# High carbohydrate to protein ratio promotes changes in intestinal microbiota and host metabolism in rainbow trout (Oncorhynchus mykiss) fed plant-based diet

Raphaël Defaix, Jep Lokesh, Mylène Ghislain, Mickael Le Béchec, Michaël Marchand, Vincent Veron, Anne Surget, Sandra Biasutti, Frédéric Terrier, Thierry Pigot, et al.

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Raphaël Defaix, Jep Lokesh, Mylène Ghislain, Mickael Le Béchec, Michaël Marchand, et al. High carbohydrate to protein ratio promotes changes in intestinal microbiota and host metabolism in rainbow trout (Oncorhynchus mykiss) fed plant-based diet. Aquaculture, 2024, 578, pp.740049. 10.1016/j.aquaculture.2023.740049. hal-04211282

HAL Id: hal-04211282

https://hal.inrae.fr/hal-04211282

Submitted on 26 Jan 2024

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# High carbohydrate to protein ratio promotes changes in intestinal microbiota and host metabolism in rainbow trout (Oncorhynchus mykiss) fed plant-based diet

Raphaël Defaix, Jep Lokesh, Mylène Ghislain, Mickael Le Béchec, Michaël Marchand, Vincent Veron, Anne Surget, Sandra Biasutti, Frédéric Terrier, Thierry Pigot, et al.

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Raphaël Defaix, Jep Lokesh, Mylène Ghislain, Mickael Le Béchec, Michaël Marchand, et al. High carbohydrate to protein ratio promotes changes in intestinal microbiota and host metabolism in rainbow trout (Oncorhynchus mykiss) fed plant-based diet. Aquaculture, 2024, 578, pp.740049. 10.1016/j.aquaculture.2023.740049. hal-04211282

HAL Id: hal-04211282

https://hal.inrae.fr/hal-04211282v1

Submitted on 26 Jan 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
1 High carbohydrate to protein ratio promotes changes in intestinal microbiota and host

2 **metabolism in rainbow trout (Oncorhynchus mykiss) fed plant-based diet.**

3

4 Raphaël Defaixª, Jep Lokeshª, Mylène Ghislainᵇ, Mickael Le Bechecᵇ, Michaël Marchandª, Vincent

5 Véronª, Anne Surgetª, Sandra Biasuttiᶜ, Frederic Terrierª, Thierry Pigotᵇ; Stéphane Panseratª, Karine

6 Ricaudª.

7 raphael.defaix@inrae.fr ; lokesh.jep@inrae.fr ; mylene.ghislain@univ-pau.fr ;

8 mickael.lebechec@univ-pau.fr ; michael.marchand@inrae.fr ; vincent.veron@inrae.fr ;

9 anne.surget@inrae.fr ; sandra.biasutti@univ-pau.fr ; frederic.terrier@inrae.fr ; thierry.pigot@univ-

10 pau.fr ; stephane.panserat@inrae.fr ; karine.brugirardricaud@univ-pau.fr

11

12 ª Université de Pau et des Pays de l'Adour, E2S UPPA, INRAE, NUMEA, Saint-Pée-sur-Nivelle, France

13 ᵇ Université de Pau et des Pays de l'Adour, E2S UPPA, CNRS, IMT Mines Ales, IPREM Pau, France

14 ᶜ Université de Pau et des Pays de l'Adour, E2S UPPA, IUT des Pays de l'Adour, Département Génie

15 Biologique, rue du ruisseau 40004 Mont de Marsan, France

16

17 Corresponding author: Karine Ricaud: karine.brugirardricaud@univ-pau.fr

18

19 Running title: Effect of carbohydrates on gut microbiota in rainbow trout

20

21

22
1 High carbohydrate to protein ratio promotes changes in intestinal microbiota and host

2 **metabolism in rainbow trout (Oncorhynchus mykiss) fed plant-based diet.**

3

4 Raphaël Defaixª, Jep Lokeshª, Mylène Ghislainᵇ, Mickael Le Bechecᵇ, Michaël Marchandª, Vincent

5 Véronª, Anne Surgetª, Sandra Biasuttiᶜ, Frederic Terrierª, Thierry Pigotᵇ; Stéphane Panseratª, Karine

6 Ricaudª.

7 raphael.defaix@inrae.fr ; lokesh.jep@inrae.fr ; mylene.ghislain@univ-pau.fr ;

8 mickael.lebechec@univ-pau.fr ; michael.marchand@inrae.fr ; vincent.veron@inrae.fr ;

9 anne.surget@inrae.fr ; sandra.biasutti@univ-pau.fr ; frederic.terrier@inrae.fr ; thierry.pigot@univ-

10 pau.fr ; stephane.panserat@inrae.fr ; karine.brugirardricaud@univ-pau.fr

11

12 Université de Pau et des Pays de l'Adour, E2S UPPA, INRAE, NUMEA, Saint-Pée-sur-Nivelle, France

13 Université de Pau et des Pays de l'Adour, E2S UPPA, CNRS, IMT Mines Ales, IPREM Pau, France

14 Université de Pau et des Pays de l'Adour, E2S UPPA, IUT des Pays de l'Adour, Département Génie

15 Biologique, rue du ruisseau 40004 Mont de Marsan, France

16

17 Corresponding author: Karine Ricaud: karine.brugirardricaud@univ-pau.fr

18

19 Running title: Effect of carbohydrates on gut microbiota in rainbow trout

20

21

22
23

**Abstract**

To ensure the sustainability of aquaculture, it is necessary to change the “menu” of carnivorous fish such as rainbow trout from a fish-based diet to one with plant-based ingredients. However, there is a major problem with the growth performance decrease of fish fed with a 100% plant-based diet due to the reduction in feed intake and feed efficiency. For the first time, we incorporated high levels of digestible carbohydrates (high-starch diet) in a 100% plant-based diet during a 12-week feeding trial in order to improve protein utilization for growth (protein sparing effect) and reduce nitrogen waste.

We measured the changes in the intestinal microbiota, Short-Chain Fatty Acid (SCFA) levels and metabolic responses in liver. Dietary carbohydrates had a strong effect on alpha and beta diversity and abundance of 12 genera, including *Ralstonia* and *Bacillus* in digesta associated microbiota whereas mucosa associated microbiota was less affected. The change in microbial diversity might be linked to the change observed in SCFA production. High levels of *Mycoplasma* were observed in the intestinal mucosa. Overall, hepatic gene expression was significantly altered by the CHO/protein ratio. Up-regulation of genes involved in glucose metabolism (*gcka*, *gckb*, *g6pcb2a*), down-regulation of genes involved in lipid metabolism (*hadh*, *acox3*, *srebp2a*, and *cyp51a*) were associated with higher enzymatic activities (such as glucokinase or pyruvate kinase) and higher glycogen levels in the liver, suggesting adequate adaptation to diet. Interestingly, strong correlations were observed between abundances of certain bacterial OTUs and gene expression in the liver.

The inclusion of digestible carbohydrates in combination with a 100% plant-based diet, could be a promising way to improve and reduce the use of plant proteins in rainbow trout. In addition, the relationship between intestinal microbiota and host metabolism needs further investigation to better understand fish nutrition.

**Keywords:** rainbow trout, gut microbiota, intermediary metabolism, aquaculture, fish nutrition
23

**Abstract**

To ensure the sustainability of aquaculture, it is necessary to change the “menu” of carnivorous fish such as rainbow trout from a fish-based diet to one with plant-based ingredients. However, there is a major problem with the growth performance decrease of fish fed with a 100% plant-based diet due to the reduction in feed intake and feed efficiency. For the first time, we incorporated high levels of digestible carbohydrates (high-starch diet) in a 100% plant-based diet during a 12-week feeding trial in order to improve protein utilization for growth (protein sparing effect) and reduce nitrogen waste.

We measured the changes in the intestinal microbiota, Short-Chain Fatty Acid (SCFA) levels and metabolic responses in liver. Dietary carbohydrates had a strong effect on alpha and beta diversity and abundance of 12 genera, including *Ralstonia* and *Bacillus* in digesta associated microbiota whereas mucosa associated microbiota was less affected. The change in microbial diversity might be linked to the change observed in SCFA production. High levels of *Mycoplasma* were observed in the intestinal mucosa. Overall, hepatic gene expression was significantly altered by the CHO/protein ratio. Up-regulation of genes involved in glucose metabolism (*gcka*, *gckb*, *g6pcb2a*), down-regulation of genes involved in lipid metabolism (*hadh*, *acox3*, *srebp2a*, and *cyp51a*) were associated with higher enzymatic activities (such as glucokinase or pyruvate kinase) and higher glycogen levels in the liver, suggesting adequate adaptation to diet. Interestingly, strong correlations were observed between abundances of certain bacterial OTUs and gene expression in the liver.

The inclusion of digestible carbohydrates in combination with a 100% plant-based diet, could be a promising way to improve and reduce the use of plant proteins in rainbow trout. In addition, the relationship between intestinal microbiota and host metabolism needs further investigation to better understand fish nutrition.

**Keywords:** rainbow trout, gut microbiota, intermediary metabolism, aquaculture, fish nutrition
46

# 1. Introduction

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Sustainable and efficient seafood production is urgently needed to meet the growth in world's nutritional requirements (FAO, 2020). Seafood products for human consumption are provided at 46.8 % (2016) by aquaculture (FAO, 2018) which allows to reduce the depletion of fish stock in the oceans caused by overfishing. Traditionally, Fish meal (FM) and fish oil (FO) coming from industrial fishing are used to feed aquaculture fishes. Although these ingredients have been partially replaced in the aqua-diets by plant-based products, the use of FM and FO is not sustainable due to the constant shortage and their continuous rising costs (Hua et al., 2019). A sustainable alternative is therefore necessary to feed the species under aquaculture instead of the FM and FO in commercial diets. The utilization of a plant-based diet as an alternative has been widely studied for more than 25 years in carnivorous fish such as rainbow trout (*Oncorhynchus mykiss*) (S.J. Kaushik et al., 1995). Although a 100% plant-based diet cover the nutritional needs of fish, long-term feeding induces reduction of growth, feed intake, and feed efficiency as well as activities of key liver enzymes that are involved in glycolysis (Véron et al., 2016). Moreover, the use of plant-protein and the presence of anti-nutritional factors lead to an unbalanced and lower amino-acid utilization (Deborde et al., 2021), has negative effects on reproduction, survival of the offspring (Lazzarotto et al., 2015), and reduced levels of long-chain polyunsaturated fatty acid in muscle (Nasopoulou and Zabetakis, 2012). Thus, it is necessary to improve and optimize the use of 100% plant-based diets. Interestingly, one strategy to improve the 100% plant-based diets could be the use of digestible carbohydrates (CHO) (Kamalam et al., 2017). CHO are one of the most abundant sources of energy-yielding nutrients on earth and have never been tested in association with 100% plant-based diet in fish. Increasing the CHO level in rainbow trout diets will reduce the proportion of plant proteins and then decrease all the negative impact of plant ingredients. In association with FM and FO diets, an appropriate amount (less than 20%) of digestible carbohydrates, such as starch, improve fish growth (Boonanuntanasarn et al., 2018), reduce environmental nitrogen pollution, ameliorate feed adhesion, and have a strong protein-sparing effect
46

# 1. Introduction

Sustainable and efficient seafood production is urgently needed to meet the growth in world's nutritional requirements (FAO, 2020). Seafood products for human consumption are provided at 46.8 % (2016) by aquaculture (FAO, 2018) which allows to reduce the depletion of fish stock in the oceans caused by overfishing. Traditionally, Fish meal (FM) and fish oil (FO) coming from industrial fishing are used to feed aquaculture fishes. Although these ingredients have been partially replaced in the aqua-diets by plant-based products, the use of FM and FO is not sustainable due to the constant shortage and their continuous rising costs (Hua et al., 2019). A sustainable alternative is therefore necessary to feed the species under aquaculture instead of the FM and FO in commercial diets. The utilization of a plant-based diet as an alternative has been widely studied for more than 25 years in carnivorous fish such as rainbow trout (*Oncorhynchus mykiss*) (S.J. Kaushik et al., 1995). Although a 100% plant-based diet cover the nutritional needs of fish, long-term feeding induces reduction of growth, feed intake, and feed efficiency as well as activities of key liver enzymes that are involved in glycolysis (Véron et al., 2016). Moreover, the use of plant-protein and the presence of anti-nutritional factors lead to an unbalanced and lower amino-acid utilization (Deborde et al., 2021), has negative effects on reproduction, survival of the offspring (Lazzarotto et al., 2015), and reduced levels of long-chain polyunsaturated fatty acid in muscle (Nasopoulou and Zabetakis, 2012). Thus, it is necessary to improve and optimize the use of 100% plant-based diets. Interestingly, one strategy to improve the 100% plant-based diets could be the use of digestible carbohydrates (CHO) (Kamalam et al., 2017). CHO are one of the most abundant sources of energy-yielding nutrients on earth and have never been tested in association with 100% plant-based diet in fish. Increasing the CHO level in rainbow trout diets will reduce the proportion of plant proteins and then decrease all the negative impact of plant ingredients. In association with FM and FO diets, an appropriate amount (less than 20%) of digestible carbohydrates, such as starch, improve fish growth (Boonanuntanasarn et al., 2018), reduce environmental nitrogen pollution, ameliorate feed adhesion, and have a strong protein-sparing effect
71 (Kamalam et al., 2012). However, carnivorous fish are metabolically adapted for high protein 72 catabolism as energy source. Indeed, when fed with more than 20% of carbohydrates, metabolic 73 disorders are observed such as a persistent post-prandial hyperglycemia explained (at least partially) 74 by a non-inhibition of gluconeogenesis pathway (Panserat et al., 2019; Polakof et al., 2012). 75 Nevertheless, these fish present mechanisms adapted to the use of glucose, such as the presence of 76 enzymes involved in starch digestion and glucose metabolism (Enes et al., 2009), the presence of 77 glucose transporter (Krasnov et al., 2001), and an inducible glucokinase (Panserat et al., 2000), 78 indicating that fish have a glucose homeostatic system (Kamalam et al., 2017). All these data have been 79 obtained with diets containing fish meal and fish oil. Many different factors can play a critical role in 80 fish nutrition and feed efficiency but among them, the gut microbiota plays an important role in several 81 large functions known to be involved in fish growth, such as energy production, nutrient metabolism 82 and fermentation of dietary non-digestible components (dietary fibers) (Bäckhed et al., 2004; Butt and 83 Volkoff, 2019; Gomes et al., 2018). Plant based-diets strongly modify gut microbiota composition in 84 salmonids (Ingerslev et al., 2014; Pérez-Pascual et al., 2021). Few studies have examined the effect of 85 CHO on fish gut microbiome (Huang et al., 2021; Lin et al., 2018; Ortiz LT, Rebole A, 2013; T. Wang et 86 al., 2021; Zhang et al., 2020) but none of them with 100% plant-based diets. The functional role of the 87 gut microbiota on the host physiology and health remains to be explored in fish (Perry et al., 2020). 88 The key role of the gut microbiota on fish health and metabolism is being increasingly described 89 (Dvergedal et al., 2020; A. Wang et al., 2021), showing strong associations between gut microbiome 90 and lipid metabolism, fish growth, and feed efficiency. To partially explain the interaction between 91 microbiota and host, short-chain fatty acids (SCFAs) are relevant candidates: they are the products of 92 fermentation of non-digestible carbohydrates that become available to the gut microbiota and have 93 been already linked to the host glucose metabolism in mammals (Morrison and Preston, 2016) but 94 their role remains to be investigated in fish. The objective of this study is to understand the effect of 95 the partial replacement of plant proteins by CHO (starch) in rainbow trout fed with a 100% plant-based 96 diet on the gut microbiota and host metabolism. For this purpose, juveniles rainbow trout were fed
71 (Kamalam et al., 2012). However, carnivorous fish are metabolically adapted for high protein 72 catabolism as energy source. Indeed, when fed with more than 20% of carbohydrates, metabolic 73 disorders are observed such as a persistent post-prandial hyperglycemia explained (at least partially) 74 by a non-inhibition of gluconeogenesis pathway (Panserat et al., 2019; Polakof et al., 2012). 75 Nevertheless, these fish present mechanisms adapted to the use of glucose, such as the presence of 76 enzymes involved in starch digestion and glucose metabolism (Enes et al., 2009), the presence of 77 glucose transporter (Krasnov et al., 2001), and an inducible glucokinase (Panserat et al., 2000), 78 indicating that fish have a glucose homeostatic system (Kamalam et al., 2017). All these data have been 79 obtained with diets containing fish meal and fish oil. Many different factors can play a critical role in 80 fish nutrition and feed efficiency but among them, the gut microbiota plays an important role in several 81 large functions known to be involved in fish growth, such as energy production, nutrient metabolism 82 and fermentation of dietary non-digestible components (dietary fibers) (Bäckhed et al., 2004; Butt and 83 Volkoff, 2019; Gomes et al., 2018). Plant based-diets strongly modify gut microbiota composition in 84 salmonids (Ingerslev et al., 2014; Pérez-Pascual et al., 2021). Few studies have examined the effect of 85 CHO on fish gut microbiome (Huang et al., 2021; Lin et al., 2018; Ortiz LT, Rebole A, 2013; T. Wang et 86 al., 2021; Zhang et al., 2020) but none of them with 100% plant-based diets. The functional role of the 87 gut microbiota on the host physiology and health remains to be explored in fish (Perry et al., 2020). 88 The key role of the gut microbiota on fish health and metabolism is being increasingly described 89 (Dvergedal et al., 2020; A. Wang et al., 2021), showing strong associations between gut microbiome 90 and lipid metabolism, fish growth, and feed efficiency. To partially explain the interaction between 91 microbiota and host, short-chain fatty acids (SCFAs) are relevant candidates: they are the products of 92 fermentation of non-digestible carbohydrates that become available to the gut microbiota and have 93 been already linked to the host glucose metabolism in mammals (Morrison and Preston, 2016) but 94 their role remains to be investigated in fish. The objective of this study is to understand the effect of 95 the partial replacement of plant proteins by CHO (starch) in rainbow trout fed with a 100% plant-based 96 diet on the gut microbiota and host metabolism. For this purpose, juveniles rainbow trout were fed
97 with 100% plant-based diets with high or low CHO (starch) for 12 weeks. In particular, we evaluated
98 the effect of the high-starch (HS) diet on (I) the zootechnical, growth and feed parameters, (II) the
99 diversity and composition of the midgut microbiota (both digesta and mucosa associated microbiota),
100 (III) the short-chain fatty acid concentration in hindgut, (IV) and the host glucose metabolism i.e gene
101 expressions, liver enzymatic activities, glycogen contents, and plasmatic parameters. The results of this
102 study will allow to have new directions to optimize alternative 100% plant-based diet for fish nutrition
103 in the future.

## 2. Methods

### 2.1. Ethical approval

106 The rearing experiment was conducted in accordance with the guidelines laid down by French and
107 European legislation for the use and care of laboratory animals (Decree no. 2013-36, February 1st 2013
108 and Directive 2010/63/EU, respectively). The fish handling protocols and the sampling for the
109 experiment were described by the INRAE ethics committee (INRAE, 2002-36, April 14, 2002). The INRAE
110 experimental station (INRAE facilities of Donzacq, Landes, France) is certified for animal services under
111 the license number A40-228.1 by the French veterinary service, which is a competent authority.

### 2.2. Diet and experimental setup

113 Two experimental 100% plant-based diets were formulated for juvenile rainbow trout. The High-starch
114 diet (HS) corresponds to a high CHO/protein ratio, with 20% of dietary carbohydrates and 42% of
115 proteins. Regarding the Low-starch diet (LS) it is a low CHO/protein ratio, with 3% of dietary
116 carbohydrates and 51% of proteins. The proteins are issue from different plant ingredients.
117 Descriptions of the diets are given in **table 1**. These diets, adjusted to meet the nutritional requirement
118 of rainbow trout, were isolipidic (23.565 % crude fat) and isoenergetic (~24.905 KJ-g dry matter). These
119 diets have been produced as extruded pellets. The fish were manually fed twice a day (with an interval
120 of 8h) during 12 weeks. For both experimental groups, eighty-one female Trout (~60g) were randomly
97 with 100% plant-based diets with high or low CHO (starch) for 12 weeks. In particular, we evaluated
98 the effect of the high-starch (HS) diet on (I) the zootechnical, growth and feed parameters, (II) the
99 diversity and composition of the midgut microbiota (both digesta and mucosa associated microbiota),
100 (III) the short-chain fatty acid concentration in hindgut, (IV) and the host glucose metabolism i.e gene
101 expressions, liver enzymatic activities, glycogen contents, and plasmatic parameters. The results of this
102 study will allow to have new directions to optimize alternative 100% plant-based diet for fish nutrition
103 in the future.

## 2. Methods

### 2.1. Ethical approval

106 The rearing experiment was conducted in accordance with the guidelines laid down by French and
107 European legislation for the use and care of laboratory animals (Decree no. 2013-36, February 1st 2013
108 and Directive 2010/63/EU, respectively). The fish handling protocols and the sampling for the
109 experiment were described by the INRAE ethics committee (INRAE, 2002-36, April 14, 2002). The INRAE
110 experimental station (INRAE facilities of Donzacq, Landes, France) is certified for animal services under
111 the license number A40-228.1 by the French veterinary service, which is a competent authority.

