Bifidobacterium longum and prebiotic interventions restore early-life high-fat/high-sugar diet-induced alterations in feeding behavior in adult mice

Domain: chemistry
Published: 2026-02-24
Editorial outcome: minor_revision

Abstract
An unhealthy diet disrupts feeding behavior and the gut microbiota, but whether early-life dietary effects persist, or can be restored later in life, remains unclear. We investigated whether microbiota-targeted interventions (FOS+GOS or Bifidobacterium longum APC1472) could restore early-life high-fat/high-sugar (HFHS) diet-induced feeding alterations in adult female and male mice. HFHS exposure exclusively in early-life induced persistent, sex-specific feeding alterations in adult mice, despite normalized body weight. Early-life HFHS diet reduced hypothalamic cells expressing feeding-related markers (POMC, GHSR, PNOC, NOD2) in adult mice. Females were more vulnerable, with reduced LEPR+ cells and disrupted arginine/tryptophan metabolism, while males showed impaired peptidoglycan sensing and steroid metabolism. We show that microbiota interventions restore these effects via distinct mechanisms. FOS+GOS induced extensive microbiome compositional shifts and sex-specific restoration of gut-brain pathways, while B. longum APC1472 induced greater behavioral restoration with minimal microbiome compositional changes. These findings highlight sex-specific vulnerabilities and mechanism-dependent therapeutic potential of microbiota-based interventions after exposure to early-life unhealthy diets.

Introduction
Early life, including the perinatal and postnatal time window, is a critical period for offspring development1,2. Environmental factors such as diet, metabolic status or stress have been shown to impact neurodevelopment and can lead to long-lasting adverse metabolic effects, such as glucose intolerance, hyperleptinemia and weight gain, with enduring metabolic risk across multiple generations of offspring3,4. Exposure to unhealthy diets, such as high-fat diet (HFD) or western diets, during early-life has been shown to have effects on offspring’s appetite regulation5–8, food intake9–11, preference for palatable foods10–12 as well as dopaminergic signaling and circuitry11,13,14 involved in hedonic feeding15,16. Understanding these early-life dietary influences on feeding behavior is crucial given the global rise in obesity and eating disorders, as well as the increasing consumption of Western-style diets, including during pregnancy and early-life periods17,18.

Feeding behavior involves highly interconnected homeostatic mechanisms that maintain energy balance based on metabolic needs, and non-homeostatic mechanisms driven by reward and motivation19–21. It follows that neurons that sense metabolic needs to maintain energy balance19–21 and neuronal populations involved in reward-based feeding22–25, along with non-neuronal cells26–29 respond to hormones regulating food intake and energy balance, for instance via nutrient sensing30. These peripheral signals reach the brain directly through the blood-brain barrier (BBB) or indirectly via vagal afferent signaling into the nucleus tractus solitarius (NTS) in the brainstem. From there, projections reach the hypothalamus, which integrates them to regulate homeostatic and reward-based feeding behaviors19,21,31. Whilst the NTS itself has been implicated as a critical regulator of meal size and satiation32–34, the hypothalamus acts in concert with brainstem and limbic pathways to integrate peripheral and central signals controlling appetite. Within the hypothalamus, the arcuate nucleus (ARC) is especially important for feeding regulation19,21,31. Its strategic location near the median eminence and adjacent to the third ventricle allows it to be one of the first points of access for peripheral signals, such as gut hormones and metabolites19,21. The anorexigenic propiomelanocortin (POMC) neurons and the orexigenic agouti-related protein (AgRP)/neuropeptide Y (NPY) neurons are two opposing neuronal populations located in the ARC and crucial for the homeostatic regulation of appetite and food intake35–37. These neurons express multiple receptors, integrating several locally produced and circulating neurotransmitters and neuropeptides, including leptin, ghrelin, NPY, and melanocortins38. Maternal dietary interventions during the perinatal period have been shown to alter the expression of these hypothalamic neuropeptides and induce metabolic programming in offspring7,39.

The gut microbiota has emerged as a modulator of hypothalamic processes, including neuroendocrine signaling and metabolic regulation40,41. This modulation may occur through multiple pathways, including vagal-brainstem-hypothalamic circuits34,35 and the blood-brain interface at the median eminence21,42,43. Recently, the gut microbiota has been postulated as a key regulator of both homeostatic37,40,44 and reward-based feeding45–48. Mice born and raised without microbiota (germ-free) have higher food intake and altered expression of hypothalamic markers of food intake regulation, including the orexigenic NPY and AgRP, and the anorexigenic POMC40. Depletion of the microbiota in mice using an antibiotic cocktail has also been shown to induce a higher consumption of and preference for palatable foods, as well as alterations in reward signaling and neuronal activation in reward-related regions including the nucleus accumbens and the ventral tegmental area45–47. Microbiota-targeted interventions, including pre-, pro-, or synbiotics or microbiota-derived metabolites such as short-chain fatty acids (SCFAs) and caseinolytic peptidase B (ClpB), have also been shown to impact the regulation of food intake49–55 and non-homeostatic feeding47,49,56.

Early life is a critical period for the establishment of the gut microbiota57, which is significantly impacted by diet58–65. Bifidobacterium species, particularly B. longum subsp. infantis, and B. breve, are key early colonizers of the infant gut microbiota due to their unique ability to metabolize human milk oligosaccharides found in breast milk66–68. Through the fermentation of complex carbohydrates, Bifidobacterium can produce metabolites such as acetate and lactate, which have been shown to modulate central nervous system (CNS) function69 and other physiological processes66,67. A reduction in the abundance of Bifidobacterium spp. has been correlated with chronic diseases, such as obesity70. Interventions with specific Bifidobacterium strains have involved beneficial effects for the host, including their metabolic status71,72. While enduring colonization of probiotics is not required to exert biological effects on the host73,74, B. longum strains have been shown to colonize the entire gastrointestinal tract (including the small intestine, cecum, and colon), with the highest abundance typically detected in the colon75–77. For instance, the putative probiotic B. longum APC1472 has previously been shown to improve metabolic health in HFD-fed mice as well as in overweight and obese humans72, and to attenuate signaling by the orexigenic hormone, ghrelin78. The prebiotics fructo-oligosaccharides (FOS) and galacto-oligosaccharides (GOS) have previously been shown to increase the abundance of Bifidobacterium79. Individual administration of FOS and GOS has been shown to prevent HFD-induced obesity and obesity-associated metabolic disorders80,81. While the gut microbiota has been shown to modulate homeostatic and hedonic feeding pathways in adult studies40,45–47,49, and, separately, to influence the host’s developing neuro-immune-endocrine pathways57,59,82, the mechanisms through which the early-life microbiota affects the homeostatic and hedonic feeding systems remain to be fully elucidated. Overall, whether early-life exposure to an unhealthy diet can prime enduring alterations in offspring homeostatic and non-homeostatic behaviors, and the underlying mechanisms, are not yet fully understood. Here, we investigate the potential of microbiota-targeted approaches, via prebiotic combination FOS+GOS or the putative probiotic B. longum APC1472, to attenuate detrimental enduring effects of an early-life high-fat/high-sugar (HFHS) diet, as a model of the western diet, on offspring’s feeding behavior in adulthood. Our findings indicate that microbiota-targeted interventions can restore the enduring, sex-specific alterations in feeding behavior and associated molecular alterations induced by an early-life HFHS diet exposure in adult female and male mice via distinct mechanisms. Importantly, early-life HFHS diet reduces the abundance of Bifidobacterium genus, which is restored by FOS+GOS administration, and B. longum APC1472 administration increases B. longum abundance in the offspring.

Results
Microbiota-targeted interventions ameliorate the effects of an early-life high-fat/high-sugar diet on adult feeding behavior in a sex-specific manner and are associated with increased Bifidobacterium or B. longum abundance

Maternal exposure to HFHS diet significantly increased weight gain (Supplementary Fig. S1C, D and Supplementary Table S1) and gonadal and mesenteric white adipose tissue weights (Supplementary Fig. S1E, F) in dams, which were restored by supplementation with the prebiotic FOS+GOS and the putative probiotic B. longum APC1472. HFHS diet did not alter brown adipose tissue, cecum, or liver weight, nor colon or small intestine length in dams when normalized to body weight (Supplementary Fig. S1E, F). The offspring of these HFHS-exposed obese dams (Fig. 1A, B; Supplementary Tables S1 and S2) showed increased body weight gain during the milk suckling and post-weaning period, which was attenuated by B. longum APC1472 in female offspring at postnatal day (PND) 28 and PND35, and in male offspring at PND35 (Fig. 1B and Supplementary Data 1). This increase in body weight, with greater gain in male than female offspring, was particularly apparent after weaning, when offspring consume HFHS themselves rather than through the mother’s milk, suggesting that this is related to food intake (Fig. 1A, B and Supplementary Fig. S1G). Female and male offspring receiving the HFHS diet removed significantly more food from the food hopper (Fig. 1C and Supplementary Fig. S1G). Interestingly, we found that early-life HFHS-exposed offspring did not consume all the food removed from the food hopper but instead, crumbled part of it (Fig. 1C and Supplementary Fig. S1G), a phenomenon previously described as food crumbling or grinding83. Supplementation with B. longum APC1472 attenuated both increased food intake and food crumbling observed in female and male offspring during the early-life exposure to the HFHS diet, to levels similar to those of control offspring (Fig. 1C and Supplementary Fig. S1G). Body weight normalized following the dietary switch from HFHS diet to control chow diet at week 5 (Fig. 1B and Supplementary Data 1). While in female mice, there were no significant differences after 1 week on the control diet, the normalization of body weight in male offspring was delayed for another week (Fig. 1B and Supplementary Data 1), in line with previous studies showing sexual dimorphism in body weight and metabolic health depending on the diet exposure and age84–86.

The food crumbling behavior toward the palatable diet persisted into adulthood (Fig. 1D) when mice, after 6 weeks on control diet, were given a choice between control chow diet and palatable HFHS diet (food preference test). Adult male and female mice exposed to the HFHS diet in early life demonstrated an increased preference for the palatable diet and removed significantly more of it from the food hoppers compared to control mice (Fig. 1D). We observed that B. longum APC1472 normalized the heightened food preference induced by the early-life HFHS exposure in adult male and female adult mice, to levels seen in control animals in females (Fig. 1D). Supplementation with the prebiotic FOS+GOS also normalized food preference and/or crumbling behavior only in male mice (Fig. 1D).

To separate the food consumption from food manipulation behavior, we measured the food crumbling, and food intake in a separate experiment using PheCOMP cages, where adult female and male mice were given access to the palatable HFHS diet during the light phase. Female, but not male mice, exposed to the early-life HFHS diet increased both food consumption (Fig. 1E) and food crumbling behavior (Fig. 1F) of the palatable diet compared to control mice. A non-significant increase in food crumbling was also observed in early-life HFHS exposed male mice compared to the control mice (ANOVA: F = 2.19, p = 0.128, η² = 0.12; Post-hoc Tukey (diet effect): mean difference = 2.73, p = 0.33) and B. longum APC1472 supplementation reduced this food manipulation or crumbling (F = 4.12, p = 0.050, η² = 0.11) (Fig. 1F). Both microbiota-targeted interventions attenuated the food intake (Fig. 1E) and the crumbling behavior (Fig. 1F) in females exposed to early-life HFHS, with B. longum APC1472 supplementation completely restoring those effects (Figs. 1E and 1F). The microbiota-targeted interventions also restored the early-life HFHS diet-induced increases in eating rate, first meal size and average meal size in the adult female mice (Supplementary Fig. S1H).

To assess differences in non-caloric preference for sweetness, we performed the saccharin preference test87–89. Interestingly, early-life HFHS diet induced a trend for increased saccharin preference in adult males (ANOVA: F = 2.23, p = 0.123, η² = 0.12; Post-hoc Tukey (diet effect): mean difference = 12.2%, p = 0.123), which was significantly reduced by the prebiotic FOS+GOS and B. longum APC1472 (Fig. 1G). Intriguingly, no differences in saccharin preference were observed in females (Fig. 1G). Next, we assessed striatal content and turnover of dopamine and serotonin (5-HT) for their involvement in the regulation of food intake and reward-based feeding15,16,90,91. While the exposure to an early-life HFHS diet did not have significant lasting effects on the content of dopamine, its metabolites DOPAC and HVA, or its precursor L-DOPA in the striatum of adult female or male mice, adult female mice showed some effects on its metabolism, including an increased L-DOPA/dopamine ratio (ANOVA: F = 9.31, p = 0.003, η² = 0.57; Post-hoc Tukey (diet effect): mean difference = 0.004, p = 0.052) and a reduced DOPAC/HVA ratio (ANOVA: F = 2.96, p = 0.085, η² = 0.30; Post-hoc Tukey (diet effect): mean difference = −0.234, p = 0.306). The latter of which was significantly increased by FOS+GOS intervention (Supplementary Fig. S2A). Early-life HFHS did not have enduring effects on DOPAC/dopamine and HVA/dopamine ratios in the striatum of adult females or males (Supplementary Fig. S2A). Interestingly, early-life HFHS diet induced a trend toward reducing 5-HT levels in the striatum of adult male mice (ANOVA: F = 9.06, p = 0.003, η² = 0.56; Post-hoc Tukey (diet effect): mean difference = −52.45, p = 0.080) and both microbiota-targeted interventions significantly increased 5-HT levels. Additionally, B. longum APC1472 normalized the non-significant reduction in serotonin turnover (5-HIAA/5-HT ratio) induced by the early-life HFHS diet in adult female mice (ANOVA: F = 9.24, p = 0.003, η² = 0.57; Post-hoc Tukey (diet effect): mean difference = −0.064, p = 0.090) (Supplementary Fig. S2A), while diet intervention did not alter 5-HIAA content in females or males. Both microbiota-targeted interventions normalized the trend toward reducing striatal noradrenaline levels in adult males exposed to an early-life HFHS (ANOVA: F = 3.13, p = 0.073, η² = 0.29; Post-hoc Tukey (diet effect): mean difference = −59.41, p = 0.068) (Supplementary Fig. S2A). Notably, early-life HFHS exposure did not have enduring effects on locomotor activity in adult female or male mice (Supplementary Fig. S2B).

To further elucidate the long-lasting impact of an early-life HFHS diet on offspring’s glucose metabolism in adulthood, we performed an intraperitoneal glucose test (IpGTT) (Supplementary Fig. S2C, D). Surprisingly, adult male offspring exposed to HFHS diet in early-life showed an improved glucose tolerance compared to control mice, as demonstrated by a higher clearing rate (Supplementary Fig. S2C, D). These effects were normalized by the administration of FOS+GOS after 60–120 min of glucose injection and of B. longum APC1472 after 90 and 120 min post-glucose injection in male mice (Supplementary Fig. S2C). FOS+GOS administration also normalized the diet-induced reduction in AUC in adult males (Supplementary Fig S2D). In females, there were no diet-induced changes in glucose clearance (Supplementary Fig. S2C, D). Interestingly, early-life HFHS diet induced a non-significant increase in basal glucose levels in adult females and males (Supplementary Fig. S2E) and supplementation with B. longum APC1472 significantly reduced these levels in female mice (Supplementary Fig. S2E).

Furthermore, adult male mice exposed to early-life HFHS diet had significantly higher circulating HDL and LDL levels, the former of which was restored by supplementation with the prebiotic FOS+GOS (Supplementary Table S3). Supplementation with B. longum APC1472 reduced the non-significant increase in gonadal and mesenteric white adipose tissue weight (normalized to body weight) induced by an early-life HFHS diet exposure in adult male mice (Supplementary Fig. S3A). Early-life HFHS diet did not have significant enduring effects on brown adipose tissue, liver, or cecum weights (normalized to body weight) in male mice. In females, whilst there were no significant diet effects in any organ measurements, B. longum APC1472 decreased colon length (normalized to body weight) (Supplementary Fig. S3A). Importantly, FOS+GOS intervention increased caecum weight compared to all other groups in both sexes (Supplementary Fig. S3), consistent with established prebiotic effects of oligosaccharides through enhanced microbial fermentation92,93. This physiological adaptation likely contributes to the observed body weight increase in FOS+GOS-administered mice (Fig. 1B). No changes were observed in the levels of glucose, triglycerides, protein, alanine transaminase, or aspartate aminotransferase or total cholesterol in plasma of adult female and male mice exposed to the early-life HFHS diet compared to controls (Supplementary Table S3). We also measured circulating levels of active ghrelin in 12-week-old fasted female and male mice, but observed no differences (Supplementary Fig. S3B).

HFHS-related changes in microbiota have been associated with alterations in feeding behavior45,46. In our study, alpha diversity analysis revealed that early-life HFHS diet induced a trend towards increased microbial richness (Chao1) in 5-week-old female mice (t = 1.47, q = 0.189, effect size = −0.25, R² marginal = 0.036) and no significant alterations in richness were observed in 5-week-old males or 10-week-old female and male mice (q > 0.2) (Supplementary Fig. S4A–C). Early-life HFHS diet did not significantly alter evenness as measured by Shannon and Simpson indexes at week 5 or 10 of age in either sex (q > 0.2) (Supplementary Fig. S4A–C). We also observed that early-life HFHS diet exposure altered beta diversity in 5-week-old male offspring (PERMANOVA: (F = 5.99, p = 0.001, R² = 0.285); pairwise (diet effect): F = 7.65, q = 0.0012, R² = 0.258 and female (PERMANOVA: F = 5.83, p = 0.001, R² = 0.280; pairwise (diet effect): F = 7.31, q = 0.002, R² = 0.249). These alterations were normalized in 10-week-old males after 5 weeks on control diet during adolescence and adulthood (Males PERMANOVA: (F = 6.19, p = 0.001, R² = 0.292); Males pairwise (diet effect): F = 1.96, q = 0.09, R² = 0.082; Females PERMANOVA: (F = 7.08, p = 0.001, R² = 0.320); Females pairwise (diet effect): F = 3.02, q = 0.002, R² = 0.121) (Supplementary Fig. S4D). The prebiotic FOS+GOS altered beta diversity compared to both control (Females pairwise: F = 6.11, q = 0.002, R² = 0.217; Males pairwise: F = 5.35, q = 0.0012, R² = 0.189) and early-life HFHS-exposed mice (Females pairwise: F = 10.15, q = 0.002, R² = 0.316; Males pairwise: F = 6.21, q = 0.0012, R² = 0.213) at 5 weeks of age in females and males. At 10 weeks, FOS+GOS continued to alter beta diversity in females (Pairwise Control Vehicle vs HFHS FOS+GOS: F = 10.88, q = 0.002, R² = 0.331; Pairwise HFHS Vehicle vs HFHS FOS+GOS: F = 13.93, q = 0.002, R² = 0.388) and males (Pairwise Control Vehicle vs HFHS FOS+GOS: F = 6.82, q = 0.002, R² = 0.229; Pairwise HFHS Vehicle vs HFHS FOS+GOS: F = 8.68, q = 0.002, R² = 0.274) (Supplementary Fig. S4D). B. longum APC1472 administration altered beta diversity compared to HFHS Vehicle in 5-week-old (Pairwise: F = 2.53, q = 0.021, R² = 0.099) and 10-week-old females (Pairwise: F = 2.07, q = 0.044, R² = 0.083), but not in males at either timepoint (5 weeks: F = 0.87, q = 0.432, R² = 0.038; 10 weeks: F = 1.24, q = 0.288, R² = 0.053). HFHS diet reduced the relative abundance of Bifidobacterium genus in dams (Supplementary Fig. S4E, F) and 5-week-old female and male and 10-week-old male offspring (Fig. 1H, I). Importantly, FOS+GOS administration was able to restore the alteration in Bifidobacterium relative abundance induced by the HFHS diet exposure in dams (Supplementary Fig. S4F) and 5- and 10-week-old female and male offspring (Fig. 1I). This suggests that the restoration by the prebiotic administration may occur via compensation for the lack of fermentable fiber and other carbohydrates in the HFHS diet. The supplementation with B. longum APC1472 increased the relative abundance of the B. longum species in dams (Supplementary Fig. S4G) and 5-week- and 10-week-old female and male offspring (Fig. 1J). We observed an increase in the Bacillota/Bacteroidota ratio following early-life HFHS exposure compared to control mice in both sexes at 5 weeks of age with persisting effects at 10 weeks only in male mice, which was restored by FOS+GOS administration in 5-week and 10-week-old female mice (Supplementary Fig. S4H).

Altogether, these results demonstrate that microbiota-targeted interventions ameliorate enduring sex-specific effects of an early-life HFHS diet on feeding behavior and glucose metabolism in adult female and male mice, through mechanisms involving increased Bifidobacterium or B. longum abundance.

Enduring sex-specific effects of an early-life exposure to an unhealthy diet on host-derived and microbiota-derived metabolites

Here, we studied the impact of an early-life HFHS diet exposure and the microbiota-targeted interventions on circulating host- and microbial-derived metabolites in 12-week-old female and male mice. We observed that early-life HFHS diet had enduring effects by differentially altering the blood metabolome of 12-week-old female and male mice in a sex-specific manner (Fig. 2A). Adult female mice exposed to the early-life HFHS diet displayed alterations in circulating metabolites belonging to different metabolite classes, including the microbiota-derived metabolites kynurenine, indole-3-carboxaldehyde, hippuric acid, or pantothenic acid, and the host-derived metabolites creatinine, creatine, tryptophan, 15(S)-HETE, lactic acid or the L-amino acids alanine and arginine (Fig. 2A). Early-life HFHS diet-exposed adult males displayed alterations in microbiota-derived metabolites including the secondary bile acid deoxycholic acid, and the tryptophan metabolite formyl kynurenine, as well as in the host-derived metabolites tryptophan, serotonin, thiamine, succinic acid, and the primary bile acids cholic acid and β-muricholic acid (Fig. 2A). Supplementation with the prebiotic FOS+GOS restored some of the altered circulating metabolites in adult mice exposed to early-life HFHS diet (Fig. 2A). In females, this included the amino acid arginine, the fatty acid metabolism intermediate decanoylcarnitine, the nucleotide metabolite deoxyuridine, and the polyamines spermidine. In males, this included the primary bile acids cholic acid and β-muricholic acid, the secondary bile acid deoxycholic acid, the nucleoside 5-methylcytidine, and corticosterone (Fig. 2A). Noteworthy, we also observed a reduction in corticosterone circulating levels in adult male mice that were exposed to an early-life HFHS diet, which was also normalized by both microbiota-targeted interventions (Supplementary Fig. S4I). Supplementation with the putative probiotic B. longum APC1472 was able to significantly restore metabolites altered by the early-life HFHS diet, including tryptophan, creatinine and spermidine in adult females, and 4-guanidinobutyric Acid (4-GBA) in adult males (Fig. 2A).

Pathway enrichment analyses of the blood metabolome identified arginine and proline, pyrimidine, and glycine, serine and threonine metabolism as significantly altered by early-life HFHS diet exposure in adult female mice (Fig. 2B), while β-alanine, arginine and proline metabolism, and lysine degradation were significantly altered in adult male mice (Fig. 2C). Tryptophan metabolism was also an enriched pathway in adult male and female mice exposed to the early-life HFHS diet, although it did not reach statistical significance (Fig. 2B, C). Supplementation with FOS+GOS significantly restored metabolites involved in arginine biosynthesis, and β-alanine, arginine and proline metabolism in early-life HFHS exposed adult female mice (Fig. 2D), and restored metabolites involved in bile acid and steroid hormone biosynthesis in adult male mice (Fig. 2E). Glutathione metabolism as well as β-alanine, arginine and proline metabolism were the pathways enriched based on the metabolites restored by B. longum APC1472 supplementation in adult female mice exposed to an early-life HFHS diet (Fig. 2F).

