# The protein ingredient quality of infant formula influences colonic physiology and microbiota and the serum metabolomic profile in neonatal mini-piglets

Lucile Chauvet, Gwenaelle Randuineau, Sylvie Guerin, Armelle Cahu, Régis Janvier, Patrice Dahirel, Marie-Bernadette Maillard, Didier Dupont, Marion Lemaire, Amélie Deglaire, et al.

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## ► To cite this version:

Lucile Chauvet, Gwenaelle Randuineau, Sylvie Guerin, Armelle Cahu, Régis Janvier, et al. The protein ingredient quality of infant formula influences colonic physiology and microbiota and the serum metabolomic profile in neonatal mini-piglets. Food Research International, 2025, 200, pp.115511-115511. 10.1016/j.foodres.2024.115511. hal-04891543

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# The protein ingredient quality of infant formula influences colonic physiology and microbiota and the serum metabolomic profile in neonatal mini-piglets

Lucile Chauvet<sup>a,b,c</sup>, Gwénaëlle Randuineau<sup>b</sup>, Sylvie Guérin<sup>b</sup>, Armelle Cahu<sup>b</sup>, Régis Janvier<sup>b</sup>, Patrice Dahirel<sup>b</sup>, Marie-Bernadette Maillard<sup>a</sup>, Didier Dupont<sup>a</sup>, Marion Lemaire<sup>c</sup>, Amélie Deglaire<sup>a,1</sup>, Isabelle Le Huérou-Luron<sup>b,*,1</sup>

## ARTICLE INFO

**Keywords:**

Infant formula

Protein ingredient

Gut physiology

Microbiota

Serum metabolites

Hypothalamus

Mini-piglets

## ABSTRACT

Despite the WHO recommendations in favor of breastfeeding, most infants receive infant formulas (IFs), which are complex matrices involving numerous ingredients and processing steps. Our aim was to understand the impact of the quality of the protein ingredient in IFs on gut microbiota and physiology, blood metabolites and brain gene expression.

Three IFs were produced using whey proteins (WPs) from cheese whey (IF-A) or ideal whey (IFs-C and -D) and caseins, either in a micellar form (IFs-A and -C) or partly in a non-micellar form (IF-D). Twenty-four Yucatan minipiglets received one of the IFs from 2- to 21-days of age. The piglets were then euthanized 84 min post-prandially. Blood, ileal and colonic digesta and tissues, the liver and the hypothalamus were sampled. Gut microbiota composition and activity, the expression of intestinal and brain genes involved in barrier, immune, endocrine, nutrient carrier and neuronal functions and serum metabolite levels were determined. Intestinal paracellular permeability was assessed with an *ex vivo* Ussing chamber. The data were analyzed using multi-factorial and univariate analyses.

The colon was the main site to be physiologically affected by the quality of the dairy protein ingredients used in IFs. Colonic paracellular permeability was significantly higher in IF-D-fed piglets than in those receiving IFs-A and -C, in line with the expression of genes encoding tight junction proteins (OCLN, CLDN3 and CLDN4). IF-D up-regulated the colonic expression of genes involved in the immune function (SOCS3, PIGR and TNFα) when compared to IF-A. Although intestinal α- and β-diversities did not significantly differ among IFs, some specific differences were observed, such as the abundances of Campylobacterota phylum and *Bacteroides* genera and fecal butyrate production, which were increased with IFs-C and -D versus IF-A. The kynurenine pathway was favored in piglets fed IF-D compared to IF-A, based on colonic gene expression and serum metabolites. In addition, levels of some serum metabolites, particularly putrescine, spermidine and spermine, were higher in piglets fed IF-D than IFs-A and -C. Overall, IF-D, which combined ideal WPs and some non-micellar caseins, appeared to differ most from IF-A in terms of its physiological consequences, suggesting that both WP and casein ingredient quality may mediate the physiological properties of IFs, probably through changes to the colonic microbiota composition and activity.

**Abbreviations:** BW, body weight; CREEA, Rennes Committee of Ethics in Animal Experimentation; GMP, Glycomacropetide; HM, Human milk; HRP, Horseradish peroxidase; IF, Infant formula; MFA, Multifactorial analysis; PCoA, Principal co-ordinate analysis; PERMANOVA, permutational multivariate analysis of variance; PLS-DA, Partial least square discriminant analysis; PND, post-natal day; RIN, RNA integrity number; SCFA, short-chain fatty acid; SEM, Standard error of the mean; VIP, Variable importance in the projection; WPs, whey proteins.

* Corresponding author at: Human Nutrition division, Institut NuMeCan, INRAE, INSERM, Univ Rennes, 16 Le Clos 35590 Saint-Gilles, France.

E-mail address: isabelle.luron@inrae.fr (I.L. Huérou-Luron).

¹ Authors contributed equally.

<footnote><sup>a</sup> STLO, INRAE, L'Institut Agro, Rennes, France</footnote>

<footnote><sup>b</sup> Institut NuMeCan, INRAE, INSERM, Univ Rennes, Saint Gilles, France</footnote>

<footnote><sup>c</sup> SODIAAL International, Centre Recherche & Innovation, Rennes, France</footnote>
# 1. Introduction

Early postnatal life is a crucial period for the development of numerous physiological functions, such as the gut barrier, immune functions and commensal microbiota. During this period, human milk (HM) is the gold standard as it ensures optimal infant growth and development, but 56 % of 0 to 5-month old infants worldwide still receive an HM substitute (Global UNICEF, 2022). Infant formulas (IFs) are the only adequate, closely regulated, HM substitute and cover the nutritional needs of 0- to 6-month old infants (European Union, 2016). The composition of IF has been improved in recent decades to mimic HM as closely as possible. Nevertheless, differences in composition and structure still remain and may partly explain the different health effects of IF vs. HM that are still observed in the short and long terms (Lemaire et al., 2018). Interest in the composition and structure of the protein fraction of IFs is recent and limited data are available regarding their impact on gut physiology and microbiota.

IFs are most commonly formulated using bovine milk proteins and contain a combination of skim milk and whey protein (WP) ingredients in order to mimic the casein:WP ratio of mature HM (40:60). The WP ingredients most widely used to produce IF come from cheese whey, while ideal whey, obtained after skim milk microfiltration, is used less frequently because of its higher cost. The compositions of cheese and ideal whey differ because the chymosin used in cheese production leads to the release of glycomacropeptide (GMP), a C-terminal hydrophilic peptide of κ-casein, thus reducing the proportion of β-lactoglobulin and α-lactalbumin in cheese whey when compared to ideal whey. GMP may exert some health-promoting activities by modulating the immune response and microbiota composition (Chen et al., 2012; Córdova-Dávalos et al., 2019; Kelleher et al., 2003; Ntemiri et al., 2017; Requena et al., 2010; Royle et al., 2008; Sawin et al., 2015; Thomá-Worringer et al., 2006). Moreover, the processing route for WP ingredients depends on the targeted end products and manufacturing companies; this may modulate their WP composition and structure (denaturation, aggregation, glycation) and consequently affect digestion kinetics, as reported for dairy matrices (van Lieshout et al., 2019), fat-free model IFs (Halabi et al., 2022) and IFs (Chauvet et al., 2023). Regarding the casein fraction, it has been shown that the casein micelles in HM are smaller

<table><caption>**Table 1**

Biochemical composition (mean ± SD) of the three rehydrated IFs.</caption><caption>Per 100 mL of rehydrated IF</caption><thead><tr><th>Item</th><th>Unit</th><th>IF-A</th><th>IF-C</th><th>IF-D</th></tr></thead><tbody><tr><td>Crude protein<sup>1</sup></td><td>g</td><td>4.4 ± 0.2</td><td>4.4 ± 0.1</td><td>4.4 ± 0.2</td></tr><tr><td>True protein<sup>1</sup></td><td>g</td><td>4.0 ± 0.1</td><td>4.3 ± 0.1</td><td>4.3 ± 0.1</td></tr><tr><td>Non-protein nitrogen<sup>1</sup></td><td>g</td><td>0.34 ± 0.03</td><td>0.18 ± 0.03</td><td>0.17 ± 0.03</td></tr><tr><td>Casein:WP<sup>2</sup></td><td>%</td><td>40:60</td><td>40:60</td><td>40:60</td></tr><tr><td>Insoluble nitrogen at pH 4.6<sup>1</sup></td><td>g</td><td>3.2 ± 0.2</td><td>2.9 ± 0.1</td><td>2.9 ± 0.1</td></tr><tr><td>Tryptophan<sup>3</sup></td><td>g</td><td>0.04 ± 0.001</td><td>0.06 ± 0.001</td><td>0.06 ± 0.001</td></tr><tr><td>Fat<sup>2</sup></td><td>g</td><td>7.6</td><td>7.6</td><td>7.6</td></tr><tr><td>Lactose<sup>2</sup></td><td>g</td><td>12</td><td>12</td><td>12</td></tr><tr><td>Ash<sup>4</sup></td><td>g</td><td>0.55</td><td>0.51</td><td>0.76</td></tr><tr><td>Energy<sup>2</sup></td><td>kcal</td><td>133</td><td>133</td><td>132</td></tr><tr><td></td><td>kJ</td><td>555</td><td>555</td><td>553</td></tr></tbody></table>

(30–70 nm) than those found in bovine milk (130–160 nm), which may partly explain the differences in gastric behavior between HM and IF (Billeaud et al., 1990; Cavell, 1981; Van Den Driessche et al., 1999). We previously showed that altering the supramolecular organization of casein modulated the structure of IFs and, in turn, the release of casein peptides, including bioactive peptides (Chauvet et al., 2023). Gut physiology and microbiota are particularly vulnerable to dietary changes in early infancy, and altering the digestive kinetics of IFs could modulate gut maturation and gut microbiota composition. Indeed, our previous work showed that HM-induced changes in microbiota profiles modulated development of the gut-brain axis in piglets, and more specifically intestinal and cerebral tryptophan (Trp) metabolism and immune and nutrient carrier functions in the early period of life (Charton et al., 2022).