### 2.2. Diet and experimental setup

113 Two experimental 100% plant-based diets were formulated for juvenile rainbow trout. The High-starch
114 diet (HS) corresponds to a high CHO/protein ratio, with 20% of dietary carbohydrates and 42% of
115 proteins. Regarding the Low-starch diet (LS) it is a low CHO/protein ratio, with 3% of dietary
116 carbohydrates and 51% of proteins. The proteins are issue from different plant ingredients.
117 Descriptions of the diets are given in **table 1**. These diets, adjusted to meet the nutritional requirement
118 of rainbow trout, were isolipidic (23.565 % crude fat) and isoenergetic (~24.905 KJ-g dry matter). These
119 diets have been produced as extruded pellets. The fish were manually fed twice a day (with an interval
120 of 8h) during 12 weeks. For both experimental groups, eighty-one female Trout (~60g) were randomly
distributed in three tanks (130 L). During the experimental period, the fish were kept under standard rearing conditions with water t 17°C, pH 7.5, water flow rate 0.3 L/s, and oxygen levels 9mg/L. The fish mortality was checked (if any) every day, and the tanks were weighted every 3 weeks to evaluate the growth and zootechnical parameters (Figure1).

2.3. Sampling

After 12 weeks of feeding, four fish from each tank (twelve fish per group) were randomly sampled.

The fish were anaesthetized with benzocaine (50 mg-L) before being euthanized with a benzocaine overdose of 150 mg-L, followed by blood collection for plasma isolation. After the blood collection, fish were aseptically dissected and the digestive tract was separated. Liver and midgut was dissected at 6h after the last meal (the peak of the postprandial regulation of metabolism in trout (Polakof et al., 2012)) and immediately frozen with liquid nitrogen and stored at -80°C for long-term storage. For microbiota analysis, the midgut was separated from the rest of the digestive tract and was cut open using sterile instruments. The midgut was chosen because microbiota of this intestine part is involved in the digestion of food in trout, moreover the existence of a glucosensing mechanism have already been demonstrated in the midgut for these carnivorous fish species (Polakof et al., 2010). The digesta was separated carefully and the mucosa samples were collected by scraping the intestinal epithelium using a glass slide and then collected into sterile tubes, frozen in liquid nitrogen and stored at -80°C for DNA extraction. Intestinal contents from hindgut were sampled 25h after last meal for SCFA analysis in 6 fish per group (2 per tanks) (Figure 1).

2.4. Diets and whole-body proximate composition

Composition of diets and whole-body fish were determined by the following procedures. Dry matter was obtained by drying the samples at 105°C for 24h. The weight of the post-dried samples was subtracted from the pre-died samples. Ash content was measured by incinerating the samples at 550°C for 16h. Protein content was measured by the Kjeldahl™ method. Lipid content was determined by the Soxtherm method. Gross energy was measured with an adiabatic bomb calorimeter (IKA, Heitersheim
distributed in three tanks (130 L). During the experimental period, the fish were kept under standard rearing conditions with water t 17°C, pH 7.5, water flow rate 0.3 L/s, and oxygen levels 9mg/L. The fish mortality was checked (if any) every day, and the tanks were weighted every 3 weeks to evaluate the growth and zootechnical parameters (Figure1).

2.3. Sampling

After 12 weeks of feeding, four fish from each tank (twelve fish per group) were randomly sampled.

The fish were anaesthetized with benzocaine (50 mg-L) before being euthanized with a benzocaine overdose of 150 mg-L, followed by blood collection for plasma isolation. After the blood collection, fish were aseptically dissected and the digestive tract was separated. Liver and midgut was dissected at 6h after the last meal (the peak of the postprandial regulation of metabolism in trout (Polakof et al., 2012)) and immediately frozen with liquid nitrogen and stored at -80°C for long-term storage. For microbiota analysis, the midgut was separated from the rest of the digestive tract and was cut open using sterile instruments. The midgut was chosen because microbiota of this intestine part is involved in the digestion of food in trout, moreover the existence of a glucosensing mechanism have already been demonstrated in the midgut for these carnivorous fish species (Polakof et al., 2010). The digesta was separated carefully and the mucosa samples were collected by scraping the intestinal epithelium using a glass slide and then collected into sterile tubes, frozen in liquid nitrogen and stored at -80°C for DNA extraction. Intestinal contents from hindgut were sampled 25h after last meal for SCFA analysis in 6 fish per group (2 per tanks) (Figure 1).

2.4. Diets and whole-body proximate composition

Composition of diets and whole-body fish were determined by the following procedures. Dry matter was obtained by drying the samples at 105°C for 24h. The weight of the post-dried samples was subtracted from the pre-died samples. Ash content was measured by incinerating the samples at 550°C for 16h. Protein content was measured by the Kjeldahl™ method. Lipid content was determined by the Soxtherm method. Gross energy was measured with an adiabatic bomb calorimeter (IKA, Heitersheim
146

Gribheimer, Germany). Starch content was determined using the Megazyme® (Bray, Ireland) total

147

starch assay procedure.

148

**2.5. Measurement of the plasma biochemical parameters**

149

Blood was sampled for plasma collection from the caudal vein and then were directly centrifuged at

150

12,000g at 4°C for 5 min, and stored in heparinized tubes at -20°C until use. Commercial kits were used

151

to determine the level of several plasma metabolites: glucose (Sobioda, Montbonnot-Saint-Martin,

152

France), lactate (kit Randox, Crumlin, United Kingdom), triglycerides (Sobioda), and cholesterol

153

(Sobioda). These kits were adapted to 96-well plates formats according to the manufacturer's

154

instructions.

155

**2.6. Hepatic metabolites measurement**

156

Liver glycogen was measured using a protocol previously described (Good et al., 1933). For glycogen

157

determination, 250mg of liver were homogenized with 1M HCL and divided in two aliquots. One part

158

was neutralized with 5M KOH, centrifuged and the supernatant was used to measure the free glucose

159

with a commercial kit (Sobioda). The second group of aliquots was hydrolyzed during a boiling step

160

(2h30 at 100°C) and then neutralized with 5M KOH. After centrifugation, the total glucose (free glucose

161

and glucose released by hydrolysis of glycogen) was measured in the supernatant. For hepatic

162

cholesterol determination 100mg of liver was homogenized in 5% Igepal (sigma Aldrich, Saint-Louis,

163

MO, USA). Samples are placed twice at 90°C during 5min and then centrifuged 2 min at 5,000g.

164

Supernatants were recovered and the cholesterol concentration measured in 96-well plates thanks to

165

a commercial kit (Sobioda).

166

167

168
146

Gribheimer, Germany). Starch content was determined using the Megazyme® (Bray, Ireland) total

147

starch assay procedure.

148

**2.5. Measurement of the plasma biochemical parameters**

149

Blood was sampled for plasma collection from the caudal vein and then were directly centrifuged at

150

12,000g at 4°C for 5 min, and stored in heparinized tubes at -20°C until use. Commercial kits were used

151

to determine the level of several plasma metabolites: glucose (Sobioda, Montbonnot-Saint-Martin,

152

France), lactate (kit Randox, Crumlin, United Kingdom), triglycerides (Sobioda), and cholesterol

153

(Sobioda). These kits were adapted to 96-well plates formats according to the manufacturer's

154

instructions.

155

**2.6. Hepatic metabolites measurement**

156

Liver glycogen was measured using a protocol previously described (Good et al., 1933). For glycogen

157

determination, 250mg of liver were homogenized with 1M HCL and divided in two aliquots. One part

158

was neutralized with 5M KOH, centrifuged and the supernatant was used to measure the free glucose

159

with a commercial kit (Sobioda). The second group of aliquots was hydrolyzed during a boiling step

160

(2h30 at 100°C) and then neutralized with 5M KOH. After centrifugation, the total glucose (free glucose

161

and glucose released by hydrolysis of glycogen) was measured in the supernatant. For hepatic

162

cholesterol determination 100mg of liver was homogenized in 5% Igepal (sigma Aldrich, Saint-Louis,

163

MO, USA). Samples are placed twice at 90°C during 5min and then centrifuged 2 min at 5,000g.

164

Supernatants were recovered and the cholesterol concentration measured in 96-well plates thanks to

165

a commercial kit (Sobioda).

166

167

168
169

## 2.7. Microbial composition analysis

170

### 2.7.1. DNA extraction and Purification

171

172

173

174

175

176

177

178

DNA from gut samples (digesta and mucosa) were extracted using the QIAamp Fast DNA Stool Mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions with the following modifications as suggested by (Lokesh et al., 2022). About 200mg of -80°C frozen samples were mixed with the pre-heated Inhibitex buffer (1/7 ratio) and homogenized with 0.5mm and 1mm zirconia beads in a bead beater for 30 sec on high speed (Beater, VWR, Radnor, USA). The homogenized samples were placed in a heat block for 10 min at 70°C and then centrifuged for 2 min at 20,000g. DNA was assessed for purity and integrity, and quantified using a microplate Spectrophotometer (Epoch2, BioTek, France).

179

### 2.7.2. Generation of the 16S rRNA gene libraries

180

181

182

183

184

185

186

187

188

189

190

191

192

The 460 base-pair (bp) V3-V4 regions of 16S rRNA genes were amplified by PCR (first stage of PCR) using 12.5µL of 2X KAPA HiFi HotStart Ready Mix (Roche, France), 5µL of amplicon PCR Forward primer 1 µM (5' TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG -3'), 5µL of amplicon PCR Reverse primer 1 µM (5' GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC - 3'), and 2.5µL of DNA (5ng/µL). Thermocycler conditions included 95°C for 3min (a pre-incubation), followed by 35 cycles of 95°C for 30sec, 55°C for 30sec, 72°C for 30sec, and a final elongation step at 72°C for 5min. Controls, including water from fish tanks, diets samples, *Escherichia coli*, and ultra-pure water were also added to the run. After confirmation of PCR amplification by agarose electrophoresis, the samples and controls were transferred to the genomic platform of Bordeaux (PGTB, Bordeaux, France). Libraries were prepared according to the standard protocol recommended by Illumina (Illumina, CA, USA). Index PCR was used to add the unique dual indexes to the sequences, by using the Nextera XT index kit according to the manufacturer's recommendation (Illumina). The thermocycling conditions were the
169

## 2.7. Microbial composition analysis

170

### 2.7.1. DNA extraction and Purification

171

172

173

174

175

176

177

178

DNA from gut samples (digesta and mucosa) were extracted using the QIAamp Fast DNA Stool Mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions with the following modifications as suggested by (Lokesh et al., 2022). About 200mg of -80°C frozen samples were mixed with the pre-heated Inhibitex buffer (1/7 ratio) and homogenized with 0.5mm and 1mm zirconia beads in a bead beater for 30 sec on high speed (Beater, VWR, Radnor, USA). The homogenized samples were placed in a heat block for 10 min at 70°C and then centrifuged for 2 min at 20,000g. DNA was assessed for purity and integrity, and quantified using a microplate Spectrophotometer (Epoch2, BioTek, France).

179

### 2.7.2. Generation of the 16S rRNA gene libraries

180

181

182

183

184

185

186

187

188

189

190

191

192

The 460 base-pair (bp) V3-V4 regions of 16S rRNA genes were amplified by PCR (first stage of PCR) using 12.5µL of 2X KAPA HiFi HotStart Ready Mix (Roche, France), 5µL of amplicon PCR Forward primer 1 µM (5' TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG -3'), 5µL of amplicon PCR Reverse primer 1 µM (5' GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC - 3'), and 2.5µL of DNA (5ng/µL). Thermocycler conditions included 95°C for 3min (a pre-incubation), followed by 35 cycles of 95°C for 30sec, 55°C for 30sec, 72°C for 30sec, and a final elongation step at 72°C for 5min. Controls, including water from fish tanks, diets samples, *Escherichia coli*, and ultra-pure water were also added to the run. After confirmation of PCR amplification by agarose electrophoresis, the samples and controls were transferred to the genomic platform of Bordeaux (PGTB, Bordeaux, France). Libraries were prepared according to the standard protocol recommended by Illumina (Illumina, CA, USA). Index PCR was used to add the unique dual indexes to the sequences, by using the Nextera XT index kit according to the manufacturer's recommendation (Illumina). The thermocycling conditions were the
same than in step 1, except that PCR was performed for only 8 cycles. After PCR cleaning, libraries were quantified using the KAPA library quantification kit for Illumina platforms (Roche). Libraries were pooled at an equimolar concentration (4nM) and sequenced on a MiSeq platform using a 250 bp Paired End Sequencing Kit v2 (Illumina).

## 2.7.3. Data analysis

The initial data analysis was performed using the FROGS pipeline according to standard procedures (Escudié et al., 2017). First, the forward and reverse reads of each sample are merged. Only amplicons with size between 380 bp and 500 bp corresponding to the size of the V3-V4 region of the 16S rRNA gene, without ambiguous bases and with the two primers were kept. The adapter sequences were removed and the sequences with not expected lengths or ambiguous bases (N) were deleted. After this step 7800687 sequences have been kept, which represents 79.42 % of initial input sequences. The clustering swarm algorithm was used to group together amplicons with a maximum of one nucleotide difference between two amplicons (Mahé et al., 2014), 476371 clusters were created. PCR-generated chimeras, typically created when an aborted amplicon acts as a primer, are removed. Clusters present in less than 4 samples, and having a minimum abundance of 0.005% are removed. After this step, 261 clusters have remained with 6716271 sequences. The PhiX database was used to remove contaminants such as chloroplasmic or mitochondrial sequences (Mukherjee et al., 2015). Then, taxonomic affiliations are carried out for each OTUs (Operational Taxonomic Unit), using the silva138.1 pintail100 16S reference database ("https://www.arb-silva.de/documentation/release-138/," n.d.). However, some multi-affiliations can be generated when a cluster is composed by several amplicons with not the exact same nucleotide sequence. Samples had an average of 61.729 +/- 18.210 sequences (minimum: 17.009; maximum: 107.484 sequences) with 202 OTUs remaining (ranging from 56 to 119 OTUs per sample).
same than in step 1, except that PCR was performed for only 8 cycles. After PCR cleaning, libraries were quantified using the KAPA library quantification kit for Illumina platforms (Roche). Libraries were pooled at an equimolar concentration (4nM) and sequenced on a MiSeq platform using a 250 bp Paired End Sequencing Kit v2 (Illumina).

## 2.7.3. Data analysis

The initial data analysis was performed using the FROGS pipeline according to standard procedures (Escudié et al., 2017). First, the forward and reverse reads of each sample are merged. Only amplicons with size between 380 bp and 500 bp corresponding to the size of the V3-V4 region of the 16S rRNA gene, without ambiguous bases and with the two primers were kept. The adapter sequences were removed and the sequences with not expected lengths or ambiguous bases (N) were deleted. After this step 7800687 sequences have been kept, which represents 79.42 % of initial input sequences. The clustering swarm algorithm was used to group together amplicons with a maximum of one nucleotide difference between two amplicons (Mahé et al., 2014), 476371 clusters were created. PCR-generated chimeras, typically created when an aborted amplicon acts as a primer, are removed. Clusters present in less than 4 samples, and having a minimum abundance of 0.005% are removed. After this step, 261 clusters have remained with 6716271 sequences. The PhiX database was used to remove contaminants such as chloroplasmic or mitochondrial sequences (Mukherjee et al., 2015). Then, taxonomic affiliations are carried out for each OTUs (Operational Taxonomic Unit), using the silva138.1 pintail100 16S reference database ("https://www.arb-silva.de/documentation/release-138/," n.d.). However, some multi-affiliations can be generated when a cluster is composed by several amplicons with not the exact same nucleotide sequence. Samples had an average of 61.729 +/- 18.210 sequences (minimum: 17.009; maximum: 107.484 sequences) with 202 OTUs remaining (ranging from 56 to 119 OTUs per sample).
## 2.8. Short-chain fatty acid measurement

### 2.8.1. Sample preparation

220 Frozen intestinal samples were weighed and added into a 114 mL micro-chamber µCTE250 (Markes international, Llantrisant, UK). The micro-chamber was heated at 100 °C with a flow rate of 60 mL min-1 of dry nitrogen. The SIFT-MS sampling was carried out at the outlet of the micro-chamber at a flow rate of 20 mL min-1.

### 2.8.2. Selected Ion Flow Tube – Mass Spectrometry (SIFT-MS) measurements

225 A Voice 200 Ultra SIFT-MS (SYFT Technologies, Christchurch, New Zealand) equipped with a dual source generating positive soft ionizing reagent ions (H3O+, O2+, NO+) with the nitrogen carrier gas (Air Liquid, Alphagaz 2) was used in this study. Full-scan mass spectra were recorded for each positive precursor ion (H3O+, O2+, NO+) in a range from 15 to 250 with an integration time of 60 s and accumulated during 16h identification was based on specific ion-molecule reactions patterns of target analytes with the three positive precursor ions described in the literature and in the database of the LabSyft software (LabSyft 1.6.2, SYFT Technologies). Product ions from ion-molecule reactions of SCFA are summarized in **Supplementary table 1**. Quantification of short-chain fatty acids was performed using the NO+ precursor ion. In SIFT-MS analysis, quantification is straight forward and requires only measurement of the count rate of the precursor ion [R] and product ions [P]. The analyte concentration in the flow tube [A] can be determined according to the following calculation:

$$ [A] = \frac{[P]}{t_r k [R]} $$

236 Where $t_r$ is the reaction time in the flow tube and $k$ is the apparent reaction rate constant.

238

239

240
## 2.8. Short-chain fatty acid measurement

### 2.8.1. Sample preparation

220 Frozen intestinal samples were weighed and added into a 114 mL micro-chamber µCTE250 (Markes international, Llantrisant, UK). The micro-chamber was heated at 100 °C with a flow rate of 60 mL min-1 of dry nitrogen. The SIFT-MS sampling was carried out at the outlet of the micro-chamber at a flow rate of 20 mL min-1.

### 2.8.2. Selected Ion Flow Tube – Mass Spectrometry (SIFT-MS) measurements

225 A Voice 200 Ultra SIFT-MS (SYFT Technologies, Christchurch, New Zealand) equipped with a dual source generating positive soft ionizing reagent ions (H3O+, O2+, NO+) with the nitrogen carrier gas (Air Liquid, Alphagaz 2) was used in this study. Full-scan mass spectra were recorded for each positive precursor ion (H3O+, O2+, NO+) in a range from 15 to 250 with an integration time of 60 s and accumulated during 16h identification was based on specific ion-molecule reactions patterns of target analytes with the three positive precursor ions described in the literature and in the database of the LabSyft software (LabSyft 1.6.2, SYFT Technologies). Product ions from ion-molecule reactions of SCFA are summarized in **Supplementary table 1**. Quantification of short-chain fatty acids was performed using the NO+ precursor ion. In SIFT-MS analysis, quantification is straight forward and requires only measurement of the count rate of the precursor ion [R] and product ions [P]. The analyte concentration in the flow tube [A] can be determined according to the following calculation:

$$ [A] = \frac{[P]}{t_r k [R]} $$

236 Where $t_r$ is the reaction time in the flow tube and $k$ is the apparent reaction rate constant.

238

239

240
241 **2.9. Gene expression analysis**

242 RNA from liver, were extracted using TRIzol™ reagent (Invitrogen, Waltham, MA, USA) according to
243 the manufacturer's instruction, and quantified by spectrophotometry (absorbance at 260 nm). The
244 qualities of the RNA extracted were assessed using agarose gel electrophoresis. One µg of RNA was
245 transcribed into cDNA using the SuperScript III reverse transcriptase (Invitrogen, Waltham, MA, USA)
246 with random primer (Promega, Madison, WI, USA). After the reverse transcription, cDNA was diluted
247 76-fold for each tissue before it uses in quantitative real-time (q) RT-qPCR. RT-qPCR was performed on
248 a C1000 Touch™ thermal cycler (BioRad, Hercules, CA, USA) using PerfeCTa SYBR green (VWR, Radnor,
249 PA, USA). Reactions were performed on a 384 well plate. The total volume of reaction was with 2µL of
250 diluted cDNA mixed with 0.24µL of forward and reverse primer (10µM), 0.52 µL of RNase-free water,
251 and 3µL of SYBR green. Thermocycling conditions included a pre-incubation at 95°C for 10 min,
252 followed by 45 cycles of denaturation at 95°C for 15sec, annealing 60°C for 10sec, and extension at
253 72°C for 15sec. Melting curves were systematically monitored (95°C for 5sec, 65°C for 60sec, 40°C for
254 30sec) at the end of the last amplification cycle to confirm the specificity of the amplification reaction.
255 Each RT-qPCR included replicate samples (duplicate of reverse transcription and PCR amplification), a
256 standard curve (a range of dilution of cDNA from a pool of all cDNA samples) in triplicate, and negative
257 controls (reverse transcriptase-free samples and RNA-free samples) in duplicate. The relative
258 quantification of gene expression was carried out by the Bio-Rad CFX Maestro software (Version
259 4.0.2325.0418). Cq (Quantification cycle) values were further converted to relative quantities. ef1α
260 gene was used as reference gene for liver (Song et al., 2018), samples. The list primer used to study
261 the genes of interest are present in **Supplementary table 2**.

