animal model. LabDiet, with its standardized formulation and vitamin C supplementation, was associated with improved digestive efficiency and enhanced cognitive performance. In contrast, the Champion diet, likely due to its diverse fiber content and plant-based ingredients, promoted greater microbial richness. These outcomes underscore the importance of dietary standardization in animal facilities, as unaccounted dietary variables can confound behavioral and physiological outcomes. Furthermore, our results emphasize the role of the gut microbiome as a mediator between diet and brain function, pointing to the potential of microbiome-derived metabolites, particularly SCFAs, to improve metabolic and cognitive health^26,36,38,129.

Methods

Ethics statement

The experimental protocols were carried out in compliance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publication No. 8023, revised 1978) and received approval from the Bioethics and Biosafety Committee of the Universidad Mayor (Protocol #15/2019). In addition, the ARRIVE guidelines were followed to carry out the present study.

Animals and management

Adult male degus (four years old) weighed 216 ± 2.7 g (mean ± SE). All animals were second-generation (F2) offspring from a laboratory-maintained breeding colony housed in the Faculty of Biological Sciences at the Pontificia Universidad Católica de Chile. Breeders in this colony are regularly rotated, and consanguineous pairings (i.e., sibling, parent–offspring, or half-sibling matings) are actively avoided to minimize inbreeding within the experimental cohort. Animals were randomly assigned to two groups and pair-housed in replicate cages per diet, with no cross-diet co-housing. Housing consisted of transparent acrylic aquaria (50 × 35 × 23 cm; length × height × depth) with hardwood chip bedding. Each cage was equipped with a clear acrylic nest box (22 × 12 × 15 cm). Animals were maintained in a ventilated room under natural photoperiod conditions (12 h light/12 h dark cycle) and ambient temperature (annual minimum = 13.4 ± 0.2 °C; annual maximum = 24.9 ± 0.2 °C). All animals received environmental enrichment throughout the experiment, including cardboard tubes and wood to support dental wear. Husbandry and procedures (including bedding type and change schedule, water supply, light cycle, handling, and sampling time) were standardized across cages; bedding was not pooled between cages.

Animals were assigned to two dietary groups (n = 10 per group). One group received a standard commercial rabbit pellet diet (Champion^®, Santiago, Chile), while the second group received a commercial guinea pig pellet diet (LabDiet^® 5025, Guinea Pig Diet; LabDiet, St. Louis, MO, USA; technical datasheet). Group size was determined a priori based on (i) the logistical feasibility of maintaining degu colonies under controlled dietary regimes over extended periods, (ii) prior work in this model reporting robust behavioral effects^54,55, and (iii) a power calculation following the National Research Council guidelines (Guidelines for the Use of Animals in Neuroscience and Behavioral Research, 2003). We used the equation , −where is the animals per group, the expected standard deviation, the minimally important difference, and a constant defined by α and statistical power (1 − β). We set α = 0.05 and 1 − β = 0.90, yielding C = 10.51. Pilot behavioral data indicated ≈ 0.50, and we targeted a detectable difference of 0.65. Under these assumptions, the calculation supports a target of 10–13 animals per group; we therefore adopted n = 10 per group as a pragmatic compromise that maintains high power for endpoints with lower dispersion, while minimizing unnecessary animal use.

