# Industrial diet intervention modulates the interplay between gut microbiota and host in semi-stray dogs

Soufien Rhimi, Amin Jablaoui, Juan Hernandez, Vincent Mariaule, Nizar Akermi, Tristan Méric, Héla Mkaouar, Magdalena Wysocka, Adam Lesner, Mohamed Ali Borgi, et al.

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## To cite this version:

Soufien Rhimi, Amin Jablaoui, Juan Hernandez, Vincent Mariaule, Nizar Akermi, et al. Industrial diet intervention modulates the interplay between gut microbiota and host in semi-stray dogs. Animal Microbiome, 2024, 6 (69), 10.1186/s42523-024-00357-w. http://hal-04804167

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# Industrial diet intervention modulates the interplay between gut microbiota and host in semi-stray dogs

Soufien Rhimi<fnref n="1" />,<fnref n="4" />, Amin Jablaoui<fnref n="1" />, Juan Hernandez<fnref n="1" />,<fnref n="2" />, Vincent Mariaule<fnref n="1" />, Nizar Akermi<fnref n="1" />, Tristan Méric<fnref n="1" />,<fnref n="2" />, Héla Mkaouar<fnref n="1" />, Magdalena Wysocka<fnref n="3" />, Adam Lesner<fnref n="3" />, Mohamed Ali Borgi<fnref n="4" />, Emmanuelle Maguin<fnref n="1" />* and Moez Rhimi<fnref n="1" />*

**Abstract**

**Background** The gut microbiota and derived metabolites play a key role in regulating host physiology. Diet is identified as a key regulatory factor of the microbiota composition and, potentially, of subsequent functionalities. Demonstrating the role of diet may be complex as most human studies are cross-sectional and dietary intervention is often accompanied by hygienic changes. The objective of the present study was to investigate the impact of an industrial diet on the modulation of the microbiota and targeted functionalities using a canine "natural" model.

**Results** We carried out a controlled dietary trial in a cohort of Tunisian semi-stray dogs. We made a transition from a natural diet to an industrial kibble diet and monitored the composition of the fecal microbiota, the concentration of short-chain fatty acids (SCFA) and bile acids (BAs), and protease activities. We demonstrated that dietary change significantly decreased fecal primary bile acids levels and protease activities. Interestingly, correlation analyses demonstrated that variation of specific microbial genera were associated with modulated physiological parameters.

**Conclusions** Our study reveals that an industrial diet induces beneficial changes in microbial composition and functions characterised by increased diversity, synthesis of SCFA and secondary bile acids production, stressing the key role of the diet-microbiota-dog crosstalk.

**Keywords** Dog, Diet intervention, Semi-stray dog, Industrial diet, Gut microbiota, Host response, Holobiont

**Background**

The role of the intestinal microbiota in health is now well-established in humans and animals. Many environmental, medicinal, hygienic, or dietary factors can modify its composition and function and thus have an impact on health and well-being. In humans, the Mediterranean diet (MD) has long been known for its protective effects on cardiovascular health and, more recently, cancer and cognitive health [1, 2]. Typically, MD is a custom food in Latin European Countries, Greece, and North Africa, rich in fruits, vegetables, whole grains, nuts, seeds, onion, garlic, and aromatic herbs. The source of fat is essentially olive oil. Proteins of animal origin are consumed in small

<footnote>*Correspondence:</footnote>

<footnote>Emmanuelle Maguin</footnote>

<footnote>emmanuelle.maguin@inrae.fr</footnote>

<footnote>Moez Rhimi</footnote>

<footnote>moez.rhimi@inrae.fr</footnote>

<footnote n="1">¹ Microbiota Interaction with Human and Animal Team (MIHA), Micalis Institute, Institut National de Recherche Pour l'Agriculture, l'Alimentation et l'Environnement, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France</footnote>

<footnote n="2">² Oniris VetAgroBio Nantes, Department of Clinical Sciences, Nantes-Atlantic College of Veterinary Medicine and Food Sciences, 44300 Nantes, France</footnote>

<footnote n="3">³ Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80308 Gdańsk, Poland</footnote>

<footnote n="4">⁴ Laboratory of Biotechnology and Biomonitoring of the Environment and Oasis Ecosystems (LBBEEO), Faculty of Sciences of Gafsa, University of Gafsa, Zarroug, 2112 Gafsa, Tunisia</footnote>
proportions [2]. In contrast, the diet of Western industri-
alised countries (WD) is characterised by the abundant
consumption of ultra-processed products rich in refined
sugars, animal fat, and poor in fiber [3]. The advent of
WD in industrialised countries is associated with an
increase in the incidence of chronic noncommunicable
diseases (cardiovascular diseases, dyslipidemia, obesity,
diabetes mellitus, chronic inflammatory diseases, can-
cers, and neurodegenerative diseases) [3].

The role of diet in the composition and functions of the
intestinal microbiota is well demonstrated within human
cohort studies. Greater biodiversity and a relative pre-
dominance of *Prevotella*, *Clostridium* of cluster XIVa and
*Faecalibacterium prausnitzii* are observed in subjects
fed with MD while *Bacteroidetes* are dominant in indi-
viduals fed with WD [4]. Among the most important gut
microbiota bio-products, short-chain fatty acids (SCFA)
and bile acids (BAs) are known to modulate homeosta-
sis. SCFA are produced mainly from the fermentation
of non-digestible dietary fiber and starch. SFCA impact
the immune and metabolic homeostasis and their con-
centrations are associated with consuming plant-based
nutrients [5, 6]. Bile acids are other microbial bio-metab-
olites that have been shown to play a key role in intes-
tinal inflammation and host metabolism. Patients with
inflammatory bowel disease (IBD) present increased fecal
levels of primary conjugated BAs, whereas secondary
BAs decrease dramatically compared with healthy sub-
jects [7, 8]. More recently, fecal proteolytic activities have
emerged as an important player in the dialogue between
the microbiota and the host [9]. Increased serine protease
(SP) activity and decrease of their inhibitor, so called ser-
pins, are observed in human and canine patients with
IBDs is likely to interact with various signalling pathways,
inflict direct tissue damage, and exacerbate gut inflam-
mation [9, 10]. As large number of SP and serpins are
encoded by the gut microbiota, determining the role of
diet in the modulation of the microbiota and the sub-
sequent regulation of microbial functions involved in
gut homeostasis is a future challenge [11]. Most studies
on the role of diet in modulating the microbiota and its
functions are cross-sectional, meaning they describe the
characteristics of cohorts of subjects who adhere to one
or the other diet [12]. Interventional studies are rare and
dietary changes are associated with other environmental
or hygienic (i.e., physical activity) modifications that pre-
vent establishing cause-effect relationships.