262 **2.10. Hepatic enzymatic analysis**

263 The hepatic enzymatic activity level of Glucokinase, Pyruvate kinase, Glucose-6-phosphatase, and Fatty
264 acid synthase were determined according to the protocol described by (Véron et al., 2016). For the
265 measurement of these enzymatic activities the livers were crushed in 10 volumes of ice-cold buffer at
241 **2.9. Gene expression analysis**

242 RNA from liver, were extracted using TRIzol™ reagent (Invitrogen, Waltham, MA, USA) according to
243 the manufacturer's instruction, and quantified by spectrophotometry (absorbance at 260 nm). The
244 qualities of the RNA extracted were assessed using agarose gel electrophoresis. One µg of RNA was
245 transcribed into cDNA using the SuperScript III reverse transcriptase (Invitrogen, Waltham, MA, USA)
246 with random primer (Promega, Madison, WI, USA). After the reverse transcription, cDNA was diluted
247 76-fold for each tissue before it uses in quantitative real-time (q) RT-qPCR. RT-qPCR was performed on
248 a C1000 Touch™ thermal cycler (BioRad, Hercules, CA, USA) using PerfeCTa SYBR green (VWR, Radnor,
249 PA, USA). Reactions were performed on a 384 well plate. The total volume of reaction was with 2µL of
250 diluted cDNA mixed with 0.24µL of forward and reverse primer (10µM), 0.52 µL of RNase-free water,
251 and 3µL of SYBR green. Thermocycling conditions included a pre-incubation at 95°C for 10 min,
252 followed by 45 cycles of denaturation at 95°C for 15sec, annealing 60°C for 10sec, and extension at
253 72°C for 15sec. Melting curves were systematically monitored (95°C for 5sec, 65°C for 60sec, 40°C for
254 30sec) at the end of the last amplification cycle to confirm the specificity of the amplification reaction.
255 Each RT-qPCR included replicate samples (duplicate of reverse transcription and PCR amplification), a
256 standard curve (a range of dilution of cDNA from a pool of all cDNA samples) in triplicate, and negative
257 controls (reverse transcriptase-free samples and RNA-free samples) in duplicate. The relative
258 quantification of gene expression was carried out by the Bio-Rad CFX Maestro software (Version
259 4.0.2325.0418). Cq (Quantification cycle) values were further converted to relative quantities. *ef1α*
260 gene was used as reference gene for liver (Song et al., 2018), samples. The list primer used to study
261 the genes of interest are present in **Supplementary table 2**.

262 **2.10. Hepatic enzymatic analysis**

263 The hepatic enzymatic activity level of Glucokinase, Pyruvate kinase, Glucose-6-phosphatase, and Fatty
264 acid synthase were determined according to the protocol described by (Véron et al., 2016). For the
265 measurement of these enzymatic activities the livers were crushed in 10 volumes of ice-cold buffer at
266 pH 7.4 (50 mmol/l TRIS, 5 mmol/l EDTA, 2 mmol/l DTT and a protease inhibitor cocktail (P2714, 267 purchased from Sigma, St Louis, MO)). After homogenization, homogenates were then centrifuged at 268 4°C, and the supernatants were used immediately for enzyme assays. The enzymes assayed were: 269 glucokinase (GK; EC 2.7.1.2) (15), pyruvate kinase (PK; EC 2.7.1.40) as described by (Kirchner et al., 270 2003), glucose-6-phosphatase (G6Pase; EC 3.1.3.9) according to (Kirchner et al., 2003), fatty acid 271 synthase (FAS; EC 2.3.1.85) from (Chang et al., 1967). Enzyme activity was measured at 37 °C in 272 duplicate following variation of absorbance of NAD(P)(H) at 340 nm. The reactions were started by 273 adding the specific substrate; a Power Wave X (BioTek Instrument, Inc.) plate reader was used. For 274 each sample, a blank with water instead of the substrate was run. One unit of enzyme activity was 275 defined as the amount of enzyme that catalyzed the transformation of 1 µmol of substrate per min at 276 37°C, except for FAS which was expressed as the amount of enzyme oxidizing 1 µmol of NADPH at 277 37°C. Enzymatic activities was expressed per mg of soluble protein. Protein concentration was 278 measured in triplicate according to Bradford (Bradford, 1976), using a protein assay kit (Bio Rad, 279 München, Germany) with bovine serum albumin as a standard. 280

## 2.11. Statistical analysis

281 Zootechnical parameters, including initial and final body weight, specific growth rate, feed intake, feed 282 efficiency, and protein retention efficiency were calculated per tank (n=3). The hepatosomatic index 283 (liver weight*100/fish weight) were obtained during the final sampling of the trial experimentation 284 (n=12). All data were presented as mean ± SEM. All statistical analysis were performed with R software 285 (version 4.0.3). Data were tested for normally distribution using the Shapiro-Wilk test and 286 homogeneity of variance using the Bartlett's test. All Data were analyzed using the one-way ANOVA. 287 These statistical tests were followed by Tukey's HSD as post hoc test when the normal distribution is 288 respected, otherwise a Wilcoxon non-parametric test is carried out. Results with a P-value < 0.05 were 289 considered statistically significant. In the figures: *, P < 0.05; **, P < 0.01; ***, P < 0.001. The microbial 290 composition was analyzed using the phyloseq package. For alpha and beta diversity, data samples were
pH 7.4 (50 mmol/l TRIS, 5 mmol/l EDTA, 2 mmol/l DTT and a protease inhibitor cocktail (P2714, purchased from Sigma, St Louis, MO)). After homogenization, homogenates were then centrifuged at 4°C, and the supernatants were used immediately for enzyme assays. The enzymes assayed were: glucokinase (GK; EC 2.7.1.2) (15), pyruvate kinase (PK; EC 2.7.1.40) as described by (Kirchner et al., 2003), glucose-6-phosphatase (G6Pase; EC 3.1.3.9) according to (Kirchner et al., 2003), fatty acid synthase (FAS; EC 2.3.1.85) from (Chang et al., 1967). Enzyme activity was measured at 37 °C in duplicate following variation of absorbance of NAD(P)(H) at 340 nm. The reactions were started by adding the specific substrate; a Power Wave X (BioTek Instrument, Inc.) plate reader was used. For each sample, a blank with water instead of the substrate was run. One unit of enzyme activity was defined as the amount of enzyme that catalyzed the transformation of 1 µmol of substrate per min at 37°C, except for FAS which was expressed as the amount of enzyme oxidizing 1 µmol of NADPH at 37°C. Enzymatic activities was expressed per mg of soluble protein. Protein concentration was measured in triplicate according to Bradford (Bradford, 1976), using a protein assay kit (Bio Rad, München, Germany) with bovine serum albumin as a standard.

280

## 2.11. Statistical analysis

Zootechnical parameters, including initial and final body weight, specific growth rate, feed intake, feed efficiency, and protein retention efficiency were calculated per tank (n=3). The hepatosomatic index (liver weight*100/fish weight) were obtained during the final sampling of the trial experimentation (n=12). All data were presented as mean ± SEM. All statistical analysis were performed with R software (version 4.0.3). Data were tested for normally distribution using the Shapiro-Wilk test and homogeneity of variance using the Bartlett's test. All Data were analyzed using the one-way ANOVA. These statistical tests were followed by Tukey's HSD as post hoc test when the normal distribution is respected, otherwise a Wilcoxon non-parametric test is carried out. Results with a P-value < 0.05 were considered statistically significant. In the figures: *, P < 0.05; **, P < 0.01; ***, P < 0.001. The microbial composition was analyzed using the phyloseq package. For alpha and beta diversity, data samples were
rarified. Beta-diversity was analyzed with the Bray-Curtis distance using permutational multivariate analysis of variance (PERMANOVA). The mixOmics package was used to perform a Partial Least Square Discriminant Analysis (PLS-DA) to determine the most discriminant OTUs. The rCCA (regularized canonical correlation analysis) function of the same package was used to understand the correlations between the bacterial OTUs and different host parameters. The core microbiota visualization was made using the phyloseq package on R and the Venn diagram using a website.

## 3. Results

### 3.1. Whole-body composition, zootechnical, hepatic and plasma metabolite levels

At the end of the feeding trial different zootechnical parameters such as final weight, specific growth rate (SGR), feed efficiency (FE) and protein efficiency ratio (PER) were measured, showing no statistical differences (Table 2). Plasma parameters such as triglycerides, glucose, lactate and cholesterol were also measured. Statistical analysis did not reveal any change between the two experimental groups (Table 3). Regarding feed utilization, protein efficiency ratio increased (p = 2.41e-4) in trout fed with high starch diet (HS), whereas no significant differences in daily feed intake and feed efficiency were measured (table 2). Whole-body composition (dry matter, gross energy, crude protein and lipid) of fish for the two experimental groups were evaluated (Supplementary table 3). The two diets did not lead to any significant differences of whole-body composition of fish. Moreover, plasma metabolites (glucose, lactate, triglycerides, cholesterol) were also not affected by the diets (Table 3). Finally, several significant changes were observed for hepatic parameters. The use of the high-starch diet increased significantly the average liver weight (hepatosomatic index). The level of glycogen stored in the liver was also significantly higher (p = 1.894e-8) in the group of fish fed with the high-starch diet. Inversely, a decrease of the hepatic cholesterol was observed when trout were fed with the high-starch diet.
rarified. Beta-diversity was analyzed with the Bray-Curtis distance using permutational multivariate
analysis of variance (PERMANOVA). The mixOmics package was used to perform a Partial Least Square
Discriminant Analysis (PLS-DA) to determine the most discriminant OTUs. The rCCA (regularized
canonical correlation analysis) function of the same package was used to understand the correlations
between the bacterial OTUs and different host parameters. The core microbiota visualization was
made using the phyloseq package on R and the Venn diagram using a website.

297

3. Results

298

3.1. Whole-body composition, zootechnical, hepatic and plasma metabolite levels

At the end of the feeding trial different zootechnical parameters such as final weight, specific growth
rate (SGR), feed efficiency (FE) and protein efficiency ratio (PER) were measured, showing no statistical
differences (Table 2). Plasma parameters such as triglycerides, glucose, lactate and cholesterol were
also measured. Statistical analysis did not reveal any change between the two experimental groups
(Table 3). Regarding feed utilization, protein efficiency ratio increased (p = 2.41e-4) in trout fed with
high starch diet (HS), whereas no significant differences in daily feed intake and feed efficiency were
measured (table 2). Whole-body composition (dry matter, gross energy, crude protein and lipid) of fish
for the two experimental groups were evaluated (Supplementary table 3). The two diets did not lead
to any significant differences of whole-body composition of fish. Moreover, plasma metabolites
(glucose, lactate, triglycerides, cholesterol) were also not affected by the diets (Table 3). Finally,
several significant changes were observed for hepatic parameters. The use of the high-starch diet
increased significantly the average liver weight (hepatosomatic index). The level of glycogen stored in
the liver was also significantly higher (p = 1.894e-8) in the group of fish fed with the high-starch diet.
Inversely, a decrease of the hepatic cholesterol was observed when trout were fed with the high-starch
diet.

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314
315 **3.2. Microbiota diversities and composition**

316 Sequence data were rarefied to 17000 sequences per sample. Alpha diversity measures including the 317 indexes Observed OTUs, Chao1, Shannon, Simpson, and InvSimpson were calculated in the digesta 318 (Figure 2A) and in mucosa (Figure 2B). A significant decrease (ANOVA, *p* < 0.05) of all the alpha 319 diversities indices only in the digesta associated microbiota was observed for the high-starch group. 320 Most of the alpha diversity indices did not differ between digesta and mucosa samples except for the 321 Shannon index which is significantly (*p* = 0.01) higher in digesta (data not shown). Beta diversities were 322 measured in the digesta (Figure 2C), and in the mucosa (figure 2D) of the midgut using the Weighted 323 UniFrac dissimilarity index and visualized using NMDS ordination. The pairwise PERMANOVA test was 324 used to compare both groups, showing a significant (*p* = 0.003) modification of the beta diversities only 325 in the digesta. At total of 202 OTUs (Operational Taxonomic Units) were detected in all samples (Figure 3A). Among them, 138 OTU i.e 68.3 % were common across the four experimental groups (Figure 3A). 326

327 The 10 most abundant common OTUs are *Ralstonia pickettii* (OTU 1), *Mycoplasma* sp. (OTU 2), 328 *Sphingomonas* sp. (OTU 4), *Cutibacterium* sp. (OTU 6), *Sphingomonas* sp. (OTU 7), *Lactobacillus* sp. 329 (OTU 20), *Ligilactobacillus* sp. (OTU 11), *Bacillus* sp. (OTU 18), *Weissella* sp. (OTU 13), and 330 *Enhydrobacter* sp. (OTU 10) (Figure 3B). After performing taxonomic affiliations on OTUs, removing 331 OTUs found in less than 4 samples, and having a minimum abundance of 0.005 %, 7 phyla, 54 families, 332 and 85 genera were observed in the overall dataset. Results are presented at phylum (Figure 4A, 4B), 333 family (Figure 4D, 4E) and genera (Figure 4G, 4H) levels. *Proteobacteria* and *Firmicutes* were the most 334 abundant phyla regardless the sample origin. Independently of the diet, we observed significant 335 difference between mucosa associated microbiota and digesta associated microbiota. In digesta, 336 *Proteobacteria* was the dominant phylum (76.21 ± 11.22 %) (Figure 4B) which is not the case in the 337 mucosa associated microbiota where *Firmicutes* dominates (66.58 ± 14.10 %) (Figure 4A). In both 338 mucosa and digesta associated microbiota, *Proteobacteria* were dominated by the genus *Ralstonia* 339 belonging to the *Burkholderiaceae* family, whereas the genus *Mycoplasma* (*Mycoplasmaceae* family) 340 dominated in *Firmicutes* but significant differences occurred regarding their relative abundance. In
315 **3.2. Microbiota diversities and composition**

316 Sequence data were rarefied to 17000 sequences per sample. Alpha diversity measures including the 317 indexes Observed OTUs, Chao1, Shannon, Simpson, and InvSimpson were calculated in the digesta 318 (Figure 2A) and in mucosa (Figure 2B). A significant decrease (ANOVA, *p* < 0.05) of all the alpha 319 diversities indices only in the digesta associated microbiota was observed for the high-starch group. 320 Most of the alpha diversity indices did not differ between digesta and mucosa samples except for the 321 Shannon index which is significantly (*p* = 0.01) higher in digesta (data not shown). Beta diversities were 322 measured in the digesta (Figure 2C), and in the mucosa (figure 2D) of the midgut using the Weighted 323 UniFrac dissimilarity index and visualized using NMDS ordination. The pairwise PERMANOVA test was 324 used to compare both groups, showing a significant (*p* = 0.003) modification of the beta diversities only 325 in the digesta. At total of 202 OTUs (Operational Taxonomic Units) were detected in all samples (Figure 3A). Among them, 138 OTU i.e 68.3 % were common across the four experimental groups (Figure 3A). 326

327 The 10 most abundant common OTUs are *Ralstonia pickettii* (OTU 1), *Mycoplasma* sp. (OTU 2), 328 *Sphingomonas* sp. (OTU 4), *Cutibacterium* sp. (OTU 6), *Sphingomonas* sp. (OTU 7), *Lactobacillus* sp. 329 (OTU 20), *Ligilactobacillus* sp. (OTU 11), *Bacillus* sp. (OTU 18), *Weissella* sp. (OTU 13), and 330 *Enhydrobacter* sp. (OTU 10) (Figure 3B). After performing taxonomic affiliations on OTUs, removing 331 OTUs found in less than 4 samples, and having a minimum abundance of 0.005 %, 7 phyla, 54 families, 332 and 85 genera were observed in the overall dataset. Results are presented at phylum (Figure 4A, 4B), 333 family (Figure 4D, 4E) and genera (Figure 4G, 4H) levels. *Proteobacteria* and *Firmicutes* were the most 334 abundant phyla regardless the sample origin. Independently of the diet, we observed significant 335 difference between mucosa associated microbiota and digesta associated microbiota. In digesta, 336 *Proteobacteria* was the dominant phylum (76.21 ± 11.22 %) (Figure 4B) which is not the case in the 337 mucosa associated microbiota where *Firmicutes* dominates (66.58 ± 14.10 %) (Figure 4A). In both 338 mucosa and digesta associated microbiota, *Proteobacteria* were dominated by the genus *Ralstonia* 339 belonging to the *Burkholderiaceae* family, whereas the genus *Mycoplasma* (*Mycoplasmaceae* family) 340 dominated in *Firmicutes* but significant differences occurred regarding their relative abundance. In
mucosa, no change in the relative abundance was observed at phylum, family, and genus levels (Figure 4A, 4D, 4G), except for three genera significantly lower in high-starch i.e *Bacillus*, *Peptoniphilus*, and *Clostridium sensu stricto 1 (Table 4)*. Only in digesta associated microbiota, these three phyla were significantly affected by the diet, resulting in an increase in the relative abundance of the *Proteobacteria* (81.84 ± 5.25% in HS and 70.58 ± 12.91% in LS, *p* = 0.010) and the *Actinobacteria* (4.39 ± 2.68% in HS and 2.48 ± 0.93% in LS, *p* = 0.02987) in trout fed with the high-starch diet. The *Firmicutes* proportion was lower in the high-starch group (13.68 ± 4.32% in HS and 26.64 ± 13.49% in LS, *p* = 0.0044). The *Ralstonia* genus was present a larger relative abundance, in the digesta associated microbiota, with a significant higher genera level with the high-starch diet (0.74 ± 0.06 in HS and 0.64 ± 0.12 in LS, *p* = 0.019) (Figure 4C, 4F, 4I). The proportion of *Bacillus*, belonging to the *Bacillaceae* family and *Firmicutes* phylum was significantly lower in the high-starch diet (0.0047 ± 0.0085 in HS and 0.033 ± 0.014 in LS, *p* = 4.45e-06). Moreover, the proportion of *Cutibacterium*, belonging to the *Propionibacteriaceae* family and *Actinobacteria* phylum was significantly higher in the high-starch group (0.037 ± 0.025 in HS and 0.020 ± 0.0083 in LS, *p* = 0.042). Furthermore, 12 bacterial genera were significantly affected by the diet in the digesta i.e. the genera *Ralstonia*, *Bacillus*, *Cutibacterium*, *Ligilactobacillus*, *Weissella*, *Anaerobacillus*, *Limosilactobacillus*, *Rickettsiella*, *Peptoniphilus*, *Lactococcus*, *Floricoccus*, and *Anoxybacillus* (Table 4), whereas in mucosa only 3 genera were impacted, all in the *Firmicutes* phylum: *Bacillus*, *Peptoniphilus* and *Clostridium sensu stricto 1* (s.s. 1).
In order to evaluate whether some bacterial taxa could distinguish the two diet groups, a PLS-DA was performed, keeping the OTU with abundance above 0.005% in at least one group. A clear separation was observed for digesta (Figure 5A) and the top 30 most contributing OTUs were identified (Figure 5B). Indeed, one-way ANOVA allowed to indicate that all of these OTUs were significantly different between the diets (Supplementary table 4). OTUs belonging to *Ralstonia pickettii*, *Peptoniphilus Koenoeneniae*, *Anoxybacillus flavithermus*, *Enterococcus cecorum*, *Mycoplasma* sp., *Rickettsiella* sp., *Streptococcus* sp., *Veillonella* sp., and *Bacillus licheniformis*, *cytotoxicus*, and *amyloliquefaciens* decrease in fish fed with the high starch diet. One OTU belonging to *Ralstonia pickettii*, and several
mucosa, no change in the relative abundance was observed at phylum, family, and genus levels (Figure 4A, 4D, 4G), except for three genera significantly lower in high-starch i.e *Bacillus*, *Peptoniphilus*, and *Clostridium sensu stricto 1 (Table 4)*. Only in digesta associated microbiota, these three phyla were significantly affected by the diet, resulting in an increase in the relative abundance of the *Proteobacteria* (81.84 ± 5.25% in HS and 70.58 ± 12.91% in LS, *p* = 0.010) and the *Actinobacteria* (4.39 ± 2.68% in HS and 2.48 ± 0.93% in LS, *p* = 0.02987) in trout fed with the high-starch diet. The *Firmicutes* proportion was lower in the high-starch group (13.68 ± 4.32% in HS and 26.64 ± 13.49% in LS, *p* = 0.0044). The *Ralstonia* genus was present a larger relative abundance, in the digesta associated microbiota, with a significant higher genera level with the high-starch diet (0.74 ± 0.06 in HS and 0.64 ± 0.12 in LS, *p* = 0.019) (Figure 4C, 4F, 4I). The proportion of *Bacillus*, belonging to the *Bacillaceae* family and *Firmicutes* phylum was significantly lower in the high-starch diet (0.0047 ± 0.0085 in HS and 0.033 ± 0.014 in LS, *p* = 4.45e-06). Moreover, the proportion of *Cutibacterium*, belonging to the *Propionibacteriaceae* family and *Actinobacteria* phylum was significantly higher in the high-starch group (0.037 ± 0.025 in HS and 0.020 ± 0.0083 in LS, *p* = 0.042). Furthermore, 12 bacterial genera were significantly affected by the diet in the digesta i.e. the genera *Ralstonia*, *Bacillus*, *Cutibacterium*, *Ligilactobacillus*, *Weissella*, *Anaerobacillus*, *Limosilactobacillus*, *Rickettsiella*, *Peptoniphilus*, *Lactococcus*, *Floricoccus*, and *Anoxybacillus* (Table 4), whereas in mucosa only 3 genera were impacted, all in the *Firmicutes* phylum: *Bacillus*, *Peptoniphilus* and *Clostridium sensu stricto 1* (s.s. 1).
In order to evaluate whether some bacterial taxa could distinguish the two diet groups, a PLS-DA was performed, keeping the OTU with abundance above 0.005% in at least one group. A clear separation was observed for digesta (Figure 5A) and the top 30 most contributing OTUs were identified (Figure 5B). Indeed, one-way ANOVA allowed to indicate that all of these OTUs were significantly different between the diets (Supplementary table 4). OTUs belonging to *Ralstonia pickettii*, *Peptoniphilus Koenoeneniae*, *Anoxybacillus flavithermus*, *Enterococcus cecorum*, *Mycoplasma* sp., *Rickettsiella* sp., *Streptococcus* sp., *Veillonella* sp., and *Bacillus licheniformis*, *cytotoxicus*, and *amyloliquefaciens* decrease in fish fed with the high starch diet. One OTU belonging to *Ralstonia pickettii*, and several
367 OTUs from *Weissella cibaria*, *Cutibacterium namnetense*, *Sphingomonas* sp., and *Streptococcus* sp.,
368 were higher in the high-starch group. Regarding mucosa associated microbiota, the separation was not
369 clear and only 6 OTUs were significantly different between the diet groups: 4 OTUs belonging to
370 *Bacillus* genus, 1 from *Peptoniphilus* and the last one from *Clostridium s.s* 1 as previously described
371 (data not shown).

