{"paper_id":"255e171c-d243-4c75-aec6-93cdc2fdd9e3","body_text":"Integrative characterization of host–microbiome-diet axes during early-life development of the murine gut | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative characterization of host–microbiome-diet axes during early-life development of the murine gut Giacomo Carta, Feng Xian, Daniel Malzl, Manuela Schmidt, David Gómez-Varela1 This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9367132/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Early-life development of the gut microbiome plays a critical role in shaping host physiology. However, a comprehensive understanding of how diet, microbial community assembly, functional capacity, and host intestinal maturation axes evolve and coordinate over time remains lacking. Most studies focus on a single axis, rely on cross-sectional sampling, or have limited functional resolution, restricting insight into developmental dynamics. Here, we characterized early-life maturation of the gut ecosystem using longitudinal, metaproteome-level analysis in a murine model. Results Using ultra-sensitive metaproteomics, we profiled fecal samples collected at seven postnatal time points from day 10 through weaning and into early adulthood (day 48) in pups from two contemporaneously raised C57BL/6 cohorts differing only in maternal origin (a long-established local colony and newly purchased pregnant females from the same vendor). Analyses accounted for time, cohort, and sex effects. Microbial communities underwent pronounced taxonomic turnover, shifting from early dominance by facultative anaerobes to obligate anaerobes after weaning, accompanied by increasing species richness and functional complexity across cohorts and sexes. Taxonomic changes supported increasing functional redundancy that converged by postnatal day 34 and remained stable into early adulthood. KEGG clustering showed this redundancy to be driven by coordinated upregulation of distinct metabolic pathways alongside maintenance of core functions. Direct detection of low-abundant dietary proteins provided evidence for dietary transitions coinciding with microbial maturation: milk proteins were detected only before weaning, while solid food components predominated afterward. Maternal origin significantly influenced microbial engraftment trajectories, leading to cohort-specific taxonomic and functional differences despite identical housing and diet. In parallel, host intestinal proteome maturation mirrored microbial succession, with coordinated shifts in metabolic, absorptive, regulatory, and effector pathways, including antimicrobial peptides and carbohydrate-modifying enzymes. Conclusions By directly integrating microbial, dietary, functional, and host axes within a longitudinal framework, this study provides a comprehensive view of murine gut ecosystem maturation during early life and offers a reference for interpreting developmental microbiome dynamics and improving experimental design and reproducibility in mouse studies. Developmental Biology Animal Science Systems Biology Animal Physiology Gut microbiome mouse development Host-microbiome interactions Diet Metaproteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background The gut microbiome plays a central role in mammalian development, shaping host physiology through effects on immune maturation, nutrient metabolism, and organ development [ 1 – 3 ]. Early life represents a critical window during which gut microbial communities are established and refined [ 4 – 7 ], with long-term consequences for health and disease susceptibility [ 7 – 13 ]. Despite extensive interest in early-life microbiome development, how microbial community assembly, functional maturation, and host intestinal development are coordinated over time is unknown. Most current knowledge of gut microbiome development derives from genomic and amplicon-based studies [ 12 , 14 – 17 ], which primarily describe taxonomic composition and, consequently, infer functional potential. While these approaches have been instrumental in defining broad patterns of microbial community assembly dynamics, they do not directly capture expressed microbial functions or host physiological responses[ 11 , 18 ]. Moreover, many studies rely on cross-sectional sampling or limited temporal resolution [ 15 , 19 , 20 ], restricting insight into dynamic host–microbiome-diet interactions during key developmental transitions such as weaning. As a result, the functional mechanisms underlying diet, early microbial colonization, host intestinal maturation, and ecosystem-level functional stabilization are only poorly characterized. Murine models, particularly C57BL/6 mice, are widely used to study microbiome-associated processes relevant to human health, owing to their genetic tractability and substantial overlap in microbial functional capacity with humans [ 21 ]. Beyond host genetics and diet, early-life microbial assembly is also shaped by vertical transmission from the mother [ 10 , 15 , 16 ]. Maternal microbiota have emerged as key biological determinants of early microbial engraftment, influencing both the initial composition and the subsequent developmental trajectory of the gut ecosystem [ 10 , 11 ]. At the same time, accumulating evidence indicates that gut microbiota composition in C57BL/6 mice varies across vendors, breeding colonies, and housing conditions, introducing a significant source of experimental variability [ 21 – 24 ]. Differences in maternal microbiota associated with animal sourcing and colony history can therefore imprint cohort-specific microbial trajectories in offspring, even under otherwise standardized experimental conditions. Yet, the extent to which such maternal-origin effects are reflected over early development, and whether they translate into functional signatures in the adult pup’s gut ecosystem, is unknown. This uncertainty has important implications for the implementation, reproducibility, and interpretation of mouse-based studies. Dietary transitions represent another major driver of microbiome maturation during early life [ 3 , 25 , 26 ]. In both humans and mice, the shift from maternal milk to solid food coincides with marked changes in microbial composition and metabolic activity [ 27 , 28 ]. However, the timing and extent of early exposure to solid food components, and how these dietary inputs intersect with microbial succession and host intestinal responses, have been difficult to assess directly due to sensitivity limits. To address these gaps, we applied longitudinal ultra-sensitive metaproteomics [ 29 ] to profile gut microbiome and host intestinal development in mice across seven postnatal time points, from early colonization through weaning and into early adulthood. Using non-invasive and sterile fecal sampling, we followed pups from two contemporaneously raised C57BL/6 cohorts that differed only in maternal origin—a long-established local colony and newly purchased pregnant females from the same vendor—while systematically accounting for sex-specific effects. This integrated approach enabled the detection of very low-abundant dietary proteins, species-resolved microbial profiling, direct measurement of expressed microbial functions, and parallel assessment of host intestinal proteome maturation. By capturing microbial, dietary, functional, and host axes within a single longitudinal framework, this study provides a comprehensive view of how developmental programming of the murine gut ecosystem emerges during early life. Methods Mice All the experiments included in this publication were approved by the University of Vienna’s Institutional Animal Care and Use Committee (IACUC; protocol n. 2023-016; February-June 2023) and carried out in compliance with the rules provided in the Guide for the Care and Use of Laboratory Animals, and EU-Directive for the protection of animals used for scientific purposes. Additionally, this work was reported according to the ARRIVE guidelines. To explore the changes in the gut microbiota-host development, 4 local (L) and 4 purchased C57BL/6J pregnant females (8–9 weeks old) from Charles River (CR) were enrolled in the study. The labelled as “Local” mothers and pups were obtained from the breeding colony established at the University of Vienna in October 2021 using founder parents obtained from the same vendor from which the new mothers (CR) were purchased. To minimize the impact of environmental factors on the evolution of the microbiota-host development, the mothers and pups from CR and our L colonies were housed in the same room on two different racks under the same humidity and temperature [ 23 ]. All mice had ad libitum access to chow (SM VRF1 – Breeding diet fortified, autoclavable / γ-irradiated, ssniff Spezialdiäten GmbH) and water, under 12:12 light/dark cycle, with light starting at 7:00 AM. Each transparent polycarbonate cage was provided with corncob bedding, and a standardized enriched environment consisting of a red transparent plastic nest box, two wooden tunnels (15x5x5 cm), and a handful of crinkled natural paper strands for building a high-quality nest [ 30 ]. Every Monday, the mice were moved into a cleaned cage with the autoclaved enrichment described above. The cage was changed the following week only if the mother delivered pups on Monday to avoid stress-related behaviors of the mother. Each pup was considered an experimental unit. Peers of the same sex, from the same cage, were co-housed in the same cage at a maximum of 5 mice per cage. All mice were weaned at 21 days after birth. No mice were single-caged after weaning. The study did not have humane endpoints since none of the procedures adopted were invasive, painful, or stressful for the animals. Mice were handled daily by trained animal facility technicians. No animal was sacrificed in the present study. Based on the significant detectable difference in Bray-Curtis index between adult male and female C57BL/6J mice obtained from different vendors, as shown by Long and coworkers [ 22 , 31 ], we used a total sample size of 32 animals (7 for sex/group + 1 possible dropout) with an effect size of η 2 = .14 (Power [1-β]=.95 and α = .05) with an actual power of .969. Assay strategies and exclusion criteria The same male experimenter performed the fecal samples (FS) collection from pups and mothers from the L and the CR cohorts [ 32 – 34 ]. The tools and the experimenter's hands were disinfected with water and 70% ethanol between each animal manipulation. The experimental groups were assessed randomly (Microsoft Excel random list) at each time point from 8 to 12 AM. From Day 8 (D8), all the pups were tail-marked with a surgical pen, and the marks were renewed every 2 days [ 35 , 36 ]. Pups were a priori excluded from the experiment if they did not provide FS after 2 collection attempts per day for two consecutive days. For this reason, to reach the above-mentioned sample size of 32 animals, we used a total of 42 females (18 CR and 28 Local) and 35 males (13 CR and 22 Local) were used to assure reliable fecal sample amount. Fecal samples collected from pups and mothers. The FSs from pups were collected as soon as possible after birth. FSs were collected with an interval of at least 2 days between one collection and the other, since a smaller time interval provided no collection of fecal pellets. The FS obtained from the mothers (4 from each group) was collected at 26 days after the pups' delivery (5 days after weaning to minimize the mother stress consequences on pups during lactation) [ 37 , 38 ]. This way, microbial community similarity structure and baseline values for the offsprings’ microbial composition were provided [ 15 ]. The FSs were collected by stimulating the anogenital reflex, mimicking the maternal licking to stimulate defecation and urination in the mice[ 39 , 40 ]. The pups were stimulated with soft repeated light strokes (cranio-caudal direction) on the perianal region with a wet cotton swab. This technique was adopted for all the animals at each time point. During the stimulation, a sterile plastic 1,5 ml tube was positioned below the mouse tail, and the FSs were collected from the animal by gravity to reduce as much as possible urine and fur contamination. Fresh FSs were collected in the morning (8–12 AM), placed immediately in autoclaved 1.5 ml plastic sterile tubes pre-cooled on ice, and stored at -80°C. Results reported in this study were obtained from a total of 16 females (8 CR and 8 L) and 16 males (8 CR and 8 L). A total of 224 samples were analysed. Protein extraction, protein digestion, and peptide clean-up The sequence of the FS collected was randomized, and samples were relocated in new boxes and processed for analysis by a blinded operator. The protein preparation protocol as previously described[ 41 ], started by taking 30–50 mg of FS that was mixed with 200 µL of lysis buffer (5% SDS, 2 M urea, 50 mM Tris-HCl, protease inhibitor 1x) and vortexed vigorously for 1 min. The samples were then placed in a Thermomixer (serial number: 5382JR638726, Eppendorf, Hamburg, Germany) at 1,200 rpm and 70°C for 15 min. The samples were then ultrasonicated using a Bioruptor Pico (Diagenode, Seraing, Belgium, program: 15 cycles of 30 s \"on\" and 30 s \"off\", frequency level: Low, water temperature: +20°C). At room temperature, the tubes were then centrifuged for 5 minutes at 16,000 g. The supernatant was moved into a new tube, centrifuged again, and stored at − 80°C until protein digestion with SP3 was performed. In the case of early D (10–12 days) when the FSs range from 0.1 to 10 mg all the sample collected was processed as reported above. A modified version of the one-pot solid-phase enhanced sample preparation method (SP3) of Hughes et al.[ 42 ] is used for protein purification and digestion was used. In brief, 50 µg of protein was added to a 1.5 mL LoBind tube (Eppendorf, Hamburg, Germany), and 50 µL of lysis buffer was added. Samples went through the processes of protein reduction (5 mM dithiothreitol, DTT, incubated for 30 min at + 60°C) and alkylation (20 mM iodoacetamide, IAA, 30 min in the dark at room temperature). The remaining IAA in the sample was quenched by adding DTT at a final concentration of 5 mM. Premixed Sera-Mag SpeedBead beads were added in a volume of 10 µL (50 mg/mL, Cytiva, Marlborough, MA) to a 50 µg protein sample. One volume of absolute ethanol was immediately added to initiate the protein binding to the beads, followed by incubation and agitation at 1,000 rpm for 5 min at 24°C in a Thermomixer. After standing on a magnetic rack for 2 minutes, the supernatant was removed, and the beads were rinsed three times with 500 µL of 80% ethanol. The obtained beads were reconstituted in 50 µL of digestion buffer (50 mM ammonium bicarbonate, pH 8). 2 µg of sequencing grade trypsin/LysC (Promega, Madison, USA) was used for protein digestion in a Thermomixer with agitation at 950 rpm for 18 h at 37°C. After a 30-second spin, acetonitrile (ACN) was added to each sample to reach a final concentration of 95%. The mixture was incubated at room temperature for 8 minutes and then placed on a magnetic rack for 2 minutes. After the supernatant was discarded 900µL of 100% ACN was added, 15 seconds on a magnetic rack and removed from each tube. To elute the peptides, the washed beads were reconstituted in 20 µL of liquid chromatography-mass spectrometry (LC-MS) grade water. Peptide concentrations were measured in duplicate at 205 nm using a NanoPhotometer N60 (serial number: TG2022, Implen, Munich, Germany). A final concentration of 0.1% Formic acid (FA) was added to acidify peptides and the tubes were stored at − 20°C until LC-MS/MS analysis. Liquid Chromatography Mass Spectrometry (LC-MS/MS) Nanoflow reversed-phase liquid chromatography (Nano-RPLC) was conducted using a NanoElute system (Bruker Daltonik, Bremen, Germany). A total of 250 ng of digested peptides were loaded onto a trap column (Neo-Trap C18, 5 mm x 300 µm, Thermo Fischer) and then separated on a 25 cm x 75 µm column, packed with 1.7 µm C18 particles (IonOpticks, Fitzroy, Australia), using a 60 min gradient. Mobile phase A consisted of 100% water (LC-MS grade) with 0.1% formic acid (FA), while mobile phase B was 100% acetonitrile with 0.1% FA. The separation was performed at a flow rate of 250 nL/min, except for the final 7 min, during which the flow rate was increased to 400 nL/min. The gradients included a linear increase of mobile phase B from 4% to 20% over the first 35 minutes, followed by an increase to 35% over 17 minutes. Subsequently, mobile phase B was ramped up to 85% within 0.5 minutes and held at this level for 7 minutes to elute hydrophobic peptides. Peptide eluates were analyzed on a hybrid TIMS quadrupole TOF mass spectrometer (timsTOF Pro2, Bruker Daltonik) coupled via a CaptiveSpray ion source. Data acquisition was performed in data-independent acquisition (DIA) mode, in combination with parallel accumulation serial fragmentation (PASEF). The TIMS analyzer operated at 100% duty cycle with an accumulation and ramp time of 100 ms. Ion mobility separation was set in the range of 0.65–1.40 (1/k0). Precursors within an m/z range of 400–1200 were targeted in 12 scan cycles with 3 isolation steps in each cycle, comprising 32 isolation windows of 25 Th, resulting in a total cycle time of 1.38 seconds. Collision energy was ramped linearly from 65 eV at 1/k0 = 1.6 to 20 eV at 1/k0 = 0.6. DIA-PASEF data processing To process DIA-PASEF data[ 41 ], DIA-NN (Linux version 1.8.1) was used in library-free mode and based on a microbial database of 48,970 protein sequences and the standard Uniprot proteome of Mus musculus (accessed on 20230427). Within DIA-NN, a deep learning-based method was used to predict theoretical peptide spectra, retention time, and ion mobility. Trypsin/P was used for in silico digestion, with a maximum of 2 missed cleavages. Peptide identification allowed up to two variable modifications per peptide, including methionine oxidation and N-terminal acetylation. Carbamidomethylation on cysteine was selected as a fixed modification. Peptides were