The domestic dog is a species of interest because it
shares its owners' living environment and eating habits
and can easily be subjected to dietary changes. Similarly
to the main types of diets described in humans (MD vs
WD), two main trends emerge in animal health. On
one hand, a so-called "natural diet" made from fresh,

unprocessed products, and on the other hand, an indus-
trial diet mainly composed of kibble. The "natural diet"
is composed of fresh products that are not or minimally
processed in proportions that are often unbalanced com-
pared to the dog's nutritional recommendations. Kibbles
are marketed by large companies specialising in animal
feed and are nutritionally balanced. The industrial pro-
cess of manufacturing kibble requires the presence of a
minimum quantity of starch to enable the extrusion pro-
cess. The semi-stray dogs from Tunisia have the advan-
tage of feeding on "natural products" in a rural setting
in a developing country. Semi-wandering allows a con-
trolled dietary trial making it possible to study the effects
of diet on the gut microbiota in a complex and stable
environment.

Our study aimed to evaluate the effects of the transi-
tion from a “natural diet” to an industrial kibble diet
using a Tunisian cohort of dogs. Analysis of the holobiont
will shed light on the effect of diet intervention on the gut
microbiota composition and the host response.

Methods

Animals

Thirty-two dogs were recruited from several farms belonging to Sidi Bouzid governorate in the center of Tunisia in the clinical practice of one of the co-authors (SR). The selected dogs were all semi-stray, which means that they were free within the perimeter of the farm, in contact with other species (cats, cattle, sheep, poultry, and horses) and self-nourished by hunting, what they found on the ground and sometimes fed by their owners (food waste, bread). The dogs were considered healthy based on normal clinical examination, normal blood cell count and biochemistry, and negative fecal flotation analysis for parasites.

The clinical investigator (SR) completed a question-
naire for each dog. The collected information included
age, sex, reproductive status, breed, medical history,
weight, and body condition score (BCS). A nine-point
body condition scoring system was used, with BCS
between 1 and 3 being “thin”, BCS between 4 and 5 being
“ideal” and BCS over 6 being “overweight” or “obese” [13].
Dogs that had received medication in the last 6 months
were not included. Dogs demographic data are summa-
rized in Table 1. The detailed dietary habits of the par-
ticipants were also collected to estimate the proportion
of the main ingredients.

Dietary intervention

The 32 dogs were randomly divided into 2 groups. Group
1 (n=10) served as a control and maintained the eating
habits described above. Group 2 (n=22) underwent a dietary transition to a kibble industrial food (Table 2).

To ensure a gradual dietary transition in group 2, the supply of kibble was gradually increased over 10 days until full energy requirements were reached. Energy requirements (ER) were calculated according to the formula ER (kcal) = 156 × W<sup>0.67</sup> × 1.2 where W is the optimal weight. On the 10th day (D10), the dogs were confined in a large enclosure located inside the farm in order to cut off access to food sources other than the industrial food

provided while maintaining physical activity and exposure to the usual environment (Fig. 1).

### Blood sample collection

Blood samples were collected in a Na-heparin tube on day (D) 0 and D40 from dogs fasted for 12 h to assess blood glucose and C-reactive protein concentrations. One drop of fresh blood from each sample was used for glycemia measurement. Subsequently, the blood was centrifuged at room temperature (825 × g for 10 min), and the plasma was carefully separated into Eppendorf tubes.

<table><caption>**Table 1** Demographic data of the cohort</caption><thead><tr><th>Groups</th><th>Breed</th><th>Sex (M/F)</th><th>Age (years)<br>Median (1st<br>quartile-3rd<br>quartile)</th></tr></thead><tbody><tr><td>Industrially-nourished (n = 22)</td><td>Mixed local breed (15)<br>German shepherd (3)<br>Rottweiler (2)<br>Epagneul (1)<br>Pointer (1)</td><td>13/9</td><td>3 (2-3.9)</td></tr><tr><td>Self-nourished (n = 10)</td><td>Mixed local breed (10)</td><td>8/3</td><td>3 (2-3.3)</td></tr></tbody></table>

<table><caption>**Table 2** Composition and nutritional analysis of the kibble industrial diet</caption><tr><td>Ingredients</td><td>Corn, dehydrated poultry proteins, corn flour, animal fats, dehydrated beef and pork proteins, hydrolyzed animal proteins, corn gluten, wheat, beet pulp, rice, fish oil, soybean oil, mineral salts, yeasts and yeast components, crustacean hydrolysates (source of glucosamine) and cartilage hydrolyzate (source of chondroitin), additives</td></tr><tr><td>Nutritional analysis (dry matter)</td><td>Protein 25%, Fat content 17%, Crude ash 6.2%, Crude fiber 1.8%</td></tr></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Study design</figcaption></figure>
The plasma samples were promptly frozen at -80 °C for subsequent analysis.

## Glycemia monitoring

Glycemia was assessed in dogs by measuring the concentration of glucose in the blood before and after diet change. One drop of blood from each sample was immediately used to measure glycemia using a portable digital glucometer (Sinocare Safe-Accu, Safecare Bio-tech, Yuhang, China) following the manufacturers' recommendations.

## C-reactive protein concentration

C-reactive protein (CRP) concentrations for each plasma sample were assessed using a commercially available method, as previously outlined by Carney et al. [14]. The measurement assays were conducted using the Canine C-Reactive Protein ELISA Kit from BD Biosciences, San Jose, CA, following the manufacturers' recommendations.