372 **3.3. Short-chain fatty acid**

373 Short-chain fatty acid concentrations, including acetic, propionic, butyric, and valeric acid were
374 measured in samples collected from the fish hindgut. A significant increase of the valeric acid
375 concentration, going from 5.23 ± 1.27 µg/g for the low-starch group to 17.96 ± 14.00 µg/g for the high-
376 starch group were obtained according to a Wilcoxon non-parametric test. The level of acetic, butyric,
377 and propionic acid did not differ between the diets (Figure 6A) but show the same trend as valeric acid.
378 Clustering of the samples based on the count rates of the product ions derived from the SCFAs using
379 PLS-DA showing a clear separation between the two diet groups (Figure 6B) and the 15 most
380 discriminating ions were identified and showed significant differences (Figure 6C). Lactate
381 fragmentation induce the production of multiple ions including H3O+ 45, and H3O+ 81 (Figure 6D). A
382 significant higher proportion of lactate (H3O+ 81 signal) was measured in the hindgut sample in trout
383 fed with the high-starch diet.

384 **3.4. Gene expression in liver**

385 We evaluated the expression of different genes involved in gluconeogenesis, glycolysis, lipogenesis
386 and fatty acid oxidation in the liver, the center of the intermediary metabolism. ANOVA revealed that
387 there was not a significant effect of CHO on the expression of *glut2a* and *glut2b*, two genes involved
388 in glucose transport (Table 5). The mRNA level of genes implicated in the first (*gcka*, *gckb*), the third
389 (*pfkla*, *pfklb*) and last (*pk*) glycolysis steps was measured. The expression of genes involved in the first
390 steps of glycolysis (*gcka*: *p*<0.001 **; *gckb*: *p*<0.01 **) increased whereas the expression of the gene
391 implicated in the last-step (*pk*: *p*<0.001 ***) decreased in the high-starch group. In the same way, the
367 OTUs from *Weissella cibaria*, *Cutibacterium namnetense*, *Sphingomonas* sp., and *Streptococcus* sp., 368 were higher in the high-starch group. Regarding mucosa associated microbiota, the separation was not 369 clear and only 6 OTUs were significantly different between the diet groups: 4 OTUs belonging to 370 *Bacillus* genus, 1 from *Peptoniphilus* and the last one from *Clostridium s.s* 1 as previously described 371 (data not shown).

372 **3.3. Short-chain fatty acid**

373 Short-chain fatty acid concentrations, including acetic, propionic, butyric, and valeric acid were 374 measured in samples collected from the fish hindgut. A significant increase of the valeric acid 375 concentration, going from 5.23 ± 1.27 µg/g for the low-starch group to 17.96 ± 14.00 µg/g for the high- 376 starch group were obtained according to a Wilcoxon non-parametric test. The level of acetic, butyric, 377 and propionic acid did not differ between the diets (Figure 6A) but show the same trend as valeric acid. 378 Clustering of the samples based on the count rates of the product ions derived from the SCFAs using 379 PLS-DA showing a clear separation between the two diet groups (Figure 6B) and the 15 most 380 discriminating ions were identified and showed significant differences (Figure 6C). Lactate 381 fragmentation induce the production of multiple ions including H3O+ 45, and H3O+ 81 (Figure 6D). A 382 significant higher proportion of lactate (H3O+ 81 signal) was measured in the hindgut sample in trout 383 fed with the high-starch diet.

384 **3.4. Gene expression in liver**

385 We evaluated the expression of different genes involved in gluconeogenesis, glycolysis, lipogenesis 386 and fatty acid oxidation in the liver, the center of the intermediary metabolism. ANOVA revealed that 387 there was not a significant effect of CHO on the expression of *glut2a* and *glut2b*, two genes involved 388 in glucose transport (Table 5). The mRNA level of genes implicated in the first (*gcka*, *gckb*), the third 389 (*pfkla*, *pfklb*) and last (*pk*) glycolysis steps was measured. The expression of genes involved in the first 390 steps of glycolysis (*gcka*: *p*<0.001 **; *gckb*: *p*<0.01 **) increased whereas the expression of the gene 391 implicated in the last-step (*pk*: *p*<0.001 ***) decreased in the high-starch group. In the same way, the
pck1 (*p*<0.05 *) and *fbp1b1* (*p*<0.05 *) mRNA gene expression involved in gluconeogenesis were significantly lower in the high-starch group, while in this group the mRNA level of *g6pcb2a* (*p*<0.05 *) was increased. High-starch diet did not affect the expression of genes involved in lipogenesis. By contrast, the *hadh* (*p*<0.01 **) and *acox3* (*p*<0.05 *) expression implicated in beta oxidation of lipids decreased in trout fed with the high-starch diet. Regarding cholesterol biosynthesis gene, the expression of the *srebp2a*, *hmgcs*, and *cyp51a* genes decreased significantly in high-starch diet group.

### 3.5. Hepatic enzymatic activities

To validate the gene expression at functional level, the specific activities of glucokinase, pyruvate kinase, glucose-6-phosphatase, and fatty acid synthase were measured in the liver. The enzymes involved in the first (glucokinase) and in the last (PK) steps of glycolysis were both significantly increased in trout fed with high starch diet (Figure 7A, B). Indeed, the average activity of glucokinase was higher in the high-starch group (+ 0.023 mU/mg of protein, *p*<0.001 ***). Glucose-6-phosphate, implicated in gluconeogenesis presented a significant (*p*<0.05 *) higher specific activity in high-starch group, 0.36 ± 0.098 mU/mg of protein, than low-starch group, 0.26 ± 0.11 mU/mg of protein (Figure 7C). Then, for lipogenesis the key enzyme, Fatty Acid Synthase (FAS), did not show significant difference between the two groups (Figure 7D).

### 3.6. Correlations between the OTUs and the hepatic gene expression, zootechnical, liver, plasma parameters and enzymatic activities

The correlations between discriminatory bacterial genera and hepatic gene expression was firstly evaluated using regularized canonical correlation analysis (rCCA) (Figure 8A). *Anoxybacillus*, *Ligilactobacillus*, *Bacillus*, *Lactobacillus*, *Lentilactobacillus*, *Rickettsiella*, *Mycoplasma*, *Brevundimonas*, and *Fibrella* were positively correlated with *fbp1b1*, and *pck1* genes evolved in gluconeogenesis, as well as *pk* involved in glycolysis. These genera were all negatively correlated with *pck2* (gluconeogenesis) and *g6pcb2a* (glycolysis). *Sphingomonas*, *Undibacterium*, *Acinetobacter*, *Veillonella*, and *Salmonella*, were positively correlated with *g6pca*, *g6pcb2a*, *g6pcb2b*, *fbp1b1*, *pck2* (implicated in
pck1 (*p*<0.05 *) and *fbp1b1* (*p*<0.05 *) mRNA gene expression involved in gluconeogenesis were significantly lower in the high-starch group, while in this group the mRNA level of *g6pcb2a* (*p*<0.05 *) was increased. High-starch diet did not affect the expression of genes involved in lipogenesis. By contrast, the *hadh* (*p*<0.01 **) and *acox3* (*p*<0.05 *) expression implicated in beta oxidation of lipids decreased in trout fed with the high-starch diet. Regarding cholesterol biosynthesis gene, the expression of the *srebp2a*, *hmgcs*, and *cyp51a* genes decreased significantly in high-starch diet group.

3.5. Hepatic enzymatic activities

To validate the gene expression at functional level, the specific activities of glucokinase, pyruvate kinase, glucose-6-phosphatase, and fatty acid synthase were measured in the liver. The enzymes involved in the first (glucokinase) and in the last (PK) steps of glycolysis were both significantly increased in trout fed with high starch diet (Figure 7A, B). Indeed, the average activity of glucokinase was higher in the high-starch group (+ 0.023 mU/mg of protein, *p*<0.001 ***). Glucose-6-phosphate, implicated in gluconeogenesis presented a significant (*p*<0.05 *) higher specific activity in high-starch group, 0.36 ± 0.098 mU/mg of protein, than low-starch group, 0.26 ± 0.11 mU/mg of protein (Figure 7C). Then, for lipogenesis the key enzyme, Fatty Acid Synthase (FAS), did not show significant difference between the two groups (Figure 7D).

3.6. Correlations between the OTUs and the hepatic gene expression, zootechnical, liver, plasma parameters and enzymatic activities

The correlations between discriminatory bacterial genera and hepatic gene expression was firstly evaluated using regularized canonical correlation analysis (rCCA) (Figure 8A). *Anoxybacillus*, *Ligilactobacillus*, *Bacillus*, *Lactobacillus*, *Lentilactobacillus*, *Rickettsiella*, *Mycoplasma*, *Brevundimonas*, and *Fibrella* were positively correlated with *fbp1b1*, and *pck1* genes evolved in gluconeogenesis, as well as *pk* involved in glycolysis. These genera were all negatively correlated with *pck2* (gluconeogenesis) and *g6pcb2a* (glycolysis). *Sphingomonas*, *Undibacterium*, *Acinetobacter*, *Veillonella*, and *Salmonella*, were positively correlated with *g6pca*, *g6pcb2a*, *g6pcb2b*, *fbp1b1*, *pck2* (implicated in
gluconeogenesis), and negatively with *pk* and *pck1*. *Ralstonia* was negatively correlated with all of these gene expressions. Secondly, OTUs abundances were correlated with zootechnical, liver, and plasmatic parameters as well as enzymatic activities (Figure 8B). The first 9 OTUs i.e. *Lactococcus*, *Floricoccus*, *Ligilactobacillus*, *Lactobacillus*, *Peptoniphilus*, *Anoxybacillus*, *Bacillus*, *Rickettsiella* and *Mycoplasma* were positively correlated with FBM (Final Body Mass), DGI (Daily Growth Intake), SGR, DFI, FE and the level of liver cholesterol. These 9 OTUs were negatively correlated with the PER, HSI, as well as the level of glycogen and all the enzymatic activities and the plasma parameters. Inversely, the 9 OTUs, *Ralstonia*, *Burkholderia*, *Weissella*, *Cutibacterium*, *Limosilactobacillus*, *Acinetobacter*, *Fusobacterium*, *Macrococcus*, and *Anaerobacillus*, were positively correlated with PER, HSI, as well as the enzymatic activities and the level of glucose and plasmatic lactate. These OTUs were negatively correlated with the zootechnical parameters implicated in growth parameters as well as the DFI and the FE and the level of liver cholesterol.

## 4. Discussion

It is widely known that the vertebrate gut microbiome plays a critical role in digestive system by breaking down nutrients. Bacteria can provide vitamins, fatty acids, as well as nutrients to the host (Dhanasiri et al., 2011), and can also affect the gut morphology (Taschuk and Griebel, 2012). Diet composition is an important factor that could modify both intestinal microbiome (its structure and its metabolic function) and host metabolic response in fish (Ingerslev et al., 2014; Naya-Català et al., 2021; Yang et al., 2021). Although the gut microbiota diversity in fish has been characterized in the last decade (Mansfield et al., 2010), the influence of changes in nutrient composition linked to carbohydrates and proteins levels needs to be investigated in order to improve the use of all plant-based diet for farmed carnivorous fish. Therefore, the goal of this study was to evaluate the effect of dietary carbohydrates in rainbow trout fed with 100% plant-based diet. The use of high-starch diet have already been studied in diets containing FM and/or FO showing strong changes in the fish
gluconeogenesis), and negatively with *pk* and *pck1*. *Ralstonia* was negatively correlated with all of these gene expressions. Secondly, OTUs abundances were correlated with zootechnical, liver, and plasmatic parameters as well as enzymatic activities (Figure 8B). The first 9 OTUs i.e. *Lactococcus*, *Floricoccus*, *Ligilactobacillus*, *Lactobacillus*, *Peptoniphilus*, *Anoxybacillus*, *Bacillus*, *Rickettsiella* and *Mycoplasma* were positively correlated with FBM (Final Body Mass), DGI (Daily Growth Intake), SGR, DFI, FE and the level of liver cholesterol. These 9 OTUs were negatively correlated with the PER, HSI, as well as the level of glycogen and all the enzymatic activities and the plasma parameters. Inversely, the 9 OTUs, *Ralstonia*, *Burkholderia*, *Weissella*, *Cutibacterium*, *Limosilactobacillus*, *Acinetobacter*, *Fusobacterium*, *Macrococcus*, and *Anaerobacillus*, were positively correlated with PER, HSI, as well as the enzymatic activities and the level of glucose and plasmatic lactate. These OTUs were negatively correlated with the zootechnical parameters implicated in growth parameters as well as the DFI and the FE and the level of liver cholesterol.

# 4. Discussion

It is widely known that the vertebrate gut microbiome plays a critical role in digestive system by breaking down nutrients. Bacteria can provide vitamins, fatty acids, as well as nutrients to the host (Dhanasiri et al., 2011), and can also affect the gut morphology (Taschuk and Griebel, 2012). Diet composition is an important factor that could modify both intestinal microbiome (its structure and its metabolic function) and host metabolic response in fish (Ingerslev et al., 2014; Naya-Català et al., 2021; Yang et al., 2021). Although the gut microbiota diversity in fish has been characterized in the last decade (Mansfield et al., 2010), the influence of changes in nutrient composition linked to carbohydrates and proteins levels needs to be investigated in order to improve the use of all plant-based diet for farmed carnivorous fish. Therefore, the goal of this study was to evaluate the effect of dietary carbohydrates in rainbow trout fed with 100% plant-based diet. The use of high-starch diet have already been studied in diets containing FM and/or FO showing strong changes in the fish
441 microbiota and metabolism (Huang et al., 2021; T. Wang et al., 2021; Zhang et al., 2020) but has never
442 been studied with a 100% plant-based diet. We used a similar approach in this study.

443 **4.1. The high-starch diet strongly affects the composition and biodiversity of the gut microbiota**

444 Firstly, *Proteobacteria* and *Firmicutes* phyla were the most abundant phyla regardless the location and
445 the diet according to previous studies in salmon and rainbow trout (Gajardo et al., 2017, 2016; Li et al.,
446 2021; Villasante et al., 2022, 2019). Several studies have already shown that a high-starch diet
447 decreases the intestinal microbiota diversity on the fish intestinal microbiota when combined with
448 diets based on FM and FO in rainbow trout (Geurden et al., 2014), largemouth bass (Huang et al., 2021;
449 Zhou et al., 2021), Chinese perch (Zhang et al., 2020), and Nile tilapia (Xu et al., 2022). Moreover, the
450 comparison between a plant-based diet (but not 100% plant based-diet) and a FM/FO diet have been
451 studied in rainbow trout, revealing that the *Firmicutes* phylum were dominant with the plant-based
452 diet, while with the FM / FO diet, the *Proteobacteria* were dominant (Ingerslev et al., 2014). We
453 sampled the microbiota in the midgut region by collecting the digesta, and by scrapping the mucus
454 layer (mucosa associated microbiota) according to (Li et al., 2021). The results obtained in this study
455 allows us to demonstrate that regardless of the diet, a significant difference between digesta and
456 mucosa associated microbiota were observed according to previous works in mammals and fish (Bruni
457 et al., 2022; Gajardo et al., 2017, 2016; Li et al., 2021). Most studies investigating diet effect on gut
458 microbiota in fish were focused only on digesta or on both mucosa and content mixed together.
459 Investigating only one or a mix could allow us to misinterpret the response of intestinal microbiota to
460 dietary changes. Interestingly, in our study, *Ralstonia* dominates in digesta microbiota when
461 *Mycoplasma* is highly abundant in mucus samples (Lokesh et al., 2023; Rasmussen et al., 2021). In the
462 last version of silva database (n°138.1) used in this study ("https://www.arb-
463 silva.de/documentation/release-138/," n.d.), *Mycoplasma* is now part of the *Bacilli* class explaining the
464 high abundance of *Firmicutes* in mucosa associated microbiota. The mucosa associated microbiota in
465 salmonids is well known to be dominated by *Mycoplasma*, a potential intracellular bacterium (Cheaib
441 microbiota and metabolism (Huang et al., 2021; T. Wang et al., 2021; Zhang et al., 2020) but has never
442 been studied with a 100% plant-based diet. We used a similar approach in this study.

443 **4.1. The high-starch diet strongly affects the composition and biodiversity of the gut microbiota**