The aim of the present study was therefore to evaluate the effects of the quality of different bovine milk protein ingredients (WP and caseins) in IFs on gut physiology and microbiota, and any associated changes affecting serum metabolomic profiles and the hypothalamus. This was investigated using the mini-piglet, a well-established and suitable animal model of human infants (Darragh & Moughan, 1995; Moughan et al., 1992; Roura et al., 2016). For this purpose, three powdered IFs were formulated to meet the nutritional needs of the piglets and produced following the same procedure at a semi-industrial scale and using different commercial protein ingredients, differing in terms of their protein composition and structure. Gut microbiota composition and activity, the intestinal and brain expression of genes involved in barrier, immune, endocrine and nutrient transport functions, and neuronal functions (in the brain) were determined. In addition, the metabolomic profile related to microbiota activity and Trp metabolism was explored in the serum of the piglets.

# 2. Material and methods

## 2.1. Experimental infant formulas

The three experimental IFs were manufactured at a semi-industrial scale at Bionov by Sodiaal (Rennes, France) using the same processing route and same ingredients (except for the protein ingredients; casein and WP as described previously) (Chauvet et al., 2023). IFs-A and -C contained concentrated skim milk powder as a source of caseins (micellar form), while IF-D contained a mix of sodium caseinate (75 % of total caseins) and 25 % of the same casein concentrated skim milk powder as IFs-A and -C. As for WPs, IF-A was based on a demineralized cheese whey powder and IFs-C and -D on a demineralized ideal whey powder, as previously used (Chauvet et al., 2024). The present IFs were formulated to meet piglet energy and protein requirements and presented similar energy, protein, lipid and carbohydrate contents (Table 1).

## 2.2. Animal study and diets

The experiment was conducted at INRAE experimental facilities (Pig Physiology and Phenotyping Experimental Facility, Saint-Gilles, France, doi https://doi.org/10.15454/1.5573932732039927E12; Agreement No. D35-275-32) in accordance with the current ethical standards of the European Community (Directive 2010/63/EU), and French legislation on animal experimentation and ethics. The CREEA (Rennes Committee of Ethics in Animal Experimentation) and French Ministry of Higher Education and Research Ethics Committees approved the protocol (authorization #30985-2021041213063537). The animals were observed daily throughout the experimental protocol to ensure their welfare and they did not receive any medication or antibiotic treatments.

A total of twenty-four female and male Yucatan piglets (Saint-Gilles, France) were studied in two replicate groups. The piglets were separated from their dams at postnatal day (PND) 2 to allow them access to sow

<footnote n="1">¹ Determined by the Kjeldahl method; ² Expected according to the formulation; ³ Determined by the method described in Charton et al. (2022); ⁴ minerals and vitamins were supplied by ingredients incorporated in powdered IFs and a premix of minerals and vitamins (0.1 g/100 mL rehydrated IF, Piglet premix Step 1, Mg2Mix, France). Mean values per 100 mL of rehydrated IFs: Ca 113.8 mg, K 90.0 mg, P 83.5 mg, Cl 69.1 mg, Na 38.2 mg, Mg 6.6 mg, Zn 2.5 mg, Fe 2.4 mg, Cu 2.8 µg, Mn 1.1 µg, I 0.1 µg, Se 0.02 µg, vitamin A 320 U.I, vitamin D3 4 U. I, vitamin E 3.2 mg, vitamin K3 0.12 mg, vitamin B1 0.04 mg, vitamin B2 0.08 mg, vitamin B3 0.40 mg, vitamin B5 0.20 mg, vitamin B6 0.06 mg, vitamin B8 4.0 µg, vitamin B12 0.4 µg, vitamin C 4 mg, choline chloride 10 mg. WP: whey protein.</footnote>
<caption>**Table 2**</caption>

Relative expression of genes involved in barrier, immune and nutrient-carrier functions, and tryptophan metabolism pathways in the colon of piglets fed IFs-A, -C and -D.