restricted to lengths between 7 and 50 amino acids. The m/z range was defined according to the DIA-PASEF acquisition method: 400–1200 for precursor ions and 100–1700 for fragment ions. Mass accuracy for both MS1 and MS2 was set to automatic determination. Protein inference was performed at the protein name level, with the “Heuristic protein inference” option disabled. Raw data files were processed in parallel. DIA-NN first searched each file individually against the predicted spectra library to generate a quant file. In the second step, DIA-NN built an empirical spectral library based on all quant files[ 43 ]. And lastly, all files were reprocessed against the empirical spectral library using the match-between-runs (MBR) feature. Quantification was carried out using retention time–dependent cross-run normalization with the “Robust LC (high precision)” setting. The resultant precursor intensities were further processed using the R package DIA-NN (v1.0.1; https://github.com/vdemichev/diann-rpackage ), to extract and compute the MaxLFQ, quantitative intensity for all detected peptides and protein groups with q-value < 0.01[ 44 ]. Microbiota Taxonomy, function, functional redundancy, and taxa-function analyses The built-in taxonomy and function databases were created by importing into iMetaLab (v2.3.0), peptide and protein tables were generated with MaxLFQ[ 45 ]. Tables with peptide and intensities data (microbial and host) were adopted for taxonomical annotation (Ignore blanks under rank: Unique peptide count ≥ 3, Superkingdom), while protein identifications with corresponding intensities were adopted for functional annotation (with default parameters). The taxonomy annotation output from iMetalab was further processed in R by removing peptides without taxonomic annotations and taxa annotated by fewer than three distinct peptides (Supplementary Table 1). Log2 intensities of peptides common to the same taxa were summed for each sample. Alpha and Beta diversity indexes, engraftment analyses, and taxa-function were calculated at the Species level. The relative abundance of a species was defined as the percentage of the peptide summed intensity average for each sex in each cohort. Analyses performed at the cage level are based on the cage shared by the mothers and their respective pups before weaning. Mothers’ Core Species (Species detected in at least 3 Mothers of the same cohort), and the Pups’ Core Species (Species detected in at least 3 pups of the same cages) were considered to assess the microbiota engraftment in pups of the same cohort over time. From the iMetaLab function annotation table, the KEGG pathways identified by fewer than 3 proteins were removed (Supplementary Table 2). The functional redundancy of the gut microbiota at the proteome level (FRp) was calculated as described by Li and colleagues [ 46 ]. In brief, data preparation was performed using Python 3.12.6 and PhyCharm Community Edition 2024.2.3. To define the Species-specific functions Meta4P[ 47 ] was used by importing the tables with the peptide intensities for each microbial in each sample (Supplementary Table 1) and the taxonomic and functional annotation tables (Supplementary Table 2). Meta4P results were filtered by the Species-NOG pathway identified by at least 3 peptides. The log2-transformed intensities of common peptides annotated in the same protein were summed across each sample. Consensus Clustering Analysis was adopted to evaluate the stability of sample clustering and determine the optimal number of clusters (k). We performed consensus clustering on KEGG pathway z-score profiles. Consensus clustering was chosen over the Calinski–Harabasz (CH) index, Davies–Bouldin (DB) index, and the Gap statistic for determining the optimal number of clusters because it provides a more robust, resampling-based estimate of cluster stability. Analyses were conducted separately for each mouse cohort with pooled sexes. Consensus clustering was then performed for k values ranging from 2 to 6, using the following parameters: 500 bootstrap resampling iterations, with each iteration subsampling 80% of the samples and 80% of the features without replacement. For each k, Silhouette Scores were calculated. All analyses were performed in Python using numpy, pandas, scikit-learn, and seaborn. Host proteome analysis The protein intensity, from the DIA-NN host protein sequences, was summed among common peptides in the same sample and divided by the total intensity of the sample (Protein Relative Abundance). Considering the factors time, cohort, and sex of the pups, and their interactions when detecting a significant difference with the Kruskal-Wallis test with BH correction, a soft clustering (allowing proteins to belong to multiple clusters) with the Mfuzz R package was performed[ 48 ]. First, data were pre-processed by averaging replicates at each condition and standardizing each protein’s profile (z-scoring). By examining the Mfuzz Dmin plot, the number of clusters at the Dmin inflection point (the \"elbow point\" — where the minimum centroid distance stops decreasing rapidly and begins to level off) was used for the optimal number of cluster definition. With the estimated fuzzifier (m ≈ 1.25), the plots of the mean profile for each cluster were generated considering proteins with the highest membership score (> 0.7–0.9) to each cluster detected. Proteins with the highest membership were searched for Multiple Proteins by Names / Identifiers on String 12.0 ( https://stringdb.org ; 23/05/2025) and protein full STRING networks were obtained with confidence as a parameter for meaning of the network edges, with a minimum required interaction score of 0.7. The host proteins were matched by Peptide sequence or Protein Name to the 152 known antimicrobial peptides (AMP) reported by Valdes and colleagues [ 49 ]. The protein intensity was summed among common peptides in the same sample and divided by the total intensity of the sample (relative AMPs abundance). Due to the high number of low-abundant AMPs, a reduced dataset considering the 10 most abundant AMPs among all the samples was adopted to perform analyses of the main phenomena of the AMPs evolution along time and to detect differences between pups of different Mothers’ origin and sexes. Finally, the Carbohydrate-active enzymes (CAZymes) that play a crucial role in the synthesis, modification, and breakdown of carbohydrates were analysed. The dataset was created by selecting among all host proteins those annotated on UniProt (search performed on 28/02/2025) matching with the CAZYmes categories listed by Wardman and colleagues[ 50 ]. Food proteome analysis The food protein dataset was generated using the FASTA file created by Valdes and colleagues [ 49 ] through FragPipe ( https://fragpipe.nesvilab.org/ ) to generate a reduced protein list. Proteins originating from plant genera containing the species reported in the ingredients of the mice chow (5 genera Triticum; Glycine; Zea; Avena; Beta) and the mouse milk proteins were downloaded from UniProt (search performed on 28.02.2025; Supplementary Table 3). Only those unique protein markers for each genus were included in the food database to avoid any detection bias. Lactoferrin is a secreted protein present in the milk and the intestinal mucosa [ 51 , 52 ]; for this reason, it was differentiated from the other milk proteins in the analyses. Protein intensity was summed among common peptides in the same sample and divided by the total intensity of the sample (relative food protein abundance). Statistical analyses The lists of Species detected in pups and mothers were compared with those reported in the literature as core shared species between C57BL/6J mice and humans [ 27 , 28 ]. The α-diversity (Richness Shannon, and Simpson indexes) and β-diversity metrics (Bray-Curtis and Jaccard dissimilarities) were calculated by using vegan (version 2.6-8 packages in R 4.3.1(R Core Team, 2018). For β-diversity indexes, permutation tests (with 999 permutations) using the Adonis [PERMANOVA] method with the Benjamini-Hochberg (BH) correction for pairwise multilevel comparisons under a reduced model were employed to detect the relative contributions to microbial community variability from time, sex, and cohort. The same strategy was adopted to compare the metabolic-informed taxonomical index between the pups and their mothers. The analyses of the data from fecal samples were performed with \"rstatix”, “ggplot2”, “ggpubr”, “diann”, “lme4”, “emmans”, “limma”, \"inflection\", “stringr”, “ggVenDiagram”, ”purrr”, “Mfuzz”, “vegan”, “pairwiseAdonis”, and “biomaRt” R packages. All data is represented as mean ± SD (standard deviation of the mean) unless indicated otherwise. All replicates are biological unless indicated otherwise. For taxonomical, protein, and functional analyses, the statistical analysis of the three-sided linear mixed-effects model (lmem) was adopted, and estimated marginal means with BH correction to perform the post hoc analyses of the significant interactions between the factors time, sex, and cohort detected. All statistical tests are reported in the respective figure caption. Significant interactions for taxonomical and functional analyses were assessed for the normality of the data distribution. Two-way ANOVA and three-way ANOVA for repeated measures with BH correction were adopted for normally distributed data. For non-normally distributed data, the Kruskal-Wallis test followed by Post-hoc Dunn's test for pairwise multiple comparisons with BH correction, was adopted. In all panels: p > 0.05; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. Results Unveiling Taxonomic Developmental Programs During Early Life To characterize the temporal assembly of the gut microbiota during early life, we performed longitudinal ultra-sensitive metaproteomic profiling of fecal samples collected from postnatal day (D) 10, the earliest time point at which fecal material was available, through D48 (Fig. 1a). Samples were obtained from male and female pups belonging to two C57BL/6 cohorts that differed only in maternal origin: pups born to a long-established local breeding colony (L) and pups born to pregnant females newly acquired from the same vendor (Charles River; CR). Importantly, both cohorts were born contemporaneously and raised under identical housing and dietary conditions (see Methods), allowing us to assess developmental trajectories of gut microbiota maturation while minimizing environmental confounders and acknowledging any potential genetic drift due to inbred local mice. Across both cohorts and sexes, gut microbial α-diversity increased markedly with age (Figs. 1b and S1a; Supplementary Tables 4–6; Mixed-effects models, all p < 0.0001). Species richness, Shannon, and Simpson indices rose steeply during the first three postnatal weeks and reached a plateau after D26, consistent with rapid community diversification during the pre-weaning period followed by stabilization after dietary transition to solid food. No significant differences in α-diversity were detected between male and female pups within the same cohort at any time point (Supplementary Tables 4–6). Temporal restructuring of the microbial community was further supported by β-diversity analyses. Bray–Curtis and Jaccard dissimilarities revealed significant separation of samples by developmental stage in both cohorts (PERMANOVA, p < 0.001; Figs. 1c and S1b, Supplementary Tables 8–9), with early-life communities at D10–D12 clustering distinctly from those observed after weaning (D34–D48). These age-dependent shifts were consistent across sexes, indicating a reproducible and coordinated remodeling of gut community structure during maturation. Beyond ecological indices, metaproteomic profiling enabled species-resolved taxonomic analysis of microbial turnover across development. The number of confidently detected species (see Methods) increased from 36 at D10 to 198 at D48 (Supplementary Table 9), following highly similar trajectories across cohorts and sexes (Figs. 1d and S1c). Early-life samples were dominated by facultative anaerobes, primarily Lactobacillus species, including L. murinus , L. reuteri , and L. johnsonii . As development progressed, these taxa were progressively replaced by obligate anaerobes such as Anaerotruncus sp. G3 (2012), Bacteroides caecimuris , and members of the Lachnospiraceae family. This taxonomic turnover coincided temporally with the weaning period and the transition from maternal milk to solid chow, consistent with the establishment of a more anaerobic and metabolically complex gut ecosystem during postnatal maturation. Dynamics of dietary protein during early-life Because dietary inputs are a primary driver of gut microbiota assembly dynamics during early life[ 53 ], we next quantified food-derived proteins in pup fecal samples to pinpoint the transition from maternal milk to solid chow. Although dietary proteins accounted for only a small fraction of the total fecal proteome, we reproducibly detected them across developmental stages, which enables direct assessment of dietary transitions in vivo and underscores the sensitivity of our metaproteomic method. Across both cohorts and sexes, we revealed a marked and reproducible shift in dietary protein profiles over time (Fig. 2a). Proteins originating from maternal milk were detected exclusively during the pre-weaning period. Among milk-associated proteins, α-caseins were consistently detected through D20 but were absent thereafter, reflecting the cessation of milk intake around weaning (Fig. 2b). In contrast, Lipid Transferase CIDEA showed low abundance at D10–D12 and was no longer detectable by D20, suggesting early functional changes in lipid processing concurrent with microbial and dietary maturation. Notably, lactotransferrin was detected across the entire developmental window in both cohorts (Fig. 2b; Supplementary Table 10). This persistence likely reflects a transition in the source of lactotransferrin, from maternal milk before weaning to endogenous secretion by the intestinal mucosa after weaning [ 51 , 52 ]. Indicative of a complete transition to solid food, main chow-derived components, like soybeans and wheat, progressively increased and dominated after weaning. Functional specialization underlies increasing microbiome functional redundancy To determine how age-dependent increases in taxonomic complexity and concurrent dietary transitions translate into functional organization of the gut microbiome, we quantified ecological functional redundancy using the normalized functional redundancy index of the proteome (nFRp). In both mouse cohorts, nFRp increased significantly over time, reaching a maximum around postnatal day 34 (Fig. 3a; lmem, Time; p < 0.0001; Supplementary Table 11). Although transient differences between cohorts were observed at intermediate time points, nFRp values converged by day 41, indicating a shared trajectory toward functional stabilization (lmem, Time × Cohort; p < 0.001, Supplementary Table 11). No significant effect of the pups’ sex on the nFRp was detected over time and among the cohorts. This temporal increase in functional redundancy paralleled the rise in microbial species richness (Figs. 1b and S1a), linking taxonomic expansion to the accumulation of overlapping functional capacity at the community level. The increase in functional redundancy reflected both an expansion in the number of detectable microbial functions and coordinated changes in functional expression. The total number of represented KEGG pathways increased from 105 at day 10 to 140 at day 48 (Supplementary Table 12). Consensus clustering of KEGG pathway expression profiles identified two major temporal patterns in each cohort (Fig. 3b and 3c), with no detectable sex-specific effects. The predominant cluster, comprising approximately 80% of KEGG functions (Supplementary Table 13), showed progressive upregulation during early development followed by stabilization around day 34, closely mirroring the nFRp trajectory in both cohorts. These functions encompassed pathways associated with the transition toward anaerobic metabolism (Fig. 1d), dietary change (Fig. 2a), and gut niche establishment, including carbohydrate and lipid metabolism, utilization of host-derived glycans, and functions related to microbial interaction and stress tolerance. In contrast, a smaller cluster of pathways exhibited stable or modestly decreasing expression across development, including core biosynthetic and housekeeping functions such as peptidoglycan biosynthesis and amino acid metabolism. Collectively, these patterns indicate that early-life increases in functional redundancy are driven by coordinated upregulation of specialized metabolic pathways alongside maintenance of core microbial functions. The mother of origin shapes taxonomic and functional engraftment trajectories Analysis of maternal fecal samples collected 26 days after delivery revealed substantial overlap in gut microbiota composition between local (L) and Charles River (CR) mothers, with approximately 65% of maternal species shared (Supplementary Fig. 2a). Nonetheless, each maternal group harbored distinct subsets of taxa, with L mothers carrying more than one quarter of core species not detected in CR dams. Longitudinal tracking of maternal species engraftment in offspring revealed a significant increase in maternal contribution over developmental stages (Fig. 4a; Supplementary Table 14). This pattern was consistent with the age-dependent restructuring of microbial communities observed in β-diversity analyses (Fig. 1c and Supplementary Fig. 1b) and with the progressive functional maturation of the gut microbiome after weaning (Fig. 3a). From D34 onward, CR pups exhibited significantly higher levels of maternal species acquisition compared to L pups (Fig. 4a), indicating cohort-specific engraftment trajectories. These differences were independent of pup sex, as confirmed by sex-stratified analyses (Supplementary Fig. 2b). Analysis of pup core species—defined as taxa detected in at least three pups per cage at D26—revealed limited