Diet composition for Champion^® was obtained from published proximate analyses⁶⁷, and composition for LabDiet^® 5025 from the manufacturer’s technical datasheet (Guinea Pig Diet 5025; LabDiet, St. Louis, MO, USA). Briefly, the Champion diet—formulated primarily for rabbit fattening, contains alfalfa, oats, barley, wheat bran, sunflower meal, rapeseed meal, soybeans/soybean meal, and alfalfa meal among its principal ingredients. It is supplemented with vitamins (A, D₃, E, K, B₁, pantothenic acid, niacin, choline, and folic acid), minerals (e.g., calcium carbonate, sodium chloride, cobalt, copper, zinc, manganese, iodine, iron, magnesium, and potassium), and includes antioxidants and coccidiostats. In contrast, LabDiet^® 5025 is a life-stage guinea pig diet designed to support reproduction, lactation, growth, and maintenance. Its ingredients include dehydrated alfalfa meal, dehulled soybean meal, ground soybean hulls, wheat middlings, ground corn, ground oats, preserved animal fat (with BHA and citric acid), cane molasses, dicalcium phosphate, ground wheat, dried whey, salt, calcium carbonate, magnesium oxide, L-ascorbyl-2-polyphosphate (vitamin C), DL-methionine, menadione dimethylpyrimidinol bisulfite (vitamin K), choline chloride, vitamin A acetate, cholecalciferol (vitamin D₃), folic acid, pyridoxine hydrochloride, DL-alpha tocopheryl acetate (vitamin E), manganese oxide, calcium pantothenate, zinc oxide, ferrous carbonate, thiamine mononitrate, nicotinic acid, copper sulfate, vitamin B₁₂ supplement, riboflavin supplement, cobalt carbonate, zinc sulfate, calcium iodate, and sodium selenite. Because proprietary ingredient proportions are not disclosed for Champion^®, cross-diet comparisons are presented using comparable proximate metrics; the proximate composition of both diets is summarized in Table 1 . Degus had ad libitum access to their assigned diet and fresh water for 15 months. Body mass was recorded monthly. At the end of the study, behavioral tests were performed and fecal samples were collected for microbiome analysis.

Physiological parameters

Fatty acid analyses

Total lipid quantification and extraction were performed on diet pellets and on fecal samples collected from degus to determine the total fatty acid composition. For each diet, 200 mg pellet samples were analyzed, and fatty acid profiles were determined by gas chromatography in triplicate. Diet samples were stored at 4 °C until processing. In addition, between five and six fecal samples per individual were collected and stored at -80 °C until laboratory analysis. Lipid extraction followed the method of Bligh and Dyer (1959)¹³⁴. Briefly, samples were homogenized in cold chloroform:methanol (2:1, v/v) containing 0.01% butylated hydroxytoluene as an antioxidant.

Preparation of fatty acid methyl esters (FAMEs) and gas chromatography

FAMEs from both diet and fecal samples were prepared using boron trifluoride (12% methanolic solution), followed by treatment with 0.5 N methanolic sodium hydroxide. After evaporation under a nitrogen stream, the resulting FAMEs were extracted with 0.5 mL of hexane¹³⁵. Separation and quantification of FAMEs were conducted using gas-liquid chromatography on an Agilent Hewlett-Packard system (model 7890 A, CA, USA) equipped with a capillary column (DB FFAP, 30 m × 0.250 mm × 0.250 μm) and a flame ionization detector (FID)¹²⁶. The injector temperature was set at 250 °C and the FID at 300 °C. The oven temperature was initially set at 140 °C and programmed to increase to 220 °C at a rate of 5 °C per minute. Nitrogen was used as the carrier gas (flow rate: 35 cm/s), with a split ratio of 50:1.

Identification and quantification of FAMEs were performed by comparing retention times and peak areas (%) with those of a commercial lipid standard (Nu-Check Prep Inc.). C23:0 was used as an internal standard (Nu-Check Prep Inc., Elysian, MN, USA). Data analysis was performed using Hewlett-Packard Chemstation software (Palo Alto, CA, USA).

Short-chain fatty acid (SCFA) analysis and gas chromatography

SCFAs were extracted from fecal samples previously stored at -80 °C. Samples were homogenized in deionized water at a concentration of 200 mg/mL. The pH was adjusted to a range of 2 to 3. Samples were centrifuged at 10,000 rpm for 10 min, and 195 µL of the supernatant was collected¹³⁶. An internal standard (2-ethylbutyric acid) was added to each sample at a final concentration of 1 mM in 200 µL¹²⁶. SCFAs were separated and quantified by gas-liquid chromatography using the same Agilent Hewlett-Packard system (model 7890 A, CA, USA), capillary column (DB FFAP, 30 m × 0.250 mm × 0.250 μm), and FID as described for FAME analysis.