<table><thead><tr><th>Function</th><th>Subfunction</th><th>Gene (detailed name)</th><th>IF-A (n = 8)</th><th>IF-C (n = 8)</th><th>IF-D (n = 8)</th><th>Diet effect (p-value)</th></tr></thead><tbody><tr><td rowspan="10">Barrier</td><td rowspan="10">Barrier</td><td>CDH1 (Cadherin 1)</td><td>0.92±0.10</td><td>1.02±0.07</td><td>1.02±0.08</td><td>0.52</td></tr><tr><td>CLDN2 (Claudin 2)</td><td>1.65±0.26</td><td>1.07±0.18</td><td>1.11±0.26</td><td>0.13</td></tr><tr><td>CLDN3 (Claudin 3)</td><td><b>1.17<sup>a</sup>±0.14</b></td><td><b>1.04<sup>ab</sup>±0.11</b></td><td><b>0.92<sup>b</sup>±0.13</b></td><td><b>0.04</b></td></tr><tr><td>CLDN4 (Claudin 4)</td><td><b>1.24<sup>a</sup>±0.19</b></td><td><b>1.13<sup>a</sup>±0.19</b></td><td><b>0.85<sup>b</sup>±0.19</b></td><td><b>0.02</b></td></tr><tr><td>CLDN7 (Claudin 7)</td><td>0.92±0.08</td><td>1.01±0.10</td><td>0.96±0.10</td><td>0.57</td></tr><tr><td>OCLN (Occludin)</td><td><b>1.66<sup>a</sup>±0.18</b></td><td><b>1.06<sup>b</sup>±0.19</b></td><td><b>1.10<sup>b</sup>±0.17</b></td><td><b>0.01</b></td></tr><tr><td>BD2 (Beta defensin 2)</td><td>1.08±0.14</td><td>1.02±0.14</td><td>0.97±0.17</td><td>0.94</td></tr><tr><td>MUC1 (Mucin 1)</td><td><b>1.30±0.17</b></td><td><b>1.07±0.20</b></td><td><b>0.81±0.20</b></td><td><b>0.08</b></td></tr><tr><td>MUC2 (Mucin 2)</td><td>1.12±0.14</td><td>1.06±0.14</td><td>1.71±0.50</td><td>0.50</td></tr><tr><td>Defense</td><td>ZO1 (Zonulin 1)</td><td>0.94±0.08</td><td>1.02±0.11</td><td>1.03±0.13</td><td>0.74</td></tr><tr><td rowspan="10">Nutrient transporter</td><td rowspan="10">Amino acid transporter</td><td>SLC1A1(Sodium-Dependent Glutamate/Aspartate transporter 3)</td><td>1.42±0.35</td><td>1.06±0.15</td><td>1.02±0.16</td><td>0.45</td></tr><tr><td>SLC1A5 (Sodium- Neutral Amino Acid exchanger, ASCT2)</td><td>1.00±0.06</td><td>1.01±0.09</td><td>0.93±0.12</td><td>0.49</td></tr><tr><td>SLC15A1 (Peptide transporter 1)</td><td><b>2.32±0.46</b></td><td><b>1.73±0.60</b></td><td><b>1.17±0.48</b></td><td><b>0.07</b></td></tr><tr><td>SLC38A2 (Sodium-coupled neutral amino acid transporter 2)</td><td>0.93±0.07</td><td>1.01±0.07</td><td>0.85±0.05</td><td>0.32</td></tr><tr><td>SLC38A5 (Sodium-coupled neutral amino acid transporter 5)</td><td>0.94±0.06</td><td>1.01±0.08</td><td>0.90±0.11</td><td>0.47</td></tr><tr><td>SLC6A19 (Sodium-Dependent Neutral Amino Acid Transporter B0)</td><td><b>9.87<sup>a</sup>±1.90</b></td><td><b>2.02<sup>b</sup>±1.01</b></td><td><b>0.73<sup>b</sup>±0.08</b></td><td><b>&lt; 0.001</b></td></tr><tr><td>SLC7A1(high affinity cationic AA transporter, CAT-1)</td><td>0.96±0.13</td><td>1.03±0.12</td><td>0.96±0.14</td><td>0.95</td></tr><tr><td>SLC7A7(cationic and neutral AA transporter, y + LAT1)</td><td>1.44±0.17</td><td>1.11±0.23</td><td>0.91±0.20</td><td>0.11</td></tr><tr><td>SLC7A9 (cationic AA plus cysteine transporter, b0,+AT)</td><td><b>2.01±0.50</b></td><td><b>1.18±0.26</b></td><td><b>0.90±0.26</b></td><td><b>0.06</b></td></tr><tr><td>FFAR2 (Free fatty acid receptor 2)</td><td>1.49±0.30</td><td>1.12±0.22</td><td>1.37±0.21</td><td>0.56</td></tr><tr><td rowspan="3">SCFA receptor/ transporter</td><td>FFAR3 (Free fatty acid receptor 3)</td><td><b>2.14±0.43</b></td><td><b>1.16±0.29</b></td><td><b>0.93±0.20</b></td><td><b>0.09</b></td></tr><tr><td>MCT4 (Monocarboxylate Transporter 4)</td><td>1.53±0.36</td><td>1.17±0.32</td><td>1.21±0.31</td><td>0.32</td></tr><tr><td>NIACR1 (Niacin receptor 1)</td><td><b>1.54±0.19</b></td><td><b>1.19±0.35</b></td><td><b>1.19±0.14</b></td><td><b>0.06</b></td></tr><tr><td rowspan="10">Tryptophan pathways</td><td rowspan="10">Kynurenine pathway</td><td>IDO (Indoleamine 2,3-Dioxygenase 1)</td><td><b>0.88±0.29</b></td><td><b>1.28±0.35</b></td><td><b>2.12±0.73</b></td><td><b>0.03</b></td></tr><tr><td>KMO (Kynurenine 3-monooxygenase)</td><td>1.10±0.13</td><td>1.07±0.16</td><td>1.04±0.12</td><td>0.95</td></tr><tr><td>KYAT (Kynurenine aminotransferase)</td><td>1.00±0.08</td><td>1.06±0.14</td><td>1.03±0.20</td><td>0.96</td></tr><tr><td>KYNU (Kynureninase)</td><td>1.02±0.13</td><td>1.06±0.17</td><td>1.36±0.28</td><td>0.44</td></tr><tr><td>5-HTR2B (5-Hydroxytryptamine receptor 2B)</td><td>1.02±0.15</td><td>1.06±0.17</td><td>1.34±0.28</td><td>0.61</td></tr><tr><td>AAAD (Aromatic amino acid decarboxylase)</td><td><b>0.80±0.08</b></td><td><b>1.08±0.16</b></td><td><b>0.77±0.10</b></td><td><b>0.06</b></td></tr><tr><td>MAO (Monoamine oxydase)</td><td>1.18±0.13</td><td>1.01±0.11</td><td>0.86±0.12</td><td>0.21</td></tr><tr><td>SERT (Serotonin transporter)</td><td><b>1.86<sup>a</sup>±0.29</b></td><td><b>1.48<sup>ab</sup>±0.44</b></td><td><b>0.69<sup>b</sup>±0.16</b></td><td><b>0.04</b></td></tr><tr><td>Tph1 (Tryptophan hydroxylase 1)</td><td>0.87±0.13</td><td>1.05±0.14</td><td>1.24±0.20</td><td>0.29</td></tr><tr><td rowspan="10">Immunity</td><td rowspan="10">Anti-inflammatory</td><td>BAFF (B-cell activating factor)</td><td>1.01±0.05</td><td>1.11±0.21</td><td>1.26±0.23</td><td>0.83</td></tr><tr><td>IL10 (Interleukin 10)</td><td>1.31±0.34</td><td>1.13±0.22</td><td>1.31±0.17</td><td>0.83</td></tr><tr><td>IL10Ra (Interleukin 10 Receptor Subunit Alpha)</td><td>1.19±0.16</td><td>1.05±0.14</td><td>1.18±0.23</td><td>0.69</td></tr><tr><td>SOCS3 (Suppressor Of Cytokine Signaling 3)</td><td><b>0.80<sup>b</sup>±0.17</b></td><td><b>1.07<sup>ab</sup>±0.24</b></td><td><b>1.49<sup>a</sup>±0.29</b></td><td><b>0.02</b></td></tr><tr><td>TGFbeta (Transforming Growth Factor Beta)</td><td>1.27±0.14</td><td>1.05±0.17</td><td>1.01±0.18</td><td>0.16</td></tr><tr><td>MYD88 (Myeloid Differentiation Primary Response Gene)</td><td>0.95±0.07</td><td>1.01±0.08</td><td>0.92±0.06</td><td>0.34</td></tr><tr><td>PIGR (Polymeric Immunoglobulin Receptor)</td><td><b>1.00<sup>b</sup>±0.60</b></td><td><b>1.46<sup>ab</sup>±0.49</b></td><td><b>2.54<sup>a</sup>±0.53</b></td><td><b>0.01</b></td></tr><tr><td>TLR2 (Toll-like receptor 2)</td><td>1.20±0.08</td><td>1.05±0.17</td><td>1.02±0.10</td><td>0.34</td></tr><tr><td>TLR4 (Toll-like receptor 4)</td><td>0.94±0.07</td><td>1.02±0.09</td><td>1.25±0.07</td><td>0.27</td></tr><tr><td rowspan="8">Pro-inflammatory</td><td>CCL2 (C-C Motif Chemokine Ligand 2)</td><td>1.03±0.11</td><td>1.06±0.18</td><td>1.20±0.27</td><td>0.83</td></tr><tr><td>IL1B (Interleukin 1-Beta)</td><td>1.44±0.76</td><td>1.25±0.40</td><td>2.48±0.85</td><td>0.24</td></tr><tr><td>IL1BR (Interleukin 1-Beta receptor)</td><td>1.36±0.27</td><td>1.03±0.12</td><td>1.35±0.30</td><td>0.61</td></tr><tr><td>IL8 (Interleukin 8)</td><td>1.32±0.18</td><td>1.14±0.23</td><td>1.69±0.43</td><td>0.78</td></tr><tr><td>TNFA (Tumor necrosis factor alpha)</td><td><b>1.38±0.56</b></td><td><b>1.22±0.35</b></td><td><b>2.65±0.66</b></td><td><b>0.07</b></td></tr><tr><td>TNFR1 (Tumor necrosis factor alpha receptor 1)</td><td>1.09±0.09</td><td>1.01±0.08</td><td>0.77±0.15</td><td>0.39</td></tr><tr><td rowspan="2">Endocrine</td><td rowspan="2">Endocrine</td><td>CCK (Cholecystokinin)</td><td>2.22±0.61</td><td>1.39±0.55</td><td>0.80±0.12</td><td>0.13</td></tr><tr><td>CCKBR (Cholecystokinin B receptor)</td><td>2.60±0.59</td><td>1.32±0.40</td><td>1.61±0.75</td><td>0.23</td></tr></tbody></table>

Results are expressed as mean +/- SEM; significant differences at P < 0.05 or tendencies at p < 0.1 are represented in bold; a,b non-common letters indicate that piglet groups differed significantly, P < 0.05.

colostrum during the first two days of life. After that, they were housed in individual stainless-steel metabolic cages until PND21 and fed one of the three IFs via an automatic milk feeder, as previously described (Morise et al., 2009). To account for interlitter variations, three piglets with a body weight (BW) close to the mean birth weight of the litter were

selected from each litter and assigned to one of the three IFs. Allocation to the IFs was thus balanced between the groups in terms of birth weight, BW at PND2 and sex. The mean birth weight was similar in all groups (945 ± 112 g). The formulas were rehydrated each day to 25 % dry matter in water before distribution, in order to obtain 4.4 g crude
proteins/100 mL. The rehydrated experimental formulas contained on average twice more energy, 3 times more proteins, twice more lipids and 1.5 times more lactose than a standard IF for human infants, such as that used by Chauvet et al. (2024). Indeed, the Yucatan sow milk (SM) contained on average twice more energy (SM 114 kcal/100 mL vs. HM 65 kcal/100 mL), 3 times more proteins (crude protein SM 5.1 g/100 mL vs. HM 1.67 g/100 mL), 2.2 times more lipids (SM 7.2 g/100 mL vs. HM 3.21 g/100 mL) and an equal amount of lactose (SM 7.0 g/100 mL vs. HM 7.5 g/100 mL) as HM. So as to provide sufficient energy and nutrients to the piglets, the composition of the IFs used in the present study was thus adjusted to meet the nutritional needs of the piglets. The daily allowance was allocated to ten meals distributed automatically throughout the day. BW was measured twice a week and the feeding schedules were adjusted accordingly. The daily net energy supplied was 1450 kJ / kg BW<sup>0.75</sup>. The intake of formula was recorded automatically on a daily basis.

## 2.3. Sample collection

On the last day of the experiment (at PND21), the piglets were euthanized at 84 ± 6 min postprandially in the experimental slaughterhouse by electric stunning, followed immediately by exsanguination. The blood was collected in dry tubes and placed at room temperature for 1 h until blood clotting. Serum samples obtained after centrifugation (1000 g, 10 min, 4 °C) were stored at -80 °C until metabolomic analysis. Immediately after euthanasia, digesta from the ileum (60 cm before the ileocecal junction), the first-third of the colon and the feces were frozen immediately in liquid nitrogen and stored at -80 °C for microbiota analysis. Ileal and proximal colonic tissue segments were dissected. Ten-cm ileal and ten-cm proximal colon specimens were kept in ice-cold Dulbecco's Minimum Essential Medium (Gibco, ThermoFisher) for immediate Ussing chamber analysis. About 100 mg of ileal and proximal colonic tissues were sampled and kept in a RNA later solution for 24 h at 4 °C and then stored at -20 °C for RNA extraction and gene expression analysis. Adjacent segments (10-cm) were fixed in 4 % paraformaldehyde for 48 h until further dehydration in ethanol and embedding in paraffin, for morphometry analysis and goblet cell counting. Two samples of liver (100 mg each) and of the hypothalamus region of the brain were also extracted and frozen immediately in liquid nitrogen and stored at -80 °C for RNA extraction and gene expression analysis.

## 2.4. Microbiota analysis and short-chain fatty acid (SCFA) assay

**Bacterial DNA extraction.** The extraction of total bacterial DNA from fecal, colonic and ileal digesta was performed as described in the instruction guide for the Quick-DNA Fecal/Soil Microbe Miniprep Kit (ZYMO Research). After extraction, PCR amplification of the V3-V4 region of 16S rDNA was sequenced using Illumina Miseq technology (Illumina 2018) (INRAE GenoToul platform, Toulouse).