## Fecal sample collection and storage

Fecal samples were collected at D0 and D40 after spontaneous defecation, placed in a stool pot, stored in a cool box, and transferred within 3 h to the laboratory. Samples from each dog were stored at -80 °C until processing.

## DNA extraction and microbiota analysis

Metagenomic DNA was extracted from 200 mg of feces from each subject as described previously [15]. The amplification of the V3-V4 region was performed during 30 amplification cycles at 65°C with designed primer pairs of the 16S ribosomal RNA locus (F343 5'CTT TCCCTACACGACGCTCTTCCGATCTACGGRAGG CAGCAG3' and R784 5'GGAGTTCAGACGTGTGCT CTTCCGATCTTACCAGGGTATCTAATCCT3'). The obtained amplicons (510 bp) were purified and subjected to second PCR to add dual indices and sequencing adaptor requested for Illumina Miseq technology sequencing. The purification and loading of the amplification products onto the Illumina MiSeq cartridge were done according to the manufacturer's instructions. Raw sequences were analysed using the bioinformatics pipeline FROGS (Find Rapidly OTU with Galaxy Solution) [16]. The sequences were clustered into operational taxonomic units (OTUs) of 97% similarity and a cut-off value of 0.03. OTUs were classified against the Silva Ribosomal Database at an 80% confidence interval using a naive Bayesian method. The OTU sequence data were calculated and presented as relative abundance ± SEM. α and β diversity analyses were accomplished with the phyloseq R package. We evaluated α-diversity using Observed, Shannon and Simpson indices. Community compositions

profiles between groups were evaluated using permutational multivariate analysis of variances (PERMANOVA). Mann Whitney test was applied to analyze the differences in phyla, families, and genera between the two groups. Benjamini–Hochberg corrections were used to avoid false positives (significance threshold=0.05) [17]. We calculated pairwise correlations between each specific genus and host parameters using Spearman's non-parametric rank correlation coefficient. To calculate the Spearman correlations matrix, we used R 4.2.3, the corrplot and Hmisc package. We also performed statistical analysis of the microbiota data using the Rhea scripts pipeline [18].

## Fecal bile acids and short-chain fatty acids quantification

Fecal bile acids, including cholic acid (CA), chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), lithocholic acid (LCA), hyodeoxycholic acid (HDCA), and ursodeoxycholic acid (UDCA), were quantified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) from ~50 mg of fecal samples as described previously [19].

Short-chain fatty acids (SCFA), including acetate, butyrate, isobutyrate, propionate, valerate and isovalerate, were quantified by gas chromatography–mass spectrometry from ~50 mg of fecal samples as described previously [20].

## Proteolytic activity assays

Fecal waters were prepared by adding 200 mg of thawed feces to 1 ml of assay buffer (20 mM Tris–HCl, 200 mM NaCl, pH 8). The samples underwent a 1-min sonication (10 s on / 10 s off, amplitude 39%), and were centrifuged at 4 °C, 10 000 rpm for 20 min. Fresh fecal water preparation were used for protease activity assays.

The proteolytic activity assessment consists of using specific chromogenic or fluorogenic substrates to monitor each of the specified activities, as detailed in Table 3. Azocasein was used as the substrate to quantify total activity, and the reaction was stopped with 10% (w/v) added trichloroacetic acid (TCA, Sigma). All reactions were performed in Tris–HCl buffer (20 mM Tris–HCl, 200 mM NaCl, pH 8). Fecal supernatants were diluted to the adequate concentration and 20 µl of the dilution were added to the reaction mixture containing 20 µl of the convenient substrate in a final volume of 200 µl. To confirm each protease family among the total proteolytic activity, inhibition assays were achieved using selective inhibitors of serine proteases, cysteine proteases and metalloproteases that are, respectively, phenylmethanesulfonyl fluoride (PMSF, 1 mM), E-64 (10 µM) and EDTA (1 mM). Other synthetic inhibitors were also used to determine
<table><caption>**Table 3** Specific substrates and inhibitors</caption><thead><tr><th>Type</th><th>Specificity</th><th>Sequence</th></tr></thead><tbody><tr><td rowspan="10">Substrates</td><td>Cysteine-like</td><td>AB2-Ile-Leu-Pro-Glu-ANB-NH2 (Chromogenic)</td></tr><tr><td>Metalloprotease-like</td><td>MCA-Lys-Pro-Leu-Gly-Leu-DNP-Dpa-Ala-Arg-NH2 (Fluorogenic)</td></tr><tr><td>Trypsin-like</td><td>AB2-Val-Val-Ser-Lys-ANB-NH2 (Chromogenic)</td></tr><tr><td>Chymotrypsin-like</td><td>AB2-Lys-His-Trp-Tyr-ANB-NH2 (Chromogenic)</td></tr><tr><td>NE-like</td><td>AB2-Met-Pro-Val-Ala-Trp-Glu-Tyr-(3-NO2)-NH2 (Fluorogenic)</td></tr><tr><td>PR3-like</td><td>AB2-Tyr-Tyr-ABU-Asn-Glu-Pro-Tyr-(3-NO2)-NH2 (Fluorogenic)</td></tr><tr><td>CatG-like</td><td>MCA-Phe-Val-Thr-Gnf-Ser-Trp-AB2-NH2 (Fluorogenic)</td></tr><tr><td>Kallikrein</td><td>H-D-Pro-Phe-Arg-pNA · 2 HCl (Chromogenic)</td></tr><tr><td>Chymase-like</td><td>N-succinyl-AAPF-pNA (Chromogenic)</td></tr><tr><td rowspan="9">Inhibitors</td><td>Cysteine-like</td><td>E-64</td></tr><tr><td>Metalloproteases</td><td>EDTA</td></tr><tr><td>Serine-like</td><td>PMSF</td></tr><tr><td>Trypsin-like</td><td>Soybean Trypsin Inhibitor (STI)</td></tr><tr><td>Chymotrypsin-like</td><td>Na-Tosyl-Phe Chloromethyl Ketone (TPCK)</td></tr><tr><td>NE-like</td><td>N-(Methoxysuccinyl)-Ala-Ala-Pro-Val-chloromethyl ketone</td></tr><tr><td>PR3-like</td><td>Bt-Val-Tyr-Asp-nValP(O-C6H4-4-Cl)2</td></tr><tr><td>CatG-like</td><td>Ac-Phe-Val-Thr-PhgP(4-guanidine)-(OC6H4-4-S-Me)2</td></tr></tbody></table>

Trypsin-like activity (100 µM), Chymotrypsin-like activity (0.1 µM), Neutrophil Elastase-like activity (NE, 10 µM), and Proteinase-3-like (PR3, 0.1 µM) activity. The reactions were then incubated in 96-well plates for 30 min at 37 °C. Absorbance (410 nm) or fluorescence (Excitation: 360 nm, Emission: 460 nm) were measured at room temperature on a Perkinelmer® plate reader.