cage-specific effects, with most species shared across multiple maternal cages (Fig. 4b). To assess whether maternal origin also influenced functional maturation, we compared metabolic-informed taxonomic profiles between mothers and their corresponding offspring. β-diversity analyses based on Bray–Curtis and Jaccard dissimilarities showed that pups from both cohorts were functionally distinct from their mothers early in life but progressively converged toward their respective maternal profiles over time (Fig. 4c–d; Supplementary Table 15). Despite this convergence, pups from the two cohorts remained significantly different from each other in taxonomic–functional composition across all time points from D12 to D48 (Supplementary Table 15), indicating that maternal origin imprints cohort-specific functional trajectories during gut microbiome development. Maturation of the host intestinal proteome parallels the development of the gut microbiome in early life To determine how microbiome maturation is mirrored by host intestinal physiology, we analyzed host-derived proteins quantified by metaproteomics across time points. In total, more than 3,700 host proteins were detected across both mouse cohorts. Over the full developmental period, 1,327 and 691 host proteins were significantly regulated in L and CR pups, respectively, whereas only 69 proteins showed differential temporal regulation between cohorts (lmem, Time × Cohort; p < 0.0001; Supplementary Table 16), indicating a largely conserved host developmental program. The pups’ sex over time did not affect the host proteins significantly, while a few proteins were detected to be significantly different between same sex peers of the two cohorts in a few time points at weaning and after (lmem, Time × Cohort × Sex; p < 0.0001; Supplementary Table 16). Cluster analysis of host protein expression profiles identified four major temporal patterns that captured the principal structure of the data (shown for L pups in Fig. 5a; CR pups shown in Supplementary Fig. 3). Protein–protein interaction network analysis and Gene Ontology enrichment revealed distinct functional trajectories across clusters of L pups (Fig. 5b–e), which were broadly recapitulated in CR pups (Supplementary Fig. 3). Cluster 1 comprised proteins that progressively increased in abundance across development and was enriched for metabolic and catabolic processes, including organic substance metabolism, proteolysis, lipid-related pathways, and intestinal absorption (Fig. 5b). These changes coincide temporally with the transition from milk to only solid food nutrition and with the expansion of strict anaerobic microbial taxa, consistent with maturation of epithelial metabolic and absorptive functions during weaning. Cluster 2 displayed a biphasic pattern, with higher abundance early in life, a marked reduction around weaning, and partial recovery thereafter. Proteins in this cluster were enriched for small-molecule and organic acid metabolism, cellular respiration, fatty acid oxidation, and oxidative phosphorylation (Fig. 5c). These functions reflect metabolic programs with greater reliance on oxidative energy metabolism, prominent in the neonatal intestine, that are transiently downregulated during dietary and microbial transitions toward anaerobic metabolism and fermentation. Cluster 3 included proteins that were highly abundant shortly after birth and progressively decreased over time, and was enriched for cytoskeletal organization, actin filament dynamics, nucleotide metabolism, and the tricarboxylic acid cycle (Fig. 5d). These pathways are consistent with early postnatal programs supporting rapid epithelial growth, tissue organization, and high metabolic demand, which diminish as intestinal architecture and barrier function become established during development. Cluster 4 exhibited a transient increase during early-to-mid development and was enriched for regulatory processes, including negative regulation of proteolysis and hydrolase activity, wound healing, blood coagulation, and responses to tissue injury (Fig. 5e), indicating dynamic regulation of tissue integrity during periods of rapid microbial expansion. Together, these findings indicate that host intestinal functional maturation follows developmental trajectories that align closely with the taxonomic and functional development of the gut microbiome. Expression of host antimicrobial peptides and carbohydrate-active enzymes during early-life To illustrate how global host proteome maturation translates into functionally relevant effector pathways at the host–microbiome interface, we focused on two biologically distinct classes of host proteins: antimicrobial peptides (AMPs), which contribute directly to microbial community regulation [ 54 , 55 ], and host-encoded carbohydrate-active enzymes (CAZymes), which modulate carbohydrate availability in the intestinal lumen [ 50 ]. Of the 152 annotated AMPs considered in our analysis [ 49 ], 90 were reproducibly detected in pups (Supplementary Table 17). The cumulative abundance of the ten most abundant AMPs (representing the 82% of the AMPs' cumulative abundance) differed modestly but significantly between early and late postnatal stages (Supplementary Fig. 4a and Supplementary Table 16). Interestingly, these ten AMPs showed strong time-dependent dynamics (Fig. 6a). No AMP was uniquely detected in either cohort, and no cohort- or sex-specific AMP repertoires were observed. Also, AMP composition and cumulative abundance were largely conserved between L and CR pups, with similar developmental trajectories across cohorts (Fig. 6b and Supplementary Fig. 4b). Host-encoded CAZymes displayed discrete and stage-specific expression patterns. Polypeptide N-acetylgalactosaminyltransferase 4 (Galnt4), an enzyme involved in glycoprotein glycosylation, was highly expressed during the neonatal period (D10–D12) and declined significantly thereafter in both cohorts (Fig. 6c). Conversely, host sialate O-acetylesterase showed a marked increase in expression from D20 onward (Fig. 6d). This enzyme, which regulates sialic acid availability and has been implicated in intestinal homeostasis and carbohydrate metabolism, increased in parallel with gut microbiome maturation and the expansion of microbial pathways associated with host-derived glycan utilization. Together, these results delineate discrete developmental windows during which host antimicrobial and glycan-modifying activities are differentially deployed, highlighting coordinated maturation of host effector functions alongside gut microbiome colonization. Discussion In this study, we present a longitudinal metaproteomic analysis of gut microbiome and host intestinal development in mice, spanning early colonization, the weaning transition, and the emergence of adult-like community structure and function. By integrating 4 different developmental axes - species-resolved microbial profiling, dietary protein detection, functional redundancy analysis, and host proteome characterization - we show how microbial community turnover, functional stabilization, and host intestinal maturation proceed in parallel during early life. Our non-invasive longitudinal design and ultra-sensitive metaproteomic approach enabled repeated sampling of individual animals from postnatal day 10 onward and captured an integrative view of developmental transitions at a resolution previously missed in early-life studies[ 3 – 6 , 56 , 57 ]. Early-life microbial community assembly dynamics and niche construction Early-life gut communities were dominated by facultative anaerobes, particularly Lactobacillus species such as L. murinus , L. reuteri , and L. johnsonii . Metaproteomic profiles of these taxa were enriched for functions related to carbohydrate utilization and host–microbe interactions, consistent with a proposed role for early colonizers in niche construction [ 3 , 58 – 63 ]. Such activities likely shape the physicochemical environment of the neonatal gut and influence subsequent microbial assembly [ 3 , 9 , 13 ]. These early colonizers were progressively replaced by obligate anaerobes, including Bacteroides , Anaerotruncus , and members of the Lachnospiraceae family. This taxonomic turnover coincided with weaning and is consistent with a transition toward a more anaerobic and metabolically complex gut environment driven by dietary change and reduced luminal oxygen availability [ 3 – 6 ]. Dietary transitions and early exposure to solid food Direct detection of dietary proteins provided proteome-level evidence for progressive changes in nutrient exposure during development. Although dietary proteins represented a minor fraction of the fecal proteome (less than 0.01% as shown in Fig. 2b), their reproducible detection enabled direct investigation of dietary transitions in vivo. Milk-derived proteins were detected exclusively before weaning, whereas chow-derived proteins increased thereafter. Unexpectedly, proteins annotated as chow-derived constituted > 90% of the food-derived identifications from D10 through weaning. This finding does not imply that pups derive most nutrition from solid food at this stage. We hypothesize that it indicates that exogenous dietary proteins detectable in pup feces largely originate from the chow diet. Several mechanisms could explain this early signal despite predominantly milk-based feeding, including indirect oral exposure through maternal grooming and transfer of chow residues via maternal fur/nipples, and ingestion of diet-derived material associated with copography of maternal feces[ 12 ]. Early exploratory ingestion of chow dust or softened crumbs may further contribute as pups approach weaning. While milk-mediated transfer of diet-derived peptides cannot be excluded, the dominance of chow-associated proteins among food-derived detections is more consistent with environmental and dam-mediated exposure routes. Our sampling strategy via stimulation of fecal production through the anogenital reflex [ 39 ], in a sterile tube, reduces the possibility for cross-contamination with food, animal fur, and components present in the cage bedding. We note that food-derived proteins comprise only a small fraction of the total fecal proteome; thus, enrichment within this category may reflect detectability and exposure rather than primary caloric input. Importantly, the pattern was reproducible across cohorts/sex/animals and increased progressively across development, consistent with a gradual transition in dietary exposure before formal weaning. After weaning, chow-associated proteins such as those from soybeans and wheat progressively increased and became dominant, consistent with reported transitions from milk-oriented gut communities to those associated with diverse solid foods. Functional redundancy and stabilization of the gut ecosystem Despite pronounced taxonomic turnover, a substantial fraction of microbial functions remained comparatively stable across development, highlighting the role of functional redundancy in maintaining ecosystem resilience. Core pathways involved in cell-wall biosynthesis, amino-acid metabolism, and intrinsic stress resistance were preserved, even as the taxa encoding these functions changed. In parallel, the normalized functional redundancy index (nFRp) increased during development and converged across cohorts by postnatal day 34, indicating a shared trajectory toward functional stabilization. Together, these patterns suggest that increasing taxonomic diversity supports the accumulation of overlapping functional capacity, buffering the gut ecosystem as it matures. Maternal origin and reproducibility of microbiome development Comparison of pups born to long-established local colony mothers and pups born to newly purchased pregnant females revealed that maternal microbiota origin shapes engraftment trajectories [ 15 , 16 , 20 , 64 , 65 ]. Although maternal communities shared many species, each cohort harbored distinct taxa that influenced offspring microbiome assembly. Maternal species acquisition increased with age, and pups progressively converged toward their respective maternal profiles at both taxonomic and functional levels. Cohort-specific differences in engraftment persisted despite identical housing and dietary conditions, underscoring maternal microbiota as a key determinant of early-life microbial development[ 15 , 16 , 65 ]. At the same time, convergence of functional redundancy across cohorts suggests that distinct taxonomic trajectories can nonetheless yield similar ecosystem-level functional outcomes. Host intestinal proteome maturation and host–microbiome coupling Host proteome analysis revealed conserved developmental trajectories that closely paralleled microbial community turnover. Early-life host protein profiles were enriched for pathways associated with epithelial growth and oxidative metabolism, consistent with rapid intestinal development in the neonatal period [ 56 , 57 ]. With maturation, host protein expression shifted toward enhanced metabolic versatility, absorptive capacity, and regulation of tissue homeostasis. These coordinated host and microbial changes indicate that microbiome maturation and host intestinal development are tightly coupled components during early-life. Deployment of host effector pathways: AMPs and CAZymes Host antimicrobial peptides showed stable overall abundance across development, sexes, and cohorts, but pronounced temporal reorganization of individual AMP profiles. Distinct subsets of AMPs predominated before versus after weaning, indicating compositional reprogramming rather than changes in total antimicrobial output. Host-encoded carbohydrate-active enzymes displayed discrete, stage-specific expression patterns, with glycoprotein-modifying enzymes enriched early in life and enzymes regulating sialic acid availability increasing after weaning. These shifts highlight developmental windows during which host effector functions at the host–microbiome interface is differentially deployed. Limitations and future directions This study has several limitations. All mice used in our study belong to C57BL/6 strain. C57BL/6 mice are the most widely used model for microbiota-related human disease studies, given that they share approximately 11.8% of gut microbial species and 90% of functional pathways with humans [ 27 , 66 ]. Our experimental design comparing locally bred mothers to newly purchased animals acknowledges potential genetic or microbial drift. While our results show that overall developmental trajectories are preserved across cohorts, caution is warranted when translating specific taxonomic or functional differences to other mouse strains. In general, taxonomic and functional annotation in metaproteomics remains constrained by reference databases [ 29 , 67 , 68 ], which limits a complete biological view of the gut development process. Although both sexes followed shared maturation programs, we detected a limited but significant number of sex-specific changes. Our results open the door to more pronounced differences when considering menstrual cycle biology, therefore, supporting the recognition of sex as an important factor in mouse-based research[ 69 ]. Finally, while metaproteomics captures expressed functions, complementary measurements of microbial metabolites would further strengthen functional interpretation [ 70 , 71 ]. Conclusions Our results provide an integrated view of gut microbiome and host intestinal development during early life in mice. Our results expand our understanding of early-life host-microbiome interactions and highlight key considerations for experimental design and reproducibility in mouse studies. Declarations Ethics approval and consent to participate: All the experiments included in this publication were approved by the University of Vienna’s Institutional Animal Care and Use Committee (IACUC; protocol n. 2024-001) and carried out in compliance with the rules provided in the Guide for the Care and Use of Laboratory Animals, and EU-Directive for the protection of animals used for scientific purposes. Additionally, this work was reported according to the ARRIVE guidelines. Consent for publication: “Not applicable” Availability of data and material: The datasets generated and/or analysed during the current study are available in the PRIDE repository with the dataset identifier [PXD074520] Username: [email protected] Password: qgnCJc9Vj9N9. Source data are provided with this paper. Competing interests: MS received research awards and travel support by the German Pain Society (DGSS) both of which were sponsored by Astellas Pharma GmbH (Germany). MS received research awards by the Austrian Pain Society. MS received one-time consulting honoraria by Grunenthal GmbH (Germany). None of these sources influenced the content of this study, and MS declares no conflict of interest. DGV and MS have an ongoing scientific collaboration with Bruker (Bruker Center of Excellence for Metaproteomics, University of Vienna). All authors declare that they have no conflicts of interest. Funding: This research was funded in part by the Austrian Science Fund (FWF) [10.55776/P36554 and 10.55776/P35856] and the University of Vienna. Authors' contributions: GC performed the sample collection and data analyses, and contributed in writing the manuscript. FX participated in the LC-MS sample and data analyses, DM participated in the data analyses. MS contributed to the study's planning, to manuscript revision, and to funding acquisition. DVG contributed to the study design, planning, supervision, data analysis, manuscript preparation, and funding acquisition. All authors read and approved the final manuscript Acknowledgements We thank Elisabeth Clifford (Division of Pharmacology & Toxicology, University of Vienna, Austria), Natascha Deutsch, and Peter Höflich as animal caretakers, and Allison Barry for assistance during data analysis. 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Gut Microbes 16(1):2379862. 10.1080/19490976.2024.2379862 Kim N, Kim CY, Ma J, Yang S, Park DJ, Ha SJ et al (2024) MRGM: an enhanced catalog of mouse gut microbial genomes substantially broadening taxonomic and functional landscapes. Gut Microbes 16(1):2393791. 