Fatty acid analyses of diet and fecal samples were conducted using the Hewlett-Packard Chemstation software, which facilitated the identification and quantification of compounds by comparing the retention times and peak areas (%) of the sample chromatograms to those obtained from a commercial SCFA standard.

Digestive efficiency.

To evaluate the effect of each diet on the animals’ natural physiological capabilities, the apparent digestibility coefficient (ADC) was measured. The ADC represents the percentage of ingested matter, energy, or nutrients that is absorbed by the body⁶⁷. It was calculated as ADC = [(Qi – Qc) / Qi] × 100, where Qi is the daily food intake and Qc is the daily fecal output. The term “apparent” is used because this method may underestimate true digestive efficiency due to the presence of metabolic waste, non-reabsorbed digestive secretions, and microbial biomass in the feces⁶⁷. For the calculation, each degu was given a known amount of food (18 g of pellets), and both food intake and fecal output were recorded over four consecutive days.

Behavioral parameters

The animals were subjected to seven behavioral tests administered once daily during their active phase (between 09:00 and 14:00). At the end of each session, the animals were returned to their home cages. To minimize the effects of behavioral experiences on the results, the tests were conducted in order from least to most intrusive. The order of tests was as follows: (i) burrowing test, (ii) nesting test, (iii) open field test, (iv) novel object open field test, (v) light/dark box, (vi) three-chambered social interaction test, and (vii) novel location recognition/novel object recognition test (NLR/NOR).

Burrowing test

The burrowing test is a simple, yet sensitive behavioral assay used to assess spontaneous activity in rodents by measuring their ability to empty a tube filled with substrate. This test evaluates burrowing behavior-a natural and species-typical activity observed in many rodents-which serves various functions such as foraging, shelter construction, object burial, or even recreational digging (Deacon, 2009). The protocol followed was based on the methodology described by Deacon (2009). In brief, plastic cylinders measuring 30 cm in length and 10.5 cm in diameter were used. One end of each cylinder was left open and elevated 5 cm above the ground using evenly spaced screws to mimic landing gear. The opposite end was sealed with a flat plastic cap. Each cylinder was filled with 1300 g of familiar food pellets and placed in a terrarium. A single animal was gently introduced into the terrarium and allowed to interact with the apparatus for two hours. After the session, the animal was returned to its home cage, and the remaining weight of pellets in the cylinder was recorded. This procedure was repeated for three consecutive days per animal to ensure consistency and reliability of measurements. To reduce anxiety and prevent neophobic responses, the cylinders were filled with the same food pellets used in the animals’ daily diet, ensuring familiarity with the substrate.

Nesting test 

The nesting test is used to evaluate general behavior, affective state, and sensorimotor coordination in rodents⁹⁹. For this test, each animal was placed in a small transparent terrarium containing wood shavings as bedding and a pre-weighted piece of cardboard nesting material (Nestlet), positioned at the opposite end of the terrarium from the food sources. Each Nestlet was weighed at the beginning and end of the test to quantify the degree of interaction with the material. A Nestlet fragment weighing approximately 0.1 g (about 4% of the full Nestlet) was considered “unbroken.” Measurements were recorded at the start and end of both morning and afternoon sessions. Nest-building behavior was assessed using a standardized 5-point rating scale based on the extent of Nestlet shredding and the structure of the resulting nest. This scale provides both quantitative and qualitative evaluations of nesting behavior, reflecting the animal’s engagement with its environment and ability to organize materials into functional structures:

1. Untouched Nestlet: more than 90% of the Nestlet remains intact: minimal or no interaction observed.

2. Partially torn Nestlet: Between 50% and 90% of the Nestlet remains intact: limited interaction.

3. Mostly shredded Nestlet: less than 50% of the Nestlet remains intact; the material is spread across the terrarium without forming a defined nest (less than 90% is within a quarter of the cage floor area).