Next, we assessed the content of SCFAs, including acetate, butyrate, propionate and valerate, and the BCFAs isobutyrate and isovalerate in the cecum of 5- and 12-week-old female and male mice (Supplementary Fig. S5A, B), to investigate whether these microbiota-derived metabolites may contribute to the early-life HFHS diet-induced alterations in adult feeding behavior. At week 5, females exposed to the early-life HFHS showed a non-significant increase in acetate (t = −1.58, p = 0.141, d = 0.85) and butyrate (t = −2.06, p = 0.076, d = 1.20) levels, with no restoration by FOS+GOS or B. longum APC1472 interventions, even though FOS+GOS induced a non-significant reduction in butyrate levels (t = 2.08, p = 0.116, d = −1.00) (Supplementary Fig. S5A). Early-life HFHS exposed males showed significant reductions in all SCFAs and BCFAs analyzed compared to control at week 5, with FOS+GOS and B. longum APC1472 administration restoring acetate levels (Supplementary Fig. S5A). No significant changes were observed in the SCFAs levels in 12-week-old female or male mice exposed to the early-life HFHS diet compared to control (Supplementary Fig. S5B).

To explore the microbial origins potentially responsible for SCFA production, we examined the SCFA-producing genera altered by the early-life HFHS diet exposure (Supplementary Data 2 and Supplementary Fig. S6D, together with other taxonomic levels (phyla, order, family) in Supplementary Fig. S6A–C; comprehensive data available at https://github.com/CristinaCuesta-Marti/earlylifeHFHS_feeding_microbiota_2025/tree/outputs/overall_stats_lm). At week 5, early-life HFHS altered 42 bacterial genera in females and 37 in males (q < 0.2). Among the altered genera, key SCFA producers included Intestinimonas and Bifidobacterium (altered in both sexes), while Roseburia, Faecalibaculum, Anaerotruncus, and Lachnoclostridium were female-specific, and Lachnospiraceae subgroups (FCS020, NK4A136) were male-specific alterations. At week 10, early-life HFHS induced persistent alterations in 20 genera in females and 29 in males (q < 0.2). Several SCFA-producing genera showed persistent alterations across both timepoints: Intestinimonas in both sexes, Clostridium sensu stricto 1 in females, and Dorea, Lachnospiraceae subgroups (including FCS020 and UCG-006) in males. Notably, FOS+GOS administration restored 15 genera in 10-week-old females and 20 genera in 10-week-old males (Supplementary Data 2). FOS+GOS consistently restored Intestinimonas at both timepoints in both sexes and Romboutsia at both timepoints in females. B. longum APC1472 restored 5 genera in 10-week-old females and 9 genera in 10-week-old males (Supplementary Data 2). Interestingly, B. longum APC1472 restored Lachnospiraceae family members across timepoints in both sexes. Overall, these findings highlight sex-specific alterations of the microbiome induced by the early-life HFHS diet and sex- and intervention-specific restoration effects.

To explore persistent functional alterations relevant to gut-brain communication underlying the microbiota compositional changes observed at week 10, we performed functional profiling of KEGG orthologs using the PICRUSt294 pipeline. At week 10, females exposed to the early-life HFHS diet had long-term alterations in 13 gut-brain modules (GBMs) (Supplementary Fig. S7A). FOS+GOS restored 10 of these modules, including degradation of dopamine, tryptophan, 17-beta-estradiol, glutamate, and inositol, while B. longum APC1472 restored only acetate degradation and butyrate synthesis in 10-week-old females (Supplementary Fig. S7A). Early-life HFHS altered 5 GBMs, comprising synthesis of propionate, DOPAC, kynurenine, and nitric oxide in 10-week-old male mice, with FOS+GOS restoring 4 modules (all except nitric oxide synthesis), while B. longum APC1472 showed a trend toward restoring DOPAC synthesis (t = −1.30, q = 0.208, d = 0.53) (Supplementary Fig. S7A).

To identify functional pathways linking persistent microbiota changes at week 10 to systemic metabolic outcomes, we filtered PICRUSt2-derived pathways to retain only those involved in the metabolism of blood metabolites significantly altered by early-life HFHS diet or restored by interventions in adult mice (12 weeks of age) (q < 0.2). At 10 weeks, females exposed to the early-life HFHS diet had alterations in 13 metabolic pathways predominantly involved in arginine metabolism, fatty acid biosynthesis, and tryptophan metabolism (Supplementary Fig. S7B). FOS+GOS administration restored 5 pathways, mainly involved in arginine and fatty acid metabolism, and B. longum APC1472 restored only fatty acid β-oxidation. In males, early-life HFHS altered 3 pathways related to fatty acid metabolism and arginine biosynthesis, with both interventions showing restorative effects on fatty acid β-oxidation. These findings highlight that most circulating metabolites altered by early-life HFHS in adult mice corresponded to the predicted functional capacities of the microbiota related to respective metabolic pathways.

Altogether, these results indicate that an early-life HFHS exposure has sex-specific lasting alterations in circulating metabolites, including microbiota-derived ones, as well as microbiota-SCFA profiles and microbiota functionality in adult mice, which can be ameliorated via microbiota-targeted interventions.

Early-life HFHS has an enduring and sex-specific impact on hypothalamic transcriptomic profile in adult mice

To identify potential molecular signatures contributing to the observed behavioral phenotypes, we assessed the long-lasting effect of an early-life HFHS diet on the transcriptomic profile of the hypothalamus of 12-week-old female and male mice, given the hypothalamus’s central role in feeding regulation and energy homeostasis19,21. We showed that an early-life HFHS diet had long-lasting sex-specific effects on the transcription of hypothalamic genes with a higher number of altered genes in adult female than male mice (5070 vs. 1826) (Fig. 3A–C). We observed that supplementation with either the prebiotic FOS+GOS or the B. longum APC1472 impacted the hypothalamic transcriptomic profile, restoring some of the molecular alterations induced by early-life HFHS in a sex-specific manner, with greater restorative effects observed in adult females supplemented with B. longum APC1472 (Fig. 3D). Analysis of commonly restored genes revealed that FOS+GOS supplementation specifically restored Hcar1 expression in both male and female mice, encoding a receptor involved in metabolic regulation and anti-inflammatory responses. B. longum APC1472 commonly restored Fen1 and Piezo2 across both sexes, genes associated with DNA repair mechanisms and mechanosensation, respectively. These findings indicate few shared molecular pathways through which these microbiota-targeted interventions ameliorate HFHS-induced hypothalamic transcriptomic alterations independent of sex (Supplementary Data 3).

We then performed targeted gene enrichment analysis, using Gene Ontology (GO), to determine the functional pathways of genes altered by early-life HFHS exposure95,96. Interestingly, pathways critical for energy and metabolic homeostasis as well as neuroendocrine function were significantly altered in adult females exposed to the early-life HFHS (Fig. 3E). These included carbohydrate, lipid, amino acid, glycerophospholipid and pyruvate metabolic processes, along with regulation of cellular response to insulin stimulus, cell surface receptor signaling pathway, and Notch signaling pathway (Fig. 3E). Adult females exposed to early-life HFHS also had altered pathways involved in the response to nutrients and environmental cues, and cellular response to nitrogen compounds (Fig. 3E). Neurogenesis and glutamatergic synapse pathways, which are key for neurodevelopment and synaptic regulation, were also enriched in adult female mice exposed to early-life HFHS (Fig. 3E). Early-life HFHS exposed adult female mice also had altered pathways involved in genetic and molecular regulation, including regulation of RNA splicing, peptidyl amino acid modification, and pyrimidine metabolism (Fig. 3E). Adult males exposed to early-life HFHS showed altered pathways key for energy, metabolic, and neuroendocrine function, including glucose, carbohydrate, amino acid, lipid, organic acid, and pyruvate metabolic processes, as well as steroid and sterol biosynthetic processes (Fig. 3F). Pathways key for the response to nutrients and environmental cues, including response to nutrients, response to peptidoglycan and cellular response to nitrogen compounds were also enriched in adult males exposed to early-life HFHS (Fig. 3F). Adult males also had altered pathways crucial for neurodevelopment as well as genetic and molecular regulation, such as neurogenesis, neuron development, and nucleobase-containing compound and purine metabolic processes (Fig. 3F).

Next, we assessed functional gene pathways restored by supplementation with each of the microbiota-targeted interventions. We identified common gene pathways restored by the interventions in both male and female mice, while others were specific to each intervention and/or biological sex. Supplementation with the prebiotic FOS+GOS was able to restore some of the key pathways for energy, metabolic, and neuroendocrine function, including glycerophospholipid, lipid, amino acid, and L-amino acid metabolic processes in adult females exposed to early-life HFHS diet (Fig. 3G). FOS+GOS also restored other pathways in the adult females, including the response to nutrient, nucleobase-containing compound and pyrimidine metabolic processes and cell surface receptor signaling (Fig. 3G). In males exposed to early-life HFHS, FOS+GOS administration restored genes involved in pathways key for energy, metabolic and neuroendocrine function, including carbohydrate and L-amino acid metabolic processes and steroid and sterol biosynthetic processes (Fig. 3H). In addition, pathways related to environmental sensing, including response to nutrients, response to peptidoglycan, and cellular response to nitrogen compounds as well as nucleobase-containing compound metabolic processes, were enriched in adult male mice supplemented with the prebiotic FOS+GOS (Fig. 3H).

Supplementation with the putative probiotic B. longum APC1472 in adult females exposed to early-life HFHS significantly restored genes involved in key pathways for energy, metabolic and neuroendocrine function, including glycerophospholipid, tryptophan and nicotinamide nucleotide metabolic processes, as well as cellular response to nitrogen compounds, cell surface receptor signaling, and Notch signaling (Fig. 3I). An enrichment in pathways involved in neurodevelopment as well as genetic and molecular regulation, including neurogenesis, regulation of RNA splicing and alternative mRNA splicing, peptidyl amino acid modification and pyrimidine metabolism was observed in early-life HFHS exposed adult females supplemented with B. longum APC1472 (Fig. 3I). In adult males exposed to early-life HFHS, B. longum APC1472 administration restored genes involved in energy, metabolic, and neuroendocrine function, including the pathways carbohydrate, amino acid, lipid, monocarboxylic acid, pyruvate, tryptophan, and nicotinamide nucleotide metabolic processes (Fig. 3J). An enrichment in the response to peptidoglycan, alternative mRNA splicing, neurogenesis and purine metabolic processes was also identified in early-life HFHS-exposed adult males supplemented with the B. longum APC1472 (Fig. 3J).

To identify pathways of interest linking circulating metabolites and hypothalamic gene expression in gut-brain communication, we performed integrated pathway analyses using the previously reported circulating metabolites and hypothalamic genes that were significantly altered by early-life HFHS diet exposure and those restored by each microbiota-targeted intervention (q < 0.2) in 12-week-old female and male mice (Supplementary Fig. S8). Integrative analysis revealed that early-life HFHS diet altered developmental programming and signaling pathways in adult females, with mTOR signaling, axon guidance, and glutamatergic synapse, as well as FoxO, ErbB, PI3K-Akt, and EGFR tyrosine kinase inhibitor resistance, showing the highest statistical significance (FDR < 0.05). Additional pathways showing nominal enrichment (p-value < 0.05) included insulin signaling, as well as pyruvate, glycerophospholipid, and tryptophan metabolism in adult females (Supplementary Fig. S8A). In adult males, integrative analysis revealed that early-life HFHS diet altered energy homeostasis and signaling pathways, including the AMPK signaling pathway, insulin resistance and cAMP signaling (FDR < 0.1), as well as focal adhesion, oxytocin signaling, insulin signaling, and beta-alanine metabolism (p-value < 0.05). Additional pathways showed trends toward enrichment, including mTOR signaling, and PI3K-Akt signaling, along with metabolic pathways such as cholesterol and glycerophospholipid metabolism (Supplementary Fig. S8B).

While integrative analysis of pathways restored by the microbiota-targeted interventions did not yield any FDR-significant results in either sex, several nominally significant pathways (p-value < 0.05) were observed, highlighting potential effects on developmental, signaling, and metabolic processes. Integrative analysis revealed that FOS+GOS administration restored glycerophospholipid metabolism and pyrimidine metabolism pathways in adult females, with arginine and proline metabolism also showing restoration, consistent with the findings in the blood metabolome (Supplementary Fig. S8C). In adult males receiving FOS+GOS, the pentose phosphate pathway demonstrated nominal enrichment, while additional pathways, including fatty acid metabolism, tryptophan metabolism, and bile acid biosynthesis, showed nominal trends (Supplementary Fig. S8D). Integrative analysis in adult females revealed that B. longum APC1472 restored (nominal p-value enrichment) developmental programming pathways, with DNA replication showing the highest statistical significance, followed by ErbB signaling and Hedgehog signaling, as well as metabolic pathways including pantothenate and CoA biosynthesis, consistent with the observed HFHS-induced alterations in the blood metabolome. The energy homeostasis pathways, mTOR signaling and AMPK signaling, showed trends toward restoration (Supplementary Fig. S8E). In adult males, B. longum APC1472 administration mainly restored pathways related to neuroendocrine function, such as growth hormone synthesis, secretion, and action pathway, alongside trends in metabolic homeostasis pathways including glucagon signaling and AMPK signaling, as well as neurotransmitter pathways, particularly dopaminergic synapse (Supplementary Fig. S8F).

These results demonstrate the enduring effect of exposure of an early-life HFHS diet on the hypothalamic transcriptomic profile of genes involved in metabolic, cellular, and immune processes in adulthood in a sex-specific manner, which can be ameliorated by a microbiota-targeted interventions. Multi-omics integrative analysis further revealed sex-specific alterations and restoration of pathways linking circulating metabolites to hypothalamic transcriptomics.

Enduring molecular impact of an early-life HFHS diet on POMC neurons and GHSR and LEPR peptidergic receptors in the arcuate nucleus of the hypothalamus of adult male and female mice

Using hypothalamic transcriptomic analysis via RNA sequencing, we identified non-significant downregulation of Lepr (t = 2.10, q = 0.064, d = 1.21), of Pomc (t = −0.99, q = 0.348, d = −0.57), and Ghsr (t = 1.07, q = 0.321, d = 0.62) expression in adult female mice exposed to early-life HFHS diet compared to control mice (Fig. 4A). Using RNAscope in situ hybridization, we identified significant alterations in Pomc, Ghsr, and Lepr expression in the ARC nucleus of adult female and male mice exposed to the early-life HFHS diet (Fig. 4B–E and Supplementary Figs. S9, S10). Specifically, we observed a reduction in the number of cells expressing Ghsr, Lepr, and Pomc individually (or GHSR+, LEPR+ and POMC+, respectively) in the ARC of adult females exposed to early-life HFHS, while GHSR+ and POMC+ cells were only reduced in adult males exposed to early-life HFHS compared to control (Fig. 4C), which may highlight a specific role for ARC Lepr in the feeding behavior observed in females. We also observed that both microbiota-targeted interventions restored the number of cells expressing Ghsr and Pomc in adult females exposed to early-life HFHS, while supplementation with B. longum APC1472 also restored the alteration in Lepr-expressing cells (Fig. 4C). Both microbiota-targeted interventions also restored the alteration in the number of cells expressing Pomc in adult males exposed to early-life HFHS (Fig. 4C). Moreover, the average Ghsr expression in GHSR+ cells in the ARC was down-regulated in adult female and male mice exposed to the early-life HFHS diet, which reversed by the microbiota-targeted interventions only in females (Fig. 4D). Adult females exposed to early-life HFHS also had lower average Lepr expression in Lepr-expressing cells and lower Pomc expression in POMC+ cells in the ARC, the latter of which was restored by the microbiota-targeted interventions in adult females (Fig. 4D).

We also observed a reduction in the number of POMC+ cells co-expressing Lepr in the ARC of both female and male mice exposed to the early-life HFHS diet compared to control (Fig. 4E). Both microbiota-targeted interventions restored the number of POMC+ cells co-expressing Lepr in the ARC in adult females (Fig. 4E). Early-life HFHS exposure downregulated the average expression of Lepr in double LEPR+ and POMC+ cells only in adult females exposed to early-life HFHS and induced a non-significant reduction in Pomc expression in these double positive cells (p = 0.053) (Supplementary Fig. S10B). Both microbiota-targeted interventions were able to restore the alterations in average expression of Lepr expression in cells co-expressing Pomc and Lepr in adult female mice (Supplementary Fig. S10B). We also found that early-life HFHS diet reduced the number of double GHSR+ and LEPR+ cells in the ARC of adult female and male mice, and only B. longum APC1472 was able to restore the alteration induced in females (Fig. 4E). Adult female and male mice exposed to the early-life HFHS diet had lower average Ghsr expression in double GHSR+ and LEPR+ cells, and females also showed a reduced Lepr expression in these double positive cells (Supplementary Fig. S10B). Both microbiota-targeted interventions restored the alterations in Ghsr expression in double GHSR+ and LEPR+ cells only in adult females (Supplementary Fig. S10B).

These findings indicate that early-life exposure to a HFHS diet has long-lasting effects on neuropeptide-expressing ARC neuronal populations, which are crucial for feeding regulation. Supplementation with the microbiota-targeted interventions showed greater restorative effects in females exposed to early-life HFHS compared to males. Together, these data demonstrate a key sex-specific modulating role of the gut microbiota on early-life diet effects and neuronal priming of feeding behavior.

Enduring molecular impact of an early-life HFHS diet on PNOC GABAergic neurons and Nod2-expressing cells in the arcuate nucleus of the hypothalamus of adult male and female mice

Using hypothalamic transcriptomic analysis via RNA sequencing, we identified that exposure to early-life HFHS diet induced non-significant changes in Pnoc expression (downregulation: t = 2.21, q = 0.07, d = 1.28) and Nod2 expression (upregulation: t = −1.63, q = 0.139, d = −0.94) in adult male mice, the latter of which was restored by both microbiota-targeted interventions (Fig. 5A). To further explore the observed findings of these two potential players in the regulation of appetite and feeding97,98, we next performed RNAscope in situ hybridization of Pnoc and Nod2 in the ARC.

RNAscope analysis revealed significant changes in the number of PNOC+ and NOD2+ cells in the ARC nucleus of early-life HFHS-exposed mice (Fig. 5B, C and Supplementary Fig. S11). Furthermore, a significant reduction in the number of Pnoc-expressing cells was observed in the ARC of adult female and male mice exposed to early-life HFHS, which was restored by both microbiota-targeted interventions in both sexes (Fig. 5C). While both sexes showed a decrease in the number of ARC cells expressing Pnoc (PNOC+ cells), only male mice had a reduced average expression of Pnoc in PNOC+ cells compared to control, which was restored by microbiota-targeted interventions (Fig. 5C).

The number of ARC cells expressing Nod2 (NOD2+ cells) was reduced in adult males and females exposed to early-life HFHS compared to control, which was restored by both microbiota-targeted interventions in males and by B. longum APC1472 in females (Fig. 5C).

The average expression of Nod2 in NOD2+ cells in the ARC was reduced in male mice that were exposed to the early-life HFHS diet compared to control, which was restored by B. longum APC1472 (Fig. 5C).

Here, we identified that GABAergic PNOC ARC cells (PNOC+ cells in the ARC) can co-express Nod2 (Fig. 5B, C and Supplementary Fig. S11). We found that early-life HFHS diet reduced the number of PNOC+ cells co-expressing Nod2 in the ARC of adult female and male mice (Fig. 5C). These effects were restored by the supplementation with the prebiotic FOS+GOS and B. longum APC1472 in both sexes (Fig. 5C). Male but not female mice exposed to the early-life HFHS diet exhibited reduced average Pnoc expression in PNOC+ cells co-expressing Nod2, which was restored by the microbiota-targeted interventions (Fig. 5C). No differences were found in average Nod2 expression in PNOC+ cells co-expressing Nod2 in the ARC of female or male mice (Fig. 5C).

We also found that early-life HFHS diet exposure reduced the total number of Nod2-expressing cells in the ARC alongside the third ventricle in adult male mice, which was restored by the microbiota-targeted interventions (Fig. 5B, D). The early-life HFHS diet did not alter the average Nod2 expression in NOD2+ cells alongside the third ventricle in adult female or male mice (Fig. 5D). Together, these findings suggest that exposure to early-life HFHS diet has enduring effects on GABAergic neurons expressing Pnoc and on Nod2-expressing cells in the ARC, which can be ameliorated by microbiota-targeted interventions.

Methods
Animals, diets, and ethical approval
All experiments were conducted in accordance with the European Directive 2010/63/EC, the requirements of the S.I. No 543 of 2012 and were approved by the HPRA (AE19130/P179) and the Animal Experimentation Ethics Committee of University College Cork, Cork, Ireland. Male and female C57BL/6J breeders (Envigo, UK) were mated after 2 weeks of acclimatization in specific pathogen-free (SPF) animal research facilities. All mice were housed in IVC cages (Tecniplast, UK) in a 55% ± 10% humidity- and 21°C ± 1°C temperature-controlled room. The holding room was maintained on a 12-h light/dark cycle, and food and water were provided ad libitum, except where specified. Sample size determination was based on a priori power calculations performed with G*Power (v.3.1.9.4) and accounting for litter effects. Both male and female animals were included in the study, and sex-based analyses were performed and reported (see Section “Bioinformatics and statistical analysis”). Dams were mated and individually housed at embryonic day 17. On the day of birth (PND 0), dams were randomly assigned to experimental groups using a simple randomization procedure (7 dams/group, aiming to avoid any cage/litter effects). After birth, the dams’ food was switched from chow (Teklad 2918, Inotiv) to either control (10 kcal% fat and no sucrose; D12450K Ssniff Spezialdiäten. GmbH, Germany) or high-fat/high-sugar (HFHS) diet (45 kcal% fat and 17 kcal% sucrose; D12451 modified with reduction of sucrose from 20.17% to 17% and increase of starch from 7% to 10.17% (Product N S9912-E760) Ssniff Spezialdiäten. GmbH, Germany) according to the group (1. Control Vehicle, 2. HFHS Vehicle, 3. HFHS diet+prebiotic combination FOS+GOS, 4. HFHS diet+putative probiotic B. longum APC1472) (Fig. 1A). Vehicle consisted of drinking water provided using a system with a filter for solids and ultraviolet light water filters for bacteria (used throughout the study unless otherwise specified). All diets, including chow, were provided ad libitum (except where specified), and full compositions of the control and HFHS diets are available in Supplementary Table S4. After weaning (PND 21), offspring were group-housed by sex with their littermates within treatment groups and dams were euthanized two days after weaning (PND 23). At PND 35, 5-week-old offspring were euthanized depending on sex and litter size to keep 2–3 mice per litter/sex (on average) to reach the target sample size n = 12 per group per sex at 12 weeks of age. At PND 35, these offspring that had consumed the HFHS diet were switched to the control diet until the end of the study in order to investigate the impact of the HFHS diet during the entire early-life period. Male and female offspring were culled at week 12 (n = 12/sex/group, with half of the mice being perfused (n = 6/sex/group)). As further described in Section “Microbiota-targeted intervention”, the prebiotic combination FOS+GOS and the B. longum APC1472 were administered in drinking water to the dams from the birth of offspring until PND 21, and to offspring from PND 21 until the end of the study.