10.1080/19490976.2024.2393791 Alves G, Hamaneh MB, Ogurtsov AY, Yu YK (2026) Taxonomic-Level Protein Quantification in Metaproteomics Using a Biomass-Constrained Expectation-Maximization Approach. J Am Soc Mass Spectrom 37(2):424–439. 10.1021/jasms.5c00332 Blakeley-Ruiz JA, Kleiner M (2022) Considerations for constructing a protein sequence database for metaproteomics. Comput Struct Biotechnol J 20:937–952. 10.1016/j.csbj.2022.01.018 Gundersen BB (2015) Solving for (se)x. Lab Anim (NY) 44(10):371–372. 10.1038/laban.876 Kumar A, Xu C, Dakal TC (2026) Microbiome based precision medicine through integrated multiomics and machine learning. Microbiol Res 303:128384. 10.1016/j.micres.2025.128384 Peters DL, Wang W, Zhang X, Ning Z, Mayne J, Figeys D (2019) Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome. Proteomics 19(16):e1800363. 10.1002/pmic.201800363 Additional Declarations The authors declare no competing interests. Supplementary Files FigureS1.png Figure S1 Taxonomic maturation of gut microbiota in Charles Riverpups (a) The pups’ diversity indexes were calculated at the Species level at each time point of the experiment. Data are plotted as mean (thick line) ± 0.95 confidence intervals with each sex represented in a different color. Since the data are not normally distributed, the analyses for α diversity indexes are based on a Mixed-effects model with Benjamin Hoch correction for multiple comparisons. The α diversity indexes significantly changed over Time. Richness: F(1;68)= 288,07; p<0.000, Shannon F(1;68)= 667,00; p<0.000, and Simpson(1;68)= 1239,02; p<0.000. (b) Non-metric multidimensional scaling plots of β-diversity indexes (Bray-Curtis dissimilarity and Jaccard indexes) of pups' microbiota for each time point of the experiment are plotted with the minimal stress (stress = 0.1456706) after 999 iterations. Permutation test for adonis [PERMANOVA] with Benjamin Hoch correction for pairwise multilevel comparison under reduced model revealed for Bray Curtis index significant overall effect of Time F(1;6)= 14,13; p<0.001. Also, for the Jaccard index it was detected a significant overall effect of Time F(1;13)= 8,74; p<0,001. (c) Relative abundance is reported as the percentage of the mean peptides’summed intensity for each Species amongthe pups of the same sex (Fe = Female, Ma = Male). Species detected with relative abundance below 1% had been reported as \"Other\". Above each column of the histogram is reported the number of mice of the same sex analyzed. FigureS2.png Figure S2 Engraftment and evolution of the gut microbiota over time according to pup sexes and the mother's gut microbiota. (a)Venn diagram shows the relationship between the maternal core species of the two cohorts (species detected in all the mothers of each of the cohorts) on day 26 after the delivery of the pups. n = 8 (4 mothers for each cohort). (b) Maternal engraftment of the pups over time, divided by cohort and sex. In the plot are reported the percentages of species belonging to the mothers’ core species (species detected in at least 3 mothers of the same cohort) detected in same sex peers from the two cohorts over time. The error bar represents the standard deviation SD. n = 16 for each cohort (8 Females and 8 Males). FigureS3.png Figure S2 Engraftment and evolution of the gut microbiota over time according to pup sexes and the mother's gut microbiota. (a)Venn diagram shows the relationship between the maternal core species of the two cohorts (species detected in all the mothers of each of the cohorts) on day 26 after the delivery of the pups. n = 8 (4 mothers for each cohort). (b) Maternal engraftment of the pups over time, divided by cohort and sex. In the plot are reported the percentages of species belonging to the mothers’ core species (species detected in at least 3 mothers of the same cohort) detected in same sex peers from the two cohorts over time. The error bar represents the standard deviation SD. n = 16 for each cohort (8 Females and 8 Males). figureS4.png Figure S4. Antimicrobial Peptides (AMPs) detected during the postnatal development in local and CR pups. (a) Cumulative abundance of the 10 most abundant AMPs detected during the development of local pups. A linear mixed-effects model (lmem) revealed a significant effect of Time F[1,6] = 8.96, p = .0005 (Table 16). n = 16 mice (8 Females and 8 Males). (b)Relative AMPs abundance between sex-matched local and CR pups during development. Linear Mixed-Effects Models with Benjamini–Hochberg correctionrevealed a significant abundance difference between the Male pups of the two cohorts at days 20, 34, and 48 after birth (Time Cohort Sex F[1,6] = 2.39, p = .026; Table All AMPs local and CR). n = 16 mice for each cohort (8 Females and 8 Males). Females =Fe; Males = Ma; local = L; Charles River = CR; data are plotted based on the days after birth 10, 12, 20, 26, 34, 41, 48. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9367132\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":620256761,\"identity\":\"ae9daf05-66df-4df3-886a-cc062b0b4c02\",\"order_by\":0,\"name\":\"Giacomo Carta\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-4059-7089\",\"institution\":\"Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Giacomo\",\"middleName\":\"\",\"lastName\":\"Carta\",\"suffix\":\"\"},{\"id\":620256762,\"identity\":\"02274f8c-dfc7-4e84-ad39-95940fc2155c\",\"order_by\":1,\"name\":\"Feng Xian\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-8345-0108\",\"institution\":\"Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Feng\",\"middleName\":\"\",\"lastName\":\"Xian\",\"suffix\":\"\"},{\"id\":620256763,\"identity\":\"96da0d48-20ad-43ae-b8c9-262a01328116\",\"order_by\":2,\"name\":\"Daniel Malzl\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0001-5765-6375\",\"institution\":\"CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daniel\",\"middleName\":\"\",\"lastName\":\"Malzl\",\"suffix\":\"\"},{\"id\":620256764,\"identity\":\"1003fd64-e40d-4b38-9d94-bbf0107a3cd1\",\"order_by\":3,\"name\":\"Manuela Schmidt\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-1972-3519\",\"institution\":\"Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Manuela\",\"middleName\":\"\",\"lastName\":\"Schmidt\",\"suffix\":\"\"},{\"id\":620256765,\"identity\":\"20cb44cc-6e73-47bf-88fa-93feca0723fd\",\"order_by\":4,\"name\":\"David Gómez-Varela1\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"https://orcid.org/0000-0003-2502-9419\",\"institution\":\"Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"David\",\"middleName\":\"\",\"lastName\":\"Gómez-Varela1\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-09 10:25:39\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":true,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":true},\"doi\":\"10.21203/rs.3.rs-9367132/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9367132/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":106627012,\"identity\":\"752dcf53-1eee-4275-b5a2-ff17895f88cd\",\"added_by\":\"auto\",\"created_at\":\"2026-04-10 15:08:05\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1856798,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTaxonomic maturation of gut microbiota in local pups\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003e Experimental design to characterize the microbiota composition of 16 pups (eight females and eight males) for each Cohort (Local, L, and Charles River, CR) is shown schematically. Before weaning, pups were naturally fed by their moms with milk ad libitum. However, chow was provided in the cage for the mother’s feeding. After weaning on day 21, the pups were caged and divided by sex with siblings from the same mother for 27 days and fed exclusively with chow. The microbiota from pups' fecal samples was examined on days 10, 12, 20, 26, 34, 41, and 48 after birth. \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Alpha diversity indices (Richness, Shannon, Simpson) were calculated at the species level at each time point. Data are shown as mean (thick line) ± 95% confidence intervals, with sexes indicated by different colors. As data were not normally distributed, mixed-effects models with Benjamini–Hochberg correction for multiple testing were applied. All alpha diversity indices showed a significant effect of Time (Richness: F(1,82)=315.12, p\\u0026lt;0.0001; Shannon: F(1,82)=851.18, p\\u0026lt;0.0001; Simpson: F(1,82)=990.80, p\\u0026lt;0.0001). \\u003cstrong\\u003e(c)\\u003c/strong\\u003e Non-metric multidimensional scaling plots based on Bray–Curtis dissimilarity and Jaccard indices illustrate beta diversity at each time point (stress = 0.19; 999 iterations). Permutation test for adonis [PERMANOVA] with Benjamin Hoch correction for pairwise multilevel comparison under reduced model revealed for Bray Curtis index significant overall effect of Time F(1;13)= 6,45; p\\u0026lt;0.001. Also, for the Jaccard index,a significant overall effect of Time F(1;13)= 4,46; p\\u0026lt;0.001 was detected. \\u003cstrong\\u003e(d)\\u003c/strong\\u003e Relative abundance is reported as the percentage of the mean peptides’summed intensity for each Species among the pups of the same sex (Fe = Female, Ma = Male). Species detected with relative abundance below 1% had been reported as \\\"Other\\\". Above each column of the histogram is reported the number of mice of the same sex analyzed.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/cc987b49aae07e69ebbce42e.png\"},{\"id\":106627013,\"identity\":\"e61bbfee-deb2-48da-95f7-e9602aa4338a\",\"added_by\":\"auto\",\"created_at\":\"2026-04-10 15:08:06\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1006903,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDetection of Food Proteins\\u003c/strong\\u003e \\u003cstrong\\u003edetected during the postnatal development\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003e All proteins are presented as mean normalized relative abundances across the same category (time, cohort, sex). The mean normalized relative abundance was calculated as the summed peptide intensity for each protein divided by the total peptide intensity of the sample. The Kruskal-Wallis test, followed by post-hoc Dunn's test for multiple comparisons with BH correction, revealed no significant abundance difference between cohorts. \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Mean normalized relative abundances of the maternal milk proteins between local and CR pups throughout development; n = 16 pups per cohort and time point (8 females and 8 males).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/2a015925aeb0ea3e740f5406.png\"},{\"id\":106726892,\"identity\":\"a91eadec-0bc9-4c42-894e-cbdae763ae5d\",\"added_by\":\"auto\",\"created_at\":\"2026-04-12 18:37:32\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1171238,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunctional redundancy and gut microbiota functional clustering.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003enFRp values for pups of different cohorts are reported. Estimated marginal means with Benjamin Hochberg correction from Linear Mixed-Effects Models detected a significant overall effect of time F(6;145)= 15,26; p\\u0026lt;0.0001, and cohort* time F(1;145)= 4,31; p\\u0026lt;0.0004. n = 32; 16 for each experimental group (8 Females and 8 Males). Each point in the violin plot represents a pup’ssample. \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Temporal clustering of microbial metabolic pathways in the CR cohort. Pathways were clustered based on their standardized expression (Z-score) profiles across time. Two distinct temporal patterns emerged: Cluster 0 (blue, dashed line, n=11 pathways) and Cluster 1 (orange, solid line, n=46 pathways). Lines represent cluster means, while shaded areas indicate the standard deviation of the mean (SD). \\u003cstrong\\u003e(c)\\u003c/strong\\u003eTemporal clustering of microbial metabolic pathways in the L cohort. Pathways were clustered based on their standardized expression (Z-score) profiles across time. Two distinct temporal patterns emerged: Cluster 0 (green, dashed line, n=12 pathways) and Cluster 1 (pink, solid line, n=54 pathways). Lines represent cluster means, while shaded areas indicate the standard deviation of the mean (SD).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/02f803f632ef811bed0f09da.png\"},{\"id\":106627014,\"identity\":\"cc019d9b-7c73-4ab5-a546-e4f25e737aac\",\"added_by\":\"auto\",\"created_at\":\"2026-04-10 15:08:06\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2216751,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eEngraftment of the mother's of origin gut microbiota over time.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003e Engraftment of the two pups cohort over time. The plot reports the percentages of the mothers’ core species (detected in at least 3 Mothers of the same cohort) over time. Estimated marginal means with Benjamin Hochberg correction from Linear Mixed-Effects Models detected a significant overall effect of Cohort F(1;6)= 14,33; p\\u0026lt;0.0007, and Time*Cohort F(1;6)= 7,38; p\\u0026lt;0.00001. (see table S13). Differences between different time points are reported. ** \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.01, and **** \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.00001. n = 16 pups for each cohort and time point (8 Females and 8 Males). \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Venn diagrams illustrates the relationship, in the local (i.) and Charles River (ii) cohorts, between the mothers’ core species (species detected in at least 3 mothers) and the pups’ core species (species detected in at least 3 pups of the same cages) on day 26 after the delivery of the pups, categorized by the provenance cage. n = 20 mice (8 Females and 8 Males, and 4 mothers). \\u003cstrong\\u003e(c) \\u003c/strong\\u003eThe non-metric multidimensional scaling plots of the β-diversity Bray-Curtis index of pups metabolic-informed taxonomical indexes, and their mothers of origin, over time, are plotted with the minimal stress (stress = 0.1949407) after 999 iterations. Permutation test for adonis [PERMANOVA] with Benjamin Hoch correction for pairwise multilevel comparison under reduced model revealed a significant effect of Time*Cohort *Category F(1;15)= 7.685; p\\u0026lt;0.001. \\u003cstrong\\u003e(d)\\u003c/strong\\u003e The non-metric multidimensional scaling plots of the β-diversity Jaccard index of pups metabolic-informed taxonomical indexes and their mothers of origin, over time, are plotted with the minimal stress (stress = 0.1962058) after 999 iterations. Permutation test for adonis [PERMANOVA] with Benjamin Hoch correction for multilevel comparison under reduced model revealed a significant overall effect of Time*Cohort *Category F(1;15)= 4.734; p\\u0026lt;0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/1f25efe158eb793364a64f7a.png\"},{\"id\":106726946,\"identity\":\"ff6c0e50-98e6-491d-b2a5-b01e915256cf\",\"added_by\":\"auto\",\"created_at\":\"2026-04-12 18:37:47\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":9994397,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTemporal dynamics, clustering, and functional enrichment of host gut proteins in L pups.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003e Protein clusters are plotted as the mean standardized intensity, considering proteins with the highest membership score (\\u0026gt; 0.7–0.9) in each detected cluster. Each line represents an individual protein, with colour intensity reflecting membership strength (blue representing low and red representing high intensity). \\u003cstrong\\u003e(b–e)\\u003c/strong\\u003eProtein–protein interaction networks and Gene Ontology enrichment analyses for proteins with the highest membership score (\\u0026gt; 0.7–0.9). Protein full STRING networks obtained with evidence as a parameter for the meaning of the network edges, with a minimum required interaction score of 0.7, were generated using STRING12.0 (\\u003ca href=\\\"https://stringdb.org/\\\"\\u003ehttps://stringdb.org\\u003c/a\\u003e; 23/05/2025),and visualized with functional annotation. Nodes represent proteins, and edges represent predicted interactions. The adjacent bar plots display the top enriched biological processes (GO terms) along with their corresponding gene counts and statistical significance (FDR). n = 16 mice (8 females and 8 males).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/db5fe1f536a8c7f5de0516da.png\"},{\"id\":106627018,\"identity\":\"39605cd0-379d-41f6-8b09-eb2fa28ae146\",\"added_by\":\"auto\",\"created_at\":\"2026-04-10 15:08:06\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2981464,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTemporal expression of antimicrobial peptides (AMPs) and carbohydrate-active enzymes\\u003c/strong\\u003e \\u003cstrong\\u003e(CAZymes)\\u003c/strong\\u003e \\u003cstrong\\u003eduring postnatal early-life in local pups.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003e Relative abundance (presented as mean normalized relative abundances calculated as the summed peptide intensity for each protein divided by the total peptide intensity of the sample ) of the top 10 most abundant AMPs. All of these exhibit a significant abundance difference over time (Kruskal-Wallis test, followed by Post-hoc Dunn's test for multiple comparisons with Benjamini–Hochberg (BH) correction; all significance levels are indicated in Supplementary Table17). n = 16 mice (8 Females and 8 Males). \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Relative AMPs abundance between local and Charles River pups (local = L; Charles River = CR) over developmental time. Linear Mixed-Effects Models with BH correction revealed a significant abundance difference between the two cohorts at days 34 and 48 after birth (Time*cohort F[1,6] = 2.39, p = .026). n =16 mice for each cohort (8 Females and 8 Males). \\u003cstrong\\u003e(c-d)\\u003c/strong\\u003e Mean relative abundance of two carbohydrate-active enzymes: Polypeptide N-acetylgalactosaminyltransferase 4 andSialate O-acetylesterase among the two cohort pups (local = L; Charles River = CR). The Kruskal–Wallis test, followed by post-hoc Dunn’s test for multiple comparisons with BH correction, revealed significant temporal differences in CAZyme abundance. Statistically significant differences in AMPs and CAZyme abundance are indicated as follows: * compared to D10; # compared to D12.p \\u0026lt; 0.05 (*, #), p \\u0026lt; 0.01 (**, ##), p \\u0026lt; 0.001 (***, ###), p \\u0026lt; 0.0001 (****, ####). n = 16 mice for each cohort (8 Females and 8 Males).