Keywords:

Ce qui compte, c'est d'agir à la bonne échelle » Une échelle d'action publique repose, en effet, sur la croyance que certains problèmes s'imposent comme relevant d'un niveau territorial plutôt qu'un autre.

Parmi ses multiples attributs, le territoire est notamment un espace de fabrication de l'action publique. À ce titre, la question de l'échelle trouve toute sa pertinence. À quelle échelle doit-on gérer les risques naturels ? Le développement territorial ? Le changement climatique ? La ressource en eau ? La question des échelles de prise en charge des problèmes publics est discutée depuis longtemps, par les décideurs politiques, et du côté de la recherche notamment par les géographes, les politistes ou encore les sciences de gestion. Cette question conduit à considérer l'adéquation du périmètre d'action en fonction du problème considéré : bassin, zonage, région, nation, etc., renvoient à une envergure spatiale différente, à des frontières différentes, à des niveaux d'action publique différents mais également à des acteurs différents -autant de choix qui auront des conséquences sur la définition et le traitement du problème. Ainsi, les acteurs en charge du problème inondation diffèreront si ce dernier est défini à l'échelle nationale ou locale. Alors, à quelle échelle territoriale et administrative un problème doit-il être traité pour une plus grande efficacité de l'action ? Existe-il une bonne échelle de traitement des problèmes ? Est-ce que les échelles sont inhérentes au type de problème considéré ? La réponse à ces questions est encore débattue aujourd'hui.

Les réformes de décentralisation, et du changement d'échelle d'action publique qui les accompagne, ont souvent été justifiées par un principe fonctionnaliste, selon lequel certains territoires, à déterminer, feraient assez naturellement fonction de cadre de gestion efficace des problèmes. Le principe de subsidiarité, fondamental dans le fonctionnement de l'Union Européenne, traduit cette approche fonctionnaliste : seule la gestion des problèmes dont le traitement serait moins efficace à l'échelon inférieur est confiée à l'échelon communautaire. Pour les problèmes environnementaux plus spécifiquement, selon certains spécialistes (S. Nahrath, F. Varone, J.-D. Gerber, in : VertigO, 2009), il existerait une bonne échelle de gestion des problèmes qui coïnciderait avec la matérialité environnementale de ce dernier. Cela peut conduire à s'affranchir des classiques circonscriptions administratives pour investir des « circonscriptions écologiques », bassins versants ou massifs forestiers, par exemple. Néanmoins, l'exemple des politiques de l'eau nuance la portée opératoire de cette vision fonctionnaliste (voir Encadré 8).

Encadré 8 -Les échelles de gestion de la ressource en eau

Les politiques de l'eau sont emblématiques dans la question des échelles d'action. En effet, l'eau est gérée en France à l'échelle du bassin versant ou bassin hydrographique. Cette échelle s'impose comme le territoire institutionnel de la gestion de la ressource en eau, d'abord à l'échelle des grands bassins versants avec la loi sur l'eau de 1964, puis à l'échelle locale avec la loi de 1992 instaurant l'échelle intermédiaire des sous-bassins. Cette échelle de gestion a été justifiée de manière fonctionnaliste (selon laquelle l'action publique y serait plus efficace) par les arguments fournis par une science naturelle, l'hydrologie (J. Linton, What Is Water ?, 2010). Ainsi, l'échelle de gestion choisie pour l'eau est celle de la matérialité du problème. La gestion de l'eau doit être organisée dans le périmètre hydrologique où l'eau s'écoule, entre la source et l'exutoire. Cela fait sens quand on imagine les rivalités de gestion entre amont et aval, la gestion des pollutions et de la quantité d'eau en période d'étiages. Il est légitime de vouloir associer les acteurs présents sur un bassin versant pour décider de la gestion de la ressource en eau sur ce territoire. Néanmoins, découper la France en bassins versants n'est pas sans poser de problèmes. D'une part, il existe des incertitudes scientifiques sur le découpage par bassins versants. En effet, les échanges d'eaux souterraines entre bassins existent et ne sont pas tous identifiés. Le découpage par bassins versants vus comme des entités déconnectées les unes des autres ne reflète donc pas toujours une réalité physique claire. Par ailleurs, le découpage par grands bassins hydrographiques se surimpose à des frontières intercommunales, départementales ou régionales. Et c'est dans le cadre de ces frontières historiques que sont également conduites nombre de politiques d'aménagement ou de développement affectant fortement la ressource en eau (S. Ghiotti, in : Développement durable et territoires, 2006). Enfin, l'action publique à l'échelle des grands bassins hydrographiques a des limites opératoires. Ainsi, la planification à l'échelle des grands bassins est-elle déclinée et complétée localement lorsque les spécificités de la ressource en eau ou de son usage l'exigent, comme la présence de conflits ou de situations de pénurie d'eau. Le périmètre de cette planification locale doit en principe être défini à une échelle hydrographique cohérente. Toutefois, des considérations d'efficacité peuvent conduire à retenir d'autres options, comme l'illustre le cas du Schéma d'aménagement et de gestion des eaux (Sage) Nappes profondes défini à l'échelle du département de la Gironde.