### Statistical analyses

Shapiro-Wilk testing was used to assess the normality of quantitative variables. Normally distributed variables are presented as mean and standard deviation (SD), while non-normally distributed results are presented as median and interquartile range. The R 4.2.3 software was used for statistical analysis. Differences in the study parameters between dogs before and after diet change and between groups were determined using the Mann Whitney test. Statistical significance

is indicated as * for *p* < 0.05, ** for *p* < 0.01, and *** for *p* < 0.001. Statistical significance was considered at *p* < 0.05.

### Results

#### Cohort description

The characteristics of the two groups in terms of body weight, and BCS, at D0 and D40, are reported in Table 4. The recruited dogs display similar characteristics in the 2 groups at D0. They are mostly local breed dogs and young adults with a median age of 3 years. The gender distribution shows a statistically insignificant difference between the groups (males represent 59% of the industrially-nourished group and 72% of the self-nourished group). The animal included were medium-sized (median weight of 16.5 kg and 17 kg in the industrially-nourished and self-nourished groups, respectively). The median BCS at D0 was 3, revealing that animals were thin at the beginning of the study.

<table><caption>**Table 4** Comparison of body weight and body condition score at D0 and D40</caption><thead><tr><th rowspan="3">Groups</th><th colspan="2">Body weight (kg)</th><th colspan="2">Body condition score*</th></tr><tr><th colspan="2">Median (1st quartile–3rd quartile)</th><th colspan="2">Median (1st quartile–3rd quartile)</th></tr><tr><th>D0</th><th>D40</th><th>D0</th><th>D40</th></tr></thead><tbody><tr><td>Industrially-nourished (n = 22)</td><td>16.5<sup>a</sup> (13.3–20.8)</td><td>20.8<sup>a</sup> (17–25.8)</td><td>3<sup>a</sup> (3–4)</td><td>4.5<sup>a</sup> (4–5)</td></tr><tr><td>Self-nourished (n = 10)</td><td>17<sup>b</sup> (14.8–19.8)</td><td>18<sup>b</sup> (16–20)</td><td>4 (3.5–4)</td><td>4 (4–4)</td></tr></tbody></table>

Significant differences (*p* < 0.05) between groups are shown with the same superscript symbols (a–b)

*Body condition score was established based on a maximal mark of 9. Ideal body condition score is between 4 and 5
Moreover, the dietary questionnaire completed by the owners allowed us to estimate the typical diet of semi-stray dogs. Table scraps are given daily and are typically those from Mediterranean cuisine composed of wheat (bread, pasta, or semolina), olives and olive oil, bones of mutton, beef, and poultry, fruits and vegetables (peeling of carrots, onions, garlic, ...) (Table 5).

As reported in Table 4, body weight significantly increased between D0 and D40 in both groups (+26% body weight in the industrially-nourished group and +6% body weight in the self-nourished group). The industrially-nourished group also presents a significant increase in BCS at D40 compared to D0, ranging from a median of 3 to 4.5, which ranged from “thin” to “ideal”.

### Effect of diet change on blood glucose and CRP concentrations

In our study, we characterized the blood concentration of glucose and CRP to evaluate the impact of the selected diet on glucostasis and the inflammatory response. We did not find significant differences in glycemia before and after diet change nor with the control group. Similarly, CRP levels were not significantly altered by diet change (Supplementary Fig. 1).

### Characterization of the fecal SCFA profile in semi-stray dogs before and after diet change

Our data showed that the fecal concentration of isobutyrate, acetate and isovalerate increased by 2.8-fold ($p < 0.001$), 1.6-fold ($p < 0.01$) and 2.8-fold ($p < 0.001$) respectively in semi-stray dogs after the diet change (Fig. 2). No statistical difference was observed between valerate, butyrate and propionate concentration before and after the dietary intervention (Supplementary Fig. 2).

### Characterization of fecal bile acids profile in semi-stray dogs before and after diet change

The analysis of BA profiles revealed a significant decrease of primary BA levels, namely cholic acid (CA) and chenoxycholic acid (CDCA) in dogs fecal samples after

diet change by 22.5-fold ($p < 0.001$) and 14-fold ($p < 0.01$) respectively (Fig. 3). Moreover, our results also show an increase in two secondary BA namely hyodeoxycholic acid (HDCA; 4.4-fold higher) and lithocholic acid (LCA; 2.2-fold higher) in dog fecal samples at D40 compared to D0 (Fig. 3). On the contrary, fecal levels of two other secondary BA deoxycholic acid (DCA) and ursodeoxycholic acid (UDCA) were unchanged at D40 (Supplementary Fig. 3).

### Fecal proteolytic profile in semi-stray dogs before and after diet change

Analysis of total proteolytic activity revealed a two-fold decrease after the diet change compared to samples obtained before the diet change ($p < 0.001$; Fig. 4A). Several specific protease inhibitors were tested to determine the respective contribution of protease families to proteolytic activities (Fig. 4B). The proteolytic activity was significantly reduced by approximately 60% in fecal samples of both groups ($p < 0.01$) in the presence of PMSF, a broad-spectrum SP inhibitor, demonstrating a major contribution of SPs. In the presence of the metalloprotease inhibitor EDTA, proteolytic activity in Group 2 was slightly reduced by 20% (not statistically significant). When using the cysteine protease inhibitor E-64, we observed only a 16% decrease in the activity (not statistically significant).