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/69468843429652b889040f7a.png\"},{\"id\":108180872,\"identity\":\"d014daf5-402c-4f46-a106-5633609dfca2\",\"added_by\":\"auto\",\"created_at\":\"2026-04-30 08:54:35\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":17140313,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/fa6f48f3-52c1-4db0-8b0d-24006339e1ad.pdf\"},{\"id\":106725937,\"identity\":\"71f2e1bf-0cdc-4a76-a6e1-12bc11f5ec85\",\"added_by\":\"auto\",\"created_at\":\"2026-04-12 18:34:34\",\"extension\":\"png\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1500259,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S1\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTaxonomic maturation of gut microbiota in Charles Riverpups\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003e The pups’ diversity indexes were calculated at the Species level at each time point of the experiment. Data are plotted as mean (thick line) ± 0.95 confidence intervals with each sex represented in a different color. Since the data are not normally distributed, the analyses for α diversity indexes are based on a Mixed-effects model with Benjamin Hoch correction for multiple comparisons. The α diversity indexes significantly changed over Time. Richness: F(1;68)= 288,07; p\\u0026lt;0.000, Shannon F(1;68)= 667,00; p\\u0026lt;0.000, and Simpson(1;68)= 1239,02; p\\u0026lt;0.000. \\u003cstrong\\u003e(b) \\u003c/strong\\u003eNon-metric multidimensional scaling plots of β-diversity indexes (Bray-Curtis dissimilarity and Jaccard indexes) of pups' microbiota for each time point of the experiment are plotted with the minimal stress (stress = 0.1456706) after 999 iterations. Permutation test for adonis [PERMANOVA] with Benjamin Hoch correction for pairwise multilevel comparison under reduced model revealed for Bray Curtis index significant overall effect of Time F(1;6)= 14,13; p\\u0026lt;0.001. Also, for the Jaccard index it was detected a significant overall effect of Time F(1;13)= 8,74; p\\u0026lt;0,001.\\u003cstrong\\u003e (c)\\u003c/strong\\u003e Relative abundance is reported as the percentage of the mean peptides’summed intensity for each Species amongthe pups of the same sex (Fe = Female, Ma = Male). Species detected with relative abundance below 1% had been reported as \\\"Other\\\". Above each column of the histogram is reported the number of mice of the same sex analyzed.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/60af24a54da58f92197133f0.png\"},{\"id\":106726689,\"identity\":\"0285ae10-497b-4e6f-9ac2-34f007f69b7e\",\"added_by\":\"auto\",\"created_at\":\"2026-04-12 18:37:03\",\"extension\":\"png\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":316069,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S2\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEngraftment and evolution of the gut microbiota over time according to pup sexes and the mother's gut microbiota.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003eVenn diagram shows the relationship between the maternal core species of the two cohorts (species detected in all the mothers of each of the cohorts) on day 26 after the delivery of the pups. n = 8 (4 mothers for each cohort). \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Maternal engraftment of the pups over time, divided by cohort and sex. In the plot are reported the percentages of species belonging to the mothers’ core species (species detected in at least 3 mothers of the same cohort) detected in same sex peers from the two cohorts over time. The error bar represents the standard deviation SD. n = 16 for each cohort (8 Females and 8 Males).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/1cc1c0a7fc7030166360d2ba.png\"},{\"id\":106726692,\"identity\":\"73d7bbc5-e95a-4ab2-a8dd-b0c106f6d6f1\",\"added_by\":\"auto\",\"created_at\":\"2026-04-12 18:37:03\",\"extension\":\"png\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11306277,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S2\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEngraftment and evolution of the gut microbiota over time according to pup sexes and the mother's gut microbiota.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003eVenn diagram shows the relationship between the maternal core species of the two cohorts (species detected in all the mothers of each of the cohorts) on day 26 after the delivery of the pups. n = 8 (4 mothers for each cohort). \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Maternal engraftment of the pups over time, divided by cohort and sex. In the plot are reported the percentages of species belonging to the mothers’ core species (species detected in at least 3 mothers of the same cohort) detected in same sex peers from the two cohorts over time. The error bar represents the standard deviation SD. n = 16 for each cohort (8 Females and 8 Males).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FigureS3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/5a945c37dd95138619a50a2e.png\"},{\"id\":106627016,\"identity\":\"ff0a168a-6eed-4140-85c8-152f3e1844eb\",\"added_by\":\"auto\",\"created_at\":\"2026-04-10 15:08:06\",\"extension\":\"png\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2800784,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure S4. Antimicrobial Peptides (AMPs) detected during the postnatal development in local and CR pups.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(a)\\u003c/strong\\u003e Cumulative abundance of the 10 most abundant AMPs detected during the development of local pups. A linear mixed-effects model (lmem) revealed a significant effect of Time F[1,6] = 8.96, p = .0005 (Table 16). n = 16 mice (8 Females and 8 Males). \\u003cstrong\\u003e(b)\\u003c/strong\\u003eRelative AMPs abundance between sex-matched local and CR pups during development. Linear Mixed-Effects Models with Benjamini–Hochberg correctionrevealed a significant abundance difference between the Male pups of the two cohorts at days 20, 34, and 48 after birth \\u0026nbsp;(Time*Cohort*Sex F[1,6] = 2.39, p = .026; Table All AMPs local and CR). n = 16 mice for each cohort (8 Females and 8 Males). Females =Fe; Males = Ma; local = L; Charles River = CR; data are plotted based on the days after birth 10, 12, 20, 26, 34, 41, 48.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"figureS4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9367132/v1/5c3232bda6a2309bc01195b9.png\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eIntegrative characterization of host–microbiome-diet axes during early-life development of the murine gut\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eThe gut microbiome plays a central role in mammalian development, shaping host physiology through effects on immune maturation, nutrient metabolism, and organ development [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Early life represents a critical window during which gut microbial communities are established and refined [\\u003cspan additionalcitationids=\\\"CR5 CR6\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e], with long-term consequences for health and disease susceptibility [\\u003cspan additionalcitationids=\\\"CR8 CR9 CR10 CR11 CR12\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Despite extensive interest in early-life microbiome development, how microbial community assembly, functional maturation, and host intestinal development are coordinated over time is unknown.\\u003c/p\\u003e \\u003cp\\u003eMost current knowledge of gut microbiome development derives from genomic and amplicon-based studies [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR15 CR16\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e], which primarily describe taxonomic composition and, consequently, infer functional potential. While these approaches have been instrumental in defining broad patterns of microbial community assembly dynamics, they do not directly capture expressed microbial functions or host physiological responses[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Moreover, many studies rely on cross-sectional sampling or limited temporal resolution [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], restricting insight into dynamic host\\u0026ndash;microbiome-diet interactions during key developmental transitions such as weaning. As a result, the functional mechanisms underlying diet, early microbial colonization, host intestinal maturation, and ecosystem-level functional stabilization are only poorly characterized.\\u003c/p\\u003e \\u003cp\\u003eMurine models, particularly C57BL/6 mice, are widely used to study microbiome-associated processes relevant to human health, owing to their genetic tractability and substantial overlap in microbial functional capacity with humans [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Beyond host genetics and diet, early-life microbial assembly is also shaped by vertical transmission from the mother [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Maternal microbiota have emerged as key biological determinants of early microbial engraftment, influencing both the initial composition and the subsequent developmental trajectory of the gut ecosystem [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAt the same time, accumulating evidence indicates that gut microbiota composition in C57BL/6 mice varies across vendors, breeding colonies, and housing conditions, introducing a significant source of experimental variability [\\u003cspan additionalcitationids=\\\"CR22 CR23\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Differences in maternal microbiota associated with animal sourcing and colony history can therefore imprint cohort-specific microbial trajectories in offspring, even under otherwise standardized experimental conditions. Yet, the extent to which such maternal-origin effects are reflected over early development, and whether they translate into functional signatures in the adult pup\\u0026rsquo;s gut ecosystem, is unknown. This uncertainty has important implications for the implementation, reproducibility, and interpretation of mouse-based studies.\\u003c/p\\u003e \\u003cp\\u003eDietary transitions represent another major driver of microbiome maturation during early life [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. In both humans and mice, the shift from maternal milk to solid food coincides with marked changes in microbial composition and metabolic activity [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. However, the timing and extent of early exposure to solid food components, and how these dietary inputs intersect with microbial succession and host intestinal responses, have been difficult to assess directly due to sensitivity limits.\\u003c/p\\u003e \\u003cp\\u003eTo address these gaps, we applied longitudinal ultra-sensitive metaproteomics [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e] to profile gut microbiome and host intestinal development in mice across seven postnatal time points, from early colonization through weaning and into early adulthood. Using non-invasive and sterile fecal sampling, we followed pups from two contemporaneously raised C57BL/6 cohorts that differed only in maternal origin\\u0026mdash;a long-established local colony and newly purchased pregnant females from the same vendor\\u0026mdash;while systematically accounting for sex-specific effects. This integrated approach enabled the detection of very low-abundant dietary proteins, species-resolved microbial profiling, direct measurement of expressed microbial functions, and parallel assessment of host intestinal proteome maturation. By capturing microbial, dietary, functional, and host axes within a single longitudinal framework, this study provides a comprehensive view of how developmental programming of the murine gut ecosystem emerges during early life.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMice\\u003c/h2\\u003e \\u003cp\\u003e All the experiments included in this publication were approved by the University of Vienna\\u0026rsquo;s Institutional Animal Care and Use Committee (IACUC; protocol n. 2023-016; February-June 2023) and carried out in compliance with the rules provided in the Guide for the Care and Use of Laboratory Animals, and EU-Directive for the protection of animals used for scientific purposes. Additionally, this work was reported according to the ARRIVE guidelines.\\u003c/p\\u003e \\u003cp\\u003eTo explore the changes in the gut microbiota-host development, 4 local (L) and 4 purchased C57BL/6J pregnant females (8\\u0026ndash;9 weeks old) from Charles River (CR) were enrolled in the study. The labelled as \\u0026ldquo;Local\\u0026rdquo; mothers and pups were obtained from the breeding colony established at the University of Vienna in October 2021 using founder parents obtained from the same vendor from which the new mothers (CR) were purchased. To minimize the impact of environmental factors on the evolution of the microbiota-host development, the mothers and pups from CR and our L colonies were housed in the same room on two different racks under the same humidity and temperature [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. All mice had \\u003cem\\u003ead libitum\\u003c/em\\u003e access to chow (SM VRF1 \\u0026ndash; Breeding diet fortified, autoclavable / γ-irradiated, ssniff Spezialdi\\u0026auml;ten GmbH) and water, under 12:12 light/dark cycle, with light starting at 7:00 AM. Each transparent polycarbonate cage was provided with corncob bedding, and a standardized enriched environment consisting of a red transparent plastic nest box, two wooden tunnels (15x5x5 cm), and a handful of crinkled natural paper strands for building a high-quality nest [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Every Monday, the mice were moved into a cleaned cage with the autoclaved enrichment described above. The cage was changed the following week only if the mother delivered pups on Monday to avoid stress-related behaviors of the mother. Each pup was considered an experimental unit. Peers of the same sex, from the same cage, were co-housed in the same cage at a maximum of 5 mice per cage. All mice were weaned at 21 days after birth. No mice were single-caged after weaning. The study did not have humane endpoints since none of the procedures adopted were invasive, painful, or stressful for the animals. Mice were handled daily by trained animal facility technicians. No animal was sacrificed in the present study.\\u003c/p\\u003e \\u003cp\\u003eBased on the significant detectable difference in Bray-Curtis index between adult male and female C57BL/6J mice obtained from different vendors, as shown by Long and coworkers [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e], we used a total sample size of 32 animals (7 for sex/group\\u0026thinsp;+\\u0026thinsp;1 possible dropout) with an effect size of η\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;.14 (Power [1-β]=.95 and α\\u0026thinsp;=\\u0026thinsp;.05) with an actual power of .969.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eAssay strategies and exclusion criteria\\u003c/h3\\u003e\\n\\u003cp\\u003eThe same male experimenter performed the fecal samples (FS) collection from pups and mothers from the L and the CR cohorts [\\u003cspan additionalcitationids=\\\"CR33\\\" citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. The tools and the experimenter's hands were disinfected with water and 70% ethanol between each animal manipulation. The experimental groups were assessed randomly (Microsoft Excel random list) at each time point from 8 to 12 AM. From Day 8 (D8), all the pups were tail-marked with a surgical pen, and the marks were renewed every 2 days [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. Pups were \\u003cem\\u003ea priori\\u003c/em\\u003e excluded from the experiment if they did not provide FS after 2 collection attempts per day for two consecutive days. For this reason, to reach the above-mentioned sample size of 32 animals, we used a total of 42 females (18 CR and 28 Local) and 35 males (13 CR and 22 Local) were used to assure reliable fecal sample amount.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eFecal samples collected from pups and mothers.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe FSs from pups were collected as soon as possible after birth. FSs were collected with an interval of at least 2 days between one collection and the other, since a smaller time interval provided no collection of fecal pellets. The FS obtained from the mothers (4 from each group) was collected at 26 days after the pups' delivery (5 days after weaning to minimize the mother stress consequences on pups during lactation) [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. This way, microbial community similarity structure and baseline values for the offsprings\\u0026rsquo; microbial composition were provided [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe FSs were collected by stimulating the anogenital reflex, mimicking the maternal licking to stimulate defecation and urination in the mice[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. The pups were stimulated with soft repeated light strokes (cranio-caudal direction) on the perianal region with a wet cotton swab. This technique was adopted for all the animals at each time point. During the stimulation, a sterile plastic 1,5 ml tube was positioned below the mouse tail, and the FSs were collected from the animal by gravity to reduce as much as possible urine and fur contamination.