Il est donc difficile de définir une bonne échelle de gestion, car il existe des incertitudes autour de la définition du périmètre le plus pertinent pour traiter un problème, et le traitement des problèmes peut nécessiter d'agir conjointement à des niveaux différents. Malgré ces incertitudes, on peut penser que, s'il existe une bonne échelle de gestion des problèmes, il faut tout faire pour s'en approcher. Or, la littérature fonctionnaliste, c'est-à-dire celle qui insiste sur l'importance de faire coïncider l'échelle de gestion avec celle des problèmes, reconnaît que ces derniers ne se laissent pas définir facilement. Certains problèmes sont même qualifiés de pernicieux (de l'expression wicked problems en anglais), car ils font intervenir une pluralité d'acteurs et donc d'échelles de gestion, rendant impossible la définition d'une seule échelle de traitement du problème. Par exemple, on peut citer le changement climatique. La logique fonctionnaliste voudrait que le problème soit traité à l'échelle planétaire. Néanmoins, comme on l'a vu lors de l'Accord de Paris adopté en décembre 2015, cette échelle ne permet pas toujours une action publique contraignante et effective. Ce problème doit alors être traité à des échelles multiples. La question du changement climatique peut donc être gérée à l'échelle locale (stratégies d'adaptation), à l'échelle nationale (stratégie de réduction des gaz à effet de serre) et internationale (objectifs concertés d'émissions par région selon le produit intérieur brut des États).

Par ailleurs, au-delà de la pluralité des échelles, la question de la pertinence des frontières du problème est éminemment relative car dépendante des lieux, des temps, et des personnes impliquées dans le traitement du problème. De fait, les problèmes ne sont pas gérés partout à la même échelle : l'approvisionnement en eau potable est organisé sur une base régionale en Angleterre, départementale en Italie, encore très locale en France. Les politiques d'éducation (écoles primaires) sont centralisées au niveau national en France quand dans d'autres pays, comme en Allemagne ou en Suisse, elles sont gérées à des niveaux subsidiaires (Cantons/Länder) et les établissements scolaires jouissent d'une indépendance importante. La décentralisation a des visages différents dans les pays européens. Par ailleurs, l'appréciation de la bonne échelle de gestion peut également évoluer dans le temps. Ainsi, si l'on a considéré longtemps que la protection contre les inondations relevait de l'État central, au nom de la solidarité nationale, ce principe est aujourd'hui remis en cause en raison de l'efficacité et de l'efficience de l'action publique et en faveur d'une décentralisation (J. Guerrin, Une inondation négociée ?, 2014).

Enfin, selon les acteurs interrogés, ce qui constitue le périmètre d'une échelle pertinente pour un problème donné peut différer. La pertinence d'une échelle dépend en effet de diverses variables. Des géographes américains ont développé un courant de recherche intitulé politics of scale ou politique échelles. Selon ces auteurs, la définition d'une échelle d'action publique est un acte qui peut s'expliquer par les intérêts des acteurs, et produire des effets politiques (N. Brenner, in : Progress in Human Geography, 2001). En effet, définir une échelle de gestion des problèmes participe à désigner un acteur ou une institution comme responsable de son traitement. Cela peut avoir pour effet de renforcer le pouvoir de ces derniers, légitimer leur existence administrative ou encore leur rôle politique. A contrario, cela peut avoir pour effet de léser d'autres acteurs qui vont dès lors s'opposer à cette définition scalaire. Par exemple, la restauration de zones d'expansion de crues (c'est-à-dire l'inondation contrôlée de territoires protégés contre les inondations) comme solution au problème inondation peut sembler une solution adéquate pour les citadins qui verront