**SCFA assay.** After centrifugation at 1700 g for 15 min at 4 °C, supernatants of the intestinal contents diluted with *ortho*-phosphoric acid solution (0.5 %) were stored at -20 °C until the SCFA assay. Acetate, butyrate, valerate, isobutyrate and isovalerate were quantified by High-Performance Liquid Chromatography (HPLC, Ultimate 3000, Thermo Fisher Scientific, Courtaboeuf, France), using a Rezek ROA organic acid H + column (300*7.8 mm, Phenomenex, California), with H₂SO₄ 0.005 M as the mobile phase at a flow rate of 0.4 mL/min at 60 °C. Two detectors were used: a UV detector (DIONEX-UVD 1704) operated at 210 nm and a refractometer (RI 2031 Plus Jasco). The data were processed using Chromeleon™ software. Quantification was performed using multi-standard external calibration. Standards of butyrate, valerate, isobutyrate and isovalerate were obtained from Merck, St. Quentin Fallavier, France, and the acetate standard came from PanReac, Lyon, France.

## 2.5. Histomorphometry

Morphometric analysis was performed after the alcian blue and periodic acid Schiff staining of 7 µm sections of formalin-fixed, paraffin-embedded ileal tissues. The sections were examined and analyzed as previously described by Charton et al. (2022). Villus length and crypt depth were measured in at least 15 – 20 crypt-villus units per piglet. Goblet cells were also counted per villus and per crypt under the same staining conditions.

## 2.6. Ex vivo permeability measurement

Ileal and colonic permeability measurements were performed using Ussing chambers (Physiological Instruments, San Diego, USA) as described by Charton et al. (2022). Permeability was determined using tracer molecules: Na-FITC for paracellular permeability and horseradish peroxidase (HRP) for transcellular permeability. Na-FITC levels were measured by fluorimetry (LB940 Mithras fluorimeter, Berthold Technologies, Thoiry, France), while those of HRP were determined using spectrophotometry (Multiskan spectrum, Thermo Labsystem, Midland, Canada) after an enzymatic reaction using o-dianisidine as the substrate (Sigma-Aldrich).

## 2.7. Gene expression analysis

Total RNA was extracted using the “NucleoSpin® RNA” kit (Macherey Nagel) for intestinal tissues and liver and the Rneasy Plus Universal Mini kit (Qiagen) for hypothalamic tissue. The RNA thus extracted was quantified using a Denovix spectrophotometer. RNA quality and integrity controls were performed with the Agilent RNA 6000 Nano kit utilizing an Agilent 2100 Bioanalyzer (Agilent Technologies France, Massy, France) and by calculating the RIN (RNA Integrity Number). Reverse transcription was then performed on 2 µg extracted RNA using the High Capacity Complementary DNA Reverse Transcription Kit according to the manufacturer's instructions (Applied Biosystems). Quantitative real-time PCR was performed on 384-well plates with a real-time PCR machine using the PowerSYBR green PCR Master Mix kit (4368813, ThermoFisher Scientific) for detection. Based on their inter-sample stability, *RPL4* and *PCK1* were used as housekeeping genes in the ileum while *HPTR1*, *PCK1*, *RPL4* and *PIIA* were used as housekeeping genes in the colon. *RPL4*, *GADPH* and *YWHAZ* were used as housekeeping genes for the liver, and *HPTR1* and *ACTB* were used for the hypothalamus. The targeted genes (n = 49) in ileal and colonic tissues were related to immune, barrier and endocrine functions, digestion/nutrient carriers and Trp pathways. In the hypothalamus the targeted genes (n = 19) were related to immune, barrier, endocrine and neurosynaptogenesis functions, neurotransmitters, nutrient carriers and Trp pathways. In the liver, only three genes of interest from the Trp pathway (Tph1, KMO, KYAT) were analyzed. All target genes are listed in Table 2 (colon) and Supplemental Tables 1 (ileum and liver) and 2 (hypothalamus). The relative expressions of the target genes were determined using the 2^ΔΔCt^ method.

## 2.8. Metabolomic analysis

Twenty six metabolites related to microbiota activity and Trp metabolism were analyzed in serum. Metabolomic analyses were performed with Biocrates MxP500 Quant on a 6500 + Sciex LC-MS/MS instrument. Quantitation was performed with Analyst software to obtain the concentration of each analyte in each sample. The data tables were then uploaded into MetIDQ software for plate validation.

## 2.9. Statistical analysis

Data are presented as mean ± standard error of the mean (SEM).
**Microbiota data analysis.** Raw sequences obtained from microbiota
Growth

<figure><img src="image_5.png" /><figcaption>Fig. 1. Growth curve of IF-A, IF-C and IF-D piglets. Data are shown as means ± SEM.</figcaption></figure>

analysis were analyzed using the FROGS bioinformatic pipeline (Find Rapidly OTU with Galaxy Solution) software (Escudié et al., 2018). A descriptive analysis of the structure (α and β diversity) of the microbiota was conducted using the Phyloseq function (EdgeR package,

Bioconductor). The α-diversity indices were Observed species and Chao1 representing bacterial richness, and Shannon and InvSimpson representing bacterial evenness. Significant differences between dietary groups were assessed using ANOVA (aov function). Phylogenetic β

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Multiple factor analysis (MFA) using discriminant variables from Partial Least Square – Discriminant analysis grouped by function or phylum (n = 13 groups of variables). (A) Plots of individuals on dimensions 1 and 2. (B) Plot of the contributions of groups on dimension 1. (C) Plot of the contributions of the first 20 variables on dimension 1. Red dotted line: level of significance corresponding to the inverse of the number of groups of variables (panel B) or the number of variables (panel C) used in the analysis; Trp: Tryptophan, SCFA: Short-chain fatty acids, M: Microbiota, C: Colon, F: feces, I: Ileum, Actino: Actinomycetota, Desulf.: Desulfobacterota, Bact.: Bacteroidota. Gene labels are composed by Site_Gene name, Microbiota labels of genus are composed by M_Phylum_Site_Genus. Genes are listed in Table 2.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** (A) Microbial α-diversity (InvSimpson index) in colonic samples and (B) Most abundant phyla in colonic digesta of piglets fed IF-A, IF-C and IF-D. Campylobacterota, Patescibacteria, Synergistota and Verrucomicrobiota abundance was < 0.03 %, not shown in Fig. 2.B; Campylobacterota abundance was higher in IF-C and IF-D than in IF-A, P < 0.005.</figcaption></figure>

diversity (Jaccard, Bray-Curtis, weighted and unweighted Unifrac distances) was studied and group differences were evaluated using principal co-ordinate analysis (PCoA) and permutational multivariate analysis (PERMANOVA) of variance using distance matrices. Differences in phyla, families and OTUs were assessed with pairwise comparisons, after aggregation at the desired taxonomic rank (phyloseq' tax_glam function) using the EdgeR package (Bioconductor). Multiple testing corrections (False Discovery Rate) were used to avoid false positives (significance threshold = 0.05).

*Unidimensional analysis.* All statistical analyses were performed using R software, version 4.2.2. Differences between IF groups regarding BW, gene expression, SCFA, permeability, morphometry and metabolomic data were assessed using a linear model with the following factors: IF, litter and sex, interactions between IF and litter and between IF and sex, followed by a Tukey post-hoc test. When the p-value for sex, litter or the interaction was non-significant, the factor was removed from the linear model. The normality distribution and homoscedasticity of the residuals of each linear model were tested using Shapiro-Wilk and Levene's tests, respectively. Models were considered as acceptable for *p* > 0.1. When the raw data did not fulfil these model assumptions, a natural logarithmic transformation of the data was performed before running the linear models. If the assumptions were still not satisfied, the data were tested with a non-parametric Kruskal-Wallis test. Differences were considered to be statistically significant for *p* ≤ 0.05 and a trend for difference at 0.05 < *p* < 0.1.

*Multidimensional analysis.* Partial Least Squares – Discriminant Analysis (PLS-DA) was used to assess the relationship between diet (IF) and intestinal (ileal, colonic and fecal) variables. Discriminant variables (196 out of 398 variables) were retained when their VIP (Variable Importance in the Projection) was ≥ 1 on at least one of the two components. A multi-factor analysis (MFA) was then performed, using the FACTOMINE R package, on the variables retained from the PLS-DA. The variables were divided into 13 groups: barrier-related, immunity-

related, endocrine function-related, nutrient carrier-related, Trp metabolism-related, SCFA concentration variables and microbial genera gathered in seven phyla (Actinomycetota, Bacillota, Bacteroidota, Campylobacterota, Desulfobacterota, Fusobacteriota and Pseudomonadota).

*Data correlation.* Pearson correlation coefficients were determined between gut microbiota variables, intestinal variables (permeability, histomorphometry and gene expression), serum metabolite concentrations and hypothalamic gene expression. Relevant significant correlations (|r| ≥ 0.7, *p* < 0.05) are presented in the discussion section of the paper.

## 3. Results

### 3.1. Animal growth and experimental data

Throughout the experimental period, the piglets remained healthy and continued to grow, with an average daily BW gain of 88 ± 23 g/day. Formula composition had no significant effect on BW gain (Fig. 1). However, growth was sex-dependent, with an average of 81 ± 23 g/day for females and 95 ± 23 g/day for males.