444 Firstly, *Proteobacteria* and *Firmicutes* phyla were the most abundant phyla regardless the location and
445 the diet according to previous studies in salmon and rainbow trout (Gajardo et al., 2017, 2016; Li et al.,
446 2021; Villasante et al., 2022, 2019). Several studies have already shown that a high-starch diet
447 decreases the intestinal microbiota diversity on the fish intestinal microbiota when combined with
448 diets based on FM and FO in rainbow trout (Geurden et al., 2014), largemouth bass (Huang et al., 2021;
449 Zhou et al., 2021), Chinese perch (Zhang et al., 2020), and Nile tilapia (Xu et al., 2022). Moreover, the
450 comparison between a plant-based diet (but not 100% plant based-diet) and a FM/FO diet have been
451 studied in rainbow trout, revealing that the *Firmicutes* phylum were dominant with the plant-based
452 diet, while with the FM / FO diet, the *Proteobacteria* were dominant (Ingerslev et al., 2014). We
453 sampled the microbiota in the midgut region by collecting the digesta, and by scrapping the mucus
454 layer (mucosa associated microbiota) according to (Li et al., 2021). The results obtained in this study
455 allows us to demonstrate that regardless of the diet, a significant difference between digesta and
456 mucosa associated microbiota were observed according to previous works in mammals and fish (Bruni
457 et al., 2022; Gajardo et al., 2017, 2016; Li et al., 2021). Most studies investigating diet effect on gut
458 microbiota in fish were focused only on digesta or on both mucosa and content mixed together.
459 Investigating only one or a mix could allow us to misinterpret the response of intestinal microbiota to
460 dietary changes. Interestingly, in our study, *Ralstonia* dominates in digesta microbiota when
461 *Mycoplasma* is highly abundant in mucus samples (Lokesh et al., 2023; Rasmussen et al., 2021). In the
462 last version of silva database (n°138.1) used in this study ("https://www.arb-
463 silva.de/documentation/release-138/," n.d.), *Mycoplasma* is now part of the *Bacilli* class explaining the
464 high abundance of *Firmicutes* in mucosa associated microbiota. The mucosa associated microbiota in
465 salmonids is well known to be dominated by *Mycoplasma*, a potential intracellular bacterium (Cheaib
et al., 2021). Previous studies have shown a lack of pathogenicity genes in *Mycoplasma* strains identified in salmon suggesting a symbiotic relationship by enhancing host defense (Bozzi et al., 2021; Lian et al., 2020) and providing biotin in a nutrient poor environment (Lian et al., 2020). *Ralstonia* is already described in high abundance in the gut of marine fish (Huang et al., 2020) and in rainbow trout occasionally (Kim et al., 2007). But it is not the case in most studies on gut microbiota of salmonids. Interestingly, a study in yellowtail kingfish (*Seriola lalandi*) using a protocol allowing the authors to identify both active and inactive bacteria, showed that *Ralstonia* was more abundant after depletion of dead bacteria (Legrand et al., 2021). Regarding the effect of diet on gut microbiota diversity, we observed that it was dependent on the sample origin. Digesta associated microbiota was more affected by the diet than the mucosa associated microbiota. A significant decrease of the microbial diversity and richness (both alpha and beta diversity) was observed in the digesta for trout fed with the high-starch diet as previously described in fish fed with high CHO associated with FM/FO (Gajardo et al., 2017; Huang et al., 2021; Morrison and Preston, 2016). Inversely, no change in the alpha and beta diversities were observed in the mucosa associated microbiota, suggesting that the mucosa associated microbiota is more resilient to diet changes. In addition, it has already been shown that transient microbes in the digesta are influenced by multiple environmental factors such as diet, while mucosa contains more resident microbes that are more influenced by the host (Legrand et al., 2021). While in mammals the *Firmicutes/Bacteroidetes* ratio is helpful to understand the link with diet, in fish the ratio *Firmicutes/Proteobacteria* is more relevant regarding the low abundance of *Bacteroidetes* in fish microbiota (Desai et al., 2012). In our study, the gut microbiota of rainbow trout in the midgut digesta were mainly colonized by bacteria belonging to the *Proteobacteria* and *Firmicutes* phyla. The inclusion of high-starch diet (corresponding to a high CHO/Protein ratio) led to an increase to the abundance of *Proteobacteria* and *Actinobacteria* with a decrease in the *Firmicutes*. The *Firmicutes/Proteobacteria* ratio is more relevant in fish and the ratio decreased when rainbow trout are fed with the high-starch diet. This has been previously described in salmonids fed with FM/FO diets (Gajardo et al., 2017; Huang et al., 2021; Morrison and Preston, 2016). More specifically at genus level, the digesta is dominated by
et al., 2021). Previous studies have shown a lack of pathogenicity genes in *Mycoplasma* strains identified in salmon suggesting a symbiotic relationship by enhancing host defense (Bozzi et al., 2021; Lian et al., 2020) and providing biotin in a nutrient poor environment (Lian et al., 2020). *Ralstonia* is already described in high abundance in the gut of marine fish (Huang et al., 2020) and in rainbow trout occasionally (Kim et al., 2007). But it is not the case in most studies on gut microbiota of salmonids. Interestingly, a study in yellowtail kingfish (*Seriola lalandi*) using a protocol allowing the authors to identify both active and inactive bacteria, showed that *Ralstonia* was more abundant after depletion of dead bacteria (Legrand et al., 2021). Regarding the effect of diet on gut microbiota diversity, we observed that it was dependent on the sample origin. Digesta associated microbiota was more affected by the diet than the mucosa associated microbiota. A significant decrease of the microbial diversity and richness (both alpha and beta diversity) was observed in the digesta for trout fed with the high-starch diet as previously described in fish fed with high CHO associated with FM/FO (Gajardo et al., 2017; Huang et al., 2021; Morrison and Preston, 2016). Inversely, no change in the alpha and beta diversities were observed in the mucosa associated microbiota, suggesting that the mucosa associated microbiota is more resilient to diet changes. In addition, it has already been shown that transient microbes in the digesta are influenced by multiple environmental factors such as diet, while mucosa contains more resident microbes that are more influenced by the host (Legrand et al., 2021). While in mammals the *Firmicutes/Bacteroidetes* ratio is helpful to understand the link with diet, in fish the ratio *Firmicutes/Proteobacteria* is more relevant regarding the low abundance of *Bacteroidetes* in fish microbiota (Desai et al., 2012). In our study, the gut microbiota of rainbow trout in the midgut digesta were mainly colonized by bacteria belonging to the *Proteobacteria* and *Firmicutes* phyla. The inclusion of high-starch diet (corresponding to a high CHO/Protein ratio) led to an increase to the abundance of *Proteobacteria* and *Actinobacteria* with a decrease in the *Firmicutes*. The *Firmicutes/Proteobacteria* ratio is more relevant in fish and the ratio decreased when rainbow trout are fed with the high-starch diet. This has been previously described in salmonids fed with FM/FO diets (Gajardo et al., 2017; Huang et al., 2021; Morrison and Preston, 2016). More specifically at genus level, the digesta is dominated by
Ralstonia which increased with the high-starch diet. Interestingly, we observed in the digesta
significant higher proportion of Gram positive *Limosilactobacillus* and *Weissella* which are members of
the Lactic Acid Bacteria (LAB). These bacteria are known to produce lactic acid through glucose
metabolism and *Weissella* has been already identified at higher abundance in salmonids fed with high
content of plant ingredients (Desai et al., 2012; Gajardo et al., 2016; Villasante et al., 2019; Zarkasi et
al., 2014). Finally, 12 genera of bacteria were significantly affected by the high-starch diet in the digesta
of the midgut. Among these 12 bacterial genera, other lactic acid bacteria were observed in lower
abundance in the high-starch group, i.e. *Ligilactobacillus* showing all the complexity of gut microbiota
analysis. To go further, the PLS-DA analysis, showing clear separation of the microbial composition,
allowed us to identify the most divergent OTUs between the two groups. Most of these OTUs are part
of the genera already identified as affected by the diet such as *Weissella*, *Ralstonia* or *Ligilactobacillus*.
Regarding *Ralstonia pickettii*, in particular the OTU1 which increases in the high-starch group whereas
other OTUs belonging to *R. pickettii* were significantly in lower proportion showing we need to go
further the species level by using shotgun metagenomics to fully understand the role of these bacteria.
Interestingly, in our study, *Ralstonia pickettii* was dominant in the digesta microbiota, regardless of the
diet, and has been found in several environment such as soil, rivers or lakes. In mammals, *Ralstonia
pickettii* has been linked to the development of obesity and type 2 diabetes (Udayappan et al., 2017).
In this, the increase of *Ralstonia* was not associated to any negative effect of fish physiology. OTUs
belonging to genus *Bacillus*: *Bacillus licheniformis*, *B. cytotoxicus*, and *B. amyloliquefaciens* as
previously described, have been observed in lower abundance in the high-starch group (Burtscher et
al., 2021; Cao et al., 2019; Xu et al., 2022). In the high-starch group we can notify the higher proportion
of 4 OTUs corresponding to *Weissella cibaria*. The presence of these lactic acid-producing bacteria
could have beneficial effects on the immune system and may protect against pathogen invasion
through the intestinal surface (Nayak, 2010; Salinas et al., 2008).

516
Ralstonia which increased with the high-starch diet. Interestingly, we observed in the digesta
significant higher proportion of Gram positive *Limosilactobacillus* and *Weissella* which are members of
the Lactic Acid Bacteria (LAB). These bacteria are known to produce lactic acid through glucose
metabolism and *Weissella* has been already identified at higher abundance in salmonids fed with high
content of plant ingredients (Desai et al., 2012; Gajardo et al., 2016; Villasante et al., 2019; Zarkasi et
al., 2014). Finally, 12 genera of bacteria were significantly affected by the high-starch diet in the digesta
of the midgut. Among these 12 bacterial genera, other lactic acid bacteria were observed in lower
abundance in the high-starch group, i.e. *Ligilactobacillus* showing all the complexity of gut microbiota
analysis. To go further, the PLS-DA analysis, showing clear separation of the microbial composition,
allowed us to identify the most divergent OTUs between the two groups. Most of these OTUs are part
of the genera already identified as affected by the diet such as *Weissella*, *Ralstonia* or *Ligilactobacillus*.
Regarding *Ralstonia pickettii*, in particular the OTU1 which increases in the high-starch group whereas
other OTUs belonging to *R. pickettii* were significantly in lower proportion showing we need to go
further the species level by using shotgun metagenomics to fully understand the role of these bacteria.
Interestingly, in our study, *Ralstonia pickettii* was dominant in the digesta microbiota, regardless of the
diet, and has been found in several environment such as soil, rivers or lakes. In mammals, *Ralstonia
pickettii* has been linked to the development of obesity and type 2 diabetes (Udayappan et al., 2017).
In this, the increase of *Ralstonia* was not associated to any negative effect of fish physiology. OTUs
belonging to genus *Bacillus*: *Bacillus licheniformis*, *B. cytotoxicus*, and *B. amyloliquefaciens* as
previously described, have been observed in lower abundance in the high-starch group (Burtscher et
al., 2021; Cao et al., 2019; Xu et al., 2022). In the high-starch group we can notify the higher proportion
of 4 OTUs corresponding to *Weissella cibaria*. The presence of these lactic acid-producing bacteria
could have beneficial effects on the immune system and may protect against pathogen invasion
through the intestinal surface (Nayak, 2010; Salinas et al., 2008).
517

## 4.2. Modification in gut microbiota potentially mediates changes in SCFA concentrations in rainbow trout fed with the high-starch diet

518

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In humans, the metabolic changes in the liver in response to carbohydrates are known to be due to the activity of SCFA such as acetate and propionate, since butyrate is generally preferentially absorbed by intestinal cells (Den Besten et al., 2013; Frampton et al., 2020). Indeed, SCFA are good candidates to explain the crosstalk between diet, microbiota, host physiology and health (van der Hee and Wells, 2021). SCFA i.e. acetate, butyrate, propionate, valerate, caproate, are produced mainly in the colon after saccharolytic fermentation (Cummings et al., 1987), mostly by the *Firmicutes* and *Actinobacteria* phyla (Louis and Flint, 2017; Zhu et al., 2017). In fish, the production of SCFA have already been described (Pardesi et al., 2022) as their beneficial effects on glucose tolerance and immune function were identified in tilapia (T. Wang et al., 2021). Moreover, it has been recently demonstrated that *Bacillus amyloliquefaciens*, could alleviate the metabolic phenotypes caused by a high-carbohydrate diet by enriching the acetate-producing bacteria in Nile tilapia intestines (Xu et al., 2022). Furthermore, a study in zebrafish has revealed that *Cetobacterium* improves glucose homeostasis, mediated by a potential effect of acetate (A. Wang et al., 2021). In our study, a significant increase of the valerate concentration was observed in the microbiota of trout fed with the high-starch diet whereas acetate, butyrate and propionate showed the same trend. Regarding SCFA levels, their increase observed in the high-starch group could at least partially explain differences in gene expression in the liver (Morrison and Preston, 2016). In addition, our data suggested a high production of lactate probably linked to lactate-producing bacteria, *Weissella* and *Limosilactobacillus*, in the high-starch group microbiota.

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## 4.3. The high-starch diet did not affect growth performance and glucose homeostasis but have expected effect on glucose and lipid metabolism in liver

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541

Overall, the incorporation of 20% of dietary starch to a 100% plan-based diet has resulted in strong change in the digesta associated microbiota of the midgut. Indeed, we observed a decrease of microbiota diversity, in particular in contents as well as changes in several bacterial groups abundance
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## 4.2. Modification in gut microbiota potentially mediates changes in SCFA concentrations in rainbow trout fed with the high-starch diet

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In humans, the metabolic changes in the liver in response to carbohydrates are known to be due to the activity of SCFA such as acetate and propionate, since butyrate is generally preferentially absorbed by intestinal cells (Den Besten et al., 2013; Frampton et al., 2020). Indeed, SCFA are good candidates to explain the crosstalk between diet, microbiota, host physiology and health (van der Hee and Wells, 2021). SCFA i.e. acetate, butyrate, propionate, valerate, caproate, are produced mainly in the colon after saccharolytic fermentation (Cummings et al., 1987), mostly by the *Firmicutes* and *Actinobacteria* phyla (Louis and Flint, 2017; Zhu et al., 2017). In fish, the production of SCFA have already been described (Pardesi et al., 2022) as their beneficial effects on glucose tolerance and immune function were identified in tilapia (T. Wang et al., 2021). Moreover, it has been recently demonstrated that *Bacillus amyloliquefaciens*, could alleviate the metabolic phenotypes caused by a high-carbohydrate diet by enriching the acetate-producing bacteria in Nile tilapia intestines (Xu et al., 2022). Furthermore, a study in zebrafish has revealed that *Cetobacterium* improves glucose homeostasis, mediated by a potential effect of acetate (A. Wang et al., 2021). In our study, a significant increase of the valerate concentration was observed in the microbiota of trout fed with the high-starch diet whereas acetate, butyrate and propionate showed the same trend. Regarding SCFA levels, their increase observed in the high-starch group could at least partially explain differences in gene expression in the liver (Morrison and Preston, 2016). In addition, our data suggested a high production of lactate probably linked to lactate-producing bacteria, *Weissella* and *Limosilactobacillus*, in the high-starch group microbiota.

537

## 4.3. The high-starch diet did not affect growth performance and glucose homeostasis but have expected effect on glucose and lipid metabolism in liver

538

539

540

541

Overall, the incorporation of 20% of dietary starch to a 100% plan-based diet has resulted in strong change in the digesta associated microbiota of the midgut. Indeed, we observed a decrease of microbiota diversity, in particular in contents as well as changes in several bacterial groups abundance
without affecting the growth performance. Interestingly, while rainbow trout use high levels of protein for growth (Cleveland and Radler, 2019; Seiliez et al., 2008), decreasing the proportion of plant protein in the high-starch diet did not affect the trout final weight and even increase the protein efficiency ratio suggesting that increasing the CHO/protein ratio could prevent protein catabolism for energy needs as shown previously in fish fed with marine resources (Kamalam et al., 2017). For the first time, we showed that it is possible to incorporate high levels of digestible carbohydrates (20%) without any negative effects on zootechnical parameters and whole-body composition even in fish fed with a 100% plant-based diet. While trout are often described as poor users of glucose caused by a persistent post-prandial hyperglycemia when fed a diet containing more than 20% of carbohydrates (Polakof et al., 2012), this metabolic disorder has not been observed with the high-starch diet in our study. Other studies have also shown low blood glucose levels in mature brood stock trout fed with high levels of CHO in their diets (Callet et al., 2020). suggesting that CHO can be efficiently metabolized and/or stored as glycogen in the liver at least at later stage of development. Indeed, in rainbow trout fed with the high-starch diet, the glycogen level is higher in the liver resulting in a higher hepatosomatic index. Regarding the metabolism of glucose through glycolysis, our results reveal that the hepatic glucokinase mRNA gene expression (gcka and gckb) and their enzymatic activities were significantly higher in fish with the high-starch diet. The increase of glucokinase activity and glycogen level in trout fed with carbohydrates suggests that the rainbow trout can adapt at a metabolic level to the carbohydrate intake in fish fed with a 100% plant-based diet, as previously observed in fish fed with marine resources (Capilla et al., 2003; Pereira et al., 1995). We observed a significant lower expression of the pk gene (coding for the pyruvate kinase enzyme) involved in the last step of the glycolysis which is different to what is found in mammals (Yamada and Noguchi, 1999). However, this result is consistent in rainbow trout where it has been shown that the expression of the pk gene was poorly controlled with high levels of dietary carbohydrates, linked to a strong and constant expression of this gene (Enes et al., 2009; Panserat et al., 2001; Skiba-Cassy et al., 2013). The production of glucose in the liver through gluconeogenesis occurs from amino acids, glycerol, or lactate in mammals and fish (Kamalam et al.,
without affecting the growth performance. Interestingly, while rainbow trout use high levels of protein for growth (Cleveland and Radler, 2019; Seiliez et al., 2008), decreasing the proportion of plant protein in the high-starch diet did not affect the trout final weight and even increase the protein efficiency ratio suggesting that increasing the CHO/protein ratio could prevent protein catabolism for energy needs as shown previously in fish fed with marine resources (Kamalam et al., 2017). For the first time, we showed that it is possible to incorporate high levels of digestible carbohydrates (20%) without any negative effects on zootechnical parameters and whole-body composition even in fish fed with a 100% plant-based diet. While trout are often described as poor users of glucose caused by a persistent post-prandial hyperglycemia when fed a diet containing more than 20% of carbohydrates (Polakof et al., 2012), this metabolic disorder has not been observed with the high-starch diet in our study. Other studies have also shown low blood glucose levels in mature brood stock trout fed with high levels of CHO in their diets (Callet et al., 2020). suggesting that CHO can be efficiently metabolized and/or stored as glycogen in the liver at least at later stage of development. Indeed, in rainbow trout fed with the high-starch diet, the glycogen level is higher in the liver resulting in a higher hepatosomatic index. Regarding the metabolism of glucose through glycolysis, our results reveal that the hepatic glucokinase mRNA gene expression (gcka and gckb) and their enzymatic activities were significantly higher in fish with the high-starch diet. The increase of glucokinase activity and glycogen level in trout fed with carbohydrates suggests that the rainbow trout can adapt at a metabolic level to the carbohydrate intake in fish fed with a 100% plant-based diet, as previously observed in fish fed with marine resources (Capilla et al., 2003; Pereira et al., 1995). We observed a significant lower expression of the pk gene (coding for the pyruvate kinase enzyme) involved in the last step of the glycolysis which is different to what is found in mammals (Yamada and Noguchi, 1999). However, this result is consistent in rainbow trout where it has been shown that the expression of the pk gene was poorly controlled with high levels of dietary carbohydrates, linked to a strong and constant expression of this gene (Enes et al., 2009; Panserat et al., 2001; Skiba-Cassy et al., 2013). The production of glucose in the liver through gluconeogenesis occurs from amino acids, glycerol, or lactate in mammals and fish (Kamalam et al.,
2017; Polakof et al., 2010) and is inhibited when the animals are fed with carbohydrates (Metzger et al., 2004). In fish fed with the high-starch diet, we observed as expected a significant diminution of the *pck1*, and *fbp1b1*, gene expression involved in different steps of gluconeogenesis pathway. By contrast, the enzymatic activity of the glucose-6-phosphatase (allowing the hydrolysis of the glucose-6-phosphate to D-glucose) is higher in the high-starch diet with higher *g6pcb2a* gene expression according to previous studies that the liver glucose-6-phosphatase (*g6p*) is not well regulated in rainbow trout in fish fed with FM and FO (Kamalam et al., 2012; Marandel et al., 2015; Skiba-Cassy et al., 2013). Indeed, the duplication of genes implicated in gluconeogenesis such as *g6pcb* was suggested to contribute to the glucose intolerance and poor use of dietary carbohydrates in rainbow trout (Marandel et al., 2015). In our study, we confirmed this atypical regulation of glucose-6-phosphatase in fish fed without fish meal and fish oil, although no hyperglycemia was observed; this was observed for the first time, suggesting that the deregulation of the *g6pcb2* gene expression by carbohydrates is not sufficient to explain the low carbohydrate use in trout. The absence of differences in lipid contents in whole body composition as well as triglycerides levels in plasma in fish fed carbohydrates suggests that de novo lipogenesis is not induced in fish fed with CHO. According to lipid contents, all lipogenesis genes were not regulated by CHO which was consistent with the FAS activity which is not modified in the high starch group confirming that in rainbow trout fed with CHO (Sam et al., 2014), dietary glucose is not a strong inducer of hepatic lipogenesis. Finally, we observed as expected a decrease of the beta oxidation capacity for lipid catabolism (through hadh and *acox3* genes expression) as it was demonstrated in rainbow trout fed with a high-starch diet containing fish meal and fish oil (Song et al., 2018). We then investigated cholesterol metabolism, which is known to play an essential role in modulating membrane fluidity and essentially synthetized in liver of all animals (Dietschy et al., 1993) but can also be provided by the diet. Interestingly, we observed a significant decrease of the cholesterol concentration in the liver of high starch group associated with a decrease in the expression of several genes (*srebp2a*, *hmgcs*, *cyp51a*) involved in cholesterol biosynthesis (Khare and Gaur, 2020).
2017; Polakof et al., 2010) and is inhibited when the animals are fed with carbohydrates (Metzger et al., 2004). In fish fed with the high-starch diet, we observed as expected a significant diminution of the *pck1*, and *fbp1b1*, gene expression involved in different steps of gluconeogenesis pathway. By contrast, the enzymatic activity of the glucose-6-phosphatase (allowing the hydrolysis of the glucose-6-phosphate to D-glucose) is higher in the high-starch diet with higher *g6pcb2a* gene expression according to previous studies that the liver glucose-6-phosphatase (*g6p*) is not well regulated in rainbow trout in fish fed with FM and FO (Kamalam et al., 2012; Marandel et al., 2015; Skiba-Cassy et al., 2013). Indeed, the duplication of genes implicated in gluconeogenesis such as *g6pcb* was suggested to contribute to the glucose intolerance and poor use of dietary carbohydrates in rainbow trout (Marandel et al., 2015). In our study, we confirmed this atypical regulation of glucose-6-phosphatase in fish fed without fish meal and fish oil, although no hyperglycemia was observed; this was observed for the first time, suggesting that the deregulation of the *g6pcb2* gene expression by carbohydrates is not sufficient to explain the low carbohydrate use in trout. The absence of differences in lipid contents in whole body composition as well as triglycerides levels in plasma in fish fed carbohydrates suggests that de novo lipogenesis is not induced in fish fed with CHO. According to lipid contents, all lipogenesis genes were not regulated by CHO which was consistent with the FAS activity which is not modified in the high starch group confirming that in rainbow trout fed with CHO (Sam et al., 2014), dietary glucose is not a strong inducer of hepatic lipogenesis. Finally, we observed as expected a decrease of the beta oxidation capacity for lipid catabolism (through hadh and *acox3* genes expression) as it was demonstrated in rainbow trout fed with a high-starch diet containing fish meal and fish oil (Song et al., 2018). We then investigated cholesterol metabolism, which is known to play an essential role in modulating membrane fluidity and essentially synthetized in liver of all animals (Dietschy et al., 1993) but can also be provided by the diet. Interestingly, we observed a significant decrease of the cholesterol concentration in the liver of high starch group associated with a decrease in the expression of several genes (*srebp2a*, *hmgcs*, *cyp51a*) involved in cholesterol biosynthesis (Khare and Gaur, 2020).
Lactobacillus species have been previously identified to have cholesterol lowering effect on the hosts,
in particular on hepatic cholesterol synthesis (Khare and Gaur, 2020; Martínez Cruz et al., 2012).