\\u003c/p\\u003e \\u003cp\\u003eFresh FSs were collected in the morning (8\\u0026ndash;12 AM), placed immediately in autoclaved 1.5 ml plastic sterile tubes pre-cooled on ice, and stored at -80\\u0026deg;C.\\u003c/p\\u003e \\u003cp\\u003eResults reported in this study were obtained from a total of 16 females (8 CR and 8 L) and 16 males (8 CR and 8 L). A total of 224 samples were analysed.\\u003c/p\\u003e\\n\\u003ch3\\u003eProtein extraction, protein digestion, and peptide clean-up\\u003c/h3\\u003e\\n\\u003cp\\u003eThe sequence of the FS collected was randomized, and samples were relocated in new boxes and processed for analysis by a blinded operator.\\u003c/p\\u003e \\u003cp\\u003eThe protein preparation protocol as previously described[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e], started by taking 30\\u0026ndash;50 mg of FS that was mixed with 200 \\u0026micro;L of lysis buffer (5% SDS, 2 M urea, 50 mM Tris-HCl, protease inhibitor 1x) and vortexed vigorously for 1 min. The samples were then placed in a Thermomixer (serial number: 5382JR638726, Eppendorf, Hamburg, Germany) at 1,200 rpm and 70\\u0026deg;C for 15 min. The samples were then ultrasonicated using a Bioruptor Pico (Diagenode, Seraing, Belgium, program: 15 cycles of 30 s \\\"on\\\" and 30 s \\\"off\\\", frequency level: Low, water temperature: +20\\u0026deg;C). At room temperature, the tubes were then centrifuged for 5 minutes at 16,000 g. The supernatant was moved into a new tube, centrifuged again, and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C until protein digestion with SP3 was performed. In the case of early D (10\\u0026ndash;12 days) when the FSs range from 0.1 to 10 mg all the sample collected was processed as reported above. A modified version of the one-pot solid-phase enhanced sample preparation method (SP3) of Hughes et al.[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e] is used for protein purification and digestion was used. In brief, 50 \\u0026micro;g of protein was added to a 1.5 mL LoBind tube (Eppendorf, Hamburg, Germany), and 50 \\u0026micro;L of lysis buffer was added. Samples went through the processes of protein reduction (5 mM dithiothreitol, DTT, incubated for 30 min at +\\u0026thinsp;60\\u0026deg;C) and alkylation (20 mM iodoacetamide, IAA, 30 min in the dark at room temperature). The remaining IAA in the sample was quenched by adding DTT at a final concentration of 5 mM. Premixed Sera-Mag SpeedBead beads were added in a volume of 10 \\u0026micro;L (50 mg/mL, Cytiva, Marlborough, MA) to a 50 \\u0026micro;g protein sample. One volume of absolute ethanol was immediately added to initiate the protein binding to the beads, followed by incubation and agitation at 1,000 rpm for 5 min at 24\\u0026deg;C in a Thermomixer. After standing on a magnetic rack for 2 minutes, the supernatant was removed, and the beads were rinsed three times with 500 \\u0026micro;L of 80% ethanol. The obtained beads were reconstituted in 50 \\u0026micro;L of digestion buffer (50 mM ammonium bicarbonate, pH 8). 2 \\u0026micro;g of sequencing grade trypsin/LysC (Promega, Madison, USA) was used for protein digestion in a Thermomixer with agitation at 950 rpm for 18 h at 37\\u0026deg;C. After a 30-second spin, acetonitrile (ACN) was added to each sample to reach a final concentration of 95%. The mixture was incubated at room temperature for 8 minutes and then placed on a magnetic rack for 2 minutes. After the supernatant was discarded 900\\u0026micro;L of 100% ACN was added, 15 seconds on a magnetic rack and removed from each tube. To elute the peptides, the washed beads were reconstituted in 20 \\u0026micro;L of liquid chromatography-mass spectrometry (LC-MS) grade water. Peptide concentrations were measured in duplicate at 205 nm using a NanoPhotometer N60 (serial number: TG2022, Implen, Munich, Germany). A final concentration of 0.1% Formic acid (FA) was added to acidify peptides and the tubes were stored at \\u0026minus;\\u0026thinsp;20\\u0026deg;C until LC-MS/MS analysis.\\u003c/p\\u003e\\n\\u003ch3\\u003eLiquid Chromatography Mass Spectrometry (LC-MS/MS)\\u003c/h3\\u003e\\n\\u003cp\\u003eNanoflow reversed-phase liquid chromatography (Nano-RPLC) was conducted using a NanoElute system (Bruker Daltonik, Bremen, Germany). A total of 250 ng of digested peptides were loaded onto a trap column (Neo-Trap C18, 5 mm x 300 \\u0026micro;m, Thermo Fischer) and then separated on a 25 cm x 75 \\u0026micro;m column, packed with 1.7 \\u0026micro;m C18 particles (IonOpticks, Fitzroy, Australia), using a 60 min gradient. Mobile phase A consisted of 100% water (LC-MS grade) with 0.1% formic acid (FA), while mobile phase B was 100% acetonitrile with 0.1% FA. The separation was performed at a flow rate of 250 nL/min, except for the final 7 min, during which the flow rate was increased to 400 nL/min. The gradients included a linear increase of mobile phase B from 4% to 20% over the first 35 minutes, followed by an increase to 35% over 17 minutes. Subsequently, mobile phase B was ramped up to 85% within 0.5 minutes and held at this level for 7 minutes to elute hydrophobic peptides. Peptide eluates were analyzed on a hybrid TIMS quadrupole TOF mass spectrometer (timsTOF Pro2, Bruker Daltonik) coupled via a CaptiveSpray ion source. Data acquisition was performed in data-independent acquisition (DIA) mode, in combination with parallel accumulation serial fragmentation (PASEF). The TIMS analyzer operated at 100% duty cycle with an accumulation and ramp time of 100 ms. Ion mobility separation was set in the range of 0.65\\u0026ndash;1.40 (1/k0). Precursors within an \\u003cem\\u003em/z\\u003c/em\\u003e range of 400\\u0026ndash;1200 were targeted in 12 scan cycles with 3 isolation steps in each cycle, comprising 32 isolation windows of 25 Th, resulting in a total cycle time of 1.38 seconds. Collision energy was ramped linearly from 65 eV at 1/k0\\u0026thinsp;=\\u0026thinsp;1.6 to 20 eV at 1/k0\\u0026thinsp;=\\u0026thinsp;0.6.\\u003c/p\\u003e\\n\\u003ch3\\u003eDIA-PASEF data processing\\u003c/h3\\u003e\\n\\u003cp\\u003eTo process DIA-PASEF data[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e], DIA-NN (Linux version 1.8.1) was used in library-free mode and based on a microbial database of 48,970 protein sequences and the standard Uniprot proteome of Mus musculus (accessed on 20230427). Within DIA-NN, a deep learning-based method was used to predict theoretical peptide spectra, retention time, and ion mobility. Trypsin/P was used for \\u003cem\\u003ein silico\\u003c/em\\u003e digestion, with a maximum of 2 missed cleavages. Peptide identification allowed up to two variable modifications per peptide, including methionine oxidation and N-terminal acetylation. Carbamidomethylation on cysteine was selected as a fixed modification. Peptides were restricted to lengths between 7 and 50 amino acids. The \\u003cem\\u003em/z\\u003c/em\\u003e range was defined according to the DIA-PASEF acquisition method: 400\\u0026ndash;1200 for precursor ions and 100\\u0026ndash;1700 for fragment ions. Mass accuracy for both MS1 and MS2 was set to automatic determination. Protein inference was performed at the protein name level, with the \\u0026ldquo;Heuristic protein inference\\u0026rdquo; option disabled. Raw data files were processed in parallel. DIA-NN first searched each file individually against the predicted spectra library to generate a quant file. In the second step, DIA-NN built an empirical spectral library based on all quant files[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. And lastly, all files were reprocessed against the empirical spectral library using the match-between-runs (MBR) feature. Quantification was carried out using retention time\\u0026ndash;dependent cross-run normalization with the \\u0026ldquo;Robust LC (high precision)\\u0026rdquo; setting.\\u003c/p\\u003e \\u003cp\\u003eThe resultant precursor intensities were further processed using the R package DIA-NN (v1.0.1; \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://github.com/vdemichev/diann-rpackage\\u003c/span\\u003e\\u003cspan address=\\\"https://github.com/vdemichev/diann-rpackage\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), to extract and compute the MaxLFQ, quantitative intensity for all detected peptides and protein groups with q-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMicrobiota Taxonomy, function, functional redundancy, and taxa-function analyses\\u003c/h2\\u003e \\u003cp\\u003eThe built-in taxonomy and function databases were created by importing into iMetaLab (v2.3.0), peptide and protein tables were generated with MaxLFQ[\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Tables with peptide and intensities data (microbial and host) were adopted for taxonomical annotation (Ignore blanks under rank: Unique peptide count\\u0026thinsp;\\u0026ge;\\u0026thinsp;3, Superkingdom), while protein identifications with corresponding intensities were adopted for functional annotation (with default parameters).\\u003c/p\\u003e \\u003cp\\u003eThe taxonomy annotation output from iMetalab was further processed in R by removing peptides without taxonomic annotations and taxa annotated by fewer than three distinct peptides (Supplementary Table\\u0026nbsp;1). Log2 intensities of peptides common to the same taxa were summed for each sample. Alpha and Beta diversity indexes, engraftment analyses, and taxa-function were calculated at the Species level. The relative abundance of a species was defined as the percentage of the peptide summed intensity average for each sex in each cohort. Analyses performed at the cage level are based on the cage shared by the mothers and their respective pups before weaning. Mothers\\u0026rsquo; Core Species (Species detected in at least 3 Mothers of the same cohort), and the Pups\\u0026rsquo; Core Species (Species detected in at least 3 pups of the same cages) were considered to assess the microbiota engraftment in pups of the same cohort over time.\\u003c/p\\u003e \\u003cp\\u003eFrom the iMetaLab function annotation table, the KEGG pathways identified by fewer than 3 proteins were removed (Supplementary Table\\u0026nbsp;2). The functional redundancy of the gut microbiota at the proteome level (FRp) was calculated as described by Li and colleagues [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. In brief, data preparation was performed using Python 3.12.6 and PhyCharm Community Edition 2024.2.3. To define the Species-specific functions Meta4P[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e] was used by importing the tables with the peptide intensities for each microbial in each sample (Supplementary Table\\u0026nbsp;1) and the taxonomic and functional annotation tables (Supplementary Table\\u0026nbsp;2). Meta4P results were filtered by the Species-NOG pathway identified by at least 3 peptides.\\u003c/p\\u003e \\u003cp\\u003eThe log2-transformed intensities of common peptides annotated in the same protein were summed across each sample. Consensus Clustering Analysis was adopted to evaluate the stability of sample clustering and determine the optimal number of clusters (k). We performed consensus clustering on KEGG pathway z-score profiles. Consensus clustering was chosen over the Calinski\\u0026ndash;Harabasz (CH) index, Davies\\u0026ndash;Bouldin (DB) index, and the Gap statistic for determining the optimal number of clusters because it provides a more robust, resampling-based estimate of cluster stability. Analyses were conducted separately for each mouse cohort with pooled sexes. Consensus clustering was then performed for k values ranging from 2 to 6, using the following parameters: 500 bootstrap resampling iterations, with each iteration subsampling 80% of the samples and 80% of the features without replacement. For each k, Silhouette Scores were calculated. All analyses were performed in Python using numpy, pandas, scikit-learn, and seaborn.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eHost proteome analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eThe protein intensity, from the DIA-NN host protein sequences, was summed among common peptides in the same sample and divided by the total intensity of the sample (Protein Relative Abundance). Considering the factors time, cohort, and sex of the pups, and their interactions when detecting a significant difference with the Kruskal-Wallis test with BH correction, a soft clustering (allowing proteins to belong to multiple clusters) with the Mfuzz R package was performed[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. First, data were pre-processed by averaging replicates at each condition and standardizing each protein\\u0026rsquo;s profile (z-scoring). By examining the Mfuzz Dmin plot, the number of clusters at the Dmin inflection point (the \\\"elbow point\\\" \\u0026mdash; where the minimum centroid distance stops decreasing rapidly and begins to level off) was used for the optimal number of cluster definition. With the estimated fuzzifier (m\\u0026thinsp;\\u0026asymp;\\u0026thinsp;1.25), the plots of the mean profile for each cluster were generated considering proteins with the highest membership score (\\u0026gt;\\u0026thinsp;0.7\\u0026ndash;0.9) to each cluster detected. Proteins with the highest membership were searched for Multiple Proteins by Names / Identifiers on String 12.0 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://stringdb.org\\u003c/span\\u003e\\u003cspan address=\\\"https://stringdb.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e; 23/05/2025) and protein full STRING networks were obtained with confidence as a parameter for meaning of the network edges, with a minimum required interaction score of 0.7. The host proteins were matched by Peptide sequence or Protein Name to the 152 known antimicrobial peptides (AMP) reported by Valdes and colleagues [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. The protein intensity was summed among common peptides in the same sample and divided by the total intensity of the sample (relative AMPs abundance). Due to the high number of low-abundant AMPs, a reduced dataset considering the 10 most abundant AMPs among all the samples was adopted to perform analyses of the main phenomena of the AMPs evolution along time and to detect differences between pups of different Mothers\\u0026rsquo; origin and sexes. Finally, the Carbohydrate-active enzymes (CAZymes) that play a crucial role in the synthesis, modification, and breakdown of carbohydrates were analysed. The dataset was created by selecting among all host proteins those annotated on UniProt (search performed on 28/02/2025) matching with the CAZYmes categories listed by Wardman and colleagues[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003ch3\\u003eFood proteome analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eThe food protein dataset was generated using the FASTA file created by Valdes and colleagues [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e] through FragPipe (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://fragpipe.nesvilab.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://fragpipe.nesvilab.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) to generate a reduced protein list. Proteins originating from plant genera containing the species reported in the ingredients of the mice chow (5 genera Triticum; Glycine; Zea; Avena; Beta) and the mouse milk proteins were downloaded from UniProt (search performed on 28.02.2025; Supplementary Table\\u0026nbsp;3). Only those unique protein markers for each genus were included in the food database to avoid any detection bias. Lactoferrin is a secreted protein present in the milk and the intestinal mucosa [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]; for this reason, it was differentiated from the other milk proteins in the analyses. Protein intensity was summed among common peptides in the same sample and divided by the total intensity of the sample (relative food protein abundance).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analyses\\u003c/h2\\u003e \\u003cp\\u003eThe lists of Species detected in pups and mothers were compared with those reported in the literature as core shared species between C57BL/6J mice and humans [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe α-diversity (Richness Shannon, and Simpson indexes) and β-diversity metrics (Bray-Curtis and Jaccard dissimilarities) were calculated by using vegan (version 2.6-8 packages in R 4.3.1(R Core Team, 2018). For β-diversity indexes, permutation tests (with 999 permutations) using the Adonis [PERMANOVA] method with the Benjamini-Hochberg (BH) correction for pairwise multilevel comparisons under a reduced model were employed to detect the relative contributions to microbial community variability from time, sex, and cohort. The same strategy was adopted to compare the metabolic-informed taxonomical index between the pups and their mothers. The analyses of the data from fecal samples were performed with \\\"rstatix\\u0026rdquo;, \\u0026ldquo;ggplot2\\u0026rdquo;, \\u0026ldquo;ggpubr\\u0026rdquo;, \\u0026ldquo;diann\\u0026rdquo;, \\u0026ldquo;lme4\\u0026rdquo;, \\u0026ldquo;emmans\\u0026rdquo;, \\u0026ldquo;limma\\u0026rdquo;, \\\"inflection\\\", \\u0026ldquo;stringr\\u0026rdquo;, \\u0026ldquo;ggVenDiagram\\u0026rdquo;, \\u0026rdquo;purrr\\u0026rdquo;, \\u0026ldquo;Mfuzz\\u0026rdquo;, \\u0026ldquo;vegan\\u0026rdquo;, \\u0026ldquo;pairwiseAdonis\\u0026rdquo;, and \\u0026ldquo;biomaRt\\u0026rdquo; R packages. All data is represented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD (standard deviation of the mean) unless indicated otherwise. All replicates are biological unless indicated otherwise. For taxonomical, protein, and functional analyses, the statistical analysis of the three-sided linear mixed-effects model (lmem) was adopted, and estimated marginal means with BH correction to perform the post hoc analyses of the significant interactions between the factors time, sex, and cohort detected. All statistical tests are reported in the respective figure caption. Significant interactions for taxonomical and functional analyses were assessed for the normality of the data distribution. Two-way ANOVA and three-way ANOVA for repeated measures with BH correction were adopted for normally distributed data. For non-normally distributed data, the Kruskal-Wallis test followed by Post-hoc Dunn's test for pairwise multiple comparisons with BH correction, was adopted. In all panels: p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05; *p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; **p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; ***p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; ****p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eUnveiling Taxonomic Developmental Programs During Early Life\\u003c/h2\\u003e \\u003cp\\u003eTo characterize the temporal assembly of the gut microbiota during early life, we performed longitudinal ultra-sensitive metaproteomic profiling of fecal samples collected from postnatal day (D) 10, the earliest time point at which fecal material was available, through D48 (Fig.