4. Identifiable but flat nest: more than 90% of the Nestlet is shredded and gathered within one-quarter of the floor area, forming a nest with walls lower than the height of the degu’s body among more than 50% of its circumference.

5. Near perfect nest: more than 90% of the Nestlet is shredded and formed into a crater-like nest with walls higher than the degu’s body along more than 50% of its circumference.

Open field test

To assess differences in locomotor activity and exploratory behavior between groups, animals were subjected to a 5-minute open field test. This test involved observing each animal within a white Plexiglas box (100 × 100 × 100 cm; length × height × depth). The percentage of time spent in the corners and central arena, as well as the frequency of total crossings—specifically “central crossings,” defined using a four-paw criterion—were recorded^55,58. At the end of each session, animals were returned to their home cages, and the arena was thoroughly cleaned with a 70% ethanol solution to eliminate olfactory cues. This test provides quantitative measures of movement frequency and distance, allowing reliable evaluation of exploratory drive and anxiety-related behavior.

“Novel object” open field test

This test measures the animal’s response to a novel object placed in the center of the open field. Typically, an emotionally relaxed degu will frequently approach and investigate the novel object, whereas an anxious or hyperreactive animal is less likely to explore it. Subjects were tested in the “novel object” open field test one day after completing the standard open field test. Using the same arena, each animal was observed for 5 min. The total time spent exploring the novel object was recorded⁵⁴. Additionally, the percentage of time spent in the corners and central area of the arena was assessed. After each session, animals were returned to their home cages, and the arena was cleaned with a 70% ethanol solution to eliminate olfactory traces.

Light-dark box test

The light–dark box test is a widely used behavioral assay for assessing anxiety-like behavior in rodents¹³⁷. The apparatus consisted of a Plexiglas cage divided into two equal compartments: one brightly illuminated (light section) and one completely dark (dark section), each measuring 21 × 21 × 21 cm (length × height × depth). The two compartments were connected by a 7 × 7 cm opening at floor level. The light compartment was uncovered and illuminated by a 24 V–10 W bulb, while the dark compartment was covered with a light-proof lid to eliminate illumination. The protocol followed was adapted from Popovic et al. (2009), specifically for use in degus¹³⁸. Briefly, each animal was placed in the light compartment facing away from the entrance to the dark compartment and allowed to explore freely for 10 min. The behaviors recorded included: (i) latency to enter the dark box (time taken to first enter the dark compartment), (ii) number of transitions (crossings between light and dark compartments), and (iii) total time spent in the light compartment. After each session, the animals were returned to their home cages, and the apparatus was thoroughly cleaned with a 70% ethanol solution to remove olfactory cues.

Three-chambered social interaction test

We used the three-chamber test to assess (i) social affiliation/motivation, by comparing the time degus spent interacting with an empty wire cage versus one containing a novel conspecific, and (ii) social memory and preference for social novelty, by comparing the time spent interacting with a familiar versus a novel degu. The test was conducted in an open-field arena subdivided into three equal compartments using transparent Plexiglas walls, each with a circular opening (2.8 cm in diameter) allowing free access between chambers. The social partners used were sex-matched, unfamiliar, and unrelated degus. The test protocol consisted of three 20-minute sessions and followed the procedures described by Rivera et al. (2021)⁵⁴. Briefly, the test consisted of the following phases: Phase 1 (Habituation): The test animal was placed in the center compartment and allowed to explore all chambers freely. Phase 2 (Session 1 – “Partner I”): The test animal was returned to the center chamber, while the first social partner (Partner I) was placed inside a wire containment cup in one of the side chambers. The other side chamber contained an identical but empty cup. The test animal was allowed to explore both chambers for 20 min. At the end of this session, the test animal was returned to its home cage for 1 h, and the apparatus was cleaned with 70% ethanol to eliminate olfactory cues. Phase 3 (Session 2 – “Partner II”): The test animal was placed back into the center compartment. A second unfamiliar, unrelated degu (Partner II) was placed in a wire containment cup in the chamber that had previously been empty. The test animal could then choose to interact with either the now-familiar Partner I or the novel Partner II. Social interaction was quantified as the time the test animal spent actively exploring each containment cup (i.e., time spent touching the cup with the forepaws or nose). To evaluate social affiliation/motivation (Session 1) and social memory (Session 2), we calculated a recognition index (RI). For Session 1, RI was defined as the time spent with Partner I divided by the total time spent with Partner I and the empty cup. For Session 2, RI was calculated as the time spent with Partner II divided by the total time spent with Partners I and II. An RI ≤ 0.50 was interpreted as an absence of social affiliation/motivation during Session 1 or a lack of social memory during Session 2.