**4.4. Strong interactions between trout microbiota and host metabolism were observed in trout fed with high-starch diet**

There were positive correlations between genes glycolysis, and gluconeogenesis as well as with, the
growth parameters and OTUS belonging to *Bacillus*, *Mycoplasma*, *Rickettsiella*, *Anoxybacillus*,
*Ligilactobacillus*, and *Lactobacillus* whereas *Ralstonia* is negatively correlated with these pathways.
correlated. A positive correlation with *Bacillus* groups with these metabolic pathways were previously
observed in rainbow trout (Lokesh et al., 2022). Interestingly, *Bacillus* and *Lactobacillus* are already
used as probiotics in teleost aquaculture showing different beneficial effect on fish (Martínez Cruz et
al., 2012). However, we need further investigation, especially, information on the genomes
*Lactobacillus* and *Bacillus* to fully understand the role of these bacteria.

## 5. Conclusions

The present work allows us to show clear differences between the digesta associated microbiota and
mucosa associated microbiota. Phyla from *Proteobacteria* and *Firmicutes* are dominant in both
contents and mucosa associated microbiota. CHO/protein ratio strongly modify the bacterial
community and diversity especially in digesta-associated microbiota, associated with differences in
concentration of SCFA. Nevertheless, we cannot discard that some of these effects can also be related
to the different plant protein sources. We also identified for the first time a good utilization of dietary
carbohydrates in carnivorous rainbow trout when associated with a 100% plant-based diet, as
reflected by the growth performance and the metabolic analysis. Finally, important correlations have
been highlighted between several OTUs and the trout zootechnical and metabolic parameters. Further
investigations through the use of meta-omics approaches in future studies could provide a better
understanding of the nutrient and carbohydrate utilization by the host microbiome in order to improve
Lactobacillus species have been previously identified to have cholesterol lowering effect on the hosts, in particular on hepatic cholesterol synthesis (Khare and Gaur, 2020; Martínez Cruz et al., 2012).

**4.4. Strong interactions between trout microbiota and host metabolism were observed in trout fed with high-starch diet**

There were positive correlations between genes glycolysis, and gluconeogenesis as well as with, the growth parameters and OTUS belonging to *Bacillus*, *Mycoplasma*, *Rickettsiella*, *Anoxybacillus*, *Ligilactobacillus*, and *Lactobacillus* whereas *Ralstonia* is negatively correlated with these pathways. correlated. A positive correlation with *Bacillus* groups with these metabolic pathways were previously observed in rainbow trout (Lokesh et al., 2022). Interestingly, *Bacillus* and *Lactobacillus* are already used as probiotics in teleost aquaculture showing different beneficial effect on fish (Martínez Cruz et al., 2012). However, we need further investigation, especially, information on the genomes *Lactobacillus* and *Bacillus* to fully understand the role of these bacteria.

**5. Conclusions**

The present work allows us to show clear differences between the digesta associated microbiota and mucosa associated microbiota. Phyla from *Proteobacteria* and *Firmicutes* are dominant in both contents and mucosa associated microbiota. CHO/protein ratio strongly modify the bacterial community and diversity especially in digesta-associated microbiota, associated with differences in concentration of SCFA. Nevertheless, we cannot discard that some of these effects can also be related to the different plant protein sources. We also identified for the first time a good utilization of dietary carbohydrates in carnivorous rainbow trout when associated with a 100% plant-based diet, as reflected by the growth performance and the metabolic analysis. Finally, important correlations have been highlighted between several OTUs and the trout zootechnical and metabolic parameters. Further investigations through the use of meta-omics approaches in future studies could provide a better understanding of the nutrient and carbohydrate utilization by the host microbiome in order to improve
617 the use of a 100% plant-based diet and be a sustainable alternative to FM and FO for carnivorous fish
618 in aquaculture.

619

620 **Supplementary Information**

621 **Supplementary table 1:** Product ions from the reaction of short-chain fatty acids and Lactic acid with
622 H₃O⁺, NO⁺ and O₂⁺ precursor ions in Selected Ion Flow Tube - Mass Spectrometry (from LabSyft
623 software). **Supplementary table 2:** Primer sequences and accession numbers for qPCR analysis.

624 **Supplementary table 3.** Whole-body composition in rainbow trout fed with 100% plant-based diet
625 with high or low levels of dietary carbohydrates. **Supplementary table 4.** List of the top 30 most
626 contributing OTU, discriminated by the diet on the Midgut digesta samples, with the one-way ANOVA
627 results.

628

629 **List of abbreviations**

630 CHO: carbohydrates; HS: high-starch; LS: low-starch; SCFA: Short-Chain Fatty Acid; FM: Fish meal; FO:
631 Fish Oil; FBM: Final body mass; SGR: Specific growth rate; FE: Feed efficiency; PER: Protein efficiency
632 ratio; DGI: Daily growth intake; SD: Standard deviation; OTU: Operational taxonomic unit; PLS-DA:
633 Partial Least Squares – Discriminant Analysis; bp: base-pair; 16S rRNA: 16S ribosomal Ribonucleic
634 acid; PCR: Polymerase Chain Reaction; PERMANOVA: Permutational Analysis of Variance; FROGS:
635 Find, Rapidly, OTUs with Galaxy Solution; LAB: Lactic-Acid bacteria; Cq: quantification cycle.

636

637

638
617 the use of a 100% plant-based diet and be a sustainable alternative to FM and FO for carnivorous fish
618 in aquaculture.

619

620 **Supplementary Information**

621 **Supplementary table 1:** Product ions from the reaction of short-chain fatty acids and Lactic acid with
622 H₃O⁺, NO⁺ and O₂⁺ precursor ions in Selected Ion Flow Tube - Mass Spectrometry (from LabSyft
623 software). **Supplementary table 2:** Primer sequences and accession numbers for qPCR analysis.

624 **Supplementary table 3.** Whole-body composition in rainbow trout fed with 100% plant-based diet
625 with high or low levels of dietary carbohydrates. **Supplementary table 4.** List of the top 30 most
626 contributing OTU, discriminated by the diet on the Midgut digesta samples, with the one-way ANOVA
627 results.

628

629 **List of abbreviations**

630 CHO: carbohydrates; HS: high-starch; LS: low-starch; SCFA: Short-Chain Fatty Acid; FM: Fish meal; FO:
631 Fish Oil; FBM: Final body mass; SGR: Specific growth rate; FE: Feed efficiency; PER: Protein efficiency
632 ratio; DGI: Daily growth intake; SD: Standard deviation; OTU: Operational taxonomic unit; PLS-DA:
633 Partial Least Squares – Discriminant Analysis; bp: base-pair; 16S rRNA: 16S ribosomal Ribonucleic
634 acid; PCR: Polymerase Chain Reaction; PERMANOVA: Permutational Analysis of Variance; FROGS:
635 Find, Rapidly, OTUs with Galaxy Solution; LAB: Lactic-Acid bacteria; Cq: quantification cycle.

636

637

638
639 **Declarations**

640 **Ethics approval and consent to participate.**

641 No submission to the bioethics committee has been made since the energy and nutritional needs of
642 the animals have been covered. Moreover, all the samples were taken post mortem.

643 **Consent for publication**

644 Not applicable

645 **Availability of data and material**

646 All sequence data are available at the NCBI sequence read archive under accession numbers
647 PRJNA827991, https://www.ncbi.nlm.nih.gov/bioproject/827991.

648 **Competing interests**

649 We declare no conflicts of interest

650 **Funding**

651 Funding was provided by the CD40 (Conseil Départemental des Landes) and the "Université de Pau et
652 Pays de l'Adour" (UPPA).

653 **Authors' contributions**

654 RD designed and performed the wet lab experiments and data analysis, and drafted the first version
655 of the paper with subsequent editing by coauthors. KR and SP designed, conceived and coordinated
656 the study. JL contributed to the microbiota analysis and made the first correction of the first version.
657 MG, MLB, and TP performed the SCFA analysis. FT formulated the diets and overlooked the
658 experiment. MM, VV, SB, AS collected samples and performed several lab manipulations. All authors
659 contributed to the review of the manuscript. All authors read and approved the final manuscript.
639 **Declarations**

640 **Ethics approval and consent to participate.**

641 No submission to the bioethics committee has been made since the energy and nutritional needs of

642 the animals have been covered. Moreover, all the samples were taken post mortem.

643 **Consent for publication**

644 Not applicable

645 **Availability of data and material**

646 All sequence data are available at the NCBI sequence read archive under accession numbers

647 PRJNA827991, https://www.ncbi.nlm.nih.gov/bioproject/827991.

648 **Competing interests**

649 We declare no conflicts of interest

650 **Funding**

651 Funding was provided by the CD40 (Conseil Départemental des Landes) and the "Université de Pau et

652 Pays de l'Adour "(UPPA).

653 **Authors' contributions**

654 RD designed and performed the wet lab experiments and data analysis, and drafted the first version

655 of the paper with subsequent editing by coauthors. KR and SP designed, conceived and coordinated

656 the study. JL contributed to the microbiota analysis and made the first correction of the first version.

657 MG, MLB, and TP performed the SCFA analysis. FT formulated the diets and overlooked the

658 experiment. MM, VV, SB, AS collected samples and performed several lab manipulations. All authors

659 contributed to the review of the manuscript. All authors read and approved the final manuscript.
660

661 Acknowledgements

662 We are grateful to the genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, doi:

663 10.15454/1.5572369328961167E12) and Sigenae group for providing help and/or computing and/or

664 storage ressources thanks to Galaxy instance http://sigenae-workbench.toulouse.inra.fr. We thank

665 Frédéric Terrier, Franck Sandres and Anthony Lanuque for the fish rearing in the Donzacq (France)

666 INRAE experimental fish farm. Figure 1 created with BioRender.com.

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661 Acknowledgements

662 We are grateful to the genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, doi:

663 10.15454/1.5572369328961167E12) and Sigenae group for providing help and/or computing and/or

664 storage ressources thanks to Galaxy instance http://sigenae-workbench.toulouse.inra.fr. We thank

665 Frédéric Terrier, Franck Sandres and Anthony Lanuque for the fish rearing in the Donzacq (France)

666 INRAE experimental fish farm. Figure 1 created with BioRender.com.

667

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681
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754 FAO, 2020. WORLD FISHERIES AND AQUACULTURE.

755 FAO, 2018. The future of food and agriculture – Alternative pathways to 2050.

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758 https://doi.org/10.1038/s42255-020-0188-7

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791 metabolism by dietary carbohydrates in two lines of rainbow trout divergently selected for

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779 Huang, Q., Sham, R.C., Deng, Y., Mao, Y., Wang, C., Zhang, T., Leung, K.M.Y., 2020. Diversity of gut

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782 Huang, X., Zhong, L., Kang, Q., Liu, S., Feng, Y., Geng, Y., Chen, D., Ou, Y., Yang, S., Yin, L., Luo, W.,

783 2021. A High Starch Diet Alters the Composition of the Intestinal Microbiota of Largemouth Bass

784 Micropterus salmoides, Which May Be Associated With the Development of Enteritis. Front.

785 Microbiol. 12, 1–11. https://doi.org/10.3389/fmicb.2021.696588

786 Ingerslev, H.C., von Gersdorff Jørgensen, L., Lenz Strube, M., Larsen, N., Dalsgaard, I., Boye, M.,

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788 *mykiss*) is affected by first feeding and diet type. Aquaculture 424–425, 24–34.

789 https://doi.org/10.1016/j.aquaculture.2013.12.032

790 Kamalam, B.S., Medale, F., Kaushik, S., Polakof, S., Skiba-Cassy, S., Panserat, S., 2012. Regulation of

791 metabolism by dietary carbohydrates in two lines of rainbow trout divergently selected for

792 muscle fat content. J. Exp. Biol. 215, 2567–2578. https://doi.org/10.1242/jeb.070581

793 Kamalam, B.S., Medale, F., Panserat, S., 2017. Utilisation of dietary carbohydrates in farmed fishes:

794 New insights on influencing factors, biological limitations and future strategies. Aquaculture

795 467, 3–27. https://doi.org/10.1016/j.aquaculture.2016.02.007

796 Khare, A., Gaur, S., 2020. Cholesterol-Lowering Effects of Lactobacillus Species. Curr. Microbiol. 77,

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814 Li, Y., Bruni, L., Jaramillo-Torres, A., Gajardo, K., Kortner, T.M., Krogdahl, Å., 2021. Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon. Anim. Microbiome 3. https://doi.org/10.1186/s42523-020-00071-3

818 Lian, C.A., Yan, G.Y., Huang, J.M., Danchin, A., Wang, Y., He, L.S., 2020. Genomic Characterization of a Novel Gut Symbiont From the Hadal Snailfish. Front. Microbiol. 10, 1–10.
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811 Legrand, T.P.R.A., Wos-Oxley, M.L., Wynne, J.W., Weyrich, L.S., Oxley, A.P.A., Thibault, C., Legrand, P.R.A., 2021. Dead or alive: microbial viability treatment reveals both active and inactive bacterial constituents in the fish gut microbiota. https://doi.org/10.1111/jam.15113

814 Li, Y., Bruni, L., Jaramillo-Torres, A., Gajardo, K., Kortner, T.M., Krogdahl, Å., 2021. Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon. Anim. Microbiome 3. https://doi.org/10.1186/s42523-020-00071-3

818 Lian, C.A., Yan, G.Y., Huang, J.M., Danchin, A., Wang, Y., He, L.S., 2020. Genomic Characterization of a Novel Gut Symbiont From the Hadal Snailfish. Front. Microbiol. 10, 1–10.
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821 Lin, S.M., Shi, C.M., Mu, M.M., Chen, Y.J., Luo, L., 2018. Effect of high dietary starch levels on growth, hepatic glucose metabolism, oxidative status and immune response of juvenile largemouth bass, Micropterus salmoides. Fish Shellfish Immunol. 78, 121–126.
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824 https://doi.org/10.1016/j.fsi.2018.04.046

825 Lokesh, J., Delaygues, M., Defaix, R., Le Bechec, M., Pigot, T., Dupont Nivet, M., Kerneis, T., Labbé, L.,

826 Goardon, L., Terrier, F., Panserat, S., Ricaud, K., 2023. Interaction between genetics and inulin

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830 K., 2022. Prebiotics modify host metabolism in rainbow trout (*Oncorhynchus mykiss*) fed with a

831 total plant-based diet: Potential implications for microbiome-mediated diet optimization.

832 Aquaculture 561, 738699. https://doi.org/10.1016/j.aquaculture.2022.738699

833 Louis, P., Flint, H.J., 2017. Formation of propionate and butyrate by the human colonic microbiota.

834 Environ. Microbiol. 19, 29–41. https://doi.org/10.1111/1462-2920.13589

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836 clustering method for amplicon-based studies. PeerJ 2014, 1–13.

837 https://doi.org/10.7717/peerj.593

838 Mansfield, G.S., Desai, A.R., Nilson, S.A., Van Kessel, A.G., Drew, M.D., Hill, J.E., 2010.

839 Characterization of rainbow trout (*Oncorhynchus mykiss*) intestinal microbiota and

840 inflammatory marker gene expression in a recirculating aquaculture system. Aquaculture 307,

841 95–104. https://doi.org/10.1016/j.aquaculture.2010.07.014

842 Marandel, L., Seiliez, I., Véron, V., Skiba-Cassy, S., Panserat, S., 2015. New insights into the nutritional

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844 duplication trail. Physiol. Genomics 47, 253–263.

845 https://doi.org/10.1152/physiolgenomics.00026.2015

846 Martínez Cruz, P., Ibáñez, A.L., Monroy Hermosillo, O.A., Ramírez Saad, H.C., 2012. Use of Probiotics

847 in Aquaculture. ISRN Microbiol. 2012, 1–13. https://doi.org/10.5402/2012/916845
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825 Lokesh, J., Delaygues, M., Defaix, R., Le Bechec, M., Pigot, T., Dupont Nivet, M., Kerneis, T., Labbé, L.,

826 Goardon, L., Terrier, F., Panserat, S., Ricaud, K., 2023. Interaction between genetics and inulin

827 affects host metabolism in rainbow trout fed a sustainable all plant-based diet. Br. J. Nutr. 1–40.

828 https://doi.org/10.1017/s0007114523000120

829 Lokesh, J., Ghislain, M., Reyrolle, M., Bechec, M. Le, Pigot, T., Terrier, F., Roy, J., Panserat, S., Ricaud,

830 K., 2022. Prebiotics modify host metabolism in rainbow trout (*Oncorhynchus mykiss*) fed with a

831 total plant-based diet: Potential implications for microbiome-mediated diet optimization.

832 Aquaculture 561, 738699. https://doi.org/10.1016/j.aquaculture.2022.738699

833 Louis, P., Flint, H.J., 2017. Formation of propionate and butyrate by the human colonic microbiota.

834 Environ. Microbiol. 19, 29–41. https://doi.org/10.1111/1462-2920.13589

835 Mahé, F., Rognes, T., Quince, C., de Vargas, C., Dunthorn, M., 2014. Swarm: Robust and fast

836 clustering method for amplicon-based studies. PeerJ 2014, 1–13.

837 https://doi.org/10.7717/peerj.593

838 Mansfield, G.S., Desai, A.R., Nilson, S.A., Van Kessel, A.G., Drew, M.D., Hill, J.E., 2010.

839 Characterization of rainbow trout (*Oncorhynchus mykiss*) intestinal microbiota and

840 inflammatory marker gene expression in a recirculating aquaculture system. Aquaculture 307,

841 95–104. https://doi.org/10.1016/j.aquaculture.2010.07.014

842 Marandel, L., Seiliez, I., Véron, V., Skiba-Cassy, S., Panserat, S., 2015. New insights into the nutritional

843 regulation of gluconeogenesis in carnivorous rainbow trout (*Oncorhynchus mykiss*): A gene

844 duplication trail. Physiol. Genomics 47, 253–263.

845 https://doi.org/10.1152/physiolgenomics.00026.2015

846 Martínez Cruz, P., Ibáñez, A.L., Monroy Hermosillo, O.A., Ramírez Saad, H.C., 2012. Use of Probiotics

847 in Aquaculture. ISRN Microbiol. 2012, 1–13. https://doi.org/10.5402/2012/916845
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849 Inhibition of hepatic gluconeogenesis and enhanced glucose uptake contribute to the

850 development of hypoglycemia in mice bearing interleukin-1beta- secreting tumor.

851 Endocrinology 145, 5150-5156. https://doi.org/10.1210/EN.2004-0323

852 Morrison, D.J., Preston, T., 2016. Formation of short chain fatty acids by the gut microbiota and their

853 impact on human metabolism. Gut Microbes 7, 189-200.

854 https://doi.org/10.1080/19490976.2015.1134082

855 Mukherjee, S., Huntemann, M., Ivanova, N., Kyrpides, N.C., Pati, A., 2015. Large-scale contamination

856 of microbial isolate genomes by illumina Phix control. Stand. Genomic Sci. 10, 1-4.

857 https://doi.org/10.1186/1944-3277-10-18

858 Nasopoulou, C., Zabetakis, I., 2012. LWT - Food Science and Technology Bene fits of fish oil

859 replacement by plant originated oils in compounded fish feeds. A review. LWT - Food Sci.

860 Technol. 47, 217-224. https://doi.org/10.1016/j.lwt.2012.01.018

861 Naya-Català, F., do Vale Pereira, G., Piazzon, M.C., Fernandes, A.M., Calduch-Giner, J.A., Sitjà-

862 Bobadilla, A., Conceição, L.E.C., Pérez-Sánchez, J., 2021. Cross-Talk Between Intestinal

863 Microbiota and Host Gene Expression in Gilthead Sea Bream (*Sparus aurata*) Juveniles: Insights

864 in Fish Feeds for Increased Circularity and Resource Utilization. Front. Physiol. 12, 1-20.

865 https://doi.org/10.3389/fphys.2021.748265

866 Nayak, S.K., 2010. Probiotics and immunity: A fish perspective. Fish Shellfish Immunol. 29, 2-14.

867 https://doi.org/10.1016/J.FSI.2010.02.017

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871 Panserat, S., Marandel, L., Seiliez, I., Skiba-Cassy, S., 2019. New Insights on Intermediary Metabolism
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849 Inhibition of hepatic gluconeogenesis and enhanced glucose uptake contribute to the

850 development of hypoglycemia in mice bearing interleukin-1beta- secreting tumor.

851 Endocrinology 145, 5150-5156. https://doi.org/10.1210/EN.2004-0323

852 Morrison, D.J., Preston, T., 2016. Formation of short chain fatty acids by the gut microbiota and their

853 impact on human metabolism. Gut Microbes 7, 189-200.