To further characterize the protease contributing to the SP activity in semi-stray dogs, we used specific substrates for subfamilies of SPs. We were able to demonstrate a significant decrease of 70% of trypsin-like activity in dogs after diet change when compared to D0 ($p < 0.01$; Fig. 5A) and the control group ($p < 0.05$; Fig. 5A). We used specific inhibitors for each targeted protease subfamily to confirm the specificity of the designed substrates. Our results showed a significant 88% decrease in proteolytic activity in the presence of trypsin inhibitor ($p < 0.001$; Fig. 5B). Using specific substrates for chymotrypsin, we showed a significant increase of chymotrypsin activity by 1.3-fold higher in semi-stray dogs after a diet change

<table><caption>**Table 5** Description of the food ration of semi-stray dogs</caption><thead><tr><th>Food category</th><th>Main ingredients</th><th>Ingestion frequency</th></tr></thead><tbody><tr><td rowspan="5">Table scraps</td><td>Whole grains, bread, beans, nuts and seeds</td><td>Daily</td></tr><tr><td>Peeling of vegetable and fruits</td><td>Daily</td></tr><tr><td>Olive Oil</td><td>Daily</td></tr><tr><td>Dairy products mainly in the form of fermented products (yogurt, cheese ...)</td><td>Once a week</td></tr><tr><td>Bones</td><td>Once a week</td></tr><tr><td>Hunting</td><td>Rabbits, wild pigs</td><td>Once a week</td></tr><tr><td>Carrion</td><td>Carrion, placenta</td><td>Once a month</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Fig. 2** Characterization of fecal SCFA profile in self-nourished and industrially-nourished dogs before (D0) and after (D40) diet change. Data are presented as means ± standard error of the mean (SEM). Statistical analyses were performed using Kruskal–Wallis followed by Dunn's test to compare SCFA profile in semi-stray dogs before and after self-nourished and industrially-nourished diet change. \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001. SCFA, short-chain fatty acids</figcaption></figure>

when compared to D0 and control group (*p* < 0.05; Fig. 5C). This result was confirmed by a decreased of 76% in chymotrypsin activity in the presence of its specific inhibitor. Similarly, we also evaluated elastolytic activity (neutrophil elastase-NE and proteinase 3-PR3) in the different samples. Interestingly, we showed a decrease of 88% and 50% in fecal NE-like and PR3-like activity after diet change when compared to D0 (*p* < 0.001; Fig. 5E for NE-like and *p* < 0.01; Fig. 5G for PR3). Furthermore, we validated the increased activity using specific inhibitors for NE and PR3. As shown in Fig. 5F, H, we noted a decrease in proteolytic activity of 76% and 79% for NE-like and PR3-like activities respectively in the presence of their specific inhibitors (*p* < 0.001; Fig. 5F, H).

On the other hand, the use of specific substrates for other serine proteases (cathepsin-G, kallikrein,

chymase-like), metalloproteases, and cysteine proteases showed no significant difference in proteolytic activity between dogs before, after diet change and control group (data not shown).

**Effect of diet change on fecal microbiota composition in semi-stray dogs**

A significant modification of the gut microbiota composition in industrially-nourished semi-stray dogs at D40 compared to D0 was observed. Analysis of gut microbiota composition at the phylum level demonstrates an increase in the abundance of *Bacteroidota* and *Fusobacteriota* along with a decrease in the abundance of *Actinobacteria* and *Proteobacteria* phyla (Fig. 6A). Our data shows a significant increase in Shannon and Simpson indexes (*p* < 0.001) in group 2 dogs at D40 compared to
<figure><img src="image_6.png" /><figcaption>**Fig. 3** Quantification of fecal BAs level in self-nourished and industrially-nourished dogs before (D0) and after (D40) diet change. Data are presented as means ± SEM. Statistical analyses were performed using Kruskal–Wallis followed by Dunn's test to compare bile acid profile in semi-stray dogs before and after self-nourished and industrially-nourished diet change. \*\*p < 0.01; \*\*\*p < 0.001. BAs, bile acids</figcaption></figure>

D0, demonstrating an increase in bacterial abundance and richness (Fig. 6B). Beta diversity analysis, based on the Principal Coordinate Analysis (PCoA), revealed a distinct clustering of the gut microbiota between dogs fecal samples before and after the transition from the shelf-nourished diet to an industrial diet (PERMANOVA; *p* = 0.0004) (Fig. 6C).

As shown in Fig. 6D, we demonstrated a shift in microbiota composition at the genus level with the enrichment of *Bacteroides* (*p* < 0.05) and particularly of beneficial bacteria such as, *Butyricicoccus* (*p* < 0.001), *Erysipelatoclostridium* (*p* < 0.001), *Lachnospiraceae* NK4A136 (*p* < 0.05), *Tyzzerella* (*p* < 0.01) and *Faecalibacterium* (*p* < 0.01). In contrast, we also showed a decrease in bacterial abundance of *Parabacteroides* (*p* < 0.001), *Enterococcus* (*p* < 0.01), and *Streptococcus* (*p* < 0.001) genera.

## Gut microbiota association with differentially upregulated biological functions

We further investigated the relationship between altered gut microbiota and biological functions by performing a correlation analysis (Fig. 7). For SCFA, our data showed a significant negative correlation between isobutyrate and *Butyricicoccus*, *Faecalibacterium* and *Tyzzerella* genera abundance. Moreover, we observed a significant negative correlation for isovalerate and *Butyricicoccus* and *Tyzzerella* genera. Only after diet change, we observed a significant negative correlation between isovalerate and *Erysipeloclostridium*.