Novel object recognition test

The novel object recognition test is a widely used behavioral assay to evaluate cognition, particularly working memory, attention, and preference for novelty in rodents¹³⁸. The test was conducted in a white Plexiglas open-field arena (63 × 40 × 30 cm; length × height × depth). We followed the protocol that has previously been used in degus^55,58. Briefly, the procedure consisted of one 10-minute familiarization session followed by two consecutive 5-minute test sessions, with 1-hour inter-session intervals. Session 1 (Familiarization): Two distinct objects (Object A and Object B) were placed in opposite corners of the animal’s home cage. The test animal was allowed to explore both objects freely for 10 min. Afterward, the objects were removed and cleaned with 70% ethanol, and the animal was returned to its home cage. Session 2 (Novel Location Recognition, NLR): One of the previously explored objects (Object B) was relocated to an adjacent, previously unoccupied corner of the cage. The animal was then reintroduced and allowed to explore for 5 min. Objects were again removed, disinfected with ethanol, and the animal returned to its home cage for another hour. Session 3 (Novel Object Recognition, NOR): Object B was replaced with a new but similarly sized and textured object. The animal was again given 5 min to explore.

During each session, exploration time, defined as approaching the object within 1–3 cm, was recorded. For both NLR and NOR, a Recognition Index (RI) was calculated as the time spent exploring Object B divided by the total time spent exploring Objects A and B. An RI > 0.5 indicates a preference for the novel location or novel object, consistent with intact recognition memory and cognitive function in unstressed animals.

All sessions were recorded using a digital overhead video camera (LifeCam Studio Full HD, Microsoft Corp., Redmond, WA, USA). Behavioral scoring was performed by a trained researcher experienced in rodent behavior and blinded to the dietary groups to ensure unbiased observations.

Microbiome analysis

Stool sample collection and DNA isolation

After 15 months on their respective diets, fecal samples were collected from each group of degus, placed in Eppendorf tubes, and stored at − 80 °C until DNA extraction. DNA was extracted using the PowerFecal Pro Kit (QIAGEN GmbH, Hilden, Germany), following the manufacturer’s instructions. DNA integrity was assessed via electrophoresis using an Agilent 2200 TapeStation, and DNA concentration was quantified using the Qubit dsDNA High Sensitivity Assay Kit (Thermo Fisher Scientific). The purified DNA samples were subsequently sent to the Zymo Research Central Laboratory (Irvine, CA, USA) for further analysis.

Targeted library preparation

The DNA samples were prepared for targeted sequencing using the Quick-16S™ Plus NGS Library Prep Kit (Zymo Research, Irvine, CA, USA). Custom-designed primers developed by Zymo Research were used to maximize coverage of the 16S rRNA gene while maintaining high sensitivity. Specifically, the Quick-16S™ Primer Set V3–V4 was employed for this project.

Library preparation followed an optimized protocol in which PCR amplification was carried out in real-time PCR machines to monitor amplification cycles and minimize the formation of PCR chimeras. Final PCR products were quantified using qPCR-based fluorescence measurements and then pooled at equimolar concentrations. The pooled library was subsequently purified using the Select-a-Size DNA Clean & Concentrator™ kit (Zymo Research, Irvine, CA, USA), and quantified using both the Agilent TapeStation^® (Agilent Technologies, Santa Clara, CA, USA) and the Qubit^® fluorometer (Thermo Fisher Scientific, Waltham, MA, USA).