854 https://doi.org/10.1080/19490976.2015.1134082

855 Mukherjee, S., Huntemann, M., Ivanova, N., Kyrpides, N.C., Pati, A., 2015. Large-scale contamination

856 of microbial isolate genomes by illumina Phix control. Stand. Genomic Sci. 10, 1-4.

857 https://doi.org/10.1186/1944-3277-10-18

858 Nasopoulou, C., Zabetakis, I., 2012. LWT - Food Science and Technology Bene fits of fish oil

859 replacement by plant originated oils in compounded fish feeds. A review. LWT - Food Sci.

860 Technol. 47, 217-224. https://doi.org/10.1016/j.lwt.2012.01.018

861 Naya-Català, F., do Vale Pereira, G., Piazzon, M.C., Fernandes, A.M., Calduch-Giner, J.A., Sitjà-

862 Bobadilla, A., Conceição, L.E.C., Pérez-Sánchez, J., 2021. Cross-Talk Between Intestinal

863 Microbiota and Host Gene Expression in Gilthead Sea Bream (*Sparus aurata*) Juveniles: Insights

864 in Fish Feeds for Increased Circularity and Resource Utilization. Front. Physiol. 12, 1-20.

865 https://doi.org/10.3389/fphys.2021.748265

866 Nayak, S.K., 2010. Probiotics and immunity: A fish perspective. Fish Shellfish Immunol. 29, 2-14.

867 https://doi.org/10.1016/J.FSI.2010.02.017

868 Ortiz LT, Rebole A, V.S. et al., 2013. Effects of inulin and fructooligosaccharides on growth

869 performance, body chemical composition and intestinal microbiota of farmed rainbow trout

870 (*Oncorhynchus mykiss*). Aquacult Nutr.

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873 https://doi.org/10.1146/annurev-animal-020518-115250

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895 Polakof, S., Álvarez, R., Soengas, J.L., 2010. Gut glucose metabolism in rainbow trout: Implications in
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873 https://doi.org/10.1146/annurev-animal-020518-115250

874 Panserat, S., Médale, F., Blin, G., Brèque, J., Vachot, C., Plagnes-Juan, E., Gomes, E., Krishnamoorthy, R., Kaushik, S., 2000. Hepatic glucokinase is induced by dietary carbohydrates in rainbow trout, gilthead seabream, and common carp. Am. J. Physiol. - Regul. Integr. Comp. Physiol. 278, 1164–1170. https://doi.org/10.1152/ajpregu.2000.278.5.r1164

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881 Pardesi, B., Roberton, A.M., Lee, K.C., Angert, E.R., Rosendale, D.I., Boycheva, S., White, W.L., Clements, K.D., 2022. Distinct microbiota composition and fermentation products indicate functional compartmentalization in the hindgut of a marine herbivorous fish. Mol. Ecol. 1–16. https://doi.org/10.1111/mec.16394

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888 Pérez-Pascual, D., Pérez-Cobas, A.E., Rigaudeau, D., Rochat, T., Bernardet, J.-F., Skiba-Cassy, S., Marchand, Y., Duchaud, E., Ghigo, J.-M., 2021. Sustainable plant-based diets promote rainbow trout gut microbiota richness and do not alter resistance to bacterial infection. Anim. Microbiome 3. https://doi.org/10.1186/s42523-021-00107-2

892 Perry, W.B., Lindsay, E., Payne, C.J., Brodie, C., Kazlauskaite, R., 2020. The role of the gut microbiome in sustainable teleost aquaculture. Proc. R. Soc. B Biol. Sci. 287. https://doi.org/10.1098/rspb.2020.0184

895 Polakof, S., Álvarez, R., Soengas, J.L., 2010. Gut glucose metabolism in rainbow trout: Implications in
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909 Oncorhynchus mykiss 133, 257-274.

910 Salinas, I., Myklebust, R., Esteban, M.A., Olsen, R.E., Meseguer, J., Ringø, E., 2008. In vitro studies of

911 Lactobacillus delbrueckii subsp. lactis in Atlantic salmon (Salmo salar L.) foregut: Tissue

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917 https://doi.org/10.1371/journal.pone.0105548

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919 Tesseraud, S., 2008. An in vivo and in vitro assessment of TOR signaling cascade in rainbow
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899 Comp. Physiol. B Biochem. Syst. Environ. Physiol. 182, 1015-1045.

900 https://doi.org/10.1007/s00360-012-0658-7

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902 von G., Præbel, K., Martin, M.D., Bojesen, A.M., Gilbert, M.T.P., Kristiansen, K., Limborg, M.T.

903 2021. Genome-resolved metagenomics suggests a mutualistic relationship between

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905 02105-1

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907 replacement of fish meal by soybean protein on growth, protein utilization, potential

908 estrogenic or antigenic effects, cholesterolemia and flesh quality in rainbow trout,

909 Oncorhynchus mykiss 133, 257-274.

910 Salinas, I., Myklebust, R., Esteban, M.A., Olsen, R.E., Meseguer, J., Ringø, E., 2008. In vitro studies of

911 Lactobacillus delbrueckii subsp. lactis in Atlantic salmon (Salmo salar L.) foregut: Tissue

912 responses and evidence of protection against Aeromonas salmonicida subsp. salmonicida

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917 https://doi.org/10.1371/journal.pone.0105548

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929 https://doi.org/10.3389/fphys.2018.01579

930 Taschuk, R., Griebel, P.J., 2012. Commensal microbiome effects on mucosal immune system

931 development in the ruminant gastrointestinal tract. Anim. Health Res. Rev. 13, 129-141.

932 https://doi.org/10.1017/S1466252312000096

933 Udayappan, S.D., Kovatcheva-Datchary, P., Bakker, G.J., Havik, S.R., Herrema, H., Cani, P.D., Bouter,

934 K.E., Belzer, C., Witjes, J.J., Vrieze, A., De Sonnaville, N., Chaplin, A., Van Raalte, D.H., Aalvink, S.,

935 Dallinga-Thie, G.M., Heilig, H.G.H.J., Bergström, G., Van Der Meij, S., Van Wagensveld, B.A.,

936 Hoekstra, J.B.L., Holleman, F., Stroes, E.S.G., Groen, A.K., Bäckhed, F., De Vos, W.M.,

937 Nieuwdorp, M., 2017. Intestinal Ralstonia pickettii augments glucose intolerance in obesity.

938 PLoS One 12. https://doi.org/10.1371/journal.pone.0181693

939 van der Hee, B., Wells, J.M., 2021. Microbial Regulation of Host Physiology by Short-chain Fatty Acids.

940 Trends Microbiol. 29, 700-712. https://doi.org/10.1016/j.tim.2021.02.001

941 Véron, V., Panserat, S., Le Boucher, R., Labbé, L., Quillet, E., Dupont-Nivet, M., Médale, F., 2016.

942 Long-term feeding a plant-based diet devoid of marine ingredients strongly affects certain key

943 metabolic enzymes in the rainbow trout liver. Fish Physiol. Biochem. 42, 771-785.
944 https://doi.org/10.1007/s10695-015-0174-2

945 Villasante, A., Ramírez, C., Catalán, N., Opazo, R., Dantagnan, P., Romero, J., 2019. Effect of dietary

946 carbohydrate-to-protein ratio on gut microbiota in atlantic salmon (*Salmo salar*). Animals 9.

947 https://doi.org/10.3390/ani9030089

948 Villasante, A., Ramírez, C., Rodríguez, H., Dantagnan, P., Hernández, A., Figueroa, E., Romero, J.,

949 2022. Dietary carbohydrate-to-protein ratio influences growth performance, hepatic health and

950 dynamic of gut microbiota in atlantic salmon (*Salmo salar*). Anim. Nutr.

951 https://doi.org/10.1016/j.aninu.2022.04.003

952 Wang, A., Zhang, Z., Ding, Q., Yang, Y., Bindelle, J., 2021. Intestinal Cetobacterium and acetate modify

953 glucose homeostasis via parasympathetic activation in zebrafish. Gut Microbes 13, 1–15.

954 https://doi.org/10.1080/19490976.2021.1900996

955 Wang, T., Zhang, N., Yu, X.B., Qiao, F., Chen, L.Q., Du, Z.Y., Zhang, M.L., 2021. Inulin alleviates adverse

956 metabolic syndrome and regulates intestinal microbiota composition in Nile tilapia

957 (*Oreochromis niloticus*) fed with high-carbohydrate diet. Br. J. Nutr. 126, 161–171.

958 https://doi.org/10.1017/S000711452000402X

959 Xu, R., Li, M., Wang, T., Zhao, Y.W., Shan, C.J., Qiao, F., Chen, L.Q., Zhang, W.B., Du, Z.Y., Zhang, M.L.,

960 2022. Bacillus amyloliquefaciens ameliorates high-carbohydrate diet-induced metabolic

961 phenotypes by restoration of intestinal acetate-producing bacteria in Nile Tilapia. Br. J. Nutr.

962 127, 653–665. https://doi.org/10.1017/S0007114521001318

963 Yamada, K., Noguchi, T., 1999. Nutrient and hormonal regulation of pyruvate kinase gene expression.

964 Biochem. J. 337, 1–11. https://doi.org/10.1042/BJ3370001

965 Yang, C., Jiang, M., Lu, X., Wen, H., 2021. Effects of dietary protein level on the gut microbiome and

966 nutrient metabolism in tilapia (*Oreochromis niloticus*). Animals 11.

967 https://doi.org/10.3390/ani11041024
944 https://doi.org/10.1007/s10695-015-0174-2

945 Villasante, A., Ramírez, C., Catalán, N., Opazo, R., Dantagnan, P., Romero, J., 2019. Effect of dietary
946 carbohydrate-to-protein ratio on gut microbiota in atlantic salmon (*Salmo salar*). Animals 9.

947 https://doi.org/10.3390/ani9030089

948 Villasante, A., Ramírez, C., Rodríguez, H., Dantagnan, P., Hernández, A., Figueroa, E., Romero, J.,
949 2022. Dietary carbohydrate-to-protein ratio influences growth performance, hepatic health and
dynamic of gut microbiota in atlantic salmon (*Salmo salar*). Anim. Nutr.

951 https://doi.org/10.1016/j.aninu.2022.04.003

952 Wang, A., Zhang, Z., Ding, Q., Yang, Y., Bindelle, J., 2021. Intestinal Cetobacterium and acetate modify
953 glucose homeostasis via parasympathetic activation in zebrafish. Gut Microbes 13, 1–15.

954 https://doi.org/10.1080/19490976.2021.1900996

955 Wang, T., Zhang, N., Yu, X.B., Qiao, F., Chen, L.Q., Du, Z.Y., Zhang, M.L., 2021. Inulin alleviates adverse
956 metabolic syndrome and regulates intestinal microbiota composition in Nile tilapia
(Oreochromis niloticus) fed with high-carbohydrate diet. Br. J. Nutr. 126, 161–171.

958 https://doi.org/10.1017/S000711452000402X

959 Xu, R., Li, M., Wang, T., Zhao, Y.W., Shan, C.J., Qiao, F., Chen, L.Q., Zhang, W.B., Du, Z.Y., Zhang, M.L.,
960 2022. Bacillus amyloliquefaciens ameliorates high-carbohydrate diet-induced metabolic
phenotypes by restoration of intestinal acetate-producing bacteria in Nile Tilapia. Br. J. Nutr.
961 127, 653–665. https://doi.org/10.1017/S0007114521001318

963 Yamada, K., Noguchi, T., 1999. Nutrient and hormonal regulation of pyruvate kinase gene expression.
964 Biochem. J. 337, 1–11. https://doi.org/10.1042/BJ3370001

965 Yang, C., Jiang, M., Lu, X., Wen, H., 2021. Effects of dietary protein level on the gut microbiome and
966 nutrient metabolism in tilapia (*Oreochromis niloticus*). Animals 11.

967 https://doi.org/10.3390/ani11041024
968 Zarkasi, K.Z., Abell, G.C.J., Taylor, R.S., Neuman, C., Hatje, E., Tamplin, M.L., Katouli, M., Bowman, J.P.,
969 2014. Pyrosequencing-based characterization of gastrointestinal bacteria of Atlantic salmon
970 (*Salmo salar* L.) within a commercial mariculture system. J. Appl. Microbiol. 117, 18–27.
971 https://doi.org/10.1111/jam.12514

972 Zhang, Y., Liang, X.F., He, S., Chen, X., Wang, J., Li, J., Zhu, Q., Zhang, Z., Li, L., Alam, M.S., 2020.
973 Effects of High Carbohydrate Diet-Modulated Microbiota on Gut Health in Chinese Perch. Front.
974 Microbiol. 11, 1–17. https://doi.org/10.3389/fmicb.2020.575102

975 Zhou, Y.L., He, G.L., Jin, T., Chen, Y.J., Dai, F.Y., Luo, L., Lin, S.M., 2021. High dietary starch impairs
976 intestinal health and microbiota of largemouth bass, Micropterus salmoides. Aquaculture 534,
977 736261. https://doi.org/10.1016/j.aquaculture.2020.736261

978 Zhu, X., Zhou, Y., Wang, Y., Wu, T., Li, X., Li, D., Tao, Y., 2017. Production of high-concentration n-
979 caproic acid from lactate through fermentation using a newly isolated Ruminococcaceae
980 bacterium CPB6. Biotechnol. Biofuels 10, 1–12. https://doi.org/10.1186/s13068-017-0788-y

981

982

983 **Figure Legends:**

984

985 **Figure 1:** Experimental design. Two experimental diets containing a 100% plant-based diet with 20%
986 of digestible starch (high-starch diet) or without digestible starch (low-starch diet) were produced as
987 extruded pellets. The experimental diets were given manually, twice a day, during 12 weeks to 162
988 females rainbow trout (50 – 70g) distributed in 6 tanks. The trout tanks were weight every 3 weeks for
989 zootechnical parameters (n=3). At the end of the rearing trial, 4 fish per tanks (n=12) were randomly
990 sampled for microbiota and metabolism studies. Additional trout were recovered for whole-body
991 composition.
968 Zarkasi, K.Z., Abell, G.C.J., Taylor, R.S., Neuman, C., Hatje, E., Tamplin, M.L., Katouli, M., Bowman, J.P.,
969 2014. Pyrosequencing-based characterization of gastrointestinal bacteria of Atlantic salmon
970 (*Salmo salar* L.) within a commercial mariculture system. J. Appl. Microbiol. 117, 18–27.
971 https://doi.org/10.1111/jam.12514

972 Zhang, Y., Liang, X.F., He, S., Chen, X., Wang, J., Li, J., Zhu, Q., Zhang, Z., Li, L., Alam, M.S., 2020.
973 Effects of High Carbohydrate Diet-Modulated Microbiota on Gut Health in Chinese Perch. Front.
974 Microbiol. 11, 1–17. https://doi.org/10.3389/fmicb.2020.575102

975 Zhou, Y.L., He, G.L., Jin, T., Chen, Y.J., Dai, F.Y., Luo, L., Lin, S.M., 2021. High dietary starch impairs
976 intestinal health and microbiota of largemouth bass, Micropterus salmoides. Aquaculture 534,
977 736261. https://doi.org/10.1016/j.aquaculture.2020.736261

978 Zhu, X., Zhou, Y., Wang, Y., Wu, T., Li, X., Li, D., Tao, Y., 2017. Production of high-concentration n-
979 caproic acid from lactate through fermentation using a newly isolated Ruminococcaceae
980 bacterium CPB6. Biotechnol. Biofuels 10, 1–12. https://doi.org/10.1186/s13068-017-0788-y

981

982

983 **Figure Legends:**

984

985 **Figure 1:** Experimental design. Two experimental diets containing a 100% plant-based diet with 20%
986 of digestible starch (high-starch diet) or without digestible starch (low-starch diet) were produced as
987 extruded pellets. The experimental diets were given manually, twice a day, during 12 weeks to 162
988 females rainbow trout (50 – 70g) distributed in 6 tanks. The trout tanks were weight every 3 weeks for
989 zootechnical parameters (n=3). At the end of the rearing trial, 4 fish per tanks (n=12) were randomly
990 sampled for microbiota and metabolism studies. Additional trout were recovered for whole-body
991 composition.
992

**Figure 2:** Bacterial alpha diversity represented in terms of observed OTUs, Chao1, Shannon Simpson, InvSimpson, in digesta (A) and mucosa (B) according to the experimental diversity. Alpha diversity between diet groups was compared using one-way ANOVA and p<0.05 was considered significant. Beta diversity is presented by a nMDS representation (Bray-Curtis distance, Weighted-Unifra analysis) in digesta (C) and in mucosa (D). Beta diversity was compared using pairwise PERMANOVA and p<0.05 was considered significant and indicated with asterisk. n=12

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995

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997

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999

**Figure 3:** Venn diagram showing compartmental core microbiota OTUs distribution (A) in digesta, mucosa of the fish fed with high-starch (HS) or low-starch (LS) diet. Heatmap of the core microbiota with the 12 most abundant OTUs among all groups (B).

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1003

**Figure 4:** Microbial composition of the experimental groups in mucosa (A, D, G), and digesta (B, E, H) at phylum, family, and genus level. In panel D, E, G and H only the 10, and 11 most abundant Family and genus, respectively, were presented. Means are presented as black points and SD as horizontal error lines. Only some significant differences were showed on panels C, F, I. Significative differences were measured with one-way ANOVA (P<0.05), represented by asterisk. P<0.05 *, P<0.01 **, P<0,001 ***. n=12

1004

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1010

**Figure 5:** PLS-DA analysis on high-starch and low-starch groups based on OTU abundance (A). Each red points or blue triangles represent a fish. Fish can be discriminated according to experimental group on component 1. Contribution level of the top 30 OTUs are presented (B). The bar length represents the importance of the variable in the multivariable model. Red bars correspond to the high-starch group and blue bars to the low-starch group. Significative differences for each OTUs were calculated with one-way ANOVA. P<0.05 *, P<0.01 **, P<0,001 ***. n=12

1011

1012

1013

1014

1015
992

**Figure 2:** Bacterial alpha diversity represented in terms of observed OTUs, Chao1, Shannon Simpson, InvSimpson, in digesta (A) and mucosa (B) according to the experimental diversity. Alpha diversity between diet groups was compared using one-way ANOVA and p<0.05 was considered significant. Beta diversity is presented by a nMDS representation (Bray-Curtis distance, Weighted-Unifra analysis) in digesta (C) and in mucosa (D). Beta diversity was compared using pairwise PERMANOVA and p<0.05 was considered significant and indicated with asterisk. n=12

993

994

995

996

997

998

999

**Figure 3:** Venn diagram showing compartmental core microbiota OTUs distribution (A) in digesta, mucosa of the fish fed with high-starch (HS) or low-starch (LS) diet. Heatmap of the core microbiota with the 12 most abundant OTUs among all groups (B).

1000

1001

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1003

**Figure 4:** Microbial composition of the experimental groups in mucosa (A, D, G), and digesta (B, E, H) at phylum, family, and genus level. In panel D, E, G and H only the 10, and 11 most abundant Family and genus, respectively, were presented. Means are presented as black points and SD as horizontal error lines. Only some significant differences were showed on panels C, F, I. Significative differences were measured with one-way ANOVA (P<0.05), represented by asterisk. P<0.05 *, P<0.01 **, P<0,001 ***. n=12

1004

1005

1006

1007

1008

1009

1010

**Figure 5:** PLS-DA analysis on high-starch and low-starch groups based on OTU abundance (A). Each red points or blue triangles represent a fish. Fish can be discriminated according to experimental group on component 1. Contribution level of the top 30 OTUs are presented (B). The bar length represents the importance of the variable in the multivariable model. Red bars correspond to the high-starch group and blue bars to the low-starch group. Significative differences for each OTUs were calculated with one-way ANOVA. P<0.05 *, P<0.01 **, P<0,001 ***. n=12

1011

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1015
1016

1017 **Figure 6:** SCFA levels of Acetic acid, Butyric acid, Propionic acid, and Valeric Acid in the digesta of different diets groups (A) were measured in function of the mass of the digestive tract (in gram) with SIFT-MS mass spectrometry. A PLS-DA based on the product ions on the full scan spectrum were made (B) using the H3O+, NO+, and O2+ precursor ions. Each red points or blue triangles represent a fish. The top 15 ions discriminated in SIFT-MS were presented (C). The bar length represents the importance of variation contributed by each ions (variable). Red bars correspond to the high-starch group and blue bars to the low-starch group. The proportion of ion H3O+ 45 and H3O+ 81 corresponding to the lactate fragmentation are presented (D). All significative differences (P<0.05) were calculated according to a Wilcoxon non-parametric test. P<0.05 *, P<0.01 **, P<0.001 ***. n=5

1026

1027 **Figure 7:** The enzymatic activities of Glucokinase (A), Pyruvate kinase (B), Glucose-6-phosphatase (C), and fatty-acid synthase (D) were measured in liver samples in function of the average of milligram of proteins. Significative differences were calculated with one-way ANOVA. P<0.05 *, P<0.01 **, P<0.001 ***. n=12

1031

1032 **Figure 8:** Correlations between OTU abundances and zootechnical, liver, plasma parameters, and enzymatic activities (A) or with several liver gene expressions (B), presented as heatmaps, were calculated using regularized canonical correlation analysis (rCCA). The red stars correspond to the parameters significantly affected by the diets (P<0.05). n=12

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1016

1017 **Figure 6:** SCFA levels of Acetic acid, Butyric acid, Propionic acid, and Valeric Acid in the digesta of different diets groups (A) were measured in function of the mass of the digestive tract (in gram) with SIFT-MS mass spectrometry. A PLS-DA based on the product ions on the full scan spectrum were made (B) using the H3O+, NO+, and O2+ precursor ions. Each red points or blue triangles represent a fish.