#### 3.1.1. Overall impact of the quality of proteins in the IFs on the gut

To analyze the relationship between the diet and groups of variables, a PLS-DA was performed on the complete data set. The most discriminant variables with a VIP score ≥ 1 in the PLS-DA (n = 196 out of 398 variables) were selected for the multifactorial analysis (MFA) after grouping by function (six functions) or phylum (seven phyla). IFs were discriminated on dimension 1 (11.8 % of variance recovered) (Fig. 2.A). This dimension was mainly described, in decreasing order, by variables in the barrier group, Bacillota phylum and the nutrient carrier, Trp metabolism and immunity groups (Fig. 2.B). Interestingly, dimension 1 was mainly described by variables in the colonic compartment (62 % of
<caption>**Table 3**</caption>

Differential abundances of bacterial phyla, families and OTUs in the colon of piglets fed IFs-A, -C and -D.

<table><thead><tr><th>Taxonomic level</th><th>Comparison</th><th>Phylum</th><th>Family</th><th>Genus</th><th>p-adjusted</th><th>Log2 (Fold change)</th></tr></thead><tbody><tr><td rowspan="2">Phylum</td><td>A vs. C</td><td>Campylobacterota</td><td></td><td></td><td>0.005</td><td>-5.92</td></tr><tr><td>A vs. D</td><td>Campylobacterota</td><td></td><td></td><td>&lt; 0.001</td><td>-7.17</td></tr><tr><td rowspan="4">Family</td><td>A vs. C</td><td>Campylobacterota</td><td>Helicobacteraceae</td><td></td><td>&lt; 0.001</td><td>-22.40</td></tr><tr><td>A vs. C</td><td>Bacillota</td><td>Enterococcaceae</td><td></td><td>0.024</td><td>1.99</td></tr><tr><td>A vs. D</td><td>Campylobacterota</td><td>Helicobacteraceae</td><td></td><td>&lt; 0.001</td><td>-23.65</td></tr><tr><td>A vs. D</td><td>Bacillota</td><td>Enterococcaceae</td><td></td><td>0.010</td><td>2.21</td></tr><tr><td rowspan="20">OTU</td><td rowspan="19">A vs. C</td><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>&lt; 0.001</td><td>-21.82</td></tr><tr><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>&lt; 0.001</td><td>-22.93</td></tr><tr><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>&lt; 0.001</td><td>-23.94</td></tr><tr><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>&lt; 0.001</td><td>-21.6</td></tr><tr><td>Bacteroidota</td><td>Prevotellaceae</td><td>Alloprevotella</td><td>&lt; 0.001</td><td>22.41</td></tr><tr><td>Bacteroidota</td><td>Rikenellaceae</td><td>Alistipes</td><td>&lt; 0.001</td><td>-21.59</td></tr><tr><td>Bacteroidota</td><td>Tannerellaceae</td><td>Parabacteroides</td><td>&lt; 0.001</td><td>22.81</td></tr><tr><td>Bacteroidota</td><td>Tannerellaceae</td><td>Parabacteroides</td><td>&lt; 0.001</td><td>18.36</td></tr><tr><td>Campylobacterota</td><td>Helicobacteraceae</td><td>Helicobacter</td><td>&lt; 0.001</td><td>-22.17</td></tr><tr><td>Bacillota</td><td>Ruminococcaceae</td><td>unknown genus</td><td>&lt; 0.001</td><td>22.1</td></tr><tr><td rowspan="10">C vs. D</td><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>&lt; 0.001</td><td>34.05</td></tr><tr><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>&lt; 0.001</td><td>26.19</td></tr><tr><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>&lt; 0.001</td><td>26.97</td></tr><tr><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>&lt; 0.001</td><td>24.84</td></tr><tr><td>Bacteroidota</td><td>Prevotellaceae</td><td>Prevotellaceae NK3B31 group</td><td>0.048</td><td>7.652</td></tr><tr><td>Bacteroidota</td><td>Tannerellaceae</td><td>Parabacteroides</td><td>&lt; 0.001</td><td>-16.05</td></tr><tr><td>Bacteroidota</td><td>Tannerellaceae</td><td>Parabacteroides</td><td>&lt; 0.001</td><td>-23.13</td></tr><tr><td>Desulfobacterota</td><td>Desulfovibrioaceae</td><td>Desulfovibrio</td><td>&lt; 0.001</td><td>21.94</td></tr><tr><td>Bacillota</td><td>Oscillospiraceae</td><td>NK4A214 group</td><td>0.026</td><td>9.352</td></tr><tr><td rowspan="10">A vs. D</td><td>Bacillota</td><td>Ruminococcaceae</td><td>unknown genus</td><td>&lt; 0.001</td><td>-15.17</td></tr><tr><td>Bacteroidota</td><td>Bacteroidaceae</td><td>Bacteroides</td><td>0.042</td><td>12.23</td></tr><tr><td>Bacteroidota</td><td>Prevotellaceae</td><td>Alloprevotella</td><td>&lt; 0.001</td><td>21.84</td></tr><tr><td>Bacteroidota</td><td>Prevotellaceae</td><td>Alloprevotella</td><td>0.035</td><td>-10.75</td></tr><tr><td>Bacteroidota</td><td>Prevotellaceae</td><td>Alloprevotella</td><td>0.038</td><td>-9.805</td></tr><tr><td>Bacteroidota</td><td>Rikenellaceae</td><td>Alistipes</td><td>0.014</td><td>-13.85</td></tr><tr><td>Campylobacterota</td><td>Helicobacteraceae</td><td>Helicobacter</td><td>&lt; 0.001</td><td>-23.23</td></tr><tr><td>Desulfobacterota</td><td>Desulfovibrioaceae</td><td>Desulfovibrio</td><td>0.007</td><td>14.80</td></tr><tr><td>Bacillota</td><td>Oscillospiraceae</td><td>NK4A214 group</td><td>0.014</td><td>10.18</td></tr><tr><td>Bacillota</td><td>Ruminococcaceae</td><td>Anaerotruncus</td><td>0.006</td><td>-4.3</td></tr></tbody></table>

Log2 (Fold Change), the Fold Change indicates the significant differential abundance of bacteria (at phylum, family or OTU level) in the first cited group compared to the second cited group (Deseq analysis). For example, the abundance of Campylobacterota phylum is lower in IF-A than in IF-C colon.

significant variables; Fig. 2.C) while ileal and feces variables only represented 27% and 12% of significant variables, respectively. Indeed, out of the 49 genes analyzed in the ileum, only OCLN was significantly modified, displaying a lower expression in piglets fed IF-A compared to those fed IF-C and IF-D (Supplemental Table 1). No dietary effects were observed on villus and crypt sizes while the number of villus mucin cells was smaller in the IF-C than in the IF-A and IF-D groups (Supplemental Table 3).

## 3.2. Dietary modulation of the colon compartment

As shown by MFA, the colon was the main site to be physiologically affected by the quality of the dairy protein ingredients used in IFs. To further understand the phenomenon, univariate analyses were performed on the colon variables.

### 3.2.1. Microbiota abundance and activity

Alpha-diversity (Observed, Chao1, Shannon and inverse Simpson indices) measured on colonic samples was not affected by the diet (Fig. 3.A; Supplemental Table 4), nor was β-diversity (weighted and unweighted Unifrac, Jaccard and Bray-Curtis distances; Supplemental Fig. 1). In the colon, Bacillota was the most abundant phylum in the three dietary groups. The Bacteroidota phylum was more represented in piglets fed IF-D than in those fed IF-A, and Campylobacterota phylum abundance was higher in IF-C and IF-D than in IF-A piglets (Fig. 3.B; Table 3).

At a family level, *Helicobacteraceae* were more abundant and *Enterococcaceae* less abundant in the colons of IF-C piglets and IF-D piglets than of IF-A piglets. At the OTU level, 17 colonic OTUs differed between

IFs, with 11 of them belonging to Bacteroidota, four to Bacillota, one to Campylobacterota and one to Desulfobacterota phyla (Table 3). Only three taxa displayed differing abundance in ileal digesta (Supplemental Table 5).

Despite the different bacterial profiles, only isovalerate concentrations and the sum of isovalerate and isobutyrate concentrations tended to differ among IFs in the colon (Table 4). However, it is worth noting that fecal butyrate and valerate concentrations and butyrate proportions were lower in IF-A-fed piglets than in IF-C- and IF-D-fed piglets, whereas fecal acetate proportions were higher in IF-A piglets. Further, isovalerate and isobutyrate levels were higher in IF-C than in IF-A feces, and the proportion of isobutyrate was lower in IF-C than in IF-A feces (Table 4).

### 3.2.2. Barrier function

Colonic CLDN3, CLDN4, OCLN and MUC1 genes were more markedly expressed in piglets fed IF-A than those receiving IF-D. In addition, OCLN gene expression was higher in IF-A colon than IFs-C and -D, and CLDN4 gene expression was higher in IFs-A and -C colon than in the IF-D group (Table 2). Consistent with this, paracellular (Na-FITC) permeability was lower in the colon of piglets fed IF-A (42.4 ± 3.5 ng/cm²/h) and IF-C (52.8 ± 5.1 ng/cm²/h) than those receiving IF-D (78.2 ± 7.4 ng/cm²/h). No differences in transcellular (HRP) permeability were observed (Supplemental Table 3).

### 3.2.3. Nutrient-carrier function

Among the genes encoding for AA transport, SLC6A19 was significantly more strongly expressed in IF-A colon than in IFs-C and -D colon, while SLC15A1 and SLC7A9 tended to be differently expressed between IFs (Table 2). As for SCFA receptor/transporters, expression of the
<caption>**Table 4**</caption>

Short-chain fatty acid (SCFA) levels and proportions in colonic digesta and feces of piglets fed IFs-A, -C and -D.