Control samples

The ZymoBIOMICS^® Microbial Community Standard (Zymo Research, Irvine, CA, USA) was used as a positive control for each DNA extraction, when performed. The ZymoBIOMICS^® Microbial Community DNA Standard (Zymo Research, Irvine, CA, USA) was used as a positive control for each targeted library preparation. Negative controls (i.e., blank extraction control, blank library preparation control) were included to assess the level of bioburden carried by the wet-lab process.

Sequencing and Bioinformatics Analysis.

The final library was sequenced on Illumina^® NextSeq 2000™ (Zymo Research, Irvine, CA, USA) with a p1 (cat 20075294) reagent kit (600 cycles). The sequencing was performed with a 30% PhiX spike-in. Unique amplicon sequences were inferred from raw reads using the Dada2 pipeline¹³⁹. Chimeric sequences were also removed with the Dada2 pipeline. Taxonomy assignment was performed using UCLUST from QIIME v.1.9.1. Taxonomy was assigned using the Zymo Research Database, a 16S database that is internally designed and curated, as a reference. Composition visualization, alpha-diversity, and beta-diversity analyses were performed with QIIME v.1.9.1¹⁴⁰.

Functional prediction

Functional prediction of microbial communities was performed using PICRUSt2 (v2.5.1)¹⁴¹ within the QIIME2 framework. Amplicon sequence variants (ASVs) were used as input for the picrust2_pipeline.py script with default parameters and parallel execution to estimate the abundance of gene families and MetaCyc metabolic pathways. To facilitate ecological interpretation, pathway identifiers were annotated with descriptive names using the add_descriptions.py utility (-m metacyc). Predicted pathways were collapsed into broader functional categories based on their ecological and metabolic roles, allowing the identification of general trends in microbial functional potential across both diets.

Statistical analysis

For the behavioral data analysis, the Recognition Index (RI) was calculated for the three-chamber social interaction test, as well as the novel location recognition (NLR) and novel object recognition (NOR) tests. For variables measured across consecutive days, such as body weight, digestive efficiency, burrowing test, and nesting test, a repeated-measure analysis of variance (ANOVA) was used. Comparisons between the two diet groups were conducted using Student’s t-tests, while non-parametric analyses (Mann–Whitney U test) were applied when the data did not meet the assumptions of normality.

To organize the behavioral data and allow for a comprehensive analysis of distinct behavioral domains, the tests were categorized into three main groups: (i) innate behavior was assessed using the burrowing and nesting tests; (ii) anxiety-like behavior was evaluated with the open field test, novel object open field test, and light/dark box test; (iii) cognitive performance was measured using the three-chambered social interaction test and the NLR/NOR paradigms. This classification provided a systematic framework for analyzing a broad spectrum of behavioral responses, ranging from innate behaviors to emotional reactivity and cognitive function.

To analyze the effects of diets on degu behavior in an integrated manner, we conducted a Principal Component Analysis (PCA). PCAs were performed on variables recorded from different behavioral tasks to distinguish between measures that assess different aspects of anxiety-like behavior and cognitive performance. Given that variables were measured on different scales, we used the correlation matrix to standardize the data¹⁴². The first two principal components (PC1 and PC2) were retained for interpretation, as PC1 explained the largest proportion of variance and PC2 accounted for most of the remaining variation. These PCAs enabled us to determine whether diet influenced the relationships between anxiety-like behavior and cognitive performance. To test for statistical significance in group separation, we performed a one-way PERMANOVA with 9999 permutations using an Euclidean distance matrix.

Prior to microbial diversity analyses, sequencing data were rarefied to equal sequencing depths to control for sampling artifacts across samples. Analyses were conducted using the phyloseq R package (v1.52.0)¹⁴³. Microbiome taxonomic diversity was estimated using the Chao1 and Shannon indices. Data were analyzed using t-tests and non-parametric methods (Mann–Whitney U test), depending on whether normality assumptions were met, which were assessed using the Shapiro–Wilk test.