Regarding BAs, we noticed a significant negative correlation between *Parabacteroides* abundance and two secondary BAs levels, namely LCA and HDCA. This negative correlation becomes non-significant at D40 within the industrially-nourished group.
<figure><img src="image_7.png" /><figcaption>**Fig. 4** Fecal proteolytic activity in semi-stray dogs before (D0) and after (D40) self-nourished and industrially-nourished diet change. (A) Total fecal proteolytic activity in semi-stray dogs before and after self-nourished and industrial diet. (B) Relative proteolytic activity in the presence and absence of inhibitors (PMSF, EDTA, and E-64) for protease families. "Fold increase" represents the factor by which the activity has increased compared to the reference (average proteolytic activity measured in semi-stray dogs before diet change). Relative activity corresponds to the defined maximum activity, set as 100%. Data are presented as means ± SEM. Kruskal–Wallis followed by Dunn's test was used to compare different proteolytic activities in semi-stray dogs before and after self-nourished and industrially-nourished diet change. The Mann–Whitney test was used to compare total activity in the presence and absence of the used inhibitors. \*\*p < 0.01, \*\*\*p < 0.001</figcaption></figure>

Our data showed a significant positive correlation between chymotrypsin activity and 3 bacterial genera: *Butyricicoccus*, *Faecalibacterium*, and *Lachnospiraceae* NK4A136 (Fig. 7). Interestingly, the positive correlation found between *Bacteroides* abundance and chymotrypsin activity reached statistical significance in dogs after diet intervention. Besides, we also observed a significant negative correlation between *Tyzzerella* genus and PR3 and trypsin activities.

Discussion

The influence of diet on the composition and function of the gut microbiota is well-known in humans and animals [21, 22]. However, most studies are cross-sectional and carried out in industrialised countries in Europe [23] or North America [24] or on animals in laboratory facilities. We studied the impact of diet in a cohort of dogs living in their natural environment in North Africa by carrying out a controlled dietary trial and measuring the impact

in terms of composition and targeted functions of their intestinal microbiota.

The eating habits of the animals in our cohort are typical of MD, which could make the Tunisian semi-stray dog a good model for studying the role of this diet on health (Table 5). The industrial food used in our study is a food specially formulated to meet the nutritional recommendations from the European Pet Food Industry Scientific Advisory Board. This food, therefore, contrasts with natural food because it is complete and balanced. It is not comparable with WD because it does not contain refined sugars, fats and animal proteins in excessive quantities. The proposed industrial food also contains the recommended amount of fiber. For animals included in the industrially-fed group, we chose to make a gradual dietary transition over 10 days because an abrupt transition can lead to diarrhea and significant disruption of the intestinal microbiota [25]. The size of the control group was reduced during the experiment (from 22 to 10 dogs) due to constraints linked to the

(See figure on next page.)

**Fig. 5** Characterization of fecal proteolytic activity profile in self-nourished and industrially-nourished dogs before and after diet change. (A) Trypsin-like activity. (B) Trypsin-like activity in the presence and absence of their specific inhibitor. (C) Chymotrypsin-like activity. (D) Chymotrypsin-like activity in the presence and absence of their specific inhibitor. (E) Neutrophil elastase-like activity (NE-like). (F) Neutrophil elastase-like activity in the absence and presence of their specific inhibitor. (G) Proteinase 3-like activity (PR3-like). (H) Proteinase 3-like activity in the absence and presence of their specific inhibitor. "Fold increase" represents the factor by which the activity has increased compared to the reference (average proteolytic activity measured in free-roaming dogs). Relative activity corresponds to the defined maximum activity, set as 100% (without (-) inhibitor). Data are presented as means ± SEM. Kruskal–Wallis followed by Dunn's test was used to compare different proteolytic activities in semi-stray dogs before and after self-nourished and industrially-nourished diet change. The Mann Whitney test was used to compare proteolytic activities in the presence and absence of the used inhibitors. \*p < 0.05; \*\*p < 0.01; \*\*\*p < 0.001
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 5** (See legend on previous page.)</figcaption></figure>
fact that the study was carried out in real farm condi-
tions. Therefore, we excluded in the control group the
animals for which we had doubts about whether the
owners were following the instructions carefully. While
groups are not even which can constitute a limitation,
the statistical power of our study remains significant.

During the study period, dogs from both groups
showed significant weight gain and, for the industrially-
fed group, a significant increase in BCS (Table 4). The
owners of the self-fed dogs may have felt pressure linked
to veterinary supervision, leading to a more abundant
food during the observation period. In addition, the
energy intake of the industrially-nourished group was
calculated based on the optimal weight while the animals
were “thin”. Moreover, glycemia is not affected during
industrial diet implementation, suggesting that it pro-
vides medium energy levels that sustain glucostasis (Sup-
plementary Fig. 3A). Similarly, inflammation response as
assessed by blood CRP levels was not altered in dogs dur-
ing dietary intervention (Supplementary Fig. 3B).

Short-chain fatty acids play a crucial role in maintain-
ing overall health, particularly in the context of gut health
and metabolic processes. SCFA are an energy source
for colonocytes, improve gut barrier function and lipids
and carbohydrates metabolisms, and reduce luminal pH,
providing a resistance mechanism against pathogens
[26–28]. It is important to note that while SCFAs offer
numerous health benefits, their effects are complex and
can vary depending on factors such as diet, gut micro-
biota composition, and individual health status [29].
Herein, we evaluated their production before and after
diet change. The measurement of fecal SCFA revealed a
significant increase in the contents of acetate, isobutyrate
and isovalerate after the change to industrial food com-
pared to D0 and the control group. This effect may be due
to the evolution of the populations of SCFA-producing
bacteria, the abundance of the fermentation substrate
constituted by the non-digestible fiber and starch con-
tained in the industrial kibbles, or a combination of both
mechanisms. In fact, the abundance of known bacterial
SCFA-producers increases upon diet change (e.g., *Bac-
teroides*, *Feacalibacterium*, *Butyricicoccus*). These find-
ings align with the work of Jha and Berrocoso [30] in
swine and Myint et al. [31] in dogs. Acetate is particularly

important as it has beneficial effects on host energy and
substrate metabolism, affecting appetite and insulin sen-
sitivity [32]. Acetate supplementation decreases weight
gain induced by a high-fat diet in mice [33]. Several
in vivo studies reported that acetate can modulate satiety
via the secretion of glucagon-like peptide-1 and peptide
YY, two gut-hormones involved in satiety and appetite
regulation [34, 35]. In addition, acetate can modulate
insulin response and, hence, glucose homeostasis [36].
Therefore, an industrial diet's beneficial effects may be
partly mediated by the increased acetate concentration.
In contrast to other SCFAs, we did not find a significant
difference in butyrate or propionate after the diet change.
This was an unanticipated finding, as the increased bac-
terial groups in industrially-nourished dogs are consid-
ered to be butyrate producers.