Preparation of bacteria for in vivo administration and identity confirmation
Preparation of bacteria for in vivo administration. All culturing of Bifidobacterium longum APC1472 (NCBI accession number: GCA_002833115.1) was performed under anaerobic conditions (PLAS-LABS Simplicity 888 Automatic Atmosphere Chamber with an anaerobic gas mixture (N = 80%, H = 10%, CO = 10%, temperature = 37°C)). Growth curve of B. longum APC1472 was performed manually, in duplicate, using a WPA CO8000 Cell Density Meter, and automatically, in triplicate, using a Cerillo™ Stratus Kinetic Microplate Reader. A glycerol stock of B. longum APC1472 was streak-plated on De Man, Rogosa and Sharpe (MRS) medium (BD Difco™ Lactobacilli MRS Broth, BD288130, Fisher Scientific) supplemented with 0.5% (w/v) L-cysteine hydrochloride (C1276, Sigma-Aldrich) (mMRS, from now onwards) and grown for ~48 h until single colonies were visible. A single colony was then inoculated in liquid mMRS medium and incubated until the culture reached early stationary phase (OD600 of 1.1-1.2, ~1×109 CFU/ml). To prepare the inoculum, the bacterial cell pellet was centrifuged at 4500 rpm (2740 g) for 15 min and resuspended in phosphate-buffered saline (PBS) (P4417, Sigma-Aldrich) twice to wash out any remaining medium. After the washing steps, bacterial cell pellets were resuspended in PBS + 20% (v/v) glycerol (G7893, Sigma-Aldrich), and 1.5 ml aliquots were stored at −80°C until use. The number of viable bacterial cells in the inoculum was determined via counts of colony-forming units (CFU/ml) for every batch prepared. The purity and identity of each batch of cultures was assessed via Sanger Sequencing of 16S rRNA gene (Eurofins Genomics).

Confirmation of the purity and identity of the bacterial inoculum. Total bacterial DNA was isolated using the QIAamp Fast DNA Stool Mini kit (51604, Qiagen) following the manufacturer’s instructions with some modifications: bacterial cell pellets with Inhibitex Buffer and zirconia/silica beads (0.125 g of 0.1 mm zirconia/silica beads, 0.125 g of 1.0 mm zirconia/silica beads, and one single 3.5 mm glass bead; 11079101z, 11079110z, and 11079135, respectively; BioSpec) were placed in the FastPrep-24 bead beater (116004500, MP Biomedicals), and heated in a thermoblock (95°C, 5 min) before continuing according to the manufacturer's instructions. The quality and quantity of the isolated bacterial DNA were assessed using the NanoDrop spectrophotometer (ND-1000, Nanodrop Technologies). DNA was used as a template for PCR targeting the bacterial 16S rRNA gene using the universal bacterial primers 27F2 (5’-AGAGTTTGATCATGGCTCA-3’) and 1492R3 (5’-TACGGTTACCTTGTTACGACTT-3’) (Integrated DNA Technologies) and the Phusion High-Fidelity PCR Master Mix with HF Buffer (F531, Thermo Fisher Scientific). PCR products were visualized by electrophoresis in 2% agarose gel (Ultra-Pure Agarose, 16500500, Invitrogen and 50X TAE Electrophoresis Buffer, B49, Thermo Scientific), stained with SYBR Safe DNA Gel Stain (533102, Invitrogen), and using loading dye (AM8546G, Invitrogen). PCR products were purified using the QIAquick PCR Purification Kit (28106, Qiagen), following manufacturer’s instructions. Purified PCR products were sequenced by Sanger Sequencing of 16S rRNA gene (Eurofins Genomics). Obtained sequences were analyzed in BioEdit v7.2.5/Mega v11.0.10 and identified using Basic Local Alignment Search Tool (BLAST) server at the National Center for Biotechnology Information (NCBI) (v2.14.0). Alignment with the respective source sequence (deposited in NCBI, accession number: GCA_002833115.1) was performed using MultiAlin128.

Microbiota-targeted intervention
In vivo prebiotic administration. The prebiotic FOS+GOS was prepared by combining FOS Orafti P95 (Healy Group) and GOS 90 (Tate & Lyle PLC) (Supplementary Table S5). FOS 95 and GOS 90 were dissolved under sterile conditions in water to a final concentration of 75 g/L (37.5 g/L of FOS 95 and 37.5 g/L of GOS 90), which was a dose chosen based on previous literature129, and filter-sterilized through a 0.45 µm filter. Mice in the prebiotic group received 7.5% FOS+GOS in their drinking water throughout the study (23 days for dams, 12 weeks for offspring). The prebiotic solution, which is readily soluble in water, was prepared weekly under sterile conditions, aliquoted and stored at 4°C to maintain stability. Water bottles with FOS+GOS were replenished daily to ensure freshness and were fully changed regularly (every 2–3 days maximum) to prevent any potential degradation or contamination.

In vivo probiotic administration. Aliquots with the B. longum APC1472 inoculum were defrosted daily just prior to the start of the dark phase and diluted in drinking water to administer ~1.0×109 CFU/animal/day for dams and ~5.0×108 CFU/animal/day for offspring throughout the experiment (23 days for dams, 12 weeks for offspring). For example, if a cage contained 2 pups, 2x~5.0×108 CFU were diluted in 15 ml of drinking water. The dose administered to adult mice was based on previous pre-clinical and clinical studies in our group72 and others130–133, and following evidence-based recommendations134, which were adapted for the offspring. The putative probiotic was administered for 12 h duration overnight and replaced with regular drinking water every morning.

Survival B. longum APC1472. The survival of the B. longum APC1472 in drinking water was assessed every 4 h over 24 h (Supplementary Fig. S1A). An aliquot of bacteria inoculum was defrosted and diluted in drinking water to the same concentration as administered to the offspring (~5×108 CFU/animal/day, equivalent to ~1×108 CFU/mL, dose justified in Section “In vivo probiotic administration”). CFU/ml was determined in duplicates every 4 h during 24 h period (specifically, at time 0, 4, 8, 12, 16, 20, and 24 h). Mice were supplemented with B. longum APC1472 for 12 h during the dark phase consistent with survival of this strain (Supplementary Fig. S1A) and aligned to the protocol used in a previous study72.

Daily food and drink intake and body weight
Food intake was measured daily in the morning for every cage and averaged by the number of offspring per cage. Energy intake (kcal) was calculated by multiplying the daily food intake (g) by the caloric content (kcal/g) of the corresponding diet (Supplementary Table S4) that the mice were receiving at the corresponding time. Drink intake was measured every 12 h throughout the experiment and averaged by the number of mice per cage to monitor daily and overnight consumption (Supplementary Fig. S1B). Body weight was individually measured weekly.

Behavioral tests
Saccharin preference test. Female and male mice (n = 12/group/sex) undergoing the saccharin preference test at weeks 10 of age, were singly housed in new experimental cages with the presence of two bottles (50 ml Falcons) and pellets of control diet. Mice were habituated to single housing and the presence of two water bottles for three days (3 h, 12 h, 3 h) before the content of one bottle was replaced with a 0.2% saccharin solution (109185, Sigma-Aldrich). Saccharin and water were stored in 50 ml Falcon tubes. The saccharin preference test was carried out using the following modified protocols from previous studies in our group135–137 and others138–140. Accordingly, intake (volume) of water and saccharin solution in both bottles was recorded every 12 h for 48 h in adult female and male mice. The positions of the two bottles were switched after 12 h initially and 24 h afterwards, to reduce any confounding produced by a side or day/nighttime bias. After the last measurement, mice were returned to their original group housing. Preference for saccharin (sweet solution) over water was calculated as follows: (ml of saccharin/[ml of saccharin + ml of water]) × 100.

Food preference test. The food preference test was carried out using the following modified protocols from previous studies10,141–143. Accordingly, adult female and male mice (10–11 weeks of age, n = 12/group/sex) were habituated to single housing for 48 h in a new home cage. After this habituation period, mice were returned to their original group housing. The following day, adult female and male mice were singly housed in a new home cage and presented with control diet in one hopper and HFHS diet in the other, for 48 h. Food intake of control and HFHS diet was recorded every 12 h. Positions of the two hoppers were switched after 12 h initially and 24 h afterwards, to reduce any confounding produced by a side or day/nighttime bias. After the last measurement, mice were returned to their original group housing. Food preference was measured by quantifying the control and palatable diets removed from the respective food hoppers, which in corresponding cases included both the food intake and crumbling of the palatable diet.

Meal pattern. Feeding behavior, including meal pattern, food crumbling, and total intake was characterized using PheCOMP cages (Panlab–Harvard Instruments), which allow the precise detection of water and food intake and meal patterns as they continuously record the weight of the two food and two drink hoppers. Adult female and male mice (10–11 weeks of age, n = 12/group/sex) were habituated to single housing in the PheCOMP cage for 3 h. After this habituation period, mice were returned to their original group housing. On the experimental day, adult female and male mice (10–11 weeks of age) were placed (singly housed) in the experimental PheCOMP cages with drinking water available ad libitum (two hoppers were used for drinking water to minimize side bias) and HFHS diet in one hopper. Meal pattern was recorded every 5 s for 12 h during the light phase.
Exact feeding pattern was analyzed and determined using the COMPULSE software v1.1 (Panlab–Harvard Instruments) as follows: a meal needed to be larger than 0.2 g and consumed for longer than 10 s, and two distinct meals were recorded when separated by an interval ≥ 10 min144–146. Parameters characterizing feeding patterns, such as relative total food intake, ingestion rate, number of meals, average meal size and duration, interval between meals, eating rate, first meal size, and latency of first meal, were obtained (data only shown for total intake, food crumbled, eating rate, first meal size, and average meal size). Importantly, while food crumbling and total food intake were measured separately, meal pattern analysis captured the temporal distribution of all food hopper removal events, encompassing both eating and crumbling behaviors.

Locomotor activity. Locomotor activity was analyzed using the two-dimensional infrared frames in the PheCOMP cages (Panlab–Harvard Instruments). Adult female and male mice (10–11 weeks of age, n = 12/group/sex) were habituated to single housing for 3 h. After habituation, mice were returned to their original group housing. On the experimental day, adult female and male mice (10–11 weeks of age) were placed (singly housed) in the experimental PheCOMP cages with drinking water available ad libitum (two hoppers were used for drinking water to minimize side bias) and HFHS diet in one hopper, for 12 h during the light phase. Locomotor activity was analyzed using the ActiTrack software v2.7.13 (Panlab-Harvard Instruments) with a speed threshold between 2 cm/s and 5 cm/s and a maximum speed at 10 cm/s.

Intraperitoneal glucose tolerance test
Intraperitoneal glucose tolerance (IpGTT) was assessed on 11-week-old female and male offspring (n = 12/group/sex). Briefly, mice were fasted for 6 h during the light phase, with free access to water. An intraperitoneal injection of 2 g/kg glucose ((G8270, Sigma-Aldrich) of body weight dissolved in PBS) was performed, and glucose levels were measured in samples taken from tail vein blood using the ACCU-CHEK glucometer (9221786018, Roche) and strips (7819382, Roche) immediately before and 15, 30, 60, 90, and 120 min after the intraperitoneal injection of glucose, as previously performed72, with the minor modifications detailed.

Sacrifice and murine tissue sampling
At the end of the study, 12-week-old female and male (n = 6/group/sex) mice were euthanized by decapitation. Trunk blood was collected in tubes (454020, VACUETTE, Greiner Bio-One) containing 500 KIU/ml aprotinin (PI78432, Thermo Fisher Scientific) and 1X cOmplete Protease Inhibitor Cocktail (5892970001, Roche). Blood was then centrifuged at 2000 g for 15 min at 18°C, following instructions of blood tubes’ manufacturers. Plasma was aliquoted and 0.05 N hydrochloric acid (H/1185/PB17, Fisher Scientific) was added into one aliquot for future biochemical analyses of circulating ghrelin. Adipose depots (epididymal/gonadal white adipose tissue, subcutaneous white adipose tissue, mesenteric white adipose tissue and brown adipose tissue), cecum, and liver, were dissected and weighed. Colon and small intestine were dissected and measured. Hypothalamus and striatum (including dorsal and ventral striatum and nucleus accumbens core and shell) were dissected with a forceps (macropunch) and collected. All samples were frozen on dry ice and subsequently stored at −80°C for further analysis.
Dams fecal samples were collected when their offspring reached weaning age (PND 21) (n = 7/group). Offspring fecal samples were collected at 5 and at 10 weeks of age, before starting the second round of behavioral tests and the IpGTT (n = 12/group/sex per timepoint).

Perfusion
12-week-old female and male offspring (n = 6/group/sex) were anesthetized with an intraperitoneal injection of Pentobarbital Sodium (Abbeyville Vet Hospital, 90 mg/kg of body weight) and perfused transcardially with PBS followed by 4% w/v paraformaldehyde (PFA) (P/0840/53, Fisher Scientific). Brains and livers were collected and kept in PFA overnight at 4°C, and then transferred to 25% (w/v) sucrose (S/8600/60, Fisher Scientific) for at least 48 h at 4°C. Once the brains were saturated, they were snap frozen in isopentane (2-Methylbutane, 294524E, VWR Chemicals BDH), cooled using liquid nitrogen, and stored at −80°C.

RNA isolation from the hypothalamus and striatum
Total RNA was extracted from the hypothalamus and striatum (n = 6/group/sex) using the High Pure RNA Isolation Kit (11828665001, Roche) with the following modifications from manufacturer’s recommendations: instead of the Lysis/Binding Buffer, Qiazol Lysis Reagent (79306, Qiagen) was used together with zirconia/silica beads (~0.125 g of 1.0 mm zirconia/silica beads, and 3–5 2.3 mm zirconia/silica beads, 11079101z and 11079125z, respectively; BioSpec) and, placed 1 min in the FastPrep-24 bead beater (116004500, MP Biomedicals) for disruption and homogenization steps. After the addition of Chloroform (C2432, Sigma-Aldrich), the aqueous phase was used to continue with manufacturer’s recommendations. Quality and quantity of the isolated RNA were assessed on a NanoDrop spectrophotometer (ND1000, Nanodrop Technologies) and Agilent Bioanalyser 2100 (5067-1511, Agilent Technologies), and kept at −80°C until further analysis.

Transcriptomic sequencing
mRNA from the hypothalamus (~2 µg) was used for the RNA-seq analyses (n = 6/group/sex). RNA sample quality control, mRNA library preparation (poly-A enrichment), NovaSeq X Plus (PE150) (6G raw data per sample) sequencing, and data quality control as well as Fastq-file generation, were performed by Novogene. A reference genome for the obtained sequences was generated using the following reference annotation: Mus musculus (organism), GRCm38, UCSC genome browser (GRCm38.p6), Ensemble 147. Reads were assessed and filtered for quality with Fastqc (v0.12.1) using default parameters. Genes were annotated and counted with Kallisto (v0.48.0)148 using the default parameters.

Differential gene expression and gene ontology term enrichment analyses. Transcriptomic data analysis, after genes were annotated and counted, were performed in R Studio (2023.12.1). Low-expression genes (with counts less than 10 in 75% or more of the samples) were filtered out from the dataset to minimize noise and improve downstream analysis. Differential expression, representations, and gene ontology (GO) term enrichment analyses were performed on centered log-ratio (CLR)-transformed values, as further described in Section “Bioinformatics and statistical analysis (‘Omics datasets)”. Employing the base R stats phyper implementation of the hypergeometric test on a priori selected Gene Ontology (GO) terms96 relevant to the objectives and scope of the study design, enrichment analysis was performed on genes altered by the diet (Benjamini-Hochberg correction, P-adjusted (q) < 0.2), and restored by each microbiota-targeted intervention (Benjamini-Hochberg correction, P-adjusted (q) < 0.2) on each sex. Figures were generated using ggplot2 (v3.5.2) and patchwork (v1.3.1) in R with these selected genes.

RNAscope (fluorescent in situ hybridization)
Perfused brains were cut using a cryostat and 20 µm thick section were stored in tissue storage solution (25% glycerol (G7893, Sigma-Aldrich), 25% ethylene glycol (293237, Sigma-Aldrich), 50% PBS (P4417, Sigma-Aldrich), autoclaved for RNAscope) at −20°C until further processing. We focused on genes expressed by neurons, including receptors for hormones or bacterial peptidoglycan to sense peripheral signals, in the arcuate nucleus that are linked to energy balance and feeding regulation19,21,97,98. Thus, we selected probes for POMC, GHSR, LEPR, prepronociceptin (PNOC), and Nucleotide-binding oligomerization domain 2 (NOD2). On the day before the RNAscope assay, sections containing the ARC of the hypothalamus (bregma −1.455 to −1.955 mm) were mounted onto SuperFrost Plus Adhesion Microscape slides (J1800AMNZ, Epredia), dried at room temperature and baked at 60°C o/n in the ACD HybEZ oven (PN 321710/321720, Advanced Cell Diagnostics) (n = 4–5/group/sex). Fluorescent RNAscope (ACD; Advanced Cell Diagnostics Inc., Hayward, CA) was performed to analyze simultaneous Ghsr (426141, BioTechne), Lepr (402731, BioTechne) and Pomc (314081, BioTechne) on one set, and simultaneous Nod2 (433391, BioTechne) and Pnoc (437881, BioTechne), on another set, in the ARC. Incubation steps were performed at 40°C using the humidified chamber and oven from the HybEZ II System (Advanced Cell Diagnostics). The pre-treatment protocol followed manufacturer’s instructions with the following modifications: 7 min incubation in hydrogen peroxide (322381, Advanced Cell Diagnostics), and 7 min incubation with target retrieval (322000, Advanced Cell Diagnostics). The detection protocol followed the manufacturer’s protocol for the RNAscope Multiplex Fluorescent v2 kit. For detection of the probes directed towards Ghsr and Nod2 mRNA, Opal 650 tyramide (FP1496001KT, Akoya Biosciences), was used diluted 1:2000. For Lepr mRNA, Opal 570 tyramide (FP1488001KT, Akoya Biosciences) was used diluted 1:2000. For detection of Pomc mRNA, Opal 520 tyramide (FP1487001KT, Akoya Biosciences) was used diluted 1:3000. The same Opal 520 tyramide was used diluted 1:500 for detection of the probe directed towards Pnoc.
All the sections were afterwards counterstained with DAPI (1:5,000) (62248, Thermo Fisher Scientific), coverslipped with ProLong™ Diamond Antifade Mountant (P36970, Invitrogen) mountant, and stored in the dark at 4°C, until imaging acquisition. Images were acquired using a laser scanning confocal microscope (LSM 700 inverted, Zeiss), at the Centre for Cellular Imaging at the University of Gothenburg. A Plan-Apochromat 40×/1.3 Oil DIC objective was used, with 3×3 tiling and z-stack (7 optical sections) settings, to image the RNAscope in the ARC subregion. The Z-stack images were processed using the maximum intensity projection function in the Zen Black software (Zeiss, version 2012 SP5). Channels were merged and DAPI-identified cells with ≥2 dots/cell were defined as being positive for Nod2, ≥3 dots/cell for Ghsr, and Lepr, ≥4 dots/cell for Pomc and Pnoc using QuPath software149 (v0.5.1).

Monoamine neurotransmitter measurement in striatum using HPLC and electrochemical detection
For their involvement in the non-homeostatic regulation of feeding16,90,150,151, striatal samples were processed for the measurement of monoamine concentrations, including noradrenaline, dopamine, L-3,4 dihydroxyphenylalanine (L-DOPA), homovanillic acid (HVA), 3,4-Dihydroxyphenylacetic acid (DOPAC), 5-Hydroxytryptamine or serotonin (5-HT), and 5-hydroxyindoleacetic acid (5-HIAA), using reverse-phase high-performance liquid chromatography (HPLC) as previously described152. In brief, 500 µL of homogenization buffer, composed of the mobile phase and an internal standard (2 ng/20 μL of N-methyl 5-HT, M1514, Sigma Aldrich), was added to the previously weighed tissues (n = 6/group/sex). The samples were then sonicated (Sonopuls HD2070, Bandelin), and centrifuged at 21,910 g for 20 min at 4°C using a Hettich Mikro 22R centrifuge (AGB). The supernatant was carefully collected afterwards, and 20 µL was used as the injection volume. The HPLC system (Shimadzu) included an SCL 10-Avp system controller, LC-10AS pump, and SIL-10A autoinjector (kept at 4°C), along with a CTO-10A column oven set to 30°C. Detection was performed using a Measurement Cell Sputnik electrochemical detector (EC3000, ClinLab), operating at +0.8 V with a range of ±50 nA. The column used was a Kinetex 2.6 µm C18, 100Å, 100×4.6 mm (Phenomenex), with a flow rate of 0.9 ml/min. The mobile phase was prepared (0.1 mol/L citric acid (c0759, Sigma Aldrich), 0.1 mol/L sodium dihydrogen phosphate monohydrate (102455S, VWR Chemicals BDH), 5.6 mmol/L 1-octanesulfonic acid (1.18307.0100, Sigma Aldrich), and 0.01 mmol/L EDTA disodium salt dihydrate (e4884, Sigma Aldrich)) in MQ water. Finally, HPLC-grade methanol ((9% v/v), m/4059/pb17, Fisher Chemical) was gradually added, and the pH was adjusted to 2.8, using 1 M NaOH (S8045, Sigma Aldrich).

Analysis of plasma ghrelin, corticosterone, and metabolic signature
A potential impact of the early-life HFHS diet or microbiota-targeted interventions on plasma active ghrelin levels was analyzed by enzyme-linked immunosorbent assay (ELISA), because of its role in the regulation of food intake and feeding behavior153–155, and its suggested involvement with the gut microbiota78,156. Corticosterone levels were also analyzed by ELISA, to assess any potential impact of the early-life HFHS diet or microbiota-targeted interventions on the stress response and hypothalamic-pituitary-adrenal (HPA) axis, which the microbiota has been shown to regulate74.
Accordingly, active ghrelin levels in plasma were measured using the Rat/Mouse Ghrelin (Active) ELISA kit (EZRGRA-90K, Merck Millipore) according to the manufacturer’s instructions (n = 6/group/sex). Free corticosterone levels were assayed using the Corticosterone ELISA kit (ADI-901-097, Enzo Life Sciences) according to the manufacturer’s instructions, diluting the sample with assay buffer without steroid displacement reagent (n = 6/group/sex). Plasma levels of triglycerides, glucose, insulin and cholesterol, were analyzed by chemiluminiscence in the Cobas Integra 400 (Roche Diagnostics) at the Estación Biológica de Doñana (EBD-CSIC, Seville, Spain) (n = 6/group/sex).

Biochemical analysis of metabolic signature in the liver
Liver biochemical composition, including cholesterol, glucose, protein, alanine transaminase (ALT), and aspartate aminotransferase (AST), was analyzed by chemiluminiscence using the Cobas Integra 400 (Roche Diagnostics) at the Estación Biológica de Doñana (EBD-CSIC, Seville, Spain) (n = 6/group/sex).