Differences in community composition between the microbiomes from Champion and LabDiet were analyzed using permutational multivariate analysis of variance (PERMANOVA) using the Bray-Curtis distance matrix¹⁴⁴. Additionally, a linear discriminant analysis (LDA) based on effect size (LEfSe)¹⁴⁵ was performed to identify microbiome functional traits with differential abundance between both diets, using a standard threshold of 2.0 logarithmic LDA score and an alpha value of 0.05 for Kruskal-Wallis and paired Wilcoxon tests.

To identify the core microbiome across diets, the abundance data were transformed into relative abundances using compositional normalization. The definition of the core microbiome followed standard thresholds of detection and prevalence, based on the approach outlined by Salonen et al. (2012)¹⁴⁶. Specifically, ASVs were considered part of the core if they were detected at a minimum relative abundance of 0.1% in at least 95% of the samples. Additionally, a broader screening was performed using a range of detection thresholds and prevalence levels from 5% to 100% to evaluate the robustness and sensitivity of the core microbiome. These values were visualized using line plots that depict the number of core taxa identified across different detection and prevalence combinations.

We used piecewise structural equation modeling (pSEM) to evaluate the direct and indirect effects of dietary treatment on microbial activity, physiological traits, and behavioral indices in degus subjected to a 15-month diet. To avoid over-parameterization, we did not include latent variables; instead, we constructed composite indices (e.g., anxiety behavior) from prespecified measures, screened collinearity and removed redundant predictors, and enforced model parsimony by retaining only biologically motivated paths. This strategy enhances interpretability and robustness under limited replication while aligning with common practice for ecological and physiological datasets of similar size. Analyses were performed using the piecewise SEM package in R¹⁴⁷, which fits a series of linear models and combines them into a causal network, allowing for the inclusion of hierarchical structures and non-independence among observations. The initial model specified dietary treatment as a predictor of multiple physiological traits (body mass, digestive efficiency, saturated fatty acids, and acetic acid concentration), which in turn were hypothesized to influence microbial diversity (Microbiome) and behavioral outcomes (anxiety-like behavior and cognitive performance). Under the null conceptual model (Figure Supplementary Material S1), we expect diet to have a direct and strong effect on the physiological variables, microbial composition, and behavioral outcomes. Digestive efficiency was expected to influence body mass, while both of these variables, along with acetic acid concentration would affect the microbiome, which in turn would impact anxiety-like behavior and cognitive performance. Potential interactions among variables were also considered (Figure Supplementary Material, S1).

To reduce dimensionality and avoid multicollinearity, we created two composite indices: one for anxiety-like behavior and another for cognitive performance, calculated as the mean of standardized values across relevant behavioral variables. Model selection was based on multiple criteria: the Akaike Information Criterion (AIC), individual path significance, and global goodness-of-fit statistics. A good model fit is indicated by a non-significant Fisher’s C test (p > 0.05), which suggests that the model does not omit important relationships among variables. We also considered the model’s parsimony and its ability to explain variance in key response variables.

All statistical analyses were performed in R (version 4.3.2; R Development Core Team, 2020, Vienna, Austria) and the Statistica software package (StatSoft, Tulsa, OK). Statistical significance was set at p < 0.05.

Author contributions

D.S.R.: conceptualization, experimental design, data analysis, validation, writing—original draft, review and editing; V.B.: methodology, data curation, analysis, writing, review, and editing formal; C.H.: microbiome analysis, review and editing formal; MPF.: SEM methodology and analysis, review and editing formal; M.J.V., C.F., and RV.: fatty acid analysis, review, and editing formal; I.P.: physiological and behavioral methodology and data curation; C.O., validation, writing—original draft, review and editing; F.U.: validation, writing—original draft, review and editing; L.A.C.: review and editing.All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a postdoctoral grant from Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) N◦11190603 to DSR.

Data availability

The raw data supporting the conclusions of this article are available from the corresponding author upon reasonable request.All microbiome data can be found at (https:/figshare.com/s/a904becca8bb314a8bbd).

Declarations

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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