1021 The top 15 ions discriminated in SIFT-MS were presented (C). The bar length represents the importance of variation contributed by each ions (variable). Red bars correspond to the high-starch group and blue bars to the low-starch group. The proportion of ion H3O+ 45 and H3O+ 81 corresponding to the lactate fragmentation are presented (D). All significative differences (P<0.05) were calculated according to a Wilcoxon non-parametric test. P<0.05 *, P<0.01 **, P<0.001 ***. n=5

1026

1027 **Figure 7:** The enzymatic activities of Glucokinase (A), Pyruvate kinase (B), Glucose-6-phosphatase (C), and fatty-acid synthase (D) were measured in liver samples in function of the average of milligram of proteins. Significative differences were calculated with one-way ANOVA. P<0.05 *, P<0.01 **, P<0.001 ***. n=12

1031

1032 **Figure 8:** Correlations between OTU abundances and zootechnical, liver, plasma parameters, and enzymatic activities (A) or with several liver gene expressions (B), presented as heatmaps, were calculated using regularized canonical correlation analysis (rCCA). The red stars correspond to the parameters significantly affected by the diets (P<0.05). n=12

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1039
1040 Tables:

1041 Table 1: Formulation and proximate composition of two experimental diets.

<table><thead><tr><th></th><th>High-Starch</th><th>Low-Starch</th></tr></thead><tbody><tr><td><strong>Ingredients (%)</strong></td><td></td><td></td></tr><tr><td>Wheat gluten</td><td>20</td><td>15</td></tr><tr><td>Lysamine pea protein concentrate</td><td>20</td><td>16</td></tr><tr><td>Corn gluten</td><td>5.44</td><td>7.24</td></tr><tr><td>Whole wheat</td><td>7.09</td><td>-</td></tr><tr><td>Faba Protein Concentrate</td><td>5</td><td>-</td></tr><tr><td>Pregelatinized wheat starch</td><td>15.02</td><td>-</td></tr><tr><td>Lupin flour</td><td>-</td><td>4.53</td></tr><tr><td>Soybean meal</td><td>-</td><td>15</td></tr><tr><td>Soybean protein concentrate</td><td>-</td><td>12</td></tr><tr><td>Rapeseed oil</td><td>12</td><td>10.74</td></tr><tr><td>Linseed oil</td><td>5.29</td><td>6</td></tr><tr><td>Palm oil</td><td>2</td><td>2</td></tr><tr><td>Dicalcium Phosphate</td><td>2</td><td>2</td></tr><tr><td>L-Lysine</td><td>0.5</td><td>1</td></tr><tr><td>L-Methionine</td><td>0.5</td><td>1</td></tr><tr><td>Soy Lecithin powder</td><td>2.5</td><td>2.5</td></tr><tr><td>Premix Vitamins</td><td>1.16</td><td>1.5</td></tr><tr><td>Premix minerals</td><td>1.5</td><td>1.5</td></tr><tr><td>Cellulose</td><td>-</td><td>2</td></tr><tr><td><strong>Proximate composition (% DM)</strong></td><td></td><td></td></tr><tr><td>Dry Mass (DM)</td><td>97.20</td><td>96.90</td></tr><tr><td>Starch (% DM)</td><td>19.73</td><td>3.11</td></tr><tr><td>Proteins (% DM)</td><td>42.23</td><td>51.39</td></tr><tr><td>Lipids (% DM)</td><td>23.25</td><td>23.88</td></tr><tr><td>Energy (KJ g-1 DM)</td><td>24.54</td><td>25.27</td></tr><tr><td>Ash (% DM)</td><td>5.12</td><td>6.05</td></tr></tbody></table>

1042

1043

1044

1045

1046
1040 Tables:

1041 Table 1: Formulation and proximate composition of two experimental diets.

<table><thead><tr><th></th><th>High-Starch</th><th>Low-Starch</th></tr></thead><tbody><tr><td><strong>Ingredients (%)</strong></td><td></td><td></td></tr><tr><td>Wheat gluten</td><td>20</td><td>15</td></tr><tr><td>Lysamine pea protein concentrate</td><td>20</td><td>16</td></tr><tr><td>Corn gluten</td><td>5.44</td><td>7.24</td></tr><tr><td>Whole wheat</td><td>7.09</td><td>-</td></tr><tr><td>Faba Protein Concentrate</td><td>5</td><td>-</td></tr><tr><td>Pregelatinized wheat starch</td><td>15.02</td><td>-</td></tr><tr><td>Lupin flour</td><td>-</td><td>4.53</td></tr><tr><td>Soybean meal</td><td>-</td><td>15</td></tr><tr><td>Soybean protein concentrate</td><td>-</td><td>12</td></tr><tr><td>Rapeseed oil</td><td>12</td><td>10.74</td></tr><tr><td>Linseed oil</td><td>5.29</td><td>6</td></tr><tr><td>Palm oil</td><td>2</td><td>2</td></tr><tr><td>Dicalcium Phosphate</td><td>2</td><td>2</td></tr><tr><td>L-Lysine</td><td>0.5</td><td>1</td></tr><tr><td>L-Methionine</td><td>0.5</td><td>1</td></tr><tr><td>Soy Lecithin powder</td><td>2.5</td><td>2.5</td></tr><tr><td>Premix Vitamins</td><td>1.16</td><td>1.5</td></tr><tr><td>Premix minerals</td><td>1.5</td><td>1.5</td></tr><tr><td>Cellulose</td><td>-</td><td>2</td></tr><tr><td><strong>Proximate composition (% DM)</strong></td><td></td><td></td></tr><tr><td>Dry Mass (DM)</td><td>97.20</td><td>96.90</td></tr><tr><td>Starch (% DM)</td><td>19.73</td><td>3.11</td></tr><tr><td>Proteins (% DM)</td><td>42.23</td><td>51.39</td></tr><tr><td>Lipids (% DM)</td><td>23.25</td><td>23.88</td></tr><tr><td>Energy (KJ g-1 DM)</td><td>24.54</td><td>25.27</td></tr><tr><td>Ash (% DM)</td><td>5.12</td><td>6.05</td></tr></tbody></table>

1042

1043

1044

1045

1046
1047 Table 2: Growth performance, and feed utilization, in rainbow trout fed with high-

1048 starch diet and low-starch diet

<table><thead><tr><td>Zootechnical parameters</td><td>High-Starch</td><td>Low-Starch</td><td>P value</td></tr></thead><tbody><tr><td>Initial body weight (g)</td><td>53.33</td><td>53.33</td><td>-</td></tr><tr><td>Final body weight (g)</td><td>237.80 ± 20.14</td><td>261.90 ± 9.80</td><td>NS</td></tr><tr><td>Daily growth index<sup>1</sup></td><td>2.89 ± 0.17</td><td>3.13 ± 0.07</td><td>NS</td></tr><tr><td>Specific growth rate<sup>2</sup> (%/day)</td><td>1.78 ± 0.07</td><td>1.89 ± 0.03</td><td>NS</td></tr><tr><td>Daily feed intake<sup>3</sup> (gram/fish/day)</td><td>2.16 ± 0.18</td><td>2.38 ± 0.11</td><td>NS</td></tr><tr><td>Feed efficiency<sup>4</sup></td><td>1.02 ± 0.02</td><td>1.04 ± 0.01</td><td>NS</td></tr><tr><td>Protein efficiency ratio<sup>5</sup></td><td>2.61 ± 0.05</td><td>2.21 ± 0.02</td><td>***</td></tr></tbody></table>

1049 Data are presented as mean ± SD (n=3 tanks). Statistical differences were calculated by one-way ANOVA (P<0.05). NS not significant. Daily growth index¹ = 100x (Mean final weight ¹/³ g - Mean initial weight¹/³ g)/Experiment duration (days). Specific growth rate (%/day) = 100x [Ln (final body weight. g)-Ln (initial body weight. g)]/ Experiment duration (days). Daily feed intake³ (gram/fish/day) = (Total feed consumed. g/Number of fish/Experiment duration (days)). Feed efficiency⁴ = (Final biomass. g - Initial biomass. g)/Total feed consumed (g DM). Protein efficiency ratio⁵ = (Final biomass - Initial biomass)/ Total protein consumed (g).

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1062
1047 Table 2: Growth performance, and feed utilization, in rainbow trout fed with high-

1048 starch diet and low-starch diet

<table><thead><tr><th>Zootechnical parameters</th><th>High-Starch</th><th>Low-Starch</th><th>P value</th></tr></thead><tbody><tr><td>Initial body weight (g)</td><td>53.33</td><td>53.33</td><td>-</td></tr><tr><td>Final body weight (g)</td><td>237.80 ± 20.14</td><td>261.90 ± 9.80</td><td>NS</td></tr><tr><td>Daily growth index<sup>1</sup></td><td>2.89 ± 0.17</td><td>3.13 ± 0.07</td><td>NS</td></tr><tr><td>Specific growth rate<sup>2</sup> (%/day)</td><td>1.78 ± 0.07</td><td>1.89 ± 0.03</td><td>NS</td></tr><tr><td>Daily feed intake<sup>3</sup> (gram/fish/day)</td><td>2.16 ± 0.18</td><td>2.38 ± 0.11</td><td>NS</td></tr><tr><td>Feed efficiency<sup>4</sup></td><td>1.02 ± 0.02</td><td>1.04 ± 0.01</td><td>NS</td></tr><tr><td>Protein efficiency ratio<sup>5</sup></td><td>2.61 ± 0.05</td><td>2.21 ± 0.02</td><td>***</td></tr></tbody></table>

1049 Data are presented as mean ± SD (n=3 tanks). Statistical differences were calculated by one-way ANOVA (P<0.05). NS not significant. Daily growth index¹ = 100x (Mean final weight ¹/³ g - Mean initial weight ¹/³ g)/Experiment duration (days). Specific growth rate (%/day) = 100x [Ln (final body weight. g)-Ln (initial body weight. g)]/ Experiment duration (days). Daily feed intake³ (gram/fish/day) = (Total feed consumed. g/Number of fish/Experiment duration (days)). Feed efficiency⁴ = (Final biomass. g - Initial biomass. g)/Total feed consumed (g DM). Protein efficiency ratio⁵ = (Final biomass - Initial biomass)/ Total protein consumed (g).

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1055
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1062
1063 Table 3: Hepatic and plasmatic parameters in rainbow trout fed with the high or low-starch

1064 diet

1065

<table><thead><tr><th></th><th>High-Starch</th><th>Low-Starch</th><th>P value</th></tr></thead><tbody><tr><td><strong>Hepatic parameters</strong></td><td></td><td></td><td></td></tr><tr><td>Hepatosomatic index (%)</td><td>1.28 ± 0.13</td><td>1.09 ± 0.14</td><td>**</td></tr><tr><td>Glycogen (mg/g)</td><td>32.65 ± 3.99</td><td>21.77 ± 1.86</td><td>***</td></tr><tr><td>Cholesterol (g/L)</td><td>5.30 ± 0.48</td><td>5.72 ± 0.36</td><td>*</td></tr><tr><td>Free-Glucose (mg/g)</td><td>0.45 ± 0.04</td><td>0.42 ± 0.03</td><td>NS</td></tr><tr><td><strong>Plasmatic parameters</strong></td><td></td><td></td><td></td></tr><tr><td>Glucose (g/L)</td><td>1.11 ± 0.40</td><td>1.01 ± 0.38</td><td>NS</td></tr><tr><td>Triglycerides (g/L)</td><td>3.49 ± 1.04</td><td>3.17 ± 0.80</td><td>NS</td></tr><tr><td>Lactate (g/L)</td><td>3.59 ± 0.93</td><td>3.37 ± 0.89</td><td>NS</td></tr><tr><td>Cholesterol (g/L)</td><td>1.87 ± 0.36</td><td>1.84 ± 0.44</td><td>NS</td></tr></tbody></table>

1066

1067

1068 Data are presented as mean ± SD (n=12 fish). statistical differences are calculated by one-ways ANOVA

1069 (P<0.05). NS not significant. P<0.05 *. P<0.01 **. P<0.001 ***.

1070

1071

1072

1073

1074

1075

1076

1077

1078
1063 Table 3: Hepatic and plasmatic parameters in rainbow trout fed with the high or low-starch

1064 diet

1065

<table><thead><tr><th></th><th>High-Starch</th><th>Low-Starch</th><th>P value</th></tr></thead><tbody><tr><td><strong>Hepatic parameters</strong></td><td></td><td></td><td></td></tr><tr><td>Hepatosomatic index (%)</td><td>1.28 ± 0.13</td><td>1.09 ± 0.14</td><td>**</td></tr><tr><td>Glycogen (mg/g)</td><td>32.65 ± 3.99</td><td>21.77 ± 1.86</td><td>***</td></tr><tr><td>Cholesterol (g/L)</td><td>5.30 ± 0.48</td><td>5.72 ± 0.36</td><td>*</td></tr><tr><td>Free-Glucose (mg/g)</td><td>0.45 ± 0.04</td><td>0.42 ± 0.03</td><td>NS</td></tr><tr><td><strong>Plasmatic parameters</strong></td><td></td><td></td><td></td></tr><tr><td>Glucose (g/L)</td><td>1.11 ± 0.40</td><td>1.01 ± 0.38</td><td>NS</td></tr><tr><td>Triglycerides (g/L)</td><td>3.49 ± 1.04</td><td>3.17 ± 0.80</td><td>NS</td></tr><tr><td>Lactate (g/L)</td><td>3.59 ± 0.93</td><td>3.37 ± 0.89</td><td>NS</td></tr><tr><td>Cholesterol (g/L)</td><td>1.87 ± 0.36</td><td>1.84 ± 0.44</td><td>NS</td></tr></tbody></table>

1066

1067

1068 Data are presented as mean ± SD (n=12 fish). statistical differences are calculated by one-ways ANOVA

1069 (P<0.05). NS not significant. P<0.05 *. P<0.01 **. P<0.001 ***.

1070

1071

1072

1073

1074

1075

1076

1077

1078
<caption>Table 4: Genera abundances significantly affected by diets</caption>

1080

<table><thead><tr><th>Location</th><th>Phylum</th><th>Family</th><th>Genus</th><th>High-starch</th><th>Low-starch</th><th>P value</th></tr></thead><tbody><tr><td>Digesta</td><td>Proteobacteria</td><td>Burkholderiaceae</td><td>Ralstonia</td><td>0.74 ± 0.06</td><td>0.64 ± 0.12</td><td>0.019 *</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Bacilliaceae</td><td>Bacillus</td><td>0.0047 ± 0.0085</td><td>0.033 ± 0.014</td><td>4.45e-06 ***</td></tr><tr><td>Digesta</td><td>Actinobacteria</td><td>Propionibacteriaceae</td><td>Cutibacterium</td><td>0.037 ± 0.025</td><td>0.020 ± 0.0083</td><td>0.042 *</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Lactobacillaceae</td><td>Ligilactobacillus</td><td>0.014 ± 0.0075</td><td>0.025 ± 0.016</td><td>0.035 *</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Lactobacillaceae</td><td>Weissella</td><td>0.017 ± 0.0080</td><td>0.0075 ± 0.0043</td><td>0.0014 **</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Bacillaceae</td><td>Anaerobacillus</td><td>0.0045 ± 0.0027</td><td>0.0026 ± 0.0014</td><td>0.00019 ***</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Lactobacillaceae</td><td>Limosilactobacillus</td><td>0.0044 ± 0.0033</td><td>0.0017 ± 0.0011</td><td>0.012 *</td></tr><tr><td>Digesta</td><td>Proteobacteria</td><td>Coxiellaceae</td><td>Rickettsiella</td><td>9.7E-06 ± 2.2E-05</td><td>0.0049 ± 0.0044</td><td>0.00076 ***</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Peptoniphilaceae</td><td>Peptoniphilus</td><td>0.00052 ± 0.0010</td><td>0.0022 ± 0.0016</td><td>0.0052 **</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Streptococcaceae</td><td>Lactococcus</td><td>0.00048 ± 0.00053</td><td>0.0013 ± 0.00093</td><td>0.012 *</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Streptococcaceae</td><td>Floricoccus</td><td>0 ± 0</td><td>0.0013 ± 0.00099</td><td>0.00015 ***</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Bacillaceae</td><td>Anoxybacillus</td><td>0 ± 0</td><td>0.00080 ± 0.00060</td><td>0.040 *</td></tr><tr><td>Mucosa</td><td>Firmicutes</td><td>Bacilliaceae</td><td>Bacillus</td><td>0.0049 ± 0.014</td><td>0.018 ± 0.02</td><td>0.041 *</td></tr><tr><td>Mucosa</td><td>Firmicutes</td><td>Peptoniphilaceae</td><td>Peptoniphilus</td><td>0.00011 ± 0.00032</td><td>0.0017 ± 0.0014</td><td>0.00078 ***</td></tr><tr><td>Mucosa</td><td>Firmicutes</td><td>Clostridiaceae</td><td>Clostridium s.s. 1</td><td>0 ± 0</td><td>0.00020 ± 0.00021</td><td>0.00321 **</td></tr></tbody></table>

1081

1082 Data are presented as mean ± SD (n=12 fish). statistical differences were calculated by one-ways

1083 ANOVA (P<0.05). NS not significant. P<0.05 *. P<0.01 **. P<0.001 ***.

1084

1085

1086

1087
<table><caption>Table 4: Genera abundances significantly affected by diets</caption><thead><tr><th>Location</th><th>Phylum</th><th>Family</th><th>Genus</th><th>High-starch</th><th>Low-starch</th><th>P value</th></tr></thead><tbody><tr><td>Digesta</td><td>Proteobacteria</td><td>Burkholderiaceae</td><td>Ralstonia</td><td>0.74 ± 0.06</td><td>0.64 ± 0.12</td><td>0.019 *</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Bacilliaceae</td><td>Bacillus</td><td>0.0047 ± 0.0085</td><td>0.033 ± 0.014</td><td>4.45e-06 ***</td></tr><tr><td>Digesta</td><td>Actinobacteria</td><td>Propionibacteriaceae</td><td>Cutibacterium</td><td>0.037 ± 0.025</td><td>0.020 ± 0.0083</td><td>0.042 *</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Lactobacillaceae</td><td>Ligilactobacillus</td><td>0.014 ± 0.0075</td><td>0.025 ± 0.016</td><td>0.035 *</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Lactobacillaceae</td><td>Weissella</td><td>0.017 ± 0.0080</td><td>0.0075 ± 0.0043</td><td>0.0014 **</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Bacillaceae</td><td>Anaerobacillus</td><td>0.0045 ± 0.0027</td><td>0.0026 ± 0.0014</td><td>0.00019 ***</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Lactobacillaceae</td><td>Limosilactobacillus</td><td>0.0044 ± 0.0033</td><td>0.0017 ± 0.0011</td><td>0.012 *</td></tr><tr><td>Digesta</td><td>Proteobacteria</td><td>Coxiellaceae</td><td>Rickettsiella</td><td>9.7E-06 ± 2.2E-05</td><td>0.0049 ± 0.0044</td><td>0.00076 ***</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Peptoniphilaceae</td><td>Peptoniphilus</td><td>0.00052 ± 0.0010</td><td>0.0022 ± 0.0016</td><td>0.0052 **</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Streptococcaceae</td><td>Lactococcus</td><td>0.00048 ± 0.00053</td><td>0.0013 ± 0.00093</td><td>0.012 *</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Streptococcaceae</td><td>Floricoccus</td><td>0 ± 0</td><td>0.0013 ± 0.00099</td><td>0.00015 ***</td></tr><tr><td>Digesta</td><td>Firmicutes</td><td>Bacillaceae</td><td>Anoxybacillus</td><td>0 ± 0</td><td>0.00080 ± 0.00060</td><td>0.040 *</td></tr><tr><td>Mucosa</td><td>Firmicutes</td><td>Bacilliaceae</td><td>Bacillus</td><td>0.0049 ± 0.014</td><td>0.018 ± 0.02</td><td>0.041 *</td></tr><tr><td>Mucosa</td><td>Firmicutes</td><td>Peptoniphilaceae</td><td>Peptoniphilus</td><td>0.00011 ± 0.00032</td><td>0.0017 ± 0.0014</td><td>0.00078 ***</td></tr><tr><td>Mucosa</td><td>Firmicutes</td><td>Clostridiaceae</td><td>Clostridium s.s. 1</td><td>0 ± 0</td><td>0.00020 ± 0.00021</td><td>0.00321 **</td></tr></tbody></table>

1080

1081

1082 Data are presented as mean ± SD (n=12 fish). statistical differences were calculated by one-ways

1083 ANOVA (P<0.05). NS not significant. P<0.05 *. P<0.01 **. P<0.001 ***.

1084

1085

1086

1087