Untargeted metabolomic analyses in plasma and integrated pathway analysis
Blood metabolomics was conducted by MS-OMICS (Vedbaek, Denmark) (n = 6/group/sex). Blood collection, processing, and plasma storage are described in Section “Sacrifice and murine tissue sampling”. Semi-polar metabolite profiling (reverse phase) was analyzed with MS-Omics’ semi-polar metabolites method, a slightly modified version of the protocol described by Doneanu et al.157 (Doneanu et al., 720004042en). Samples were extracted using Phree Phospholipid Removal cartridges (Phenomenex) with an equal mixture of acetonitrile and methanol (1:1 vol/vol) containing stable isotope-labeled internal standards, centrifuged (15,000×g for 5 min at 4°C), and dried. Samples were reconstituted in a 9:1 (v/v) mixture of mobile phase eluents A and B containing additional stable isotope-labeled standards, resulting in a total dilution factor of 5.5 prior to injection. A quality control (QC) sample was prepared by pooling ~30 µL aliquots from each plasma sample to create a representative average. The QC was processed alongside the experimental samples and analyzed after every sixth experimental sample throughout the sequence. The pooled QC sample was analyzed 13 times across the analytical sequence to assess technical variation, with acceptance criteria for detected compounds including signal-to-noise ratio >5, precision <20% (relative standard deviation across pooled QCs), QC-to-sample signal ratio between 0.5 and 2, and correlation between signal and QC dilution factor >0.8 to assess matrix effects. No technical replicates of individual samples were performed; technical variation was instead assessed through the pooled QC samples analyzed throughout the sequence.
The analysis was carried out in a randomized sample order using a UPLC system (Vanquish, Thermo Fisher Scientific) equipped with a C18 column (2.1×150 mm, 1.8 µm, HSS T3) coupled with a high-resolution quadrupole-orbitrap mass spectrometer (Orbitrap Exploris 240 MS, Thermo Fisher Scientific). Mobile phases consisted of 10 mM ammonium formate with 0.1% formic acid in water (A) and in methanol (B), with a 0–90% B gradient over 15 min at 300 µL/min and a 50 µL injection volume. Ionization was achieved with an electrospray ionization interface operated in positive and negative mode under polarity switching, and a QC sample was analyzed in MS/MS fragmentation mode for compound identification. MS acquisition parameters included a scan range of m/z 65–975 with a resolving power of 60,000 and mass tolerance of 3 ppm for compound detection. Data were processed, and peak areas were extracted using Compound Discoverer v3.3 (Thermo Fisher Scientific) and Skyline v23.1158. Out of the three levels of annotation for LC-MS/MS data, level 1 (annotations based on accurate mass, MS/MS spectra and known retention time compared against in-house standards analyzed on the same system) and level 2a (based on accurate mass and known retention time compared against in-house standards analyzed on the same system) were selected for further analyses, with the exclusion of non-dietary plant metabolites. The resulting count table identifying level 1 and 2a metabolites was CLR-transformed to account for the inherent compositionality of multi-omic data159 using the R package vegan160. Metabolites that were found to be altered by the early-life exposure to a HFHS diet and restored by the microbiota-targeted interventions (individually by FOS+GOS or B. longum APC1472) were mapped to their corresponding Human Metabolome Database identifier and subjected to enrichment analysis using the MetaboAnalyst online pipeline for metabolites altered by the diet (Benjamini-Hochberg correction, P-adjusted (q) < 0.2), and restored by each intervention (Benjamini-Hochberg correction, q < 0.2) on each sex161 with additional controls with False Discovery Rate (FDR), choosing the murine KEGG library as a reference162.
Integrated pathway analysis was performed incorporating the lists of differentially abundant circulating metabolites and differentially expressed genes in the hypothalamus of 12-week-old female and male mice (q < 0.2) using the MetaboAnalyst software V6.0 with default parameter settings on all KEGG pathways163. This allowed the identification of potential pathways of interest that were modulated by diet alteration and by restoration through each microbiota-targeted intervention (FOS+GOS or B. longum APC1472) via multi-omics investigation164. This analysis considers both the enrichment ratio of components of the pathway as well as the centrality in that pathway to generate statistical significance values and pathway impact metrics163. For each sex, three separate integrated analyses were performed using differentially expressed genes and abundant metabolites in 12-week-old mice: (1) those altered by dietary intervention, (2) those restored by FOS+GOS administration, and (3) those restored by B. longum APC1472 administration.

Short-chain and branched-chain fatty acid (SCFA and BCFA) analysis preparation and analysis GC-FID operation
Extraction and analysis of the short-chain fatty acids (SCFAs) acetate, butyrate, propionate and valerate, and branched-chain fatty acids (BCFA) isobutyrate and isovalerate, followed a slightly modified version of the protocol described by Lynch et al.165. Accordingly, cecal samples (0.2 g) were homogenized in 1 mL acidified water (0.008 mL/L HCl in Milli-Q water) and centrifuged at 16,000×g for 30 min at 4°C. The exact mass of each cecum sample was accounted for when calculating concentration of SCFA/BCFA post-GC. The supernatant was transferred to a new 2 mL tube and centrifuged at 16,000×g for 30 min at 4°C. This centrifugation step was repeated twice more for a total of three cycles. The final supernatant was sequentially filtered through 5 µm Ultrafree-MC-SV centrifugal filters (Merck Millipore) at 12,000×g for 10 min, followed by 0.22 µm COSTAR Spin X centrifuge tube filters (Corning) at 12,000×g for 10 min. The filtrate was divided into two 270 µL aliquots, and 30 µL of 10 mM 2-ethylbutyric acid (internal standard) was added to each aliquot. The mixture was centrifuged at 15,000×g for 10 min at 4°C, and 250 µL was transferred into 2 mL amber glass GC vials (Agilent), containing polymer feet inserts (Agilent). A standard curve was prepared prior to running samples on GC, using acetate, propionate, butyrate, and valerate (0.1–10 mM), and isobutyrate and isovalerate (0.01–1 mM) (all acids were purchased from Sigma-Aldrich).
SCFA and BCFA analysis was performed by gas chromatography with flame ionization detection (GC-FID) using an Agilent 8860 system (Agilent) equipped with a DB-FFAP column (30 m × 0.32 mm ID × 0.25 µm film thickness; Agilent). Samples were injected (1 µL) in splitless mode using an automated sampler. Helium (Air Products) served as the carrier gas at a constant flow rate of 1.3 mL/min. The oven temperature program was: initial temperature 50°C held for 0.5 min, increased to 140°C at 10°C/min and held for 0.5 min, then increased to 240°C at 20°C/min and held for 5 min (total run time 20 min). Injector and detector temperatures were maintained at 240 and 300°C, respectively. Peak integration and quantification were performed using OpenLab software (Agilent). Abundance (µmol/g) was plotted for acetate, propionate, butyrate, valerate, isobutyrate and isovalerate for cecal samples from offspring that were collected at week 5 (n = 6–14/sex/group) and 12 (n = 6–7/sex/group) of age. Of note, the week 12 female HFHS FOS+GOS group had a reduced sample size (n = 2) due to insufficient remaining cecal filtrate for GC-FID analysis. The SCFA and BCFA abundance was calculated as the average of two technical replicates per sample, and outliers were removed as described in Section “Bioinformatics and statistical analysis”.

Fecal microbiome 16S sequencing
Fecal DNA from dams (n = 7/group) and 5- and 10-week-old female and male offspring (n = 12/group/sex/timepoint) was isolated using the QIAamp Fast DNA Stool Mini kit (51604, Qiagen), following the modified manufacturer’s instructions explained in Section “Confirmation of the purity and identity of the bacterial inoculum”. The quality and quantity of the isolated DNA were assessed on a NanoDrop spectrophotometer (ND1000, Nano Scientific) and kept at −20°C until further analysis. The bacterial composition of the samples was determined through sequencing of the hypervariable V3–V4 region of the bacterial 16S rRNA gene following the protocol outlined in the Illumina 16S Metagenomic Sequencing Library Preparation guide166. The universal bacterial primers were selected from those described by Klindworth et al.167. PCR amplification of V3–V4 region was performed using the forward primer 5′- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3’ and reverse primer 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3’ (Merck, Sigma Aldrich). Each 25 µl PCR reaction contained 5 ng/µl microbial genomic DNA, 1 µM of each primer, and 12.5 µl 2× KAPA HiFi HotStart ReadyMix (Roche Diagnostics). The PCR conditions were as follows: 5 min at 95°C; 25 cycles of 30 s at 95°C, 30 s at 55°C and 30 s at 72°C; and 72°C for 5 min. Purification of amplicons was carried out by Agencourt AMPure XP beads (Beckman Coulter). A subsequent limited-cycle PCR step was performed to add multiplexing indices and Illumina sequencing adapters. Index primers were supplied as part of the Nextera XT Index Kit v2, Set A (15052163, Illumina Inc.). Each 50 µL indexing PCR reaction contained 5 µL amplified DNA, 5 µL Nextera XT Index Primer 1 (Illumina Inc.), 5 µL Nextera XT Index Primer 2 (Illumina Inc.), 25 µL 2× KAPA HiFi HotStart ReadyMix, and 10 µL PCR grade molecular water. The Agencourt AMPure XP beads were used for purification of index PCR products. Amplicons were quantified using the Qubit® dsDNA HS Assay Kit (Life Technologies) and 4200 Tapestation system (Agilent Technologies), and subsequently, normalized, and pooled. Sequencing was carried out by Genewiz Azenta Life Sciences (GENEWIZ) employing 2×300 base-pair sequencing on the Illumina MiSeq platform.

Analysis of microbiome sequencing data. Using FastQC (v0.12.1), raw sequences underwent quality checks and were further processed using the DADA2 pipeline (v1.30.0)168. After plotting quality profiles, the following filtering and trimming parameters were chosen to best handle the data: trimLeft=c(17,21), truncLen=c(285,210), maxN=0, maxEE=c(2,4), truncQ=2. Error rates were learned, and the core sample inference algorithm was applied to the dereplicated data. Forward and reverse reads were merged, and a sequence table generated, made up of amplicon sequence variants. Since two MiSeq sequencing runs were required to sequence the data, each run was processed through the above DADA2 pipeline separately. Once the sequence tables were produced for runs 1 and 2, they were then merged. Chimeras were removed from the merged sequence table, and taxonomy was assigned using the Silva taxonomy database v138169. A taxonomy table was subsequently generated. The sequence table and taxonomy table produced were imported to R Studio (v2024.04.2 (with Build 764)) using R v4.4. A phyloseq object was created with these outputs using the phyloseq package (v1.5.2). Further packages required for data analysis were ggplot2, dplyr (v1.1.4), tidyverse (v2.0.0), ggpubr (v0.6.2), vegan (v2.7.1), adonis2 function from the vegan package, car (v3.1.3), and rstatix (v0.7.2). Raw counts were normalized by transforming them to relative abundances using the transform_sample_counts function.
Microbial functional potential was inferred from 16S rRNA gene sequences of microbiome samples using PICRUSt2 v2.5.2 applied to the processed DADA2 output, generating KEGG orthologue predictions94. Additionally, we filtered the pathways identified by PICRUSt2 to retain pathways involved in the metabolism of metabolites that were significantly altered by the early-life HFHS diet or significantly restored by each microbiota-targeted intervention (FOS+GOS or B. longum ACP1472) (q < 0.2). This approach linked microbial functional potential to observed metabolic changes through biologically relevant pathways, mechanistic insights into the gut-brain-metabolome axis. The abundances of pathways were CLR-transformed before conducting statistical analyses (see Section “Omics datasets”). A pseudocount of 2/3 was used to replace zero values. In addition, the R package omixerRpm (v0.3.3) was employed to compute Gut-Brain Modules (GBMs) from the functional annotation data170.

Bioinformatics and statistical analysis
All data, including 16S rRNA, transcriptomic, metabolomic, and other molecular and behavioral data, were analyzed using R Studio (v2023.12.1). In outputs where multiple technical replicates were performed (for instance, circulating active ghrelin or HPLC analysis), the mean per individual was used with standard error of the mean (SEM). Statistical analyses were assessed using linear models using a simple implemented in base R, and linear mixed-effects models using the package lme4 (v1.1.37)171. Functions and scripts are available online (https://github.com/thomazbastiaanssen/Tjazi). The alterations by the early-life exposure to the HFHS diet were analyzed by 3-way ANOVA followed by Tukey’s two-sided pairwise post-hoc test, between Control Vehicle (or also referred to as Control diet group) and HFHS Vehicle (unless otherwise noted, #p < 0.05, ##p < 0.01, ###p < 0.001). The restoration by each individual microbiota-targeted intervention (FOS+GOS or B. longum APC1472) of features altered by the early-life exposure to the HFHS diet was assessed using orthogonal contrasts in a generalized linear model, weighing either “Control Vehicle” and “HFHS FOS+GOS” or “Control Vehicle” and “HFHS B. longum APC1472” groups against the “HFHS Vehicle” group (unless otherwise noted, *p < 0.05, **p < 0.01, ***p < 0.001 for restoration effects of each intervention). Complete statistical analyses, including main effects (sex, diet, intervention and timepoint for longitudinal data) and interaction effects (before and after removing statistically significant outliers), are available at https://github.com/CristinaCuesta-Marti/earlylifeHFHS_feeding_microbiota_2025/tree/outputs/overall_stats_lm.
Orthogonal contrasts were employed to test specific hypotheses regarding restoration effects, providing greater statistical sensitivity for these pre-planned comparisons compared to ANOVA approaches with multiple comparison adjustments. Therefore, restoration effects may reach statistical significance even when the pairwise diet effects (Control Vehicle vs HFHS Vehicle) assessed by Tukey’s post-hoc tests do not reach significance.
Additional random effects (including litter and cohort effects) were assessed using linear mixed-effects models using the lmer function in R and results of these statistical analyses are available at https://github.com/CristinaCuesta-Marti/earlylifeHFHS_feeding_microbiota_2025/tree/outputs/overall_stats_lmer. Technical outliers were analyzed using the Grubbs test in R Studio with the “outliers” package in R (v0.15)172 and a maximum of one outlier per group/sex (if any) was excluded. Data distribution was assumed to be normal, but this was not formally tested. Analyses were conducted in a manner blinded to the experimenter.