\\u0026nbsp;1a). Samples were obtained from male and female pups belonging to two C57BL/6 cohorts that differed only in maternal origin: pups born to a long-established local breeding colony (L) and pups born to pregnant females newly acquired from the same vendor (Charles River; CR). Importantly, both cohorts were born contemporaneously and raised under identical housing and dietary conditions (see Methods), allowing us to assess developmental trajectories of gut microbiota maturation while minimizing environmental confounders and acknowledging any potential genetic drift due to inbred local mice.\\u003c/p\\u003e \\u003cp\\u003eAcross both cohorts and sexes, gut microbial α-diversity increased markedly with age (Figs.\\u0026nbsp;1b and S1a; Supplementary Tables\\u0026nbsp;4\\u0026ndash;6; Mixed-effects models, all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Species richness, Shannon, and Simpson indices rose steeply during the first three postnatal weeks and reached a plateau after D26, consistent with rapid community diversification during the pre-weaning period followed by stabilization after dietary transition to solid food. No significant differences in α-diversity were detected between male and female pups within the same cohort at any time point (Supplementary Tables\\u0026nbsp;4\\u0026ndash;6).\\u003c/p\\u003e \\u003cp\\u003eTemporal restructuring of the microbial community was further supported by β-diversity analyses. Bray\\u0026ndash;Curtis and Jaccard dissimilarities revealed significant separation of samples by developmental stage in both cohorts (PERMANOVA, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; Figs.\\u0026nbsp;1c and S1b, Supplementary Tables\\u0026nbsp;8\\u0026ndash;9), with early-life communities at D10\\u0026ndash;D12 clustering distinctly from those observed after weaning (D34\\u0026ndash;D48). These age-dependent shifts were consistent across sexes, indicating a reproducible and coordinated remodeling of gut community structure during maturation.\\u003c/p\\u003e \\u003cp\\u003eBeyond ecological indices, metaproteomic profiling enabled species-resolved taxonomic analysis of microbial turnover across development. The number of confidently detected species (see Methods) increased from 36 at D10 to 198 at D48 (Supplementary Table\\u0026nbsp;9), following highly similar trajectories across cohorts and sexes (Figs.\\u0026nbsp;1d and S1c). Early-life samples were dominated by facultative anaerobes, primarily Lactobacillus species, including \\u003cem\\u003eL. murinus\\u003c/em\\u003e, \\u003cem\\u003eL. reuteri\\u003c/em\\u003e, and \\u003cem\\u003eL. johnsonii\\u003c/em\\u003e. As development progressed, these taxa were progressively replaced by obligate anaerobes such as \\u003cem\\u003eAnaerotruncus\\u003c/em\\u003e sp. G3 (2012), \\u003cem\\u003eBacteroides caecimuris\\u003c/em\\u003e, and members of the Lachnospiraceae family. This taxonomic turnover coincided temporally with the weaning period and the transition from maternal milk to solid chow, consistent with the establishment of a more anaerobic and metabolically complex gut ecosystem during postnatal maturation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDynamics of dietary protein during early-life\\u003c/h2\\u003e \\u003cp\\u003eBecause dietary inputs are a primary driver of gut microbiota assembly dynamics during early life[\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e], we next quantified food-derived proteins in pup fecal samples to pinpoint the transition from maternal milk to solid chow. Although dietary proteins accounted for only a small fraction of the total fecal proteome, we reproducibly detected them across developmental stages, which enables direct assessment of dietary transitions \\u003cem\\u003ein vivo\\u003c/em\\u003e and underscores the sensitivity of our metaproteomic method.\\u003c/p\\u003e \\u003cp\\u003eAcross both cohorts and sexes, we revealed a marked and reproducible shift in dietary protein profiles over time (Fig.\\u0026nbsp;2a). Proteins originating from maternal milk were detected exclusively during the pre-weaning period. Among milk-associated proteins, α-caseins were consistently detected through D20 but were absent thereafter, reflecting the cessation of milk intake around weaning (Fig.\\u0026nbsp;2b). In contrast, Lipid Transferase CIDEA showed low abundance at D10\\u0026ndash;D12 and was no longer detectable by D20, suggesting early functional changes in lipid processing concurrent with microbial and dietary maturation. Notably, lactotransferrin was detected across the entire developmental window in both cohorts (Fig.\\u0026nbsp;2b; Supplementary Table\\u0026nbsp;10). This persistence likely reflects a transition in the source of lactotransferrin, from maternal milk before weaning to endogenous secretion by the intestinal mucosa after weaning [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. Indicative of a complete transition to solid food, main chow-derived components, like soybeans and wheat, progressively increased and dominated after weaning.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFunctional specialization underlies increasing microbiome functional redundancy\\u003c/h2\\u003e \\u003cp\\u003eTo determine how age-dependent increases in taxonomic complexity and concurrent dietary transitions translate into functional organization of the gut microbiome, we quantified ecological functional redundancy using the normalized functional redundancy index of the proteome (nFRp). In both mouse cohorts, nFRp increased significantly over time, reaching a maximum around postnatal day 34 (Fig.\\u0026nbsp;3a; lmem, Time; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001; Supplementary Table\\u0026nbsp;11). Although transient differences between cohorts were observed at intermediate time points, nFRp values converged by day 41, indicating a shared trajectory toward functional stabilization (lmem, Time \\u0026times; Cohort; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Supplementary Table\\u0026nbsp;11). No significant effect of the pups\\u0026rsquo; sex on the nFRp was detected over time and among the cohorts. This temporal increase in functional redundancy paralleled the rise in microbial species richness (Figs.\\u0026nbsp;1b and S1a), linking taxonomic expansion to the accumulation of overlapping functional capacity at the community level.\\u003c/p\\u003e \\u003cp\\u003eThe increase in functional redundancy reflected both an expansion in the number of detectable microbial functions and coordinated changes in functional expression. The total number of represented KEGG pathways increased from 105 at day 10 to 140 at day 48 (Supplementary Table\\u0026nbsp;12). Consensus clustering of KEGG pathway expression profiles identified two major temporal patterns in each cohort (Fig.\\u0026nbsp;3b and 3c), with no detectable sex-specific effects. The predominant cluster, comprising approximately 80% of KEGG functions (Supplementary Table\\u0026nbsp;13), showed progressive upregulation during early development followed by stabilization around day 34, closely mirroring the nFRp trajectory in both cohorts. These functions encompassed pathways associated with the transition toward anaerobic metabolism (Fig.\\u0026nbsp;1d), dietary change (Fig.\\u0026nbsp;2a), and gut niche establishment, including carbohydrate and lipid metabolism, utilization of host-derived glycans, and functions related to microbial interaction and stress tolerance. In contrast, a smaller cluster of pathways exhibited stable or modestly decreasing expression across development, including core biosynthetic and housekeeping functions such as peptidoglycan biosynthesis and amino acid metabolism. Collectively, these patterns indicate that early-life increases in functional redundancy are driven by coordinated upregulation of specialized metabolic pathways alongside maintenance of core microbial functions.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eThe mother of origin shapes taxonomic and functional engraftment trajectories\\u003c/h2\\u003e \\u003cp\\u003eAnalysis of maternal fecal samples collected 26 days after delivery revealed substantial overlap in gut microbiota composition between local (L) and Charles River (CR) mothers, with approximately 65% of maternal species shared (Supplementary Fig.\\u0026nbsp;2a). Nonetheless, each maternal group harbored distinct subsets of taxa, with L mothers carrying more than one quarter of core species not detected in CR dams. Longitudinal tracking of maternal species engraftment in offspring revealed a significant increase in maternal contribution over developmental stages (Fig.\\u0026nbsp;4a; Supplementary Table\\u0026nbsp;14). This pattern was consistent with the age-dependent restructuring of microbial communities observed in β-diversity analyses (Fig.\\u0026nbsp;1c and Supplementary Fig.\\u0026nbsp;1b) and with the progressive functional maturation of the gut microbiome after weaning (Fig.\\u0026nbsp;3a). From D34 onward, CR pups exhibited significantly higher levels of maternal species acquisition compared to L pups (Fig.\\u0026nbsp;4a), indicating cohort-specific engraftment trajectories. These differences were independent of pup sex, as confirmed by sex-stratified analyses (Supplementary Fig.\\u0026nbsp;2b). Analysis of pup core species\\u0026mdash;defined as taxa detected in at least three pups per cage at D26\\u0026mdash;revealed limited cage-specific effects, with most species shared across multiple maternal cages (Fig.\\u0026nbsp;4b).\\u003c/p\\u003e \\u003cp\\u003eTo assess whether maternal origin also influenced functional maturation, we compared metabolic-informed taxonomic profiles between mothers and their corresponding offspring. β-diversity analyses based on Bray\\u0026ndash;Curtis and Jaccard dissimilarities showed that pups from both cohorts were functionally distinct from their mothers early in life but progressively converged toward their respective maternal profiles over time (Fig.\\u0026nbsp;4c\\u0026ndash;d; Supplementary Table\\u0026nbsp;15). Despite this convergence, pups from the two cohorts remained significantly different from each other in taxonomic\\u0026ndash;functional composition across all time points from D12 to D48 (Supplementary Table\\u0026nbsp;15), indicating that maternal origin imprints cohort-specific functional trajectories during gut microbiome development.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMaturation of the host intestinal proteome parallels the development of the gut microbiome in early life\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo determine how microbiome maturation is mirrored by host intestinal physiology, we analyzed host-derived proteins quantified by metaproteomics across time points. In total, more than 3,700 host proteins were detected across both mouse cohorts. Over the full developmental period, 1,327 and 691 host proteins were significantly regulated in L and CR pups, respectively, whereas only 69 proteins showed differential temporal regulation between cohorts (lmem, Time \\u0026times; Cohort; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001; Supplementary Table\\u0026nbsp;16), indicating a largely conserved host developmental program. The pups\\u0026rsquo; sex over time did not affect the host proteins significantly, while a few proteins were detected to be significantly different between same sex peers of the two cohorts in a few time points at weaning and after (lmem, Time \\u0026times; Cohort \\u0026times; Sex; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001; Supplementary Table\\u0026nbsp;16).\\u003c/p\\u003e \\u003cp\\u003eCluster analysis of host protein expression profiles identified four major temporal patterns that captured the principal structure of the data (shown for L pups in Fig.\\u0026nbsp;5a; CR pups shown in Supplementary Fig.\\u0026nbsp;3). Protein\\u0026ndash;protein interaction network analysis and Gene Ontology enrichment revealed distinct functional trajectories across clusters of L pups (Fig.\\u0026nbsp;5b\\u0026ndash;e), which were broadly recapitulated in CR pups (Supplementary Fig.\\u0026nbsp;3).\\u003c/p\\u003e \\u003cp\\u003eCluster 1 comprised proteins that progressively increased in abundance across development and was enriched for metabolic and catabolic processes, including organic substance metabolism, proteolysis, lipid-related pathways, and intestinal absorption (Fig.\\u0026nbsp;5b). These changes coincide temporally with the transition from milk to only solid food nutrition and with the expansion of strict anaerobic microbial taxa, consistent with maturation of epithelial metabolic and absorptive functions during weaning.\\u003c/p\\u003e \\u003cp\\u003eCluster 2 displayed a biphasic pattern, with higher abundance early in life, a marked reduction around weaning, and partial recovery thereafter. Proteins in this cluster were enriched for small-molecule and organic acid metabolism, cellular respiration, fatty acid oxidation, and oxidative phosphorylation (Fig.\\u0026nbsp;5c). These functions reflect metabolic programs with greater reliance on oxidative energy metabolism, prominent in the neonatal intestine, that are transiently downregulated during dietary and microbial transitions toward anaerobic metabolism and fermentation.\\u003c/p\\u003e \\u003cp\\u003eCluster 3 included proteins that were highly abundant shortly after birth and progressively decreased over time, and was enriched for cytoskeletal organization, actin filament dynamics, nucleotide metabolism, and the tricarboxylic acid cycle (Fig.\\u0026nbsp;5d). These pathways are consistent with early postnatal programs supporting rapid epithelial growth, tissue organization, and high metabolic demand, which diminish as intestinal architecture and barrier function become established during development.\\u003c/p\\u003e \\u003cp\\u003eCluster 4 exhibited a transient increase during early-to-mid development and was enriched for regulatory processes, including negative regulation of proteolysis and hydrolase activity, wound healing, blood coagulation, and responses to tissue injury (Fig.\\u0026nbsp;5e), indicating dynamic regulation of tissue integrity during periods of rapid microbial expansion.\\u003c/p\\u003e \\u003cp\\u003eTogether, these findings indicate that host intestinal functional maturation follows developmental trajectories that align closely with the taxonomic and functional development of the gut microbiome.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eExpression of host antimicrobial peptides and carbohydrate-active enzymes during early-life\\u003c/h2\\u003e \\u003cp\\u003eTo illustrate how global host proteome maturation translates into functionally relevant effector pathways at the host\\u0026ndash;microbiome interface, we focused on two biologically distinct classes of host proteins: antimicrobial peptides (AMPs), which contribute directly to microbial community regulation [\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e], and host-encoded carbohydrate-active enzymes (CAZymes), which modulate carbohydrate availability in the intestinal lumen [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOf the 152 annotated AMPs considered in our analysis [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e], 90 were reproducibly detected in pups (Supplementary Table\\u0026nbsp;17). The cumulative abundance of the ten most abundant AMPs (representing the 82% of the AMPs' cumulative abundance) differed modestly but significantly between early and late postnatal stages (Supplementary Fig.\\u0026nbsp;4a and Supplementary Table\\u0026nbsp;16). Interestingly, these ten AMPs showed strong time-dependent dynamics (Fig.\\u0026nbsp;6a).\\u003c/p\\u003e \\u003cp\\u003eNo AMP was uniquely detected in either cohort, and no cohort- or sex-specific AMP repertoires were observed. Also, AMP composition and cumulative abundance were largely conserved between L and CR pups, with similar developmental trajectories across cohorts (Fig.\\u0026nbsp;6b and Supplementary Fig.\\u0026nbsp;4b).\\u003c/p\\u003e \\u003cp\\u003eHost-encoded CAZymes displayed discrete and stage-specific expression patterns. Polypeptide N-acetylgalactosaminyltransferase 4 (Galnt4), an enzyme involved in glycoprotein glycosylation, was highly expressed during the neonatal period (D10\\u0026ndash;D12) and declined significantly thereafter in both cohorts (Fig.\\u0026nbsp;6c). Conversely, host sialate O-acetylesterase showed a marked increase in expression from D20 onward (Fig.\\u0026nbsp;6d). This enzyme, which regulates sialic acid availability and has been implicated in intestinal homeostasis and carbohydrate metabolism, increased in parallel with gut microbiome maturation and the expansion of microbial pathways associated with host-derived glycan utilization.\\u003c/p\\u003e \\u003cp\\u003eTogether, these results delineate discrete developmental windows during which host antimicrobial and glycan-modifying activities are differentially deployed, highlighting coordinated maturation of host effector functions alongside gut microbiome colonization.