The measurements of fecal BAs showed a signifi-
cant decrease in primary BAs (CA and CDCA) and an
increase in specific secondary BAs (HDCA and LCA) in
the group fed with industrial food at D40 compared to
D0 and compared to the control group. Our data suggest
an increase in the abundance of bacteria able to convert
primary BA into secondary BA after dietary change, par-
ticularly the conversion of CDCA to LCA. This is sup-
ported by the observed increase in the abundance of
*Erysipelatoclostridium* and *Lachnospiraceae* NK4A136,
two genera known to perform 7α-dehydroxylation [37].
After diet change, HDCA levels are increased indicating
a favoured conversion of LCA to HDCA in dogs. Inter-
estingly, several studies have reported beneficial effect
of HDCA including anti-inflammatory properties [38,
39]. Our data suggest that *Faecalibacterium*, *Lachno-
spiraceae*, and *Bacteroides*, known for their bile salt acid
activity, as candidate bacterial genera that could contrib-
ute to the observed shift in bile acid profile contributing
to the metabolic benefit provided by the dietary interven-
tion [40, 41].

In the present study, fecal proteolytic activity was
decreased in the group fed with the industrial food
compared to before dietary intervention and the con-
trol group. Analysis of the profile showed that the fecal
proteolytic activity was mainly due to the SP activity
and that the trypsin-like, NE-like and PR3-like activi-
ties are significantly reduced at D40. A reduction in fecal

(See figure on next page.)

**Fig. 6** Gut microbiota analysis. (A) fecal microbiota composition at phylum level. (B) Alpha diversity indices, which take into account the community richness, evenness and diversity, was measured by three different indexes: Observed number of OTUs, Shannon and Simpson. (C) Principle Coordinate Analysis (PCoA) with unweighted unifrac (UF) distance matrices and weighted Unifrac (WU) matrix for semi-stray dogs before and after self-nourished and industrially-nourished diet change respectively. (D) Genera differentially abundant in semi-stray dogs before (D0) and after (D40) industrially-nourished diet change. Data are presented as means ± SEM. Statistical analyses were performed with the Mann-Whitney. Statistical significance is denoted as follows: \*for *p* < 0.05, \*\*for *p* < 0.01, and \*\*\*for *p* < 0.001
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>**Fig. 6** (See legend on previous page.)</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Fig. 7** Spearman correlations between microbial populations and SCFAs, bile acids and proteolytic activities. For correlation analysis, the colors represent the correlation's nature, where dark blue signifies a robust positive correlation, and dark red signifies a substantial negative correlation. Statistical analyses were performed with Mann-Whitney. Statistical significance is denoted as follows: \*p < 0.05, \*\*p < 0.01, and \*\*\*p < 0.001, all after false discovery rate correction</figcaption></figure>

proteolytic activities may be a beneficial effect sought because proteolytic hyperactivity has been demonstrated to be involved in the pathogenesis of IBD in humans and dogs [42, 43]. Diet could therefore constitute an interesting tool for regulating proteolytic activity. Furthermore, the analysis of correlations with the composition of the microbiota (Fig. 7) showed a positive correlation with some bacterial genera (*Faecalibacterium*, *Lachnospiraceae*) and a negative one with others (*Butyriciococcus* for PR3, *Enterococcus*, *Streptococcus* and *Tyzzerella*). In humans with irritable bowel syndrome, Edwinson et al. documented a correlation between SPs activity and gut microbiota composition [10]. The regulation of fecal proteolytic activity may partly rely on the modulation of the microbiota towards bacterial genera expressing regulatory serpins [44].

The change from natural to industrial food led to significant changes in the intestinal microbiota composition as shown by an increase in alpha diversity and PCoA analysis (Fig. 6C). As shown in Fig. 6D, we noted an enrichment of beneficial bacteria such as *Bacteroides*, *Butyricicoccus*, *Erysipelatoclostridium*, and *Faecalibacterium*. These genera are known for their pivotal roles in maintaining a balanced and thriving gut microbiome. *Bacteroides* are important for breaking down complex carbohydrates and aiding in nutrient absorption, contributing to overall digestive efficiency [2]. *Butyricicoccus* [45] are producers of short-chain fatty acids such as butyrate, which are not only energy sources for colonocytes but also have anti-inflammatory properties and support intestinal barrier function. *Faecalibacterium* [2,

4] is a well-recognized butyrate producer and is linked to anti-inflammatory effects in the gut. The increase in these beneficial genera reflects a positive shift in the gut microbiota composition, potentially leading to improved digestive health, enhanced immune function, and a reduced risk of gastrointestinal disorders. In contrast, we also observed a decrease in bacterial abundance of *Enterococcus* (*p* < 0.01), and *Streptococcus* (*p* < 0.001) genera. These genera are commonly associated with imbalances in the gut microbiome [5–7]. Our finding are consistent with a previous study comparing dogs fed with bones and raw food (BARF) (uncooked meat and bones) to dogs fed with a commercial diet [29]. In fact, dogs fed a commercial diet shows higher abundance of *Faecalibacterium*, *Erysipelotrichaceae* and *Lachnospiraceae* and lower *Streptococcus* and *Enterococcus* abundance compared to BARF-fed dogs. In this context, dogs fed with BARF diet was characterized by a higher richness and diversity in their gut microbiota compared to the commercial diet [46]. The shotgun metagenomic analysis carried out by Alessandri et al. on wolves and domestic dogs showed that the microbiome evolved during domestication to adapt to a dietary intake rich in carbohydrates. These results indicate that the domestication of dogs significantly shaped intestinal microbial populations in favor of fibrolytic populations [47].