‘Omics datasets. ‘Omics data analysis, which includes metabolomics, transcriptomics and 16S metagenomics, was performed on CLR-transformed counts173. Pairwise comparisons were performed using t-tests between the two dietary interventions within Vehicle-treated animals, and between HFHS-exposed animals, including HFHS Vehicle-treated animals and each microbiota intervention (prebiotic FOS+GOS and putative probiotic B. longum APC1472) for P-adjusted (q) values using Benjamini–Hochberg correction (unless otherwise noted, with a q-value of 0.2 as a threshold) for multiple comparisons using the rstatix package in R (v0.7.2). Restoration effects of microbiota-targeted interventions were evaluated when both diet and intervention effects were significant, and the effect sizes showed opposing directions. Additionally, alpha diversity was analyzed using Simpson, Shannon, and Chao1 indices. Beta diversity was assessed using Aitchinson distances (i.e., Euclidean distance over the CLR-transformed values) with principal coordinates analysis (PCoA). PERMANOVA implementation from the vegan library (v2.7.1) with the Euclidean distance matrix was used to test for significant differences in microbial diversity between groups, followed by post-hoc pairwise comparisons. For sex effect and interaction effects and additional detailed statistical analysis, see https://github.com/CristinaCuesta-Marti/earlylifeHFHS_feeding_microbiota_2025/tree/outputs/overall_stats_lm. Additional random effects (including litter and cohort effects) were assessed, and results of these statistical analyses can be found in https://github.com/CristinaCuesta-Marti/earlylifeHFHS_feeding_microbiota_2025/tree/outputs/overall_stats_lmer.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The work from Cuesta-Marti et al., explores the enduring effects of early life high-fat/high sugar diet exposure on feeding\nbehavior. They find that HFHS diet indeed have lasting effects on feeding behavior in sex dependent manner, and this effect\ncan be attenuated by prebiotic mixture (FOS+GOS) or B. longum administration. The findings are novel, and very impactful\nwith clinical implications. The conclusions are based on sound experimental design. However, I have a few concerns and\nsuggestions for clarity:\n1. FOS+GOS and B. longum has enduring effects on feeding behavior for both female and males mice but the effects are not\nnecessarily through same mechanisms, authors do not discuss or explore what would be the discrepancies in the\nmechanisms. We see more changes in female mice in metabolome and transcriptome in the brain however there are some\nmissing connections to provide information how these affects connects from gut to brain. For that, there are couple things to\nconsider:\na. Is B. longum observed in the intestine, cecum, colon, small intestine even? Are they passengers in the GI tract that’s why\nthey are found in the feces abundantly? The mechanism of action is not clear. It does not shift microbiome composition.\nDoes it change the metabolome in the microbiome? An overtime abundance analysis would confirm whether their\nabundance is increased. How does B. longum restores genes in the brain unclear. Through metabolite production? But then\nit needs to be confirmed that it colonizes because it seems like it does not change the microbiome composition.\nb. It would be good to include unpublished in vitro work where B. longum produces tryptophan. Did B. longum increase\nserotonin levels in the brain, how about enterochromaffin cells? It increases turnover but not necessarily serotonin levels.\nWere tryptophan levels affected in the gut. A metabolome analysis of the gut is needed.\nc. The study needs correlation and/or multiassociation analysis to bring blood metabolome and brain transcriptome together\nand even gut microbiome and metabolome if those experiments are done.\nd. A picrust analysis of the microbiome that connects blood metabolome to microbiome is needed.\n2. There needs some clarity in the experimental design\na. Is it clear the offspring is not exposed to the HFHS diet during pre-weaning period or are the effects through mothers milk?\nThe Fig 1A is not clear when does the offspring start eating HFHS diet. Are there any measurements that they consume\nHFHS crumbles during postnatal period?\nb. What is the composition of mothers milk during postnatal period? Does B. longum travel through mothers milk?\nc. Are there any changes in dams rearing behavior between treatment groups and control groups that might affect feeding\nbehavior?\nd. Why is the reason the administration of prebiotic mixture and B. longum continues even after switching to control diet. If\nthe reason looking at early life exposure, shouldn’t they also be stopped? It does not necessarily meant early life exposure\nbecause the treatment continues to adulthood.\ne. Why aren’t there any prebiotic+Blongum group? What about synbiotic effects of FOS+GOS+ B. longum.\nf. Was the group housing done randomly in the weaning period or did siblings go to the same cage?\ng. There will be cage to cage or cohort to cohort difference in the microbiome analysis, was that considered?\nh. How many dams per group was recruited in the beginning to reach offspring n=12 per group?\n3. Authors only address Euclidean distance in beta-diversity. Whereas Euclidean is not enough for compositional data due\nto its inability to account constant sum. Therefore Aitchison distance that takes log-ratio transformation into account is\nrecommended and this will give more information about prevalance. In addition, weighted unifrac for calculating beta\ndiversities will give information about abundance based on phylogenetic taxa observed. In addition, authors should discuss\nalpha diversity in the context of species evenness and richness. This is important especially when B. longum administration\ndoes not increase the number of total bifidobacterium but B. longum numbers increase in feces.\na. Authors don’t discuss that B. longum administration does not necessarily increase total Bifidobacterium abundance\nwhereas it increases in B. longum. That seems like a potential competition between other bifidobacterium and B.longum or\nB.longum found in feces not necessarily colonized in the gut. See my earlier comment on this suggestion.\nb. Which Bifidobacterium species were enriched during FOS+GOS administration?\n4. The conclusion will need to be tailored, that could also be reflected in the graphical abstract. I think that authors did not\npay much attention in the discussion about the different effects/mechanisms prebiotic vs B. longum and male vs female.\nEarly life HFHS has different effects male vs female and FOS+GOS and B. longum attenuates these effects different for male\nand female mice. The abstract should reflect that finding, which mechanisms were found in female and which are found in\nmale. In male mice, PNOC neuron and Nod2-expressing cells are altered whereas in female mice, POMC neurons and Ghsr\nand Lepr receptors are affected. “a reduction in the number of cells expressing Ghsr, Lepr and Pomc individually in the ARC\nof adult early-life HFHS exposed female mice while Ghsr and Pomc were only reduced in adult early-life HFHS exposed\nmales compared to control\n5. Authors should also focus on the mechanisms of FOS+GOS and B.longum more clearly. Because they have different\neffects on the HFHS induced alterations however whether the mechanisms are different is unclear. It seems like FOS+GOS\nmakes shifts in the microbiome composition whereas no clear shifts were observed for B.longum however B. longum\ndisplays more lasting effects. This can be also addressed by looking at metabolomics in fecal or cecal samples.\n6. There are some typos listed:\nLine 36 not symbiotics, synbiotics..\nLine 644 should be POMC. typo\n(Remarks on code availability)\nthe code is not deposited to github", "reviewer_2": "This is a well-written paper and a comprehensive study design that included four experimental groups, two microbiota-\ntargeted interventions (prebiotic, probiotic) and two dietary control groups, and multiple analyses (including behavioural,\nmetabolomic, transcriptomic, microbiota profiling, and fluorescent in situ hybridization). The inclusion of both male and\nfemale mice and consistent presentation of results for both sexes highlighting sex-specific effects is an important contribution\nand an often-overlooked factor. The detailed inclusion of supplementary data including body weight and gain in both graph\nand table format and details of various tissue measures and weights is commendable. The measurement of food intake and\nfood crumbling also adds to the precision of the food intake recording.\nThe quality of the data appears technically sound and is presented in detail. The conclusions made are not excessive and\nare supported by the evidence provided. These findings are relevant and add to the evidence for early life effects of diet on\nlater food intake behaviours in mice while the sex differences shown highlight the importance of including both male and\nfemale mice and reporting their data separately.\nComments\nThere are some points that could improve the manuscript:\n1. The control diet used is detailed as “D12451 modified (Product N S9912-E760” but the modifications made to the D12451\ndiet are not clear. Can these modifications be clarified.\n2. How was the probiotic dosing decided upon? The dosage used in drinking water is based upon that used in previous\nresearch and adapted for the offspring but how was this adapted for the offspring?\n3. The methods state that mice were distributed evenly across experimental groups. How were these allocations decided\nand was any randomisation method used? If so please state the method used to allocate mice into experimental groups.\n4. Are the long-term changes observed in the arcuate nucleus relevant to later control diets? This point could be mentioned\nin the discussion, that the changes in responses to food later in the study appeared to be only in response to further\nexperimental tests and that body weight normalised in all groups when on control diet in the later part of the study period\ndespite earlier exposures.\n5. Does the FOS/GOS in drinking water remain adequately dissolved and suspended during the time the water is supplied?\nHas this been measured in a similar manner to the conformation of the survival of the probiotic in the drinking water to\nensure consistent dosage?\n6. Was the amount of water the mice drink checked and was this used to confirm that the mice were receiving the expected\ndose of the FOS/GOS or probiotic?\n7. Mice pups appeared to show increased body weight gain during the suckling period when dams were fed High-fat/High-\nsugar diet. It there a potential explanation for this effect through the milk given the same control diet was used during\npregnancy?\n8. In supplementary figure 1D the food and energy intake tend to be higher in High-fat/High- sugar diet fed mice given\nFOS/GOS in their drinking water. Are there any suggested reasons for this?\n9. The body weight gain appears higher in mice supplemented higher in FOS/GOS? Is this due to increased cecum weight\nand intestinal length? This could be included as a discussion point as this physiological effect of fermentable fibre in mice is\nquite significant as this difference (Supplementary Figure 2A) appeared to persist after the mice were returned to the control\ndiet.\n10. It is stated that “early-life HFHS diet reduces the abundance of Bifidobacterium genus” and that “restored by FOS+GOS\nadministration”. But it is not mentioned that the HFHS diet is deficient in any fermentable fibre that can be metabolised by\nBifidobacterium. While the D12451 control diet is lacking in added fibre it contains a lot of corn starch that can act as\nresistant starch and is a fermentable carbohydrate to Bifidobacterium. It could be mentioned that adding FOS+GOS to the\ndiet is restoring the loss of any fermentable carbohydrate in the HFHS diet.\n(Remarks on code availability)", "reviewer_3": "The paper investigates the effects of early exposure to a high fat high sugar diet on feeding behavior, blood and brain\nneurochemistry. The potential therapeutic effect of microbiota-based interventions during and after diet exposure are also\ntested. These microbiota-targeted interventions improved feeding behavior, brain neurochemistry and restore some altered\nblood metabolomics parameters. Although it is unclear if changes are prevented by the interventions during diet exposure or\nif restoration / normalization happen after diet exposure. Similarly, the dietary switch happened at PND35, blurring the\ndistinction between early-lie exposure and post-weaning /adolescence exposure\nAdditional comments:\nEnglish needs to thoroughly edited in the abstract\nThe introduction gives a broad overview of the homeostatic and hedonic systems involved in regulating feeding, a more\nfocused introduction on the perinatal influence and the role of the microbiota in modulating both systems (and how they are\nconnected) would be more appropriate.\nThe rationale for focusing on the hypothalamus is not well justified as microbiota has been shown to influence vagal /NTTS\nsignaling in the homeostatic regulation of feeding and the references provided show effect of the reward areas (NAc and\nVTA), which are also under vagal influence. Data presented in the supp materials also show a strong effect on meal size,\nagain more a vagal /NTS regulated behavior. Maternal studies have shown strong effect on hypothalamic factors but these\nare not cited in the manuscript.\nFig1\n• Text refers to figure 2 panels that are actually figure 1\n• Preference for saccharin may be related to different threshold in sweet perception, as HF feeding has been shown to alter\nsweet testing, this should be mentioned\n• Does food crumbling directly correlate to food intake? If this is the case, the observed reduction in crumbling may be result\nof the overall reduction in intake. It may also be reflective of exploratory behavior or stress\n• Results state that control and treated group have a higher preference for saccharin (males), this appears to contradictory to\nthe data displayed on the graph?\nThe offspring are only exposed to the HFHS diet for a limited period of time, after which their body weight normalized. This is\na key piece of data as it supports that observed changes in adulthood are not due to differences in food intake or adiposity.\nBody weight and food intake data should be included in the main results.\nIt is still unclear however whether the effects observed are due to 1) changes in milk composition during lactation or 2)\nchanges in feeding during that period or 3) effects post-weaning. This limitation should be addressed.\nSimilarly, despite the interventions having beneficial effects on hypothalamic transcriptomics, the animals appear to overeat\nwhen exposed to the HFHS diet. This should be discussed, it is unclear whether the changes are prevented by the\ninterventions or reversed post- diet exposure.\nMinor\nLine 11 -References are different format\nLine 30 – Vagal afferents do not terminate in proximity to the median eminence or the hypothalamus. Ref 42 focuses on the\nNAc and is out of place here\nLine 30 – Implication of the microbiota in reward driven feeding should include Kim et al. Molecular Metabolism, 2023\nSupp. Food intake should be presented in kcal not grams as diets have different kcal density\nWas any power analysis conducted ? Omics outcomes display high variances, and the groups may be underpowered\n(Remarks on code availability)", "reviewer_4": "This paper explores the effects of early-life high-fat/high-sugar (HF/HS) diet and microbiota modulations by a prebiotic mix or\nputative probiotic on later life feeding behavior, microbiota composition, and transcriptomic differences in the arcuate\nnucleus of the hypothalamus. Additionally, it explores the differences in male and female offspring showing that the effect of\nearly-life HF/HS diet is sex dependent.\nThe manuscript adds interesting results to the existing literature about early-life diet alterations impact on later life but\naddresses it with the male/female difference point of view (which is often left out).\nThe methodology is clearly explained and stated, to the exception of a few minor details, and allow reproducibility of the\nexperiments conducted.\nThe graphs are clear for the most part and the graphical abstract/protocol visual description help with the comprehension.\nI enjoyed the statistical approach taken (difference between the two diet – vehicle groups followed by measure of the\nrestoration of the parameter by the microbiota intervention), it was a nice way of analysing the data, that should be used\nmore often.\nThe introductiona and discussion places the article in context with good reference to current literature. These two sections\nare very clear and nice to read.\nHowever, some significant changes are required before publication are listed below.\nOverall, a re-organisation of the figures and a proper labeling of them throughout the manuscript are a needed change as it\nwas difficult to match the figure number with the number in the text.\nOverall comment\n- Reorder the figures and supplementary figures to make them appear in a logical order either in the text or in the figure files.\nHaving them so disordered makes it hard to follow the manuscript. Additionally in the text, some figures are misnumbered\n(ex: Fig 2B (line 468) is actually Fig 1B).\n- Statistics need to be described in a fairer and clearer way. While the statistical tests are really adapted to represent the\nrestoration by both interventions, sometimes the changes induced by HFHS vehicle are not significant compared to the\ncontrol vehicle group, but the restoration effect is significant. While this non significance doesn’t take anything away from the\ntrend/biological effect, it is sometimes stated clearly and sometimes not in the text. A more consistent depiction of the\nstatistics would benefit the manuscript.\nFor example:\no line 490 “Interestingly, both the prebiotic FOS+GOS and B. longum APC1472 restored the higher 491 preference towards\nsaccharin in adult male mice that were exposed to an early-life HFHS”, the difference between the control vehicle group and\nthe HFHS vehicle group is not significant (p=0.12) although the restoration effect by both microbiota interventions are.\nAbstract:\nThe abstract could benefit from breaking down some of the sentences into multiple sentences as it is hard to follow as it is\nwritten now.\nMaterial and methods\n“Animals, diets and ethical approval”\n- Indicate how many dams were housed per cage\n- Indicate if inside of a same treatment group offspring were mixed after weaning to avoid cage effect\n- Figure 1A and S1B are both very clear and helpful but redundant, chose one to keep in the manuscript\nBioinformatics & statistical analysis\n- “Additional random effects (including 426 litter and cohort effects) were assessed using linear mixed-effects models using\nthe lmer function in R” - I couldn’t find the results of these tests in document S2. If it is not, please add it, if it actually is\ncontained in this file, please make it clearer so the reader can find the information.\nResults\n- Please add a sentence to describe Fig S1 E and F\n- Line 459 “Interestingly, we found that early-life HFHS-exposed offspring did not consume all food removed from the food\nhopper but crumbled part of it” - - Please show data for this claim if possible as the graphs represent cumulative consumption\nand crumbling behaviour.\n- Fig S4B: harmonize the serotonin names between the text and the figures + discuss the 9 graphs that are not mentioned in\nthe text.\n- Line 503 to 512, please re-write this section to make the differences between the IpGTT results in male and female clearer.\n- Line 511, for the basal glucose graph description, “restored” has been used in the rest of the manuscript and should be\nused here as well to keep the results clear and consistent as it is going back to control vehicle group levels. For the males,\nplease add additional statistics and discussion of the effect of FOS+GOS vs control vehicle on basal glucose.\n- Line 517: what about the other organs in Fig S5?\n- Line 522 to 536: please mention the alpha-diversity results\n- FigS3D to I are not described\n- Line 577, there is no mention on the effect/absence of effect of B. longum APC1472 on the males’ metabolome.\n- For the section “Early-life HFHS has an enduring and sex-specific impact on hypothalamic transcriptomic profile in adult\nmice”, it would be interesting the mention the gene commonly restored by both male and female FOS+GOS supplementation\nin fig.3D\n- Line 647, the difference is not significant for Lepr\n- Line 651, Figure 4F doesn’t exists (or I couldn’t match the text with the figures)\n- Line 669: It is only true for females\n- Line 669: the POMC+ Ghsr expressing cells count graph doesn’t exist (or I couldn’t match the text with the figures)\n- Line 692: not significant for Pnoc\n- In the section “Enduring molecular impact of an early-life HFHS diet on PNOC GABAergic neurons and Nod2-expressing\ncells in the arcuate nucleus of the hypothalamus of adult male and female mice” please harmonize the writing conventions of\nthe genes expressed and cell populations, this section is very confusing for the reader.\n(Remarks on code availability)\nThe code was not provided so I could not review it"}, "author_response": "We appreciate the time and effort that the reviewers have dedicated to providing valuable\nfeedback on our manuscript. We are grateful to the reviewers for their insightful comments\non our paper.\nWe have been able to incorporate changes to reflect most of the suggestions provided by\nthe reviewers. We also have performed significant number of additional works:\n1. Analysis of short-chain and branched-chain fatty acids in the caecum of 5-week and\n12-week-old female and male mice.\n2. Microbial functional potential analysis using PICRUSt2 to generate KEGG orthologue\npredictions, pathway filtering and analysis focused on metabolism of significantly\naltered metabolites, and computation of Gut-Brain Modules (GBMs) in 10-week-old\nmice.\n3. Integrated pathway analysis incorporating differentially abundant circulating\nmetabolites and differentially expressed genes in the hypothalamus using\nMetaboAnalyst to identify modulated pathways.\n4. ASV-level analysis to examine strain-specific changes within the Bifidobacterium\ngenus between Control Vehicle and HFHS B. longum APC1472 female and male\nmice at 5 and 10 weeks of age.\n5. Abundance analysis of all Bifidobacterium species identified in fecal samples (B.\npseudolongum, B. longum, and B. breve) across treatment groups in 5-week and 10-\nweek old mice.\n6. Analysis and plotting the daily drink intake throughout the study.\n7. Analysis of distance traveled and time spent in the external zone (10%) of the open\nfield test to assess thigmotaxis and exploratory behavior.\nWe have submitted the new version with the manuscript with tracked changes, following the\njournal guidelines. In addition, we have updated the access code to the repository but they\nare not publicly available yet.\nA point-by-point response to the reviewers’ comments can be found below.\nReviewer #1\n(Remarks to the Author):The work from Cuesta-Marti et al., explores the enduring effects of\nearly life high-fat/high sugar diet exposure on feeding behavior. They find that HFHS diet\nindeed have lasting effects on feeding behavior in sex dependent manner, and this effect\ncan be attenuated by prebiotic mixture (FOS+GOS) or B. longum administration. The\nfindings are novel, and very impactful with clinical implications. The conclusions are based\non sound experimental design. However, I have a few concerns and suggestions for clarity:\n1. FOS+GOS and B. longum has enduring effects on feeding behavior for both female and\nmales mice but the effects are not necessarily through same mechanisms, authors do\nnot discuss or explore what would be the discrepancies in the mechanisms. We see\nmore changes in female mice in metabolome and transcriptome in the brain however\nthere are some missing connections to provide information how these affects connects\nfrom gut to brain. For that, there are couple things to consider:\n1.1. Is B. longum observed in the intestine, cecum, colon, small intestine even? Are they\npassengers in the GI tract that’s why they are found in the feces abundantly?\nWe thank the reviewer for these insightful questions regarding B. longum localization, and\ncolonization patterns following administration.\nAs shown in Fig 1H (Fig 1J now), administration of B. longum APC1472 increases the\nrelative abundance of B. longum species in fecal samples in 5- and 10-weekold male and\nfemale offspring, which might indeed suggest they are predominantly passengers or\ncolonize transiently. To date, we have not performed extensive colonization studies for our\nB. longum APC1472 strain. Therefore, we cannot conclude whether its effects are through\n(transient) colonization. However, there is ample evidence that colonization is not required\nfor beneficial effects of probiotics for the host (ISAPP, 2022). Tofani et al. (2025)\nadministered a single dose of the probiotic strain, L. reuteri ATCC PTA 6475 and showed to\nmodulate corticosterone release (Tofani et al. 2025).\nDifferent strains of B. longum have been shown to persistently or transiently colonize the\nentire gastrointestinal tract – including the small intestine, cecum, and colon—with the\nhighest abundances typically detected in the colon (Chaplin et al., 2015; Lv et al., 2015;\nGutierrez et al., 2023). For instance, B. longum BBMN68 colonized the gut of rats within one\nday of oral administration, reaching populations exceeding 10⁸ cells per gram in both cecal\nand colonic contents (Lv et al., 2015). In humans B. longum strain AH1206 persisted in the\nintestinal tract of 30 % of individuals for at least six months following a single oral\nadministration, while other B. longum strains have been detected for up to 5-10 years in\nchildren's intestines (Maldonado-Gómez et al., 2016; Chaplin et al., 2015; Shkoporov et al.,\n2013).\nTo provide context regarding gut colonization of B. longum and its relevance for host effects,\nwe have added this in the introduction (lines 53-55).\nThe ISAPP quick guide to probiotics for health professionals: History, efficacy, and safety. ISAPP\nhttps://isappscience.org/for-clinicians/resources/probiotics/ (2022).\nTofani, G. S. S. et al. Gut microbiota regulates stress responsivity via the circadian system. Cell\nMetab. 37, 167-185.e8 (2025).\nChaplin, A.V. et al. Intraspecies Genomic Diversity and Long-Term Persistence of Bifidobacterium\nlongum. PLOS ONE 10(8): e0135658 (2015).\nLv, Y. et al. Biodistribution of a Promising Probiotic, Bifidobacterium longum subsp. longum Strain\nBBMN68, in the Rat Gut. J Microbiol Biotechnol. 25(6):863-71 (2015).\nGutierrez, A. et al. Bifidobacterium and the intestinal mucus layer. Microbiome Res Rep. 2(4):36\n(2023).\nMaldonado-Gómez, M.X. et al. Stable Engraftment of Bifidobacterium longum AH1206 in the Human\nGut Depends on Individualized Features of the Resident Microbiome. Cell Host Microbe 20(4):515-\n526 (2016).\nShkoporov, A.N. et al. Draft Genome Sequences of Two Pairs of Human Intestinal Bifidobacterium\nlongum subsp. longum Strains, 44B and 1-6B and 35B and 2-2B, Consecutively Isolated from Two\nChildren after a 5-Year Time Period. Genome Announc. 1(3):e00234-13 (2013).\n1.2. The mechanism of action is not clear. It does not shift microbiome composition. Does\nit change the metabolome in the microbiome? An overtime abundance analysis would\nconfirm whether their abundance is increased. How does B. longum restores genes\nin the brain unclear. Through metabolite production? But then it needs to be\nconfirmed that it colonizes because it seems like it does not change the microbiome\ncomposition.\nWe thank the reviewer for these insightful questions.\nWith regards to the microbiome composition, we apologize for oversimplification of our\nfindings. We have now revised the section to highlight that while early-life HFHS diet did not\ninduce persistent changes in alpha and beta diversity in adult male and female mice (10\nweeks), we observed differences at genus and species level, some of which were sex-\nspecific (lines 649-664 and 730-748, and Supplementary Table S7).\nWe have only undertaken blood metabolomic analysis in adult (12 weeks) mice because\nthis is the timepoint most closely aligned with our behavioral tests. However, since changes\nin week 5 metabolome may indeed have long-term effects, we have now undertaken\nfunctional analysis (PICRUSt2 and Gut-Brain modules (GBMs), see response to point 1.4)\nof the gut microbiome (16S) at both timepoints (week 5 and 10) as well as cecal SCFAs and\nBCFAs abundance analysis at week 5 and 12 of age.\nWe have selected SCFAs and BCFAs because these microbial metabolites, particularly\nacetate and butyrate, can cross the blood-brain barrier and directly influence hypothalamic\nPOMC neurons, key regulators of appetite and energy homeostasis (Frost et al., 2014;\nPerry et al., 2016). Briefly, we found that early-life HFHS diet induced sex-specific alterations of\nSCFA/BCFA in 5-week-old pups. All analyzed SCFA/BCFA were significantly reduced by\nearly-life HFHS diet at week 5 in males while no changes were observed in females.\nFOS+GOS and B. longum APC1472 restored acetate levels in 5-week-old males.