<table><caption>**Table 4 (continued)**</caption><thead><tr><th>Site</th><th>SCFA</th><th>IF-A (n = 8)</th><th>IF-C (n = 8)</th><th>IF-D (n = 8)</th><th>Diet effect (p-value)</th></tr></thead><tbody><tr><td rowspan="12">Colon</td><td>Acetate (mmol/kg)</td><td>32.7±13.5</td><td>44.6±20.1</td><td>41.0±9.9</td><td>0.46</td></tr><tr><td>Propionate (mmol/kg)</td><td>8.1±2.6</td><td>11.0±4.4</td><td>10.7±2.3</td><td>0.71</td></tr><tr><td>Butyrate (mmol/kg)</td><td>2.6±0.7</td><td>3.5±1.5</td><td>4.8±1.1</td><td>0.3</td></tr><tr><td>Valerate (mmol/kg)</td><td>0.9±0.2</td><td>0.7±0.3</td><td>1.0±0.2</td><td>0.35</td></tr><tr><td><strong>Isovalerate (mmol/kg)</strong></td><td><strong>0.5±0.1</strong></td><td><strong>0.6±0.2</strong></td><td><strong>1.1±0.2</strong></td><td><strong>0.06</strong></td></tr><tr><td>Isobutyrate (mmol/kg)</td><td>0.2±0.1</td><td>0.3±0.1</td><td>0.5±0.1</td><td>0.14</td></tr><tr><td>Sum of non-iso SCFAs (mmol/kg)</td><td>44.3±16.8</td><td>59.8±26.1</td><td>57.4±13.1</td><td>0.50</td></tr><tr><td><strong>Sum of iso SCFAs (mmol/kg)</strong></td><td><strong>0.7±0.1</strong></td><td><strong>0.9±0.4</strong></td><td><strong>1.5±0.3</strong></td><td><strong>0.08</strong></td></tr><tr><td>Sum of SCFAs (mmol/kg)</td><td>45.0±16.9</td><td>60.8±26.4</td><td>59.0±13.3</td><td>0.50</td></tr><tr><td>Acetate (% SCFAs)</td><td>68.1±2.2</td><td>66.0±2.9</td><td>68.8±2.7</td><td>0.74</td></tr><tr><td>Propionate (% SCFAs)</td><td>19.3±0.9</td><td>20.7±1.1</td><td>18.1±2.4</td><td>0.38</td></tr><tr><td>Butyrate (% SCFAs)</td><td>7.3±0.9</td><td>8.0±1.3</td><td>8.1±0.5</td><td>0.80</td></tr><tr><td>Valerate (% SCFAs)</td><td>3.0±0.7</td><td>2.4±0.6</td><td>2.1±0.6</td><td>0.35</td></tr><tr><td>Isovalerate (% SCFAs)</td><td>1.6±0.2</td><td>2.0±0.4</td><td>2.1±0.5</td><td>0.54</td></tr><tr><td>Isobutyrate (% SCFAs)</td><td>0.7±0.1</td><td>0.9±0.1</td><td>0.9±0.1</td><td>0.14</td></tr><tr><td>Sum of non-iso SCFA (% SCFAs)</td><td>97.7±0.3</td><td>97.1±0.5</td><td>97.1±0.6</td><td>0.58</td></tr><tr><td>Sum of iso SCFA (% SCFAs)</td><td>2.3±0.3</td><td>2.9±0.5</td><td>2.9±0.6</td><td>0.58</td></tr><tr><td rowspan="12">Feces</td><td>Acetate (mmol/kg)</td><td>9.9±2.5</td><td>28.7±1.7</td><td>11.5±2.7</td><td>0.68</td></tr><tr><td>Propionate (mmol/kg)</td><td>3.0±0.7</td><td>11.1±5.8</td><td>4.6±1.3</td><td>0.22</td></tr><tr><td><strong>Butyrate (mmol/kg)</strong></td><td><strong>1.2±0.3</strong></td><td><strong>7.0±4.0</strong></td><td><strong>2.6±0.8</strong></td><td><strong>0.07</strong></td></tr><tr><td>Valerate (mmol/kg)</td><td>0.3<sup>b</sup>±0.1</td><td>1.8<sup>a</sup>±0.7</td><td>1.0<sup>a</sup>±0.4</td><td>0.05</td></tr><tr><td><strong>Isovalerate (mmol/kg)</strong></td><td><strong>0.6±0.25</strong></td><td><strong>2.3±0.8</strong></td><td><strong>1.6±0.8</strong></td><td><strong>0.06</strong></td></tr><tr><td>Isobutyrate (mmol/kg)</td><td>0.2<sup>b</sup>±0.1</td><td>1.0<sup>a</sup>±0.3</td><td>0.6<sup>ab</sup>±0.3</td><td>0.04</td></tr><tr><td>Sum of non-iso SCFAs (mmol/kg)</td><td>14.4±3.4</td><td>48.5±10.8</td><td>19.8±5.1</td><td>0.27</td></tr><tr><td><strong>Sum of iso SCFAs (mmol/kg)</strong></td><td><strong>0.9±0.3</strong></td><td><strong>3.3±1.1</strong></td><td><strong>2.2±1.1</strong></td><td><strong>0.06</strong></td></tr><tr><td>Sum of SCFAs (mmol/kg)</td><td>15.3±3.6</td><td>51.8±27.2</td><td>22.0±6.1</td><td>0.22</td></tr><tr><td><strong>Acetate (% SCFAs)</strong></td><td><strong>64.4<sup>a</sup>±2.4</strong></td><td><strong>46.6<sup>b</sup>±6.4</strong></td><td><strong>56.8<sup>b</sup>±3.3</strong></td><td><strong>0.02</strong></td></tr><tr><td>Propionate (% SCFAs)</td><td>18.8±1.9</td><td>20.8±2.0</td><td>19.2±2.6</td><td>0.8</td></tr><tr><td><strong>Butyrate (% SCFAs)</strong></td><td><strong>8.1<sup>b</sup>±0.8</strong></td><td><strong>11.9<sup>a</sup>±0.8</strong></td><td><strong>12.1<sup>a</sup>±1.2</strong></td><td><strong>0.01</strong></td></tr><tr><td>Valerate (% SCFAs)</td><td>2.6±0.5</td><td>4.1±0.6</td><td>4.1±0.6</td><td>0.14</td></tr><tr><td>Isovalerate (% SCFAs)</td><td>4.5±1.0</td><td>6.3±0.8</td><td>5.4±1.2</td><td>0.48</td></tr><tr><td><strong>Isobutyrate (% SCFAs)</strong></td><td><strong>1.5<sup>b</sup>±0.3</strong></td><td><strong>2.9<sup>a</sup>±0.3</strong></td><td><strong>2.4<sup>ab</sup>±0.5</strong></td><td><strong>0.01</strong></td></tr></tbody></table>

<table><thead><tr><th>Site</th><th>SCFA</th><th>IF-A (n = 8)</th><th>IF-C (n = 8)</th><th>IF-D (n = 8)</th><th>Diet effect (p-value)</th></tr></thead><tbody><tr><td>Sum of non-iso SCFA (%)</td><td>93.9<sup>a</sup>±1.2</td><td>83.3<sup>b</sup>±5.9</td><td>92.2<sup>a</sup>±1.7</td><td>0.05</td></tr><tr><td>Sum of iso SCFA (%)</td><td>6.1±1.2</td><td>9.2±1.0</td><td>7.8±1.7</td><td>0.28</td></tr></tbody></table>

Results are expressed as mean +/- SEM; significant differences at P < 0.05 or tendencies at p < 0.1 are represented in bold; a,b non-common letters indicate that piglet groups differed significantly, P < 0.05. Iso SCFA means isovalerate plus isobutyrate; non-iso SCFAs means the sum of SCFAs except isovalerate and isobutyrate.

FFAR3 gene in the colon and that of the NIACR1 gene tended to be modified by the diet (Table 2).

### 3.2.4. Tryptophan pathways

Among the selected genes, expression in the colon of two genes involved in Trp metabolism was significantly modulated by diet. IDO (kynurenine pathway) was significantly upregulated and SERT (serotonin pathway) significantly downregulated in the colons of piglets fed IF-D compared to IF-A. AAAD (serotonin pathway) gene expression also tended to be modulated by diet (Table 2).

### 3.2.5. Immune function and endocrine function

The expression of SOCS3, PIGR and TNFα (tendency) genes was lower in IF-A colon than in IF-D colon (Table 2). No impact of IF composition was observed on the expression of anti-inflammatory-related genes, or pro-inflammatory-related genes or on MYD88 and TLR2 (Table 2). Finally, the expression of CCK and its receptor was not affected by IF composition (Table 2).

### 3.3. Focus on the serum metabolomic profile and the hypothalamus

Further investigations of the serum metabolomic and hypothalamic gene expression profiles were undertaken to determine whether the observed changes in colonic physiology and microbiota associated with IF protein quality were also associated with changes to the serum metabolomic profile and hypothalamic functions.

MFA was performed on the serum metabolites to explore the impact of IF protein quality (Fig. 4). IFs were discriminated on dimension 1 (25.1 % of variance recovered) (Fig. 4.A). Eight metabolites contributed significantly to the description of dimension 1, with the higher contribution of four metabolites related to Trp metabolism, namely Trp, 3-indole-acetic acid (3-IAA), kynurenine and 3-indole-propionic acid (3-IPA). Three polyamines (spermidine, putrescine and spermine) resulting from microbial metabolism also contributed significantly to the separation of IFs on dimension 1 (Fig. 4.B).