The transition from a natural diet in Tunisian stray dogs to an industrial kibble diet was accompanied by beneficial changes in the composition of the microbiota and in the evaluated functional aspects (increased SCFA synthesis and BAs bioconversion, and reduction in
fecal proteolytic activities). All these effects are consid-
ered beneficial because they help maintain intestinal and
immune homeostasis [48, 49]. The results of this study
reinforce the importance of diet beyond all other envi-
ronmental factors. Indeed, the study was carried out in
a complex environment located in a developing country
with many other animal species (cats, cattle, sheep, poul-
try, and horses) in contact with dogs. The design of our
study made it possible to maintain the impact of inter-
species microbial flows throughout the experiment, and
thus showed that diet has a very high impact. Surpris-
ingly and contrary to what one may believe, the transi-
tion from a natural food found in the dog's environment
to a highly processed industrial food was accompanied by
beneficial changes in the host's health. Likely, it is not so
much whether it is processed or not that has an impact,
as much as the nutritional balance of the food. Indeed,
industrial foods meet the nutritional recommendations
formulated at the international level. The comparison of
the macronutritional analysis between the self-nourished
group and the industrially-nourished group is difficult
because the composition of the semi-stray dogs' intake
is estimated. However, industrial food is likely richer in
indigestible fiber and starch, thus making it possible to
promote a diverse and functionally beneficial microbiota.

Conclusions

In conclusion, this study demonstrates increased fecal SCFA and secondary BAs concentration, decreased proteolytic activity, and significant alteration of gut microbiota composition in Tunisian semi-stray dogs upon dietary intervention. These findings show the beneficial effect of changing from a natural diet to a nutritionally balanced industrial food on microbiota composition, functions and dog health. Further studies are needed to determine whether dietary intervention by modulating the composition and function of the gut microbiota can be used as a powerful strategy to restore altered microbial functions leading to a new strategy to prevent some diseases (e.g., digestive inflammation) or improve clinical signs.

Abbreviations

<table>
   <tr>
    <td>
     BAs
    </td>
    <td>
     Bile acids
    </td>
   </tr>
   <tr>
    <td>
     BCS
    </td>
    <td>
     Body condition score
    </td>
   </tr>
   <tr>
    <td>
     BARF
    </td>
    <td>
     Bones and raw food
    </td>
   </tr>
   <tr>
    <td>
     CDCA
    </td>
    <td>
     Chenodeoxycholic acid
    </td>
   </tr>
   <tr>
    <td>
     CA
    </td>
    <td>
     Cholic acid
    </td>
   </tr>
   <tr>
    <td>
     CRP
    </td>
    <td>
     C-reactive protein
    </td>
   </tr>
   <tr>
    <td>
     DCA
    </td>
    <td>
     Deoxycholic acid
    </td>
   </tr>
   <tr>
    <td>
     ER
    </td>
    <td>
     Energy requirements
    </td>
   </tr>
   <tr>
    <td>
     HDCA
    </td>
    <td>
     Hyodeoxycholic acid
    </td>
   </tr>
   <tr>
    <td>
     IBD
    </td>
    <td>
     Inflammatory bowel disease
    </td>
   </tr>
   <tr>
    <td>
     LCA
    </td>
    <td>
     Lithocholic acid
    </td>
   </tr>
   <tr>
    <td>
     MD
    </td>
    <td>
     Mediterranean diet
    </td>
   </tr>
   <tr>
    <td>
     NE
    </td>
    <td>
     Neutrophil elastase
    </td>
   </tr>
   <tr>
    <td>
     OTUs
    </td>
    <td>
     Operational taxonomic units
    </td>
   </tr>
  </table>

<table>
   <tr>
    <td>
     PERMANOVA
    </td>
    <td>
     Permutational multivariate analysis of variances
    </td>
   </tr>
   <tr>
    <td>
     PMSF
    </td>
    <td>
     Phenylmethanesulfonyl fluoride
    </td>
   </tr>
   <tr>
    <td>
     PCoA
    </td>
    <td>
     Principal coordinate analysis
    </td>
   </tr>
   <tr>
    <td>
     PR3
    </td>
    <td>
     Proteinase-3
    </td>
   </tr>
   <tr>
    <td>
     SP
    </td>
    <td>
     Serine protease
    </td>
   </tr>
   <tr>
    <td>
     SCFA
    </td>
    <td>
     Short-chain fatty acids
    </td>
   </tr>
   <tr>
    <td>
     SD
    </td>
    <td>
     Standard deviation
    </td>
   </tr>
   <tr>
    <td>
     SEM
    </td>
    <td>
     Standard error of the mean
    </td>
   </tr>
   <tr>
    <td>
     UF
    </td>
    <td>
     Unweighted unifrac
    </td>
   </tr>
   <tr>
    <td>
     UDCA
    </td>
    <td>
     Ursodeoxycholic acid
    </td>
   </tr>
   <tr>
    <td>
     WU
    </td>
    <td>
     Weighted unifrac
    </td>
   </tr>
   <tr>
    <td>
     WD
    </td>
    <td>
     Western diet
    </td>
   </tr>
  </table>

**Supplementary Information**

The online version contains supplementary material available at https://doi.
org/10.1186/s42523-024-00357-w.

Additional file 1.

Acknowledgements

The authors would like to express their gratitude to Dr O. Senecat for proof-
reading this manuscript.

Author contributions

SR, EM, AL, MAB, MR conceived and designed the experiments; SR, AJ, VM, HM, NA, TM, MW performed the experiments; SR, AJ, MAB, JH, EM, MR analyzed the data; SR, TM, VM, AJ, HM, MAB, JH, EM, MR wrote the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the French National Research Agency (RestorPro no ANR-23-CE14-0073-01), the twinning European project: 952583-MICAfrica and the European Union's Horizon 2020 research and innovation programme under grant agreement N°964590.

Availability of data and materials

The data that support the findings of this study are openly available in Biostudies database at https://www.ebi.ac.uk/biostudies/, reference number S-BSST1356 (16S rRNA sequencing dataset).

Declarations

Ethics approval and consent to participate

The protocol was approved by the ethical committee of the University of Gafsa (No. FSG-02-2022). The owners completed a consent form authorizing the use of the biological samples and associated personal data.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Received: 28 March 2024 Accepted: 7 November 2024

Published online: 21 November 2024

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