\nInterestingly, regardless of dietary alteration, administration of B. longum APC1472\nincreased acetate in male mice, and butyrate, propionate, and valerate levels in females at\n5 weeks of age. With regards to week 12, there were no significant effects of early-life HFHS\ndiet on SCFA/BCFA levels. However, FOS+GOS administration showed trends toward\nincreased butyrate, propionate, and acetate levels compared all the groups, though these\nfindings should be interpreted cautiously due to low sample size (n=2 for FOS+GOS females\nat week 12) and technical limitations (see below). We describe these findings results section\n(lines 718-729, Supplementary Fig. S5, see below) and included the methodology (lines\n410-439; statistical analysis in https://github.com/CristinaCuesta-\nMarti/earlylifeHFHS_eating_microbiota_2025/outputs/overall_stats).\nUnfortunately, technical limitations (procedural error) during sample preparation affected the\nSCFA/BCFA analysis for week 12 samples. To mitigate this, we reanalyzed the remaining\ncecal filtrate; however, the available volumes were considerably below optimal, requiring\nproportional adjustments to the internal standard. As a result, quantification accuracy and\ndata quality at week 12 were impaired. These results are therefore presented descriptively\nin the supplementary materials for completeness but are not emphasized as primary findings\nor integrated with other datasets.\nNew Supplementary Fig. S5\nWith regards to the mechanism of action of B. longum APC1472, the new SCFA/BCFA\nanalysis thus suggests these microbiota-derived metabolites to be a contributing factor to\nthe B. longum APC1472 beneficial effects. In addition, as shown in the functional\nmicrobiome analysis (see point 1.4), SCFAs were identified as one of the potential pathways\nby which B. longum APC1472 may be exerting its restorative effects. In addition, we found\nthat early-life HFHS depleted SCFA-producing genera in feces of week 10 mice, some of\nwhich were restored sex-specifically by B. longum APC1472 (lines 730-748). However, other\nmetabolites, such as tryptophan for females, may also play a role, and we have described\nthis in the discussion.\nAs discussed in point 1.1, changes in microbiota-derived metabolites can occur whether or\nnot colonization occurs. Therefore, increased detection of B. longum species in the feces\nconfirms its presence with minimal disruption to the overall microbiome composition and is\nconsistent with its potential to influence metabolite levels.\nFrost, G. et al. The short-chain fatty acid acetate reduces appetite via a central homeostatic\nmechanism. Nat. Commun. 5, 3611 (2014).\nPerry, R. J. et al. Acetate mediates a microbiome–brain–β-cell axis to promote metabolic syndrome.\nNature 534, 213-217 (2016).\n1.3. It would be good to include unpublished in vitro work where B. longum produces\ntryptophan. Did B. longum increase serotonin levels in the brain, how about\nenterochromaffin cells? It increases turnover but not necessarily serotonin levels.\nWere tryptophan levels affected in the gut. A metabolome analysis of the gut is\nneeded.\nThanks for this comment. We have included below the work showing that B. longum\nAPC1472 can produce tryptophan in vitro in late stationary phase, which has been recently\naccepted in npj Biofilms and Microbiomes and publicly available (edited from Main Figure\n1b; Cuesta-Marti et al. 2025, https://doi.org/10.1038/s41522-025-00820-9). As shown in this\nwork, cell-free conditioned supernatants (CCSs) of B. longum APC1472 harvested at late\nstationary phase demonstrated a higher relative abundance of tryptophan compared to\ncontrol (see figure below adapted from this publication, demonstrating the higher abundance\nof the late stationary phase in conditioned cell-free supernatants (CCSs)).\nRegarding the in vivo effects, B. longum APC1472 significantly increased the non-significant\nreduction in serotonin (5-HT) levels in the striatum of adult male mice following early-life\nHFHS diet exposure (updated in lines 605-611). In female mice, B. longum APC1472\nnormalized the diet-induced reduction in serotonin turnover (5-HIAA/5-HT ratio) rather than\nincreasing absolute serotonin levels. Additionally, B. longum APC1472 restored the\nalteration in circulating tryptophan levels in adult female mice.\nOur study focused on central serotonin rather than evaluating peripheral serotonin\nsynthesis. While we acknowledge that both enterochromaffin cell analysis (as the primary\nsource of peripheral serotonin) and gut metabolome profiling would provide valuable\ncomplementary data, our research prioritized the central nervous system. It is known that\nserotonin produced by enterochromaffin cells cannot readily cross the blood-brain barrier\ndue to its hydrophilic nature (Matthes et al., 2018). Additionally, our research question is\nfocused on understanding the central serotonin levels as the more direct indicator of\npotential central effects relevant to the observed feeding behaviors. Additionally, our in vitro\ndata demonstrated B. longum APC1472's capacity to produce tryptophan, and we sought to\ndetermine whether this translated to measurable central nervous system effects rather than\ncharacterizing the complete peripheral metabolic landscape.\nCuesta-Marti, C., Valderrama, B., Bastiaanssen, T. et al. In vitro assessment of bacterial\nsupernatants on hypothalamic gene expression: implications for appetite regulation. npj Biofilms\nMicrobiomes 11, 192 (2025). https://doi.org/10.1038/s41522-025-00820-9\nMatthes, S. & Bader, M. Peripheral serotonin synthesis as a new drug target. Trends Pharmacol.\nSci. 39, 560-572 (2018).\n1.4. The study needs correlation and/or multiassociation analysis to bring blood\nmetabolome and brain transcriptome together and even gut microbiome and\nmetabolome if those experiments are done. A picrust analysis of the microbiome that\nconnects blood metabolome to microbiome is needed.\nWe appreciate the reviewer's suggestion for integration analysis between our multi-omics\ndatasets to investigate potential sex-specific and intervention-specific mechanisms\nunderlying the observed behavioral improvements.\nWe have now performed integrated pathway analysis incorporating differentially abundant\ncirculating metabolites (blood metabolomics) and differentially expressed genes\n(transcriptomics) in the hypothalamus of 12-week-old female and male mice (q<0.2),\nas detailed in the updated methods section 2.14 (lines 399-409) and presented in\nSupplementary Fig. S8 and results section (lines 841-871 and 874-876). This analysis used\nMetaboAnalyst to integrate KEGG pathways that are altered by early-life HFHS diet and\nrestored by microbiota-targeted interventions in both blood metabolomics and hypothalamic\ntranscriptomics. This analysis revealed EGFR tyrosine kinase inhibitor resistance and\nmTOR signaling altered by diet in females and AMPK signaling and beta-alanine metabolism\nin males. FOS+GOS restored pyrimidine and purine metabolism in females and fatty acid\ndegradation and elongation in males. B. longum APC1472 restored pantothenate and CoA\nbiosynthesis and DNA replication in females and growth hormone synthesis, secretion and\naction as well as valine, leucine and isoleucine degradation in adult males. This further\nunderlines sex-specific and intervention-specific mechanisms.\nNew Supplementary Fig. S8\nFollowing the reviewer's recommendation, we also performed PICRUSt2 analysis and\nincorporated Gut Brain Modules (GBMs) analysis to examine predicted functional capacity\nof the microbiome in relation to neuroactive compounds. Following that, to integrate\nmicrobiota and blood metabolomics data we filtered PICRUSt2-derived pathways to retain\nonly those involved the metabolism of blood metabolites significantly altered by early-life\nHFHS diet or restored by interventions in adult mice (12-week-old). These results are\ndescribed in lines 759-770 and Supplementary Fig. S7 (see below). Further statistical\ninformation of GBMs can be found on https://github.com/CristinaCuesta-\nMarti/earlylifeHFHS_eating_microbiota_2025/outputs. The multi-omics findings have been\nalso discussed regarding implications for dietary and microbiota-targeted interventions\neffects in a sex-specific manner in the Discussion (lines 971-989).\nBriefly, these results suggest that early-life HFHS induces persistent changes in 13 gut-brain\nmodules (GBMs) for example neurotransmitter metabolism and tryptophan degradation in\n(females), as well as 5 GBMs related to propionate synthesis and neurotransmitter\nmetabolism (males) (Supplementary Fig. S7A). FOS+GOS and B. longum APC1472\nrestored 10 and 2 out of 13 GBMs, respectively, in females, and 4 and 1 out of 5 in males.\nRegarding the integrative analysis of PICRUSt2 and metabolomics, we found that the\npathways altered by early-life HFHS diet in females (13 pathways including arginine, fatty\nacid and tryptophan metabolism) and males (4 pathways including fatty acid and arginine\nmetabolism) in blood were also present and significantly altered in the functional fecal\nmicrobiota analysis (Supplementary Fig. S7B). FOS+GOS administration restored 5 out of\nthe 13 altered pathways (arginine and fatty acid metabolism) in blood and fecal microbiota\nin females and 1 out of 4 pathways (fatty acid β-oxidation) in males. Further, B. longum\nAPC1472 restored 1 pathway in both female and male (fatty acid β-oxidation) in blood\nmetabolome and fecal microbiome.\nThese integrative approaches allowed us to identify consistent changes across blood\nmetabolome, brain transcriptome, and gut microbiome data, providing mechanistic insights\ninto sex- and intervention-specific effects on the gut-brain-metabolome axis.\nNew Supplementary Fig. S7\n2. There needs some clarity in the experimental design\n2.1. Is it clear the offspring is not exposed to the HFHS diet during pre-weaning period or\nare the effects through mothers milk? The Fig 1A is not clear when does the offspring\nstart eating HFHS diet. Are there any measurements that they consume\nHFHS crumbles during postnatal period?\nWe thank the reviewer for this important clarification request. We have updated Figure 1A\nto more clearly indicate the timeline of diet exposure.\nBased on literature, mouse pups begin eating solid food around postnatal day 16, coinciding\nwith eye opening and functional vision (MGI; Bailoo et al. 2020). Therefore, we expect that\ndirect consumption of diets (solid food) may occur prior to weaning (PND21), but it was not\nfeasible to distinguish this from the dam’s food intake.\nAdditionally, our research aimed to investigate the effects of HFHS diet during the complete\nearly-life period, encompassing both the lactation period (through maternal milk) and the\nperiod when pups consume solid food independently (either pre- or postweaning). We\nintentionally selected this comprehensive early-life period, where maternal and direct dietary\ninfluences overlap because it reflects the window where early-life dietary programming\neffects occur.\nMouse Genome Informatics (MGI). THE POSTNATAL PERIOD. Available at:\nhttp://www.informatics.jax.org/silver/chapters/4-4.shtml.\nBailoo, J. D., Voelkl, B., Varholick, J., Novak, J., Murphy, E., Rosso, M., Palme, R. & Würbel, H.\nEffects of weaning age and housing conditions on phenotypic differences in mice. Sci. Rep. 10,\n11998 (2020).\n2.2. What is the composition of mothers milk during postnatal period? Does B. longum\ntravel through mothers milk?\nWe appreciate the reviewer's question regarding maternal milk composition and potential\nbacterial/prebiotic transfer. The scope of the current study however was to investigate\nwhether exposure to HFHS throughout the entire early life period (lactation and direct dietary\nexposure) affected adult feeding behavior, and whether this is sensitive to microbial\nintervention.\nWe agree that understanding the composition of maternal milk and the potential transfer of\nB. longum during the postnatal period is an interesting and relevant question. We had\nconsidered collecting breastmilk as it would indeed be a potential route of transmission.\nHowever, our study design did not allow us to collect mothers milk as damns were\neuthanized at PND22-23 where pups had been weaned and transitioned to solid foods, and\ndams were no longer in active lactation. Therefore, we can not examine whether the\ncomposition of the mother milk is affected by treatments (it is likely it was) or whether B.\nlongum or FOS+GOS may be transferred through this mechanism. We have added this\nlimitation to the Discussion section (lines 1129-1139).\nRegarding the possibility of B. longum APC1472 transfer through maternal breastmilk, while\nwe cannot directly assess this in our study, researchers at APC Microbiome Ireland have\ndemonstrated that Bifidobacterium strains, including B. longum, can indeed be transmitted\nfrom mother to infant via breast milk (Feehily et al., 2023). Additionally, an earlier\nrandomized controlled trial confirmed that Bifidobacterium strains, including B. breve, can\ntransfer from maternal gut to breast milk and subsequently to the infant gut (Moore et al.,\n2023). Other studies found maternal-to-infant transmission of bacterial strains belonging to\ndifferent genera including Bifidobacterium through breastfeeding (Murphy et al., 2017;\nMartín et al., 2012).\nFeehily, C., O’Neill, I. J., Walsh, C. J., Moore, R. L., Killeen, S. L., Geraghty, A. A. et al. Detailed\nmapping of Bifidobacterium strain transmission from mother to infant via a dual culture-based and\nmetagenomic approach. Nat. Commun. 14, 3015 (2023).\nMoore, R. L., Feehily, C., Killeen, S. L., Yelverton, C. A., Geraghty, A. A., Walsh, C. J. et al. Ability\nof Bifidobacterium breve 702258 to transfer from mother to infant: the MicrobeMom randomized\ncontrolled trial. Am. J. Obstet. Gynecol. MFM 5, 100994 (2023).\nMurphy, K., Curley, D., O’Callaghan, T. F., O’Shea, C-A., Dempsey, E. M. & O’Toole, P. W. et al.\nThe composition of human milk and infant faecal microbiota over the first three months of life: a pilot\nstudy. Sci. Rep. 7, 40597 (2017).\nMartín, V., Maldonado-Barragán, A., Moles, L., Rodríguez-Baños, M., Del Campo, R. & Fernández,\nL. et al. Sharing of bacterial strains between breast milk and infant feces. J. Hum. Lact. 28, 36–44\n(2012).\n2.3. Are there any changes in dams rearing behavior between treatment groups and\ncontrol groups that might affect feeding behavior?\nWe did not assess dam rearing behavior as part of our study design. While we acknowledge\nthat maternal rearing behavior could potentially influence offspring feeding behavior,\nsystematically assessing dam behavior during the lactation period would have presented\nsignificant methodological challenges and potentially introduced confounders for our study\nobjectives.\nSpecifically, assessment of maternal rearing behavior would require repeated handling and\nseparation of dams from pups during the lactation period which is often used in animal\nmodels as postnatal stressor and can alter both maternal care patterns and offspring\nneurodevelopment (Rombaut et al., 2011). For example, maternal separation during the\nlactation period produces long-lasting alterations in offspring behavior, including altered\nfeeding behavior (de Souza et al., 2020), increased anxiety and depressive-like behaviors\n(O'Mahony et al., 2011), as well as disrupted stress responses and cognitive function\n(Rincel & Darnaudéry, 2020).\nRombaut, C., Roura-Martinez, D. & Lepolard, C. Brief and long maternal separation in C57Bl6J mice:\nbehavioral consequences for the dam and the offspring. Psychopharmacology 214, 71–88 (2011).\nde Souza, J. A. et al. Early life stress induced by maternal separation during lactation alters the\neating behavior and serotonin system in middle-aged rat female offspring. Pharmacol. Biochem.\nBehav. 198, 172908 (2020).\nO'Mahony, S. M., Hyland, N. P., Dinan, T. G. & Cryan, J. F. Maternal separation as a model of brain-\ngut axis dysfunction. Psychopharmacology 214, 71–88 (2011).\nRincel, M. & Darnaudéry, M. Maternal separation in rodents: a journey from gut to brain and\nnutritional perspectives. Proc. Nutr. Soc. 79, 113-132 (2020).\n2.4. Why is the reason the administration of prebiotic mixture and B. longum continues\neven after switching to control diet. If the reason looking at early life exposure,\nshouldn’t they also be stopped? It does not necessarily meant early life exposure\nbecause the treatment continues to adulthood.\nWe thank the reviewer for requesting this important clarification regarding our intervention\nprotocol. The \"early-life exposure\" component of our study refers specifically to the HFHS\ndiet exposure during the critical developmental period, not to the putative probiotic/prebiotic\nintervention. As now noted in our revised manuscript, our study design maintained the B.\nlongum APC1472 and FOS+GOS administration throughout the experimental period to test\nthe hypothesis that B. longum APC1472 or FOS+GOS have the potential to improve early-\nlife HFHS-induced alterations. Therefore, we administered throughout life as this would\nprovide the largest window of opportunity encompassing both neuronal development\n(prevention) and adulthood (restoration). Whether administration during select periods of life\n(early-life or post-weaning) also exerts similar effects will need to be investigated in future\nstudies, now that it is evident that life-long consumption has beneficial effects. We included\nthis limitation in the Discussion section of the manuscript (lines 1129-1139).\n2.5. Why aren’t there any prebiotic+B. longum group? What about synbiotic effects of\nFOS+GOS+ B. longum.\nWe appreciate the reviewer's interest in a combined prebiotic FOS+GOS and B. longum\nAPC1472 group. This was a deliberate experimental decision to first establish the individual\nefficacy and mechanisms of each intervention to improve early-life HFHS-induced\nalterations before investigating their synergistic effects.\nThe analysis of the individual interventions allows us to first attribute effects to specific\ninterventions without confounding synergistic interactions. Next, it allows us to investigate\nthe distinct pathways through which each intervention acts independently.\nThe data from this foundational study will inform the design of future synbiotic investigations,\nincluding our planned follow-up investigation that will specifically examine the synbiotic\neffects of FOS+GOS+ B. longum APC1472 combination.\n2.6. Was the group housing done randomly in the weaning period or did siblings go to the\nsame cage?\nWe thank the reviewer for this methodological question. Our randomization occurred at the\ndam level at postnatal day 0 (PND0), immediately after delivery. Dams were individually and\nrandomly assigned to experimental groups using a simple randomization procedure.\nSpecifically, each dam was allocated sequentially to an experimental group by drawing from\na shuffled list of group identifiers (Control Vehicle, HFHS Vehicle, HFHS+FOSG+GOS,\nHFHS+B. longum APC1472). In cases where target sample size was achieved, the next\nrandomly assigned group was selected.\nAfter weaning (PND21), offspring were group-housed by sex with their littermates within\ntreatment groups. Random allocation of treatment groups after weaning was not possible\nsince the offspring were already assigned to their respective treatment group from birth. We\nhave now edited section “2.1. Animals, diets and ethical approval” to clarify the reviewer’s\nquestions.\n2.7. There will be cage to cage or cohort to cohort difference in the microbiome analysis,\nwas that considered?\nIn our initial analyses, cage-to-cage and cohort-to-cohort variability were not explicitly\nmodeled as random effects. To address this, we have re-run the statistical analyses using\nlinear mixed-effects models (lmer function in R) that incorporated cage/litter and cohort as\nrandom factors.\nThe variance partitioning from these models indicated that cage and cohort effects\ncontributed modestly to the overall variance across the different comparisons, with residual\nvariance remaining the primary source of variation in most models. Importantly, accounting\nfor these random effects did not substantially alter our main findings regarding diet and\ntreatment effects.\nThe results for statistical analysis including cage and cohort effects are now available at the\nfollowing repository: https://github.com/CristinaCuesta-\nMarti/earlylifeHFHS_eating_microbiota_2025/outputs/overall_stats_lmer and\nhttps://github.com/CristinaCuesta-\nMarti/earlylifeHFHS_eating_microbiota_2025/BINC_PROTECT_16S and we have updated\nthe methods section \"2.17.1. 'Omics datasets\" accordingly.\n2.8. How many dams per group was recruited in the beginning to reach offspring n=12\nper group?\nWe have now updated “2.1. Animals, diets and ethical approval” to clarify that we utilized 7\ndams per group to reach the sample size at endpoint. In line 86-102 it states: “Dams were\nmated and individually housed at embryonic day 17. At day of birth (PND0), dams were\nrandomly assigned to experimental groups using a simple randomization procedure (7\ndams/group, aiding to avoid any cage/litter effects). […]. After weaning (PND21), offspring\nwere group-housed by sex with their littermates within treatment groups and […]. At PND35,\n5-week-old offspring were euthanized depending on sex and litter size to keep 2-3 mice per\nlitter/sex (on average) to reach the target sample size n=12 per group at 12 weeks of age.\"\n3. Authors only address Euclidean distance in beta-diversity. Whereas Euclidean is not\nenough for compositional data due to its inability to account constant sum. Therefore\nAitchison distance that takes log-ratio transformation into account is recommended and\nthis will give more information about prevalence. In addition, weighted unifrac for\ncalculating beta diversities will give information about abundance based on phylogenetic\ntaxa observed. In addition, authors should discuss alpha diversity in the context of\nspecies evenness and richness. This is important especially when B. longum\nadministration does not increase the number of total bifidobacterium but B. longum\nnumbers increase in feces.\nWe thank the reviewers for the thoughtful comment. We agree with the suitability of Aitchison\ndistance to account for the compositional nature of -omics datasets. Indeed, we used\nAitchison’s distance which incorporates Euclidian distance over CLR-transformed data in\nour analysis and we have clarified this in the Methods Section “2.17.1.‘Omics datasets).\nFor a discussion, please refer to this relevant review\nhttps://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2017.02224/full\nand the documentation of the R package vegan, which was used to calculate the Aitchison’s\ndistance in our article: https://rdrr.io/cran/vegan/man/vegdist.html.\nRegarding alpha diversity, we have included comprehensive analysis of species richness\n(Chao1 index) and evenness (Shannon and Simpson indexes) in the Results section (lines\n649--654), to highlight the diet and intervention effects. We have included in discussion the\npoint regarding the increase of B. longum species without impacting in overall\nBifidobacterium genus abundance in feces by the putative probiotic administration (lines\n1007-1010).\n3.1. Authors don’t discuss that B. longum administration does not necessarily increase\ntotal Bifidobacterium abundance whereas it increases in B. longum. That seems like\na potential competition between other Bifidobacterium and B.longum or B.longum\nfound in feces not necessarily colonized in the gut. See my earlier comment on this\nsuggestion.\nWe have now included additional context regarding the effect of B. longum APC1472\nadministration on microbiome composition, particularly regarding the lack of effect on\nBifidobacterium genus abundance (lines 985-987 and 1007-1010). We agree that this\nfinding likely reflects strain-level dynamics where the introduced strain competes for specific\nniches with other strains without dramatically altering overall genus abundance. This is\nconsistent with a (potentially transient) colonization of the gut by B. longum APC1472.\nIt is indeed not surprising that we did not observe differences at genus level upon treatment\nwith a probiotic strain from the same genus. In this line, recent research by Khatib et al.\ndemonstrates genus-level stability can mask species- and strain-specific changes when\nanalyzed at higher taxonomic levels (Khatib et al., 2025). Additionally, even within species,\nat a finer taxonomic level, there can be differential abundance patterns across time and sex.\nWe have now performed Amplicon Sequence Variant (ASV)-level analysis of ASVs within\nthe B. pseudolongum species (within Bifidobacterium genus) to better illustrate these sub-\nspecies dynamics (see figure below). This demonstrates for example, that in control animals\nASV2 remains stable in females over time whereas it decreases in males. Contrarily, ASV56\nabundance reduces in both sexes over time in control mice. In mice treated with B. longum\nAPC1472, neither ASV2 nor ASV56 showed any changes across time or sex.\nThis analysis reveals that even within a single species, individual ASVs can show differential\nand sometimes opposing abundance patterns within a group and timepoint, confirming that\ngenus-level measures can mask important species- and strain-level dynamics and\ncompetitive interactions within Bifidobacterium genus.\nKhatib, L. et al. A three-country analysis of the gut microbiome indicates taxon associations with diet\nvary by taxon resolution and population. mSystems 10, e00544-25 (2025).\n3.2. Which Bifidobacterium species were enriched during FOS+GOS administration?\nWe thank the reviewer for this important question seeking clarification on the specific\nBifidobacterium species affected by FOS+GOS treatment.\nThe murine gut microbiome typically harbors limited Bifidobacterium, in both species\ndiversity and abundance (Hugenholtz et al., 2018; Lkhagva et al., 2021; Weldon et al., 2015).\nThe relative abundance of this genus typically ranges from undetectable to 1% of total\ncomposition. Among the few Bifidobacterium species normally present in mice, B.\npseudolongum and B. animalis are most commonly detected (Shin et al., 2016).\nTo address the reviewer's question directly, we have now plotted the absolute abundance\nof all Bifidobacterium species identified in our dataset by treatment group, sex, and timepoint\n(figure below). This analysis reveals three Bifidobacterium species in the offspring\nmicrobiome: B. pseudolongum, B. longum, and B. breve. Bifidobacterium breve was found\nonly sporadically in a small number of pups with almost negligible counts and showed no\napparent enrichment with FOS+GOS supplementation, as shown in the figure above.\nAs shown in the figure, the only species affected by FOS+GOS was B. pseudolongum. B.\npseudolongum was the most abundant species and showed specific enrichment in response\nto FOS+GOS supplementation. Statistical analysis revealed that early-life HFHS diet\nsignificantly reduced B. pseudolongum relative abundance in 5-week-old female (q = 0.011)\nand male mice (q = 0.0002), effects that did not persist at 10 weeks of age. FOS+GOS\nadministration restored the HFHS-induced reduction in B. pseudolongum relative\nabundance in 5-week-old females (q = 0.004) and 5-week-old males (q = 0.001). FOS+GOS\nadministration restored the non-significant reduction in B. pseudolongum relative\nabundance in 10-week-old females (diet effect, q = 0.14; FOS+GOS restoration, q = 0.025).\nThis finding aligns with previous studies demonstrating that FOS and GOS selectively\nincrease the prevalence of this genus in mouse models (Mao et al., 2018; Monteagudo-Mera\net al., 2016).\nHugenholtz, F., & de Vos, W. M. (2018). Mouse models for human intestinal microbiota research: a\ncritical evaluation. Cellular and Molecular Life Sciences, 75(1), 149-160.\nLkhagva, E., Chung, H. J., Hong, J., Tang, W. H. W., Lee, S. I., Hong, S. T., & Lee, S. (2021). The\nregional diversity of gut microbiome along the GI tract of male C57BL/6 mice. BMC\nmicrobiology, 21(1), 44.\nWeldon, L., Abolins, S., Lenzi, L., Bourne, C., Riley, E. M., & Viney, M. (2015). The gut microbiota\nof wild mice. PLoS One, 10(8), e0134643.\nShin, J., Lee, S., Go, M. J., Lee, S. Y., Kim, S. C., Lee, C. H., & Cho, B. K. (2016). Analysis of the\nmouse gut microbiome using full-length 16S rRNA amplicon sequencing. Scientific reports, 6(1),\n29681.\nMao, B., Gu, J., Li, D., Cui, S., Zhao, J., Zhang, H., & Chen, W. (2018). Effects of different doses of\nfructooligosaccharides (FOS) on the composition of mice fecal microbiota, especially the\nBifidobacterium composition. Nutrients, 10(8), 1105.\nMonteagudo-Mera, A., Arthur, J. C., Jobin, C., Keku, T., Bruno-Barcena, J. M., & Azcarate-Peril, M.\nA. (2016). High purity galacto-oligosaccharides enhance specific Bifidobacterium species and their\nmetabolic activity in the mouse gut microbiome. Beneficial microbes, 7(2), 247-264.\n4. The conclusion will need to be tailored, that could also be reflected in the graphical\nabstract. I think that authors did not pay much attention in the discussion about the\ndifferent effects/mechanisms prebiotic vs B. longum and male vs female. Early life HFHS\nhas different effects male vs female and FOS+GOS and B. longum attenuates these\neffects different for male and female mice. The abstract should reflect that finding, which\nmechanisms were found in female and which are found in male. In male mice, PNOC\nneuron and Nod2-expressing cells are altered whereas in female mice, POMC neurons\nand Ghsr and Lepr receptors are affected. “a reduction in the number of cells expressing\nGhsr, Lepr and Pomc individually in the ARC of adult early-life HFHS exposed female\nmice while Ghsr and Pomc were only reduced in adult early-life HFHS exposed males\ncompared to control.\nWe have substantially revised and updated the discussion (lines 971-989) and conclusion\n(lines 1105-1112), as well as the abstract and graphical abstract, to explicitly detail these\nsex-specific effects of early-life HFHS diet and intervention-specific mechanisms of\nrestoration. Specifically, we now highlight that in females, early-life HFHS exposure induced\nbroader hypothalamic vulnerability with reductions in GHSR+, LEPR+, and POMC+ cells in\nthe ARC, alongside extensive disruption of tryptophan metabolism, arginine metabolism,\nand developmental programming pathways. In contrast, males primarily showed reductions\nin PNOC+, and NOD2+ cells in the ARC, with disruptions in peptidoglycan sensing pathways\nand neurotransmitter metabolism.\nFurthermore, we now clearly delineate the differential mechanisms of the two interventions\n(lines 978-989). Whilst there were sex-specific effects, FOS+GOS acted primarily through\nextensive microbiome compositional shifts (restoring multiple gut-brain modules and SCFA-\nproducing bacteria), while B. longum APC1472 induced minimal microbiome compositional\nchanges but achieved robust and lasting improvements in feeding behaviors, which we\nhypothesize occurs through restoration via targeted metabolic pathway, for instance via\ntryptophan metabolism in females. These intervention- and sex-specific findings are now\nhighlighted throughout the revised manuscript.\n5. Authors should also focus on the mechanisms of FOS+GOS and B. longum more\nclearly. Because they have different effects on the HFHS induced alterations however\nwhether the mechanisms are different is unclear. It seems like FOS+GOS makes shifts\nin the microbiome composition whereas no clear shifts were observed for B. longum\nhowever B. longum displays more lasting effects. This can be also addressed by looking\nat metabolomics in fecal or cecal samples.\nWe thank the reviewer for this insightful observation regarding the distinct mechanisms\nunderlying FOS+GOS versus B. longum APC1472 interventions. To further substantiate the\ndifferential mechanisms of action between FOS+GOS and B. longum APC1472, we have\nconducted extensive additional analyses of our existing data and conducted SCFA/BCFA\nanalysis in cecal samples (week 5 & 12) (Supplementary Fig. S5). Additionally, we have (1)\nanalyzed the functional capacity of the microbiome using PICRUSt to investigate gut-brain-\nmodules and common pathways of altered/restored metabolites in blood (Supplementary\nFig. S7) and (2) integrated hypothalamic transcriptomics and blood metabolomics, that\nprovide more mechanistic clarity for each microbiota-targeted intervention in a sex-specific\nmanner (Supplementary Fig. S8).\nFOS+GOS induced substantial microbiome compositional shifts, restoring multiple gut-brain\nmodules (10 in females, 4 in males) and SCFA-producing bacteria, which aligned with broad\nmetabolic pathway restoration. In contrast, B. longum APC1472 induced minimal\nmicrobiome compositional and functional (1 GBM in both sexes) changes but achieved a\ngreater lasting improvement in feeding behaviors in adult females and males. Our integrative\nanalyses revealed that this likely occurs through targeted restoration of specific metabolic\npathways, including tryptophan metabolism and energy homeostasis pathways in adult\nfemales, and through DOPAC-dopaminergic pathways and metabolic homeostasis (AMPK\nsignaling, glucagon signaling) in males. We have included these additional analysis in\nresults (lines 718-770 and 841-871) and discussion (lines 971-989).\n6. There are some typos listed:\n Line 36 not symbiotics, synbiotics..\n Line 644 should be POMC. Typo\nWe thank the reviewer for identifying these typographical errors. We have corrected both\ninstances.\nReviewer #1 (Remarks on code availability):\nthe code is not deposited to github\nWe thank the reviewer for this comment. We have updated the \"Code Availability\" section\nof the manuscript to include the GitHub URL where all the code can be accessed. The\nrepository now contains all scripts used for statistical analyses, data processing,\nvisualization and the new integrative multi-omics analyses including the multi-association\nanalyses between blood metabolome and brain transcriptome, and gut microbiome data and\nblood metabolome.\nReviewer #2\n(Remarks to the Author):\nThis is a well-written paper and a comprehensive study design that included four\nexperimental groups, two microbiota-targeted interventions (prebiotic, probiotic) and two\ndietary control groups, and multiple analyses (including behavioral, metabolomic,\ntranscriptomic, microbiota profiling, and fluorescent in situ hybridization). The inclusion of\nboth male and female mice and consistent presentation of results for both sexes highlighting\nsex-specific effects is an important contribution and an often-overlooked factor. The\ndetailed inclusion of supplementary data including body weight and gain in both graph\nand table format and details of various tissue measures and weights is commendable. The\nmeasurement of food intake and food crumbling also adds to the precision of the food intake\nrecording.\nThe quality of the data appears technically sound and is presented in detail. The conclusions\nmade are not excessive and are supported by the evidence provided. These findings are\nrelevant and add to the evidence for early life effects of diet on later food intake behaviors\nin mice while the sex differences shown highlight the importance of including both male and\nfemale mice and reporting their data separately.\nComments\nThere are some points that could improve the manuscript:\n1. The control diet used is detailed as “D12451 modified (Product N S9912-E760” but\nthe modifications made to the D12451 diet are not clear. Can these modifications be\nclarified.\nWe thank the reviewer for this important clarification request. We have now clearly specified\nthe modifications that were made to the D12451 diet (lines 92-93).\nThe HFHS diet used in our study was a modified version of the D12451 diet, with specific\ncompositional changes requested to achieve our desired macronutrient profile. According to\nthe manufacturer (Ssniff Spezialdiäten GmbH, Germany), the modifications made to the\nstandard D12451 formulation included a reduction of sucrose from 20.17% to 17% (w/w)\nand a corresponding increase in corn starch from 7% to 10.17% (w/w) to compensate for\nthe sucrose reduction.\nThe 17% sucrose content was specifically selected to align with established HFHS diet\nformulations widely used in the literature investigating metabolic programming and obesity-\nrelated outcomes. This composition has been validated in numerous studies examining\nearly-life dietary effects, including investigations of metabolic dysfunction (Mishra et al.,\n2022; Pennington et al. 2017), gut microbiome alterations and feeding behavior (de Wouters\nd’Oplinter, et al. 2021, 2023).\nWe have also corrected the diet description terminology in our methods section “2.1.\nAnimals, diets and ethical approval” (lines 90-91) to specify \"kcal%\" rather than \"%\" when\nreferring to the energy contribution from macronutrients, as this more accurately reflects the\ncompositional data presented in Supplementary Table 1.\nPennington, K. A., et al. Effects of acute exposure to a high-fat, high-sucrose diet on gestational\nglucose tolerance and subsequent maternal health in mice. Biol. Reprod. 96, 435–445 (2017).\nMishra, A., et al. A. A novel model of gestational diabetes: Acute high-fat, high-sugar diet results in\ninsulin resistance and beta cell dysfunction during pregnancy in mice. PLoS One 17, e0279041\n(2022).\nde Wouters d’Oplinter, A., et al. Gut microbes participate in food preference alterations during\nobesity. Gut Microbes 13, 1959242 (2021).\nde Wouters d’Oplinter, A., et al. A. Obese-associated gut microbes and derived phenolic metabolite\nas mediators of excessive motivation for food reward. Microbiome 11, 94 (2023).\n2. How was the probiotic dosing decided upon? The dosage used in drinking water is\nbased upon that used in previous research and adapted for the offspring but how was\nthis adapted for the offspring?\nWe thank the reviewer for this important question regarding our probiotic dosing strategy.\nThe probiotic dosing regimen was designed using body weight scaling principles alongside\nwater consumption patterns, with practical considerations for long-term study\nimplementation. The maternal dose (~1.0×10⁹ CFU/animal/day for dams weighing an\naverage of 29g dams) and offspring dose (~5.0x108 CFU/animal/day) were calculated based\non both body weight scaling and average daily water intake to provide comparable exposure\nper gram body weight when pups began direct supplementation of the bacterial strain at 5-\n10g.\nThese doses were selected based on previous studies from our own laboratory and others\nin which a dose of 10⁸-10⁹ CFU/day induced both metabolic and neurobehavioral effects in\nrodents (Carasi et al., 2015; Mariman et al., 2015; Bai et al., 2016; Wang et al., 2012).\nNotably, our group has previously demonstrated that the same Bifidobacterium longum\nstrain (APC1472) improves metabolic health in HFD-fed mice (at ~2×10⁸ CFU/mL in drinking\nwater) as well as in overweight and obese humans (at 1×10¹⁰ CFU/day) (Schellekens et al.,\n2021). Furthermore, multiple probiotic strains administered at comparable doses (10⁸-1010\nCFU/day) have shown beneficial effects on metabolic parameters in both rodent models and\nin humans (Patterson et al., 2019; Schellekens et al., 2021; Gomes et al., 2017; Chang et\nal., 2011; Bernini et al., 2016).\nFor offspring, we maintained a fixed-dose approach as is commonly used in preclinical\nstudies. Since water intake mainly scales with body weight in growing mice, we modified\naccordingly the amount to drinking water in which the inoculum was resuspend to maintain\na fixed dose (CFU). We have now clarified our strategy for probiotic dose administration in\nmethods section “2.2.2. In vivo probiotic administration” (lines 166-167).\nSchellekens, H. et al. Bifidobacterium longum counters the effects of obesity: Partial successful\ntranslation from rodent to human. EBioMedicine 63, 103176 (2021).\nPatterson, E. et al. Gamma-aminobutyric acid-producing lactobacilli positively affect metabolism and\ndepressive-like behaviour in a mouse model of metabolic syndrome. Sci. Rep. 9, 16323 (2019).\nGomes, A. C. et al. The additional effects of a probiotic mix on abdominal adiposity and antioxidant\nStatus: A double-blind, randomized trial. Obesity 25, 30–38 (2017).\nChang, B. J. et al. Effect of functional yogurt NY-YP901 in improving the trait of metabolic syndrome.\nEur. J. Clin. Nutr. 65, 1250–1255 (2011).\nBernini, L. J. et al. Beneficial effects of Bifidobacterium lactis on lipid profile and cytokines in patients\nwith metabolic syndrome: A randomized trial. Effects of probiotics on metabolic syndrome. Nutrition\n32, 716–719 (2016).\n3. The methods state that mice were distributed evenly across experimental groups.\nHow were these allocations decided and was any randomisation method used? If so\nplease state the method used to allocate mice into experimental groups.\nWe thank the reviewer for this important methodological question. We have updated the\nMethods section “2.1 Animals, diets and ethical approval”, to clearly describe our\nrandomization procedure (lines 86-102).\nThe section now states: \"Dams were mated and individually housed at embryonic day 17.\nAt day of birth (PND0), dams were randomly assigned to experimental groups using a simple\nrandomization procedure (7 dams/group, aiding to avoid any cage/litter effects). […]. After\nweaning (PND21), offspring were group-housed by sex with their littermates within treatment\ngroups and […].\"\n4. Are the long-term changes observed in the arcuate nucleus relevant to later control\ndiets? This point could be mentioned in the discussion, that the changes in responses\nto food later in the study appeared to be only in response to further experimental tests\nand that body weight normalized in all groups when on control diet in the later part of\nthe study period despite earlier exposures.\nWe thank the reviewer for this important point. We did not explicitly test the effect of early-\nlife HFHS diet on food intake (chow) in behavioral tests (PheCOMP). However, our food\nintake data (daily chow intake in home cage) in adulthood suggests that there were no major\ndifferences prior to reintroduction of the HFHS diet. As can be seen in main Figure 1C, early-\nlife HFHS exposed males overall tended to eat less chow than control vehicle mice between\nweek 8 and 10 of age (adulthood). However, there was no significant difference in daily\nintake on any specific timepoint, nor any microbiota-intervention effect.\nWe have now included a sentence in the discussion (lines 963-965) to clarify that the\nmolecular changes observed in the arcuate nucleus and the altered feeding behaviors in\nadulthood occur in the absence of body weight differences between groups. As the reviewer\ncorrectly noted, body weight normalized in all groups when switched and maintained on\ncontrol diet in the later part of the study period, despite the earlier dietary exposures.\n5. Does the FOS/GOS in drinking water remain adequately dissolved and suspended\nduring the time the water is supplied? Has this been measured in a similar manner to\nthe conformation of the survival of the probiotic in the drinking water to ensure\nconsistent dosage?\nFOS (fructooligosaccharides) and GOS (galactooligosaccharides) are both readily soluble\nin water, which ensures consistent dissolution and homogeneous distribution throughout the\ndrinking water.\nTo ensure consistent dosage delivery, we implemented strict preparation and replacement\nprotocols. The prebiotic solution was prepared weekly under sterile conditions, aliquoted\nand stored at 4°C to maintain stability. Water bottles with FOS+GOS were replenished daily\nto ensure freshness and were fully changed/renewed regularly (every 2-3 days maximum)\nto prevent any potential degradation or contamination.\nThese protocols ensure that the FOS+GOS concentration and dosing remained consistent\nthroughout the study period. The high solubility of both prebiotics minimizes the potential for\nprecipitation or uneven distribution that can be associated with less soluble compounds,\nthereby supporting consistent dosing across all animals. We have updated Section 2.2.1. In\nvivo prebiotic administration in Methods to clarify this methodology.\n6. Was the amount of water the mice drink checked and was this used to confirm that\nthe mice were receiving the expected dose of the FOS/GOS or probiotic?\nWe thank the reviewer for this important question. Daily drink intake of the mice was\nmonitored throughout the study to confirm that they were receiving the expected doses of\nFOS+GOS and B. longum APC1472. We have now included this data in Supplementary Fig.\nS1B and show the graph below in this rebuttal document, displaying the daily drink intake\nby group and sex, with measurements recorded during both light and dark phases. As can\nbe seen, mice readily consumed water throughout the study regardless of treatment. If\nanything, mice tended to drink more when the drinking water contain B. longum APC1472\n(both sexes). For this reason, the amount of water containing inoculum was adapted to the\nconsumption during dark phase to ensure mice had enough water and received the desired\ndose throughout the study. Based on the average drink intake of females and males and a\nconcentration of 7.5 g/L of FOS+GOS, females received 36 mg and males 43.5 mg of\nFOS+GOS across the entire day. Statistical analysis for drink intake can be found in\nhttps://github.com/CristinaCuesta-\nMarti/earlylifeHFHS_eating_microbiota_2025/outputs/overall_stats.\n7. Mice pups appeared to show increased body weight gain during the suckling period\nwhen dams were fed High-fat/High-sugar diet. It there a potential explanation for this\neffect through the milk given the same control diet was used during pregnancy?\nWe thank the reviewer for this important observation. To clarify, the dams were maintained\non standard chow diet during the entire pregnancy. The dietary interventions (control versus\nhigh-fat/high-sugar diet) started at birth (PND0). The increased body weight gain observed\nin pups during the suckling period is most likely explained by metabolic effects transmitted\nthrough the maternal milk composition.\nBecause maternal diet is reflected in breastmilk (Aumeistere, L. et al., 2019; Ellsworth, L. et\nal., 2020), we assume that these metabolic effects in offspring are transmitted through this\npathway. However, we were unable collect milk due to our study design (dams were\neuthanized on PND23) and therefore, could not analyze its composition to conclusively\nexamine this.\nAumeistere, L. et al. Impact of maternal diet on human milk composition among lactating women in\nLatvia. Nutrients 11, 1216 (2019).\nEllsworth, L. et al. Impact of maternal overweight and obesity on milk composition and infant growth.\nMatern. Child Nutr. 16, e12979 (2020).\n8. In supplementary figure 1D the food and energy intake tend to be higher in High-\nfat/High-sugar diet fed mice given FOS/GOS in their drinking water. Are there any\nsuggested reasons for this?\nWe thank the reviewer for this important observation regarding the food and energy intake\npatterns in Supplementary Figure 1D (main Fig. 1C and Supplementary Fig. 1G now). While\nthe HFHS diet-fed mice administered with FOS+GOS showed higher food intake on certain\npunctual days during early-life compared to HFHS Vehicle females, this was not consistently\nhigher compared to other HFHS groups throughout the study period.\nThe apparent higher food intake observed on specific days in the FOS+GOS group may be\nattributed to increased food crumbling behavior rather than genuine increased consumption.\nWe observed that the FOS+GOS group exhibited more food crumbling (also observed\nduring the food preference test, Figure 1D now), which could lead to overestimation of real\nfood intake when measured by food removal from the food hoppers on those particular days.\n9. The body weight gain appears higher in mice supplemented higher in FOS/GOS?\nIs this due to increased cecum weight and intestinal length? This could be included as\na discussion point as this physiological effect of fermentable fiber in mice is quite\nsignificant as this difference (Supplementary Figure 2A) appeared to persist after the\nmice were returned to the control diet.\nWe thank the reviewer for this observation.\nFOS+GOS group indeed showed slightly higher body weight compared to the other groups\nboth the period where they were exposed to the HFHS diet and especially later in life when\nthey were switched to the control diet. Since metabolic markers were not negatively\nimpacted by FOS+GOS administration and we observed increased caecum weight and\ncolon/small intestine length (when not normalized to body weight), we ascribed this weight\ndifference to adaptive changes associated with increased fermentable fiber intake, as also\nsupported by previous literature (Bryk, G. et al., 2015; Pan, X. et al., 2009). We highlighted\nthis observation now in results (lines 639-642) where we believe it fits more appropriately\nthan in the Discussion.\nBryk, G. et al. Effect of a combination GOS/FOS® prebiotic mixture and interaction with calcium\nintake on mineral absorption and bone parameters in growing rats. Eur. J. Nutr. 54, 913-923 (2015).\nPan, X. et al. Prebiotic oligosaccharides change the concentrations of short-chain fatty acids and the\nmicrobial population of mouse bowel. J. Zhejiang Univ. Sci. B 10, 258-263 (2009).\n10. It is stated that “early-life HFHS diet reduces the abundance of Bifidobacterium\ngenus” and that “restored by FOS+GOS administration”. But it is not mentioned that\nthe HFHS diet is deficient in any fermentable fiber that can be metabolized by\nBifidobacterium. While the D12451 control diet is lacking in added fiber it contains a\nlot of corn starch that can act as resistant starch and is a fermentable carbohydrate\nto Bifidobacterium. It could be mentioned that adding FOS+GOS to the diet is\nrestoring the loss of any fermentable carbohydrate in the HFHS diet.\nWe thank the reviewer for this insightful observation regarding the fermentable carbohydrate\ncontent of the diets.\nHFHS diet does indeed have significantly lower amount of fermentable substrates. Control\ndiet (D12450K) contained 48.85% corn starch, while the HFHS diet (D12451) had only\n10.17% (Supplementary Table S1). In addition to the reduction of corn starch, HFHS diet\nhad reduced total carbohydrate content to accommodate the increase in fat content.\nTherefore, both of these factors may contribute to the reduction of bacteria like\nBifidobacterium that use fermentable fiber in addition to other carbohydrates. FOS+GOS\nsupplementation may increase not only fermentable fiber but also overall carbohydrate\ncontent of the diet.\nWe have now included this important point in our results section (lines 669-671).\nReviewer #3\n(Remarks to the Author):\nThe paper investigates the effects of early exposure to a high fat high sugar diet on feeding\nbehavior, blood and brain neurochemistry. The potential therapeutic effect of microbiota-\nbased interventions during and after diet exposure are also tested. These microbiota-\ntargeted interventions improved feeding behavior, brain neurochemistry and restore some\naltered blood metabolomics parameters.\nAlthough it is unclear if changes are prevented by the interventions during diet exposure or\nif restoration / normalization happen after diet exposure. Similarly, the dietary switch\nhappened at PND35, blurring the distinction between early-lie exposure and post-weaning\n/adolescence exposure.\nWe thank the reviewer for this important comment regarding the distinction between\nprotective versus restorative effects of our microbiota-targeted interventions.\nWe acknowledge that the dietary switch at PND35 encompasses both the early-life lactation\nperiod (birth through weaning at PND21) and the adolescent period (post-weaning through\nPND35). We selected this design to capture the entire critical developmental window during\nwhich dietary programming effects may occur. This also reflects the human reality in which\nchildren born to parents that consume an unhealthy diet will follow generally this until early\nadulthood.\nRegarding, the restoration/normalization intervention effects, we agree that our study design\nencompassing microbiome-targeting interventions throughout life makes it challenging to\ndistinguish prevention from restoration. We appreciate that the terminology used for our\nstatistical analysis (restoration effect) for cases where microbiome-targeted interventions\nsignificantly attenuated the early-life HFHS-induced alterations may be misleading in\nregards to the suggested mechanisms. We have therefore acknowledged this in the\nDiscussion section (lines 1130-1134) and clarified our terminology regarding the statistical\nanalysis in Section “2.17. Bioinformatics and statistical analysis”.\nAdditional comments:\nEnglish needs to thoroughly edited in the abstract. We have now revised and updated the\nabstract.\nThe introduction gives a broad overview of the homeostatic and hedonic systems involved\nin regulating feeding, a more focused introduction on the perinatal influence and the role of\nthe microbiota in modulating both systems (and how they are connected) would be more\nappropriate.\nWe acknowledge the reviewer's suggestion for a more focused introduction on how the\nperinatal microbiota influences feeding systems. While extensive research demonstrates\nthat gut microbiota modulates both homeostatic and hedonic feeding in adulthood and that\nthe microbiota has a critical role in host early-life development, there is currently limited\ndirect evidence specifically addressing how early-life microbiota influences homeostatic and\nhedonic feeding systems. This important knowledge gap was the key rationale for our study.\nWe have highlighted this knowledge gap in the introduction while acknowledging the current\nevidence from adult studies (lines 62-65).\nThe rationale for focusing on the hypothalamus is not well justified as microbiota has been\nshown to influence vagal /NTTS signaling in the homeostatic regulation of feeding and the\nreferences provided show effect of the reward areas (NAc and VTA), which are also under\nvagal influence. Data presented in the supp materials also show a strong effect on meal\nsize, again more a vagal /NTS regulated behavior. Maternal studies have shown strong\neffect on hypothalamic factors but these are not cited in the manuscript.\nWe thank the reviewer for this important point. We acknowledge that the microbiota\ninfluences feeding through multiple interconnected pathways, including vagal-NTS signaling\n(which regulates meal size as observed in our supplementary data) and reward circuits that\nare also under vagal influence. We have updated our introduction to better justify our focus\non hypothalamic markers by citing studies that demonstrate direct programming effects of\nmaternal diet on hypothalamic neuropeptides during early development (lines 28-30). We\nhave added the need for future studies to examine vagal-NTS pathways and brainstem\nnuclei to complement our hypothalamic findings and provide a more comprehensive\nmechanistic understanding of microbiota-mediated feeding control in our discussion (lines\n1134-1139). Additionally, we would like to highlight that we looked into dopamine, serotonin\nand noradrenalin (and their metabolites) in the striatum, a brain region involved in food\nreward, and found minor non-significant long-term effects of early-life HFHS diet\n(Supplementary Figure S2A).\nFig1\n• Text refers to figure 2 panels that are actually figure 1.\nWe thank the reviewer for this observation. We apologize for the oversight and we have\ncorrected it now.\n• Preference for saccharin may be related to different threshold in sweet perception,\nas HF feeding has been shown to alter sweet testing, this should be mentioned\nWe thank the reviewer for this excellent point. We agree that the preference for saccharin\nobserved in our study may indeed be related to alterations in sweet taste perception\nthresholds. Previous research has demonstrated that high-fat feeding as well as other\nunhealthy diets can significantly alter sweet taste sensitivity, and perception, potentially\nleading to changes in preference for sweet-tasting substances like saccharin (Choo et al.,\n2022; Bayol et al. 2007; Maliphol et al. 2013).\nWe have now included this important consideration in our discussion (lines 997-1003), as it\nprovides valuable context for interpreting the saccharin preference test results and highlights\nhow early-life dietary exposures can have enduring effects on taste and sweet perception\nand feeding preferences that extend beyond the intervention period.\nChoo, E., Wong, L., Chau, P., Bushnell, J. & Dando, R. Offspring of obese mice display enhanced\nintake and sensitivity for palatable stimuli, with altered expression of taste signaling elements. Sci.\nRep. 10, 12776 (2020).\nBayol, S. A., Farrington, S. J. & Stickland, N. C. A maternal 'junk food' diet in pregnancy and lactation\npromotes an exacerbated taste for 'junk food' and a greater propensity for obesity in rat offspring.\nBr. J. Nutr. 98, 843–851 (2007).\nMaliphol, A. B., Garth, D. J. & Medler, K. F. Diet-induced obesity reduces the responsiveness of the\nperipheral taste receptor cells. PLoS One 8, e79403 (2013).\n• Does food crumbling directly correlate to food intake? If this is the case, the observed\nreduction in crumbling may be result of the overall reduction in intake. It may also be\nreflective of exploratory behavior or stress\nWe thank the reviewer for this question regarding the relationship between food crumbling\nand food intake as well as the potential contribution of exploratory behavior and stress.\nWe have conducted correlation analyses to examine the direct relationship between food\ncrumbling and food intake during the PheComp behavioral test at week 10, which allows\nmeasurable separation of food crumbling from food intake due to the specialized cage\ndesign without bedding interference. The overall correlation analysis revealed a moderate\npositive correlation for both sexes, with a slightly stronger relationship between crumbling\nand intake behaviors in males (r = 0.53, p < 0.001) compared to females (r = 0.44, p =\n0.002), suggesting that food intake and food crumbling are indeed associated. Interestingly,\ncontrol groups (not exposed to HFHS in early-life) and B. longum APC1472 treated HFHS-\nanimals showed non-significant correlations (control females: r = -0.58, p = 0.062; control\nmales: r = -0.14, p = 0.693, B. longum APC1472 females: r = 0.41, p = 0.163, B. longum\nAPC1472 males: r = 0.22, p = 0.571). This is in line with these two groups overall consuming\nless food and displaying less food crumbling behavior (Fig. 1E, F).