Univariate analysis revealed that kynurenine levels were significantly higher in the serum of piglets fed IF-D (+30 %) than in that of piglets fed IF-A, with IF-C being intermediate (Table 5). Similarly, putrescine, spermidine and spermine levels were higher in the serum of IF-D piglets than in that of piglets fed IF-A and IF-C (Table 5). In contrast, levels of indole (involved in both Trp metabolism and microbial activity) were lower in the serum of piglets fed IF-C and IF-D than IF-A (Table 5). In addition, Asp, Glu and Lys were found at lower levels in the serum of IF-A piglets than IF-C and IF-D piglets, while Thr was at higher levels in the serum of IF-A piglets than IF-C and IF-D piglets (Table 5).

Interestingly, the expression of only two genes out of the nineteen genes studied in the hypothalamus was significantly modulated by diet (Supplemental Table 2). Relative expression of the NPY2R gene was significantly increased in piglets fed IF-C compared to those receiving IF-A and IF-D, and that of FFAR2 was decreased in piglets fed IF-C
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Multiple factor analysis of serum metabolites related to microbiota activity and Trp metabolism (n = 2 groups of variables). (A) Plot of individuals on dimensions 1 and 2. (B) Plot of the contributions of different variables on dimension 1. Red dotted line: level of significance corresponding to the inverse of the number of variables used in the analysis; Trp: Tryptophan, X3.IAA: 3-Indole-acetic acid, X3.IPA: 3-Indole-propionic acid, Suc: Succinate, Asp: Aspartic acid, Lac: Lactate, Lys: Lysine, Ala: Alanine, Gly: Glycine, Glu: Glutamic acid, X5.AVA: 5-Aminovaleric acid, GABA: γ-Aminobutyric acid, Ser: Serotonin, p.Cresol.SO4: ρ-Cresol sulfate.</figcaption></figure>

(compared to those fed IF-A), IF-D not differing significantly different from IF-A or IF-C. In the liver, the expression of genes involved in the Trp metabolism was not significantly modified by diet (Supplemental Table 1).

## 4. Discussion

The present study demonstrated for the first time that the quality of protein ingredients in IFs affected colonic physiology and microbiota, albeit moderately, in 3-week-old mini-piglets used as a human infant model. These dietary changes in colonic physiology and microbiota composition were associated with a modulation of serum concentrations of metabolites related to microbiota metabolism and Trp metabolism, without any effect on hypothalamic function. Notably, there was no dietary effect on the ileum.

In the present study, the effects of the protein ingredient quality of IFs on the gut microbiota were moderate. Although no global effect of IFs was observed on colonic α- and β-diversities, more specific differences were observed at the phylum, family and OTU levels. Interestingly, colonic Campylobacterota abundance was increased in IFs-C and -D compared to IF-A, as previously observed in HM-fed piglets compared to IF-fed piglets (Brink et al., 2019; Charton et al., 2022). The differential modulation of Campylobacterota among IFs could be related to the origin of the whey (cheese whey for IF-A vs. ideal whey for IFs-C and -D). *Alistipes* (Bacteroidota phylum) was more abundant in the colon of piglets fed IF-C and IF-D than in those receiving IF-A. The abundance of this genus was enhanced with probiotic intake in a rodent study, with expected positive issues (Parker et al., 2020). Several species of *Bacteroides* genera were more abundant in IF-C colon than in IF-A and IF-D colons. Similar results had been observed in HM-fed piglets, which had a higher fecal abundance of *Bacteroides* than IF-fed piglets (Charton et al., 2022). The observed differences in microbiota composition and SCFA content and proportions among IFs may be related to the bioavailability of specific dietary substrates. As the digestion kinetics differed among IFs (Chauvet et al., 2023), the quality of the substrate may influence the growth of different microbial species and consequently their SCFA production (Scott et al., 2013). The fecal proportions of SCFAs (acetate, butyrate and isobutyrate) were diet-dependent with significant differences between IFs-C and -D and IF-A, indicating a specific fermentative activity of the microbiota related to WP. Several studies have shown an association between SCFA production in the colon and intestinal physiological changes (Ichikawa et al., 2002; Kripke et al., 1989).

Higher paracellular permeability, the downregulated expression of genes encoding tight junction proteins (OCLN, CLDN3 and CLDN4) and

the upregulated expression of genes involved in immune function (SOCS3, PIGR, TNFa) were observed in the colon of piglets fed IF-D compared to those receiving IF-A, with IF-C being intermediate. Human milk has previously been shown to boost the immune response (Calder et al., 2006; Charton et al., 2022; Jeon et al., 2021; Laubereau et al., 2004) and to promote higher ileal and colonic paracellular permeability in HM-fed piglets than in IF-fed piglets (Charton et al., 2022) as well as in breastfed infants (Lee et al., 2017). Interestingly, colonic paracellular permeability values were within the same range between IF-A-fed piglets in the present study and the IF-fed piglets studied by Charton et al. (2022), while the values seen in IF-D-fed piglets were similar to those reported for HM-fed piglets (Charton et al., 2022). The immune boost seen with HM, and to a lesser extent with the present IF-D, may have been due to a greater absorption of molecules across the epithelium as a result of this increased permeability, allowing the acquisition of tolerance towards exogenous bacteria and antigens (Le Huërou-Luron et al., 2010). The immunomodulatory role of GMP previously observed in neonatal mammals was not confirmed in the present work as differences in the expression of genes encoding the immune function were only observed between IF-A and IF-D, but not between IF-A and IF-C (Monnai et al., 1998; Requena et al., 2010; Sawin et al., 2015; Thomä-Worringer et al., 2006). Similarly, the *in vitro* bifidogenic activity of GMP (Azuma et al., 1984; Janer et al., 2004; O’Riordan et al., 2018) was not observed in our study, likely due to the relatively small quantity of GMP present in IF-A. Also, GMP may have been hydrolyzed by brush border enzymes (Boutrou et al., 2008) before reaching the colon.

The higher expression of SLC6A19, FFRA3 and NIACR1 in the colon of IF-A piglets compared to IF-C and IF-D piglets may be related to differences in IF composition, as a similar upregulation had been reported in the colon of HM-fed piglets compared to IF-fed piglets (Charton et al., 2022). The tendency towards a higher expression of the SLC15A1 transporter, known as the major dietary peptide transporter in the small intestine (PepT1), in the IF-A colon may be due to the greater presence of absorbable peptides. In the present study no differences in IF intake and CCK and CCKBR gene expression were observed among IFs. This finding contrasts with the GMP-induced modulation of CCK secretion and appetite regulation reported in some preclinical studies (Beucher et al., 1994; Pedersen et al., 2000). The lack of difference may be due to the relatively low concentration of GMP in IF-A, as the effect of GMP on hormone secretion appears to be dose-dependent (Córdova-Dávalos et al., 2019).

The levels of several metabolites related to microbiota activity, and particularly Trp-related metabolites and polyamines, were influenced by the quality of protein ingredients in IFs. Compared to IF-A-fed piglets, the kynurenine pathway seemed to be slightly enhanced in IF-D-fed
<caption>**Table 5**</caption>

Levels of 26 metabolites related to microbiota activity and Trp metabolism in the serum of piglets fed IFs-A, -C and -D.