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this study, we present a longitudinal metaproteomic analysis of gut microbiome and host intestinal development in mice, spanning early colonization, the weaning transition, and the emergence of adult-like community structure and function. By integrating 4 different developmental axes - species-resolved microbial profiling, dietary protein detection, functional redundancy analysis, and host proteome characterization - we show how microbial community turnover, functional stabilization, and host intestinal maturation proceed in parallel during early life. Our non-invasive longitudinal design and ultra-sensitive metaproteomic approach enabled repeated sampling of individual animals from postnatal day 10 onward and captured an integrative view of developmental transitions at a resolution previously missed in early-life studies[\\u003cspan additionalcitationids=\\\"CR4 CR5\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEarly-life microbial community assembly dynamics and niche construction\\u003c/h2\\u003e \\u003cp\\u003eEarly-life gut communities were dominated by facultative anaerobes, particularly \\u003cem\\u003eLactobacillus\\u003c/em\\u003e species such as \\u003cem\\u003eL. murinus\\u003c/em\\u003e, \\u003cem\\u003eL. reuteri\\u003c/em\\u003e, and \\u003cem\\u003eL. johnsonii\\u003c/em\\u003e. Metaproteomic profiles of these taxa were enriched for functions related to carbohydrate utilization and host\\u0026ndash;microbe interactions, consistent with a proposed role for early colonizers in niche construction [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR59 CR60 CR61 CR62\\\" citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e]. Such activities likely shape the physicochemical environment of the neonatal gut and influence subsequent microbial assembly [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. These early colonizers were progressively replaced by obligate anaerobes, including \\u003cem\\u003eBacteroides\\u003c/em\\u003e, \\u003cem\\u003eAnaerotruncus\\u003c/em\\u003e, and members of the Lachnospiraceae family. This taxonomic turnover coincided with weaning and is consistent with a transition toward a more anaerobic and metabolically complex gut environment driven by dietary change and reduced luminal oxygen availability [\\u003cspan additionalcitationids=\\\"CR4 CR5\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDietary transitions and early exposure to solid food\\u003c/h2\\u003e \\u003cp\\u003eDirect detection of dietary proteins provided proteome-level evidence for progressive changes in nutrient exposure during development. Although dietary proteins represented a minor fraction of the fecal proteome (less than 0.01% as shown in Fig.\\u0026nbsp;2b), their reproducible detection enabled direct investigation of dietary transitions in vivo. Milk-derived proteins were detected exclusively before weaning, whereas chow-derived proteins increased thereafter. Unexpectedly, proteins annotated as chow-derived constituted\\u0026thinsp;\\u0026gt;\\u0026thinsp;90% of the food-derived identifications from D10 through weaning. This finding does not imply that pups derive most nutrition from solid food at this stage. We hypothesize that it indicates that exogenous dietary proteins detectable in pup feces largely originate from the chow diet. Several mechanisms could explain this early signal despite predominantly milk-based feeding, including indirect oral exposure through maternal grooming and transfer of chow residues via maternal fur/nipples, and ingestion of diet-derived material associated with copography of maternal feces[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Early exploratory ingestion of chow dust or softened crumbs may further contribute as pups approach weaning. While milk-mediated transfer of diet-derived peptides cannot be excluded, the dominance of chow-associated proteins among food-derived detections is more consistent with environmental and dam-mediated exposure routes. Our sampling strategy via stimulation of fecal production through the anogenital reflex [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e], in a sterile tube, reduces the possibility for cross-contamination with food, animal fur, and components present in the cage bedding. We note that food-derived proteins comprise only a small fraction of the total fecal proteome; thus, enrichment within this category may reflect detectability and exposure rather than primary caloric input. Importantly, the pattern was reproducible across cohorts/sex/animals and increased progressively across development, consistent with a gradual transition in dietary exposure before formal weaning. After weaning, chow-associated proteins such as those from soybeans and wheat progressively increased and became dominant, consistent with reported transitions from milk-oriented gut communities to those associated with diverse solid foods.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFunctional redundancy and stabilization of the gut ecosystem\\u003c/h2\\u003e \\u003cp\\u003eDespite pronounced taxonomic turnover, a substantial fraction of microbial functions remained comparatively stable across development, highlighting the role of functional redundancy in maintaining ecosystem resilience. Core pathways involved in cell-wall biosynthesis, amino-acid metabolism, and intrinsic stress resistance were preserved, even as the taxa encoding these functions changed. In parallel, the normalized functional redundancy index (nFRp) increased during development and converged across cohorts by postnatal day 34, indicating a shared trajectory toward functional stabilization. Together, these patterns suggest that increasing taxonomic diversity supports the accumulation of overlapping functional capacity, buffering the gut ecosystem as it matures.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMaternal origin and reproducibility of microbiome development\\u003c/h2\\u003e \\u003cp\\u003eComparison of pups born to long-established local colony mothers and pups born to newly purchased pregnant females revealed that maternal microbiota origin shapes engraftment trajectories [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e]. Although maternal communities shared many species, each cohort harbored distinct taxa that influenced offspring microbiome assembly. Maternal species acquisition increased with age, and pups progressively converged toward their respective maternal profiles at both taxonomic and functional levels.\\u003c/p\\u003e \\u003cp\\u003eCohort-specific differences in engraftment persisted despite identical housing and dietary conditions, underscoring maternal microbiota as a key determinant of early-life microbial development[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e]. At the same time, convergence of functional redundancy across cohorts suggests that distinct taxonomic trajectories can nonetheless yield similar ecosystem-level functional outcomes.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eHost intestinal proteome maturation and host\\u0026ndash;microbiome coupling\\u003c/h2\\u003e \\u003cp\\u003eHost proteome analysis revealed conserved developmental trajectories that closely paralleled microbial community turnover. Early-life host protein profiles were enriched for pathways associated with epithelial growth and oxidative metabolism, consistent with rapid intestinal development in the neonatal period [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]. With maturation, host protein expression shifted toward enhanced metabolic versatility, absorptive capacity, and regulation of tissue homeostasis.\\u003c/p\\u003e \\u003cp\\u003eThese coordinated host and microbial changes indicate that microbiome maturation and host intestinal development are tightly coupled components during early-life.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDeployment of host effector pathways: AMPs and CAZymes\\u003c/h2\\u003e \\u003cp\\u003eHost antimicrobial peptides showed stable overall abundance across development, sexes, and cohorts, but pronounced temporal reorganization of individual AMP profiles. Distinct subsets of AMPs predominated before versus after weaning, indicating compositional reprogramming rather than changes in total antimicrobial output.\\u003c/p\\u003e \\u003cp\\u003eHost-encoded carbohydrate-active enzymes displayed discrete, stage-specific expression patterns, with glycoprotein-modifying enzymes enriched early in life and enzymes regulating sialic acid availability increasing after weaning. These shifts highlight developmental windows during which host effector functions at the host\\u0026ndash;microbiome interface is differentially deployed.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eLimitations and future directions\\u003c/h2\\u003e \\u003cp\\u003eThis study has several limitations. All mice used in our study belong to C57BL/6 strain. C57BL/6 mice are the most widely used model for microbiota-related human disease studies, given that they share approximately 11.8% of gut microbial species and 90% of functional pathways with humans [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e]. Our experimental design comparing locally bred mothers to newly purchased animals acknowledges potential genetic or microbial drift. While our results show that overall developmental trajectories are preserved across cohorts, caution is warranted when translating specific taxonomic or functional differences to other mouse strains. In general, taxonomic and functional annotation in metaproteomics remains constrained by reference databases [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e], which limits a complete biological view of the gut development process. Although both sexes followed shared maturation programs, we detected a limited but significant number of sex-specific changes. Our results open the door to more pronounced differences when considering menstrual cycle biology, therefore, supporting the recognition of sex as an important factor in mouse-based research[\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e]. Finally, while metaproteomics captures expressed functions, complementary measurements of microbial metabolites would further strengthen functional interpretation [\\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eOur results provide an integrated view of gut microbiome and host intestinal development during early life in mice. Our results expand our understanding of early-life host-microbiome interactions and highlight key considerations for experimental design and reproducibility in mouse studies.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics approval and consent to participate: All the experiments included in this publication were approved by the University of Vienna\\u0026rsquo;s Institutional Animal Care and Use Committee (IACUC; protocol n. 2024-001) and carried out in compliance with the rules provided in the Guide for the Care and Use of Laboratory Animals, and EU-Directive for the protection of animals used for scientific purposes. Additionally, this work was reported according to the ARRIVE guidelines.\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication: \\u0026ldquo;Not applicable\\u0026rdquo;\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of data and material: The datasets generated and/or analysed during the current study are available in the PRIDE repository with the dataset identifier [PXD074520] \\u003cstrong\\u003eUsername:\\u0026nbsp;\\u003c/strong\\u003ereviewer_pxd074520@ebi.ac.uk \\u003cstrong\\u003ePassword:\\u0026nbsp;\\u003c/strong\\u003eqgnCJc9Vj9N9. \\u0026nbsp;Source data are provided with this paper.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests: MS received research awards and travel support by the German Pain Society (DGSS) both of which were sponsored by Astellas Pharma GmbH (Germany). MS received research awards by the Austrian Pain Society. MS received one-time consulting honoraria by Grunenthal GmbH (Germany). None of these sources influenced the content of this study, and MS declares no conflict of interest. DGV and MS have an ongoing scientific collaboration with Bruker (Bruker Center of Excellence for Metaproteomics, University of Vienna). All authors declare that they have no conflicts of interest.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding: This research was funded in part by the Austrian Science Fund (FWF) [10.55776/P36554 and 10.55776/P35856] and the University of Vienna.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors\\u0026apos; contributions: GC performed the sample collection and data analyses, and contributed in writing the manuscript. FX participated in the LC-MS sample and data\\u0026nbsp;analyses, DM participated in the data analyses. MS contributed to the study\\u0026apos;s planning, to manuscript revision, and to funding acquisition. DVG contributed to the study design, planning,\\u0026nbsp;supervision, data analysis, manuscript preparation, and funding acquisition. All authors read and approved the final manuscript\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements We thank Elisabeth Clifford (Division of Pharmacology \\u0026amp; Toxicology, University of Vienna, Austria), Natascha Deutsch, and Peter H\\u0026ouml;flich as animal caretakers, and Allison Barry for assistance during data analysis.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eStojanović O, Miguel-Aliaga I, Trajkovski M (2022) Intestinal plasticity and metabolism as regulators of organismal energy homeostasis. 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Proteomics 19(16):e1800363. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1002/pmic.201800363\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/pmic.201800363\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[{\"identity\":\"527bd394-8603-4789-89ab-512cadf3f22e\",\"identifier\":\"10.13039/501100002428\",\"name\":\"Austrian Science Fund\",\"awardNumber\":\"10.55776/P36554 and 10.55776/P35856\",\"order_by\":0}],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Gut microbiome, mouse development, Host-microbiome interactions, Diet, Metaproteomics\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9367132/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9367132/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEarly-life development of the gut microbiome plays a critical role in shaping host physiology. However, a comprehensive understanding of how diet, microbial community assembly, functional capacity, and host intestinal maturation axes evolve and coordinate over time remains lacking. Most studies focus on a single axis, rely on cross-sectional sampling, or have limited functional resolution, restricting insight into developmental dynamics. Here, we characterized early-life maturation of the gut ecosystem using longitudinal, metaproteome-level analysis in a murine model.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsing ultra-sensitive metaproteomics, we profiled fecal samples collected at seven postnatal time points from day 10 through weaning and into early adulthood (day 48) in pups from two contemporaneously raised C57BL/6 cohorts differing only in maternal origin (a long-established local colony and newly purchased pregnant females from the same vendor). Analyses accounted for time, cohort, and sex effects.\\u003c/p\\u003e\\n\\u003cp\\u003eMicrobial communities underwent pronounced taxonomic turnover, shifting from early dominance by facultative anaerobes to obligate anaerobes after weaning, accompanied by increasing species richness and functional complexity across cohorts and sexes. Taxonomic changes supported increasing functional redundancy that converged by postnatal day 34 and remained stable into early adulthood. KEGG clustering showed this redundancy to be driven by coordinated upregulation of distinct metabolic pathways alongside maintenance of core functions.\\u003c/p\\u003e\\n\\u003cp\\u003eDirect detection of low-abundant dietary proteins provided evidence for dietary transitions coinciding with microbial maturation: milk proteins were detected only before weaning, while solid food components predominated afterward. Maternal origin significantly influenced microbial engraftment trajectories, leading to cohort-specific taxonomic and functional differences despite identical housing and diet. In parallel, host intestinal proteome maturation mirrored microbial succession, with coordinated shifts in metabolic, absorptive, regulatory, and effector pathways, including antimicrobial peptides and carbohydrate-modifying enzymes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBy directly integrating microbial, dietary, functional, and host axes within a longitudinal framework, this study provides a comprehensive view of murine gut ecosystem maturation during early life and offers a reference for interpreting developmental microbiome dynamics and improving experimental design and reproducibility in mouse studies.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Integrative characterization of host–microbiome-diet axes during early-life development of the murine gut\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-10 15:08:00\",\"doi\":\"10.21203/rs.3.rs-9367132/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d843c646-3057-41d2-82d2-182f476d8199\",\"owner\":[],\"postedDate\":\"April 10th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":66005135,\"name\":\"Developmental Biology\"},{\"id\":66005136,\"name\":\"Animal Science\"},{\"id\":66005137,\"name\":\"Systems Biology\"},{\"id\":66005138,\"name\":\"Animal Physiology\"}],\"tags\":[],\"updatedAt\":\"2026-04-10T15:08:00+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-10 15:08:00\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9367132\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9367132\",\"identity\":\"rs-9367132\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}