\nRegarding relation to exploratory behavior, we found no evidence for increased exploratory\nbehavior in adult male and female mice as measured by overall locomotor activity in the\nPheComp cages (Supplementary Fig. S2B). Regarding stress, we found that early-life HFHS\nexposure did not increase either corticosterone in the blood or noradrenaline levels in the\nstriatum (Supplementary Fig. S4I, Fig. S2A) in both sexes. In fact, both were reduced in\nadult males. Regarding the potential influence of anxiety-like behavior, as this has previously\nbeen linked to stress and food crumbling behavior (Ang et al., 2024; Cameron & Speakman,\n2010), we observed that, only in male mice, early-life HFHS diet increased the time spent in\nthe external zone (10%) as shown in the figure below. Importantly, stress- and anxiety-\nrelated measured were exclusively observed in males while it was females who displayed\nsignificantly increased food crumbling and food intake after early-life HFHS exposure. Thus,\noverall these findings support the conclusion that food crumbling is primarily feeding-related\nand not stress- or exploration-related, especially in females.\nCameron, K. M. & Speakman, J. R. The extent and function of ‘food grinding’ in the laboratory mouse\n(Mus musculus). Lab Anim. 44, 298–304 (2010).\nTang, H., et al. Food Grinding Behavior: A Review of Causality and Influential Factors. Animals\n(Basel) 14, 1865 (2024).\n• Results state that control and treated group have a higher preference for saccharin\n(males), this appears to contradictory to the data displayed on the graph?\nWe thank the reviewer for pointing out this inconsistency in our manuscript. We have now\nclarified the sentence to accurately reflect our data, which shows that early-life HFHS\nexposure increased saccharin preference in adult male mice, and that both microbiota-\ntargeted interventions (prebiotic FOS+GOS and B. longum APC1472) normalized this\nelevated preference back to control levels. The corrected sentence now properly aligns with\nthe data presented in the corresponding figure (lines 592-596).\n5. The offspring are only exposed to the HFHS diet for a limited period of time, after\nwhich their body weight normalized. This is a key piece of data as it supports that\nobserved changes in adulthood are not due to differences in food intake or adiposity.\nBody weight and food intake data should be included in the main results.\nWe thank the reviewer for this important observation. We agree that the normalization of\nbody weight following the early-life HFHS exposure period is a crucial finding that supports\nour conclusion that the observed changes in adulthood are independent of differences in\nfood intake or adiposity. In response to this comment, we have moved the body weight and\nfood intake data from the supplementary materials to the main results and included it as part\nof Figure 1 (Figure 1B-1C) to emphasize the significance of this finding for interpreting our\nadult behavioral and molecular outcomes.\n6. It is still unclear however whether the effects observed are due to 1) changes in milk\ncomposition during lactation or 2) changes in feeding during that period or 3) effects\npost-weaning. This limitation should be addressed.\nWe thank the reviewer for raising this important point regarding the mechanistic distinction\nbetween maternal milk-mediated effects versus direct consumption effects (either during\nlactation or post-weaning). We have included a statement acknowledging this limitation in\nthe discussion section (lines 1120-1133). We would like to highlight that our study was\nspecifically designed to investigate the complex dietary effects in early-life, a period that\nencompasses both lactation and post-weaning periods. Additionally, collecting milk from\ndams was not possible since they were euthanized after weaning (PND23) in order to not\ninterfere with normal maternal care. This as been addressed more in detail in response to\nReviewer #1 point 2.1 and 2.2.\n7. Similarly, despite the interventions having beneficial effects on hypothalamic\ntranscriptomics, the animals appear to overeat when exposed to the HFHS diet. This\nshould be discussed, it is unclear whether the changes are prevented by the\ninterventions or reversed post- diet exposure.\nWe thank the reviewer for this important observation regarding overeating despite\n“normalization” of hypothalamic transcriptomics. We would like to highlight that despite some\ngenes and pathways being restored by our microbiota interventions in a sex-specific\nmanner, these do not include all genes altered by early-life HFHS diet (as quantitatively\nshown in Fig 4C, D). In addition, other brain regions might also play a role.\nRegarding the potential mechanism of action (prevention and/or restoration) of our\nmicrobiome-targeting interventions, we acknowledge that our study design does not allow\nfor distinguishing prevention of initial programming defects, active reversal of established\ndevelopmental alterations, or a combination of both mechanisms. We also acknowledge that\nthe terminology used for our statistical analysis (restoration effect) for cases where\nmicrobiome-targeted interventions significantly attenuated the early-life HFHS-induced\nalterations may be misleading in regards to the suggested mechanisms. However, this is\nnot intended to suggest a mechanism of action but rather refer to the directionality of the\nchange. This statement has now been included in the discussion (lines 1122-1134).\nOverall our objective was to assess whether FOS+GOS or B. longum APC1472 can improve\nearly-life HFHS-induced alterations in feeding, including overeating. We therefore tested\ncontinuous administration from PND0 through week 12 as the largest window of opportunity\nencompassing both neuronal development (prevention) and adulthood (restoration).\nWhether administration during specific periods (early-life only or post-weaning only)\nproduces different effects requires future investigation.\nMinor\n- Line 11: References are different format.\nWe have corrected the reference formatting to ensure consistency throughout the\nmanuscript according to the journal's style guidelines.\n- Line 30: Vagal afferents do not terminate in proximity to the median eminence or the\nhypothalamus. Ref 42 focuses on the NAc and is out of place here.\nWe thank the reviewer for this important correction. We acknowledge our original\nwording was unclear and did not intend to suggest direct vagal termination in the\nhypothalamus. We have revised and updated this statement to accurately reflect the\ndistinct pathways through which the gut microbiota can modulate hypothalamic function\n(lines 32-34).\nWe have also updated the references and particularly removed Ref 42 (now 47) in the\ncontext of hypothalamic involvement.\n- Line 30: Implication of the microbiota in reward driven feeding should include Kim et al.\nMolecular Metabolism, 2023.\nWe appreciate the reviewer's suggestion to include the Kim et al. (2023) Molecular\nMetabolism reference regarding microbiota involvement in reward-driven feeding. We\nhave incorporated this reference in line 36.\n- Supp: Food intake should be presented in kcal not grams as diets have different kcal\ndensity.\nWe thank the reviewer for these important methodological point regarding food intake\npresentation.\nWe chose to display food intake measurements in grams in the main manuscript (Fig\n1C), and the energy intake in Kcal in the supplementary (Supplementary Fig. S1G),\nbecause it more clearly represents the combined concept of both actual food intake and\nfood crumbling behavior, which is methodologically important for interpreting our\nbehavioral findings. We acknowledge that energy intake accounts for caloric content of\nthe diet and would allow a more direct comparison and potential metabolic interpretation\nof HFHS diet and microbiome-targeting interventions. However, given the caveat of\nincreased food crumbling in the home cage, we felt that “removal of grams from the food\nhopper” more accurately reflected our observation.\n- Was any power analysis conducted ? Omics outcomes display high variances, and the\ngroups may be underpowered.\nA comprehensive power analysis was conducted prior to study initiation as part of our\nethics application using G*Power 3.1.9.4 (effect size of = 0.43318, 80% power (1-β error)\nand α = 0.05 and accounting for litter effects and intra-cluster correlation (ICC = 0.05)).\nThe effect size was chosen to calculate the required samples size to obtain significant\neffects in feeding behaviors and 16S microbiota analyses as our primary outcomes. This\nyielded a sample size of n=12/group/sex.\nWe acknowledge that RNA sequencing, and blood metabolomics, with n=6/group/sex\n(from 4-6 different litters) may exhibit high variability which is also inherent to such\ndatasets. The sample size for these two omics datasets was lower since endpoint\nsamples were divided into two sets for further (non-primary) analysis: one for perfusion\n(RNAscope analyses) and one for fresh tissue collection. Nevertheless, n=6/group/sex\nrepresents a reasonable sample size for omics studies (RNA sequencing and\nmetabolomics). We have added this power analysis information to the Methods section\n(2.1. Animals, diets and ethical approval, lines 84-86).\nReviewer #4\n(Remarks to the Author):\nThis paper explores the effects of early-life high-fat/high-sugar (HF/HS) diet and microbiota\nmodulations by a prebiotic mix or putative probiotic on later life feeding behavior, microbiota\ncomposition, and transcriptomic differences in the arcuate nucleus of the hypothalamus.\nAdditionally, it explores the differences in male and female offspring showing that the effect\nof early-life HF/HS diet is sex dependent.\nThe manuscript adds interesting results to the existing literature about early-life diet\nalterations impact on later life but addresses it with the male/female difference point of view\n(which is often left out).\nThe methodology is clearly explained and stated, to the exception of a few minor details,\nand allow reproducibility of the experiments conducted.\nThe graphs are clear for the most part and the graphical abstract/protocol visual description\nhelp with the comprehension.\nI enjoyed the statistical approach taken (difference between the two diet – vehicle groups\nfollowed by measure of the restoration of the parameter by the microbiota intervention), it\nwas a nice way of analyzing the data, that should be used more often.\nThe introduction and discussion places the article in context with good reference to current\nliterature. These two sections are very clear and nice to read.\nHowever, some significant changes are required before publication are listed below.\nOverall, a re-organization of the figures and a proper labeling of them throughout the\nmanuscript are a needed change as it was difficult to match the figure number with the\nnumber in the text.\nOverall comment\n1. Reorder the figures and supplementary figures to make them appear in a logical order\neither in the text or in the figure files. Having them so disordered makes it hard to\nfollow the manuscript. Additionally in the text, some figures are misnumbered\n(ex: Fig 2B (line 468) is actually Fig 1B).\nWe thank the reviewer for this important feedback regarding the organization and\nreferencing of our figures. We have now reordered all figures and supplementary figures to\nappear in a logical sequence that corresponds to their appearance in the text. Additionally,\nwe have carefully reviewed the entire manuscript to correct all figure misnumbering errors,\nincluding the example noted where Fig 2B (line 468 in the original manuscript) was\nincorrectly referenced and should have been Fig 1B. We have ensured that all figure\ncitations now accurately correspond to the correct figure panels and that the figure\nnumbering follows a consistent and logical progression throughout the manuscript.\n2. Statistics need to be described in a fairer and clearer way. While the statistical tests\nare really adapted to represent the restoration by both interventions, sometimes the\nchanges induced by HFHS vehicle are not significant compared to the\ncontrol vehicle group, but the restoration effect is significant. While this non significance doesn’t take anything away from the\ntrend/biological effect, it is sometimes stated clearly and sometimes not in the text. A more consistent depiction of the\nstatistics would benefit the manuscript. For example: o line 490 “Interestingly, both the prebiotic FOS+GOS\nand B. longum APC1472 restored the higher 491 preference towards\nsaccharin in adult male mice that were exposed to an early-life HFHS”, the difference between the\ncontrol vehicle group and the HFHS vehicle group is not significant (p=0.12) although\nthe restoration effect by both microbiota interventions are.\nWe thank the reviewer for this important comment regarding the statistical presentation in\nour manuscript. We have now ensured that throughout the manuscript, when the diet effect\ndid not reach statistical significance (but trended) and the interventions significantly altered\nthose effects in the opposite direction (using orthogonal contrast), we explicitly state the\nnon-significant diet effect to provide transparency and clarity. We rephrased these cases to\navoid the word “restoration effect”, see for example the sentence highlighted by the reviewer\n(regarding saccharin preference, lines 592-596 now).\nTo clarify, we used two complementary approaches to statistically analyze our data: (1)\nANOVA followed by Tukey's post-hoc tests to assess diet effects (Control Vehicle vs HFHS\nVehicle; while considering the intervention), followed by orthogonal contrasts in a\ngeneralized linear model to specifically test the restoration effects of each microbiota-\ntargeted intervention. (2) For omics datasets, diet effects were assessed using t-tests\nbetween Control Vehicle and HFHS Vehicle groups, while restoration effects were evaluated\nby comparing HFHS Vehicle against each intervention group (HFHS FOS+GOS or HFHS\nB. longum APC1472), with restoration defined as cases where both diet and intervention\neffects were significant with opposing effect sizes.\nThese different statistical approaches inherently possess different statistical power:\northogonal contrasts are designed to test specific, pre-planned directional hypotheses and\nthus, have greater sensitivity than the more conservative pairwise comparisons with multiple\ntesting adjustments used in ANOVA/Tukey's tests. Consequently, it is statistically\nappropriate and not uncommon for restoration effects (assessed via orthogonal contrasts)\nto reach significance even when the initial diet effect (assessed via ANOVA/Tukey's) does\nnot, as these tests address different questions with different levels of statistical power.\n3. Abstract:\nThe abstract could benefit from breaking down some of the sentences into multiple\nsentences as it is hard to follow as it is written now.\nWe have now revised the abstract by breaking down the longer, complex sentences into\nmultiple shorter sentences to improve readability and flow. We have also updated the\nabstract after new analysis performed after revisions.\n4. Material and methods\na. “Animals, diets and ethical approval”\ni. Indicate how many dams were housed per cage.\nWe have clarified this methodological section (lines 86-89). It now reads: Dams were mated\nand individually housed at embryonic day 17. At day of birth (PND0), dams were randomly\nassigned to experimental groups using a simple randomization procedure (7 dams/group,\naiding to avoid any cage/litter effects).\nii. Indicate if inside of a same treatment group offspring were mixed after\nweaning to avoid cage effect.\nWe have clarified this methodological section, which now reads (lines 97-102): After\nweaning (PND21), offspring were group-housed by sex with their littermates within treatment\ngroups and [...].\nRandom allocation of offspring between litter was not feasible due to differences in\npregnancy and birth dates. Further, combining pups from different litter may have resulted\nin fighting, especially in males.\niii. Figure 1A and S1B are both very clear and helpful but redundant, chose\none to keep in the manuscript.\nWe have kept Figure 1A, and S1B has been removed to eliminate redundancy.\nb. Bioinformatics & statistical analysis\ni. “Additional random effects (including 426 litter and cohort effects) were\nassessed using linear mixed-effects models using the lmer function in\nR” - I couldn’t find the results of these tests in document S2. If it is not,\nplease add it, if it actually is contained in this file, please make it clearer\nso the reader can find the information.\nWe apologize for the oversight. The results of the linear mixed-effects models assessing\nlitter and cohort effects were not contained in document S2. We maintained the results with\noriginal statistical analysis using linear model but included a document in\nhttps://github.com/CristinaCuesta-\nMarti/earlylifeHFHS_eating_microbiota_2025/outputs/overall_stats_lmer with the statistical\noutcomes using linear mixed-effects models to include the random effects (litter and cohort\neffects), as stated in our updated methods section “2.17. Bioinformatics and statistical\nanalysis”. We have separated these analyses from our main statistical results (which are\nfound at https://github.com/CristinaCuesta-\nMarti/earlylifeHFHS_eating_microbiota_2025/outputs/overall_stats) to provide clear\ndistinction between our primary linear model analyses and the additional random effects\nassessments. Importantly, the inclusion of random effects (litter and cohort) did not alter the\npattern of statistical significance for our main findings, though in several cases the statistical\npower was enhanced, resulting in more robust effect estimates.\nc. Results\ni. Please add a sentence to describe Fig S1 E and F\nWe have now added a sentence to describe Fig S1 E and F (lines 546-551).\nii. Line 459 “Interestingly, we found that early-life HFHS-exposed offspring\ndid not consume all food removed from the food hopper but crumbled\npart of it” - - Please show data for this claim if possible as the graphs\nrepresent cumulative consumption and crumbling behavior.\nThe observation mentioned in line 459 refers to behavioral observations made in the home\ncage environment, which did not allow us to distinguish between actual food consumption\nand food crumbling behavior in the daily food intake measurements. To further investigate\nthis phenomenon more systematically, we analyzed real food intake and food crumbling\nbehavior separately using the PheCOMP cages during a 12-hour light phase period, with\nresults presented in main Figure 1E and 1F (after reordering) respectively. These data\nprovide quantitative support for the initial observation. We also have photographic\ndocumentation of the food hoppers showing the food pellets and crumbs that can be\nprovided if the reviewer would find this additional visual evidence helpful.\niii. Fig S4B: harmonize the serotonin names between the text and the\nfigures + discuss the 9 graphs that are not mentioned in the text\nWe have now harmonized the serotonin names between the text and figures, and have\nincluded a description of the 9 graphs that were previously not mentioned in the Results\nsection (lines 598-611).\niv. Line 503 to 512, please re-write this section to make the differences\nbetween the IpGTT results in male and female clearer.\nWe have now rewritten the section from lines 503 to 512 to make the differences between\nthe IpGTT results in male and female mice clearer and more accessible to readers (lines\n618-630) and we have updated them accordingly in Supplementary Fig. S2C-S2E.\nv. Line 511, for the basal glucose graph description, “restored” has been\nused in the rest of the manuscript and should be used here as well to\nkeep the results clear and consistent as it is going back to control\nvehicle group levels. For the males, please add additional statistics and\ndiscussion of the effect of FOS+GOS vs control vehicle on basal\nglucose.\nWe have chosen the word “significantly reduced” in lieu of “restored” in this case to maintain\nclarity and consistency throughout the manuscript (as requested by the reviewer in point 2)\nwhen diet-effects did not reach significance. Additionally, for this reason, we have removed\nreference to FOS+GOS effects on basal glucose in adult males. In this case, diet effect was\nnot indicating a trend.\nvi. Line 517: what about the other organs in Fig S5?\nWe have now included reference to the other organs shown in Fig S5 that were missing\nfrom line 517 (lines 633-642).\nvii. Line 522 to 536: please mention the alpha-diversity results.\nWe have now included mention to the alpha-diversity results in the section from lines 522 to\n536 (lines 649-654).\nviii. FigS3D to I are not described\nWe have now included mention to Fig. S3D in lines 654-664.\nix. Line 577, there is no mention on the effect/absence of effect of B.\nlongum APC1472 on the males’ metabolome.\nWe apologize for not including the effect of B. longum APC1472 on the males' metabolome\nin line 577. We have now included this information to provide a complete picture of the\nmetabolomic effects in both sexes (lines 703-704).\nx. For the section “Early-life HFHS has an enduring and sex-specific\nimpact on hypothalamic transcriptomic profile in adult mice”, it would be\ninteresting the mention the gene commonly restored by both male and\nfemale FOS+GOS supplementation in fig.3D.\nWe have now included information about the genes that are commonly restored by\nFOS+GOS supplementation (Hcar1) and B. longum APC1472 supplementation (Fen1 and\nPiezo2) in both males and females as shown in Fig. 3D in results (lines 785-791).\nAdditionally, we have included a supplementary table (Supplementary Table S8) detailing\nall commonly restored genes across different treatment and sex combinations, including:\ngenes commonly restored by B. longum APC1472 in both females and males, genes\ncommonly restored by FOS+GOS in both females and males, genes commonly restored by\nB. longum APC1472 and FOS+GOS in females, and genes commonly restored by B.\nlongum APC1472 and FOS+GOS in males.\nWe thank the reviewer for this very interesting point.\nxi. Line 647, the difference is not significant for Lepr.\nWe have now updated the text to accurately reflect that the downregulation of Lepr\nexpression was non-significant (q=0.064), along with the other genes mentioned (Pomc and\nGhsr), and have removed any claims of statistical significance for these findings (lines 880-\n880). We appreciate the reviewer's attention to detail in ensuring accurate reporting of our\nstatistical results.\nxii. Line 651, Figure 4F doesn’t exists (or I couldn’t match the text with the\nfigures).\nWe apologize for the mistake in referencing Figure 4F in line 651. We meant to reference\nFig. 4E and have now corrected this reference (lines 885).\nxiii. Line 669: It is only true for females.\nWe have now updated line 669 to specify that this finding is only true for females (lines 902-\n903).\nxiv. Line 669: the POMC+ Ghsr expressing cells count graph doesn’t exist\n(or I couldn’t match the text with the figures).\nPrior to submission, we removed the initial graphs including the ARC POMC+ Ghsr\nexpressing cells count due to the minor presence of POMC+ cells expressing GHSR receptor\nin the ARC (less than 10% of POMC express GHSR (Dickson and Luckman,1997).\nHowever, it seems we left by error one sentence referring to this initial graph but we have\nnow removed the sentence referencing the POMC+ Ghsr expressing cells count graph (lines\n902-903).\nDickson, S. L. & Luckman, S. M. Induction of c-fos messenger ribonucleic acid in neuropeptide Y\nand growth hormone (GH)-releasing factor neurons in the rat arcuate nucleus following systemic\ninjection of the GH secretagogue, GH-releasing peptide-6. Endocrinology 138, 771-777 (1997).\nxv. Line 692: not significant for Pnoc.\nWe apologize for the error regarding Pnoc significance. We have now updated the text to\nhighlight that this was a non-significant down-regulation rather than a significant difference\n(lines 925-926).\nxvi. In the section “Enduring molecular impact of an early-life HFHS diet on\nPNOC GABAergic neurons and Nod2-expressing cells in the arcuate\nnucleus of the hypothalamus of adult male and female mice” please\nharmonize the writing conventions of the genes expressed and cell\npopulations, this section is very confusing for the reader.\nWe thank the reviewer for highlighting the inconsistency in gene and cell population\nnomenclature throughout this section. We have revised the text to establish consistent\nconventions for describing genes and cell populations. Specifically, we now use:\n Gene names in italics: Pnoc, Nod2\n Cell populations described as: Pnoc/Nod2-expressing cells or PNOC+/NOD2+ cells\n Co-expressing populations clearly specified as: \"Pnoc-expressing cells/PNOC+ cells that\nco-express Nod2\" for instance.\nWe have also corrected grammatical inconsistencies (e.g., \"showed a decreased\" to\n\"showed a decrease\") to improve readability and scientific precision throughout that section.\nReviewer #4 (Remarks on code availability): The code was not provided so I could not review\nit\nWe thank the reviewer for this comment. We have updated the \"Code Availability\" section\nof the manuscript to include the GitHub URL where all the code can be accessed. The\nrepository now contains all scripts used for statistical analyses, data processing,\nvisualization and the new integrative multi-omics analyses including the multi-association\nanalyses between blood metabolome and brain transcriptome, and gut microbiome data and\nblood metabolome.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nI have no additional comments. The authors addressed all my previous comments and\nconcerns very thoroughly. I recommend this manuscript for publication.\nReviewer #1 (Remarks on code availability):\nThe code is not available. The link provided leads to a \"Page not found\" error.\nResponse to Reviewer #1:\nWe apologize for the broken link to our code repository. We have now corrected this issue and\nupdated both the manuscript and supplementary materials with the proper link that provides full\naccess to all analysis code. We thank the reviewer for bringing this to our attention and for their\npositive evaluation of our manuscript.\nReviewer #2 (Remarks on code availability):\nThe link has been provided but the code does not appear to be uploaded yet.\nhttps://github.com/CristinaCuesta-Marti/earlylifeHFHS_feeding_microbiota_2025\nResponse to Reviewer #2:\nWe sincerely apologize for the oversight regarding the code repository. As mentioned in our\nresponse to Reviewer #1, we have now verified that the link is fully accessible. We have updated\nboth the manuscript and supplementary materials with the correct link providing complete\naccess to all analysis, scripts and data. We thank the reviewer for bringing this to our attention.\nReviewer #4\nRemarks to the Author):\nThe work done by the authors to answer the reviewer’s comments both in the manuscript and in\nthe rebuttal is very nice and adds a lot of clarity to the paper. It really helps to understand their\nwork that comprises multiple techniques, analyses and statistical tests. Thank you for taking\nthe time to perform all the changes and answer our comments with clarity and detail\nand congratulations on this very interesting study.\nRemarks on code availability:\nUnfortunately, although the Github link to the code and other supplementary material didn't\nwork on my side and gave me a 404 error. If it is a general problem, please review that there is no\ntypos in the link so the material becomes accessible.\nResponse to Reviewer #4:\nWe sincerely thank the reviewer for their positive feedback and for recognizing the improvements\nmade to the manuscript.\nRegarding the code availability issue, we apologize for the broken GitHub link. As mentioned in\nour responses to Reviewers #1 and #2, we have now verified that the link is fully accessible. We\nhave updated both the manuscript and supplementary materials with the correct link. We thank\nthe reviewer for bringing this to our attention."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have no additional comments. The authors addressed all my previous comments and concerns very thoroughly. I recommend this manuscript for publication.", "reviewer_4": "The work done by the authors to answer the reviewer’s comments both in the manuscript and in the rebuttal is very nice and adds a lot of clarity to the paper. It really helps to understand their work that comprises multiple techniques, analyses and statistical tests. Thank you for taking the time to perform all the changes and answer our comments with clarity and detail and congratulations on this very interesting study."}, "author_response": "Response to Reviewer #1:\nWe apologize for the broken link to our code repository. We have now corrected this issue and\nupdated both the manuscript and supplementary materials with the proper link that provides full\naccess to all analysis code. We thank the reviewer for bringing this to our attention and for their\npositive evaluation of our manuscript.\nResponse to Reviewer #2:\nWe sincerely apologize for the oversight regarding the code repository. As mentioned in our\nresponse to Reviewer #1, we have now verified that the link is fully accessible. We have updated\nboth the manuscript and supplementary materials with the correct link providing complete\naccess to all analysis, scripts and data. We thank the reviewer for bringing this to our attention.\nResponse to Reviewer #4:\nWe sincerely thank the reviewer for their positive feedback and for recognizing the improvements\nmade to the manuscript.\nRegarding the code availability issue, we apologize for the broken GitHub link. As mentioned in\nour responses to Reviewers #1 and #2, we have now verified that the link is fully accessible. We\nhave updated both the manuscript and supplementary materials with the correct link. We thank\nthe reviewer for bringing this to our attention."}]

Decision letter