<table><thead><tr><th>Serum metabolite (detailed name)</th><th>IF-A (n = 8)</th><th>IF-C (n = 8)</th><th>IF-D (n = 8)</th><th>Diet effect (p-value)</th></tr></thead><tbody><tr><td><strong>Microbiota-related</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>5-AVA (5-<br>Aminovaleric acid)</td><td>14<sup>b</sup>±2</td><td>43<sup>a</sup>±15</td><td>77<sup>a</sup>±22</td><td>0.05</td></tr><tr><td><strong>AABA</strong><br>(α-Aminobutyric<br>acid)</td><td>1221±80</td><td>1046±79</td><td>1189±91</td><td>0.08</td></tr><tr><td>Ala (Alanine)</td><td>593±<br>±234</td><td>603±161</td><td>1130±172</td><td>0.37</td></tr><tr><td><strong>Asp</strong> (Aspartic acid)</td><td>4222<sup>b</sup><br>±316</td><td>4231<sup>b</sup><br>±289</td><td>5670<sup>a</sup>±345</td><td>0.005</td></tr><tr><td>Cys (Cystein)</td><td>19,041<br>±645</td><td>17,680<br>±658</td><td>18,517<br>±758</td><td>0.17</td></tr><tr><td>GABA (γ-Aminobutyric<br>acid)</td><td>49±3</td><td>46±3</td><td>51±3</td><td>0.59</td></tr><tr><td>Gln (Glutamine)</td><td>52,381<br>±2171</td><td>57,975<br>±3189</td><td>55,551<br>±3245</td><td>0.52</td></tr><tr><td><strong>Glu</strong> (Glutamic acid)</td><td>39413<sup>b</sup><br>±2006</td><td>42086<sup>ab</sup><br>±3324</td><td>49359<sup>a</sup><br>±2318</td><td>0.04</td></tr><tr><td>Gly (Glycine)</td><td>128,243<br>±5133</td><td>134,330<br>±4861</td><td>142,155<br>±5694</td><td>0.31</td></tr><tr><td>Lac (Lactate)</td><td>559,503<br>±66,728</td><td>547,613<br>±75,036</td><td>681,543<br>±101,682</td><td>0.49</td></tr><tr><td><strong>Lys</strong> (Lysine)</td><td>52808<sup>b</sup><br>±4585</td><td>64010<sup>a</sup><br>±2148</td><td>67830<sup>a</sup><br>±3625</td><td>0.02</td></tr><tr><td>p-cresol-SO4 (p-Cresol<br>sulfate)</td><td>164±14</td><td>165±17</td><td>152±21</td><td>0.70</td></tr><tr><td><strong>Putrescine</strong></td><td>136<sup>b</sup>±7</td><td>144<sup>b</sup>±7</td><td>181<sup>a</sup>±13</td><td>0.01</td></tr><tr><td>Ser (Serine)</td><td>50,594<br>±3609</td><td>44,982<br>±2690</td><td>45,558<br>±3696</td><td>0.44</td></tr><tr><td><strong>Spermidine</strong></td><td>136<sup>b</sup>±18</td><td>120<sup>b</sup>±11</td><td>228<sup>a</sup>±22</td><td>0.002</td></tr><tr><td><strong>Spermine</strong></td><td>86<sup>b</sup>±20</td><td>66<sup>b</sup>±9</td><td>159<sup>a</sup>±22</td><td>&lt; 0.001</td></tr><tr><td>Suc (Succinate)</td><td>221±18</td><td>212±18</td><td>253±21</td><td>0.30</td></tr><tr><td><strong>Thr</strong> (Threonine)</td><td>78559<sup>a</sup><br>±5170</td><td>60628<sup>b</sup><br>±3278</td><td>58760<sup>b</sup><br>±5010</td><td>0.01</td></tr><tr><td>TMAO<br>(Trimethylamine N-<br>oxide)</td><td>593±234</td><td>603±161</td><td>1130±172</td><td>0.10</td></tr><tr><td>Xanthine</td><td>125±5</td><td>137±8</td><td>144±7</td><td>0.14</td></tr><tr><td><strong>Tryptophan<br>metabolism-related</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>3-IAA (3-Indole-acetic<br>acid)</td><td>73±7</td><td>83±11</td><td>80±11</td><td>0.80</td></tr><tr><td>3-IPA (3-Indole-<br>propionic acid)</td><td>5.5±1.4</td><td>4.4±0.8</td><td>6.2±2.2</td><td>0.97</td></tr><tr><td><strong>Indole</strong></td><td>2890<sup>a</sup><br>±330</td><td>1588<sup>b</sup><br>±413</td><td>1602<sup>b</sup>±260</td><td>0.01</td></tr><tr><td><strong>Kynurenine</strong></td><td>271<sup>b</sup>±18</td><td>314<sup>ab</sup>±22</td><td>352<sup>a</sup>±18</td><td>0.03</td></tr><tr><td>Serotonin</td><td>1311±277</td><td>1378±249</td><td>1052±231</td><td>0.55</td></tr><tr><td>Trp (Tryptophan)</td><td>16,338<br>±935</td><td>18,019<br>±732</td><td>18,664<br>±709</td><td>0.13</td></tr></tbody></table>

Results are expressed as mean +/- SEM; significant differences at P < 0.05 or tendencies at P < 0.1 are represented in bold; a,b non-common letters indicate that piglet groups differed significantly, P < 0.05.

piglets (upregulated expression of IDO in the colon and increased serum kynurenine levels). In addition, the expression of SERT, which terminates serotonin signaling by enhancing the reuptake of serotonin into presynaptic neurons, was downregulated in IF-D-fed piglets when compared to IF-A-fed piglets. Further, serum Trp levels were positively correlated to the hypothalamic expression of the serotoninergic receptor 5-HT2BR, which mediates many of the central physiological functions of serotonin. Interestingly, HM resulted in a preferential metabolism of Trp into the intestinal kynurenine pathway when compared to IF in neonatal piglets (Charton et al., 2022; Rosa et al., 2021). Serum Trp and kynurenine levels were apparently influenced by microbiota composition, as evidenced by many correlations between these metabolites and microbial genera (Supplemental Fig. 2). Among the positive correlations between genera and serum Trp-related compounds and amino acids, a

notable feature of *Acidaminococcus* is its ability to ferment glutamate, which is an important precursor and intermediate in the synthesis and metabolic recycling of other amino acids, and in the urea cycle (Gough et al., 2015). In addition, and consistent with our data, *Ligilactobacillus*, a lactic acid bacteria, was associated with an increase in compounds related to Trp metabolism (Hong et al., 2021). *Eubacterium fissicatena*, *Ruminococcaceae* UCG-005 and *Subdoligranulum* have been reported to be positively correlated with the production of SCFAs. However, knowledge of the role of these genera in protein utilization remains limited and further investigation is required. Taken together, our results highlighted the subtle but diet-dependent regulation of Trp metabolism in the gut, probably via specific bacteria, with consequences on serum Trp-related compounds and hypothalamic serotoninergic receptor expression. Polyamines are compounds of interest for rapidly growing tissues, such as the developing gastrointestinal tract of the newborn (Schimmel et al., 2023). In the present study, serum polyamine levels were positively correlated with paracellular permeability and SOCS3 expression in the colon (Supplemental Fig. 3). In previous studies, polyamine levels in plasma (putrescine), distal colon digesta and feces (spermidine) were higher in HM-fed piglets than in IF-fed piglets (Charton et al, submitted; Rosa et al., 2020). Therefore, a higher concentration of polyamines, as observed in the serum of IF-D-fed piglets (putrescine, spermine, spermidine), may be beneficial to newborns, as polyamines promote maturation of the immune system and the modulation of intestinal permeability (Ferioli et al., 2000; Larqué et al., 2007; Muñoz-Esparza et al., 2021; Pérez-Cano et al., 2010; ter Steege et al., 1997). In addition, a study on newborn piglets had also shown that IF supplementation with polyamines at the physiological doses of maternal milk slightly reduced the differences observed between IF-fed and maternal milk-fed piglets in terms of intestinal growth and maturation (Sabater-Molina et al., 2009). Putrescine, spermine and spermidine were also strongly positively correlated with several genera of Clostridiales (*Clostridium innocuum* group, *Eubacterium fissicatena* group, *Flavinofractor* and *Howardella*), known to be one of the main amine-producing bacteria (Supplemental Fig. 2). Amines produced by intestinal bacteria mainly result from the decarboxylation of amino acids, but other biochemical pathways are also involved (demethylation, N-dealkylation). Finally, and confirming previously observed differences in plasma (Chauvet et al., 2024), serum Thr levels were significantly higher in piglets fed IF-A than in those receiving IFs-C and -D. These marked differences reflect the origin of the whey (cheese vs. ideal whey), leading to differences in the amino acid intake profile, combined with the immaturity of Thr degradation ability in piglets during the first weeks of life (Chen et al., 2009). The structure and composition of protein ingredients in IFs have been shown to be important to the kinetics of proteolysis and peptide release during *in vitro* digestion (Chauvet et al. 2023) and to subsequent amino acid absorption during the early postprandial phase in piglets (Chauvet et al. 2024). Here, our additional results suggest an effect on colonic physiology that could be mediated via changes in colonic microbiota composition and thereby SCFA profiles, with associated consequences on serum metabolite profiles. The manner in which whey proteins and caseins induce changes to specific genus abundances requires further investigation.

While significant differences in variables related to gut physiology, microbiota and serum metabolites were mainly observed between IF-A and IF-D piglets, the values in IF-C piglets were often intermediate between them. It is therefore probable that the impact of IF composition resulted from the combined effects of WP origin and the supramolecular organization of caseins.

## 5. Conclusion

Colonic physiology and microbiota composition were moderately modulated by the IFs, without hypothalamic gene expression profiles being affected. Nevertheless, the changes observed to the intestinal epithelial barrier and immune function, the kynurenine pathway of Trp
metabolism, and serum polyamine levels related to microbiota activity in piglets fed IF containing ideal whey and non-micellar caseins, appeared to be similar to those seen in piglets fed HM (Charton et al., 2022). This suggests that both WP and casein quality (structure and composition) may mediate some of the physiological properties of IFs.

# 6. Funding sources

The present work formed part of a CIFRE (Agreement of Training through Research) PhD, which received funding from SODIAAL International, a French dairy cooperative, and from the Association Nationale de la Recherche et de la Technologie (ANRT).

# CRediT authorship contribution statement

**Lucile Chauvet:** Writing – original draft, Visualization, Methodology, Investigation, Formal analysis. **Gwénaëlle Randuineau:** Writing – review & editing, Investigation. **Sylvie Guérin:** Writing – review & editing, Investigation. **Armelle Cahu:** Writing – review & editing, Investigation. **Régis Janvier:** Writing – review & editing, Investigation. **Patrice Dahirel:** Writing – review & editing, Investigation. **Marie-Bernadette Maillard:** Writing – review & editing, Investigation. **Didier Dupont:** Writing – review & editing, Funding acquisition, Conceptualization. **Marion Lemaire:** Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization. **Amélie Deglaire:** Writing – review & editing, Visualization, Supervision, Methodology, Funding acquisition, Conceptualization. **Isabelle Le Huérou-Luron:** Writing – review & editing, Visualization, Supervision, Methodology, Funding acquisition, Conceptualization.

# Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work was supported by SODIAAL International, a French dairy cooperative, in the context of a CIFRE (Agreement of Training through Research) PhD. Lucile Chauvet and Marion Lemaire are employed by SODIAAL International. The other authors declare no conflicts of interests.

# Acknowledgments

We would like to thank Véronique Romé from the NuMeCan EAT team for her help during blood sampling. We also thank Xavier Parere for assistance in statistical analyses. The authors acknowledge the staff at the Rennes Pig Physiology and Phenotyping Experimental Facility (UE1421 UE3P, Saint-Gilles, France, doi 10.15454/1.5573932732039927E12) for their participation in conducting this experiment and their expert assistance in animal care and feeding.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodres.2024.115511.

# Data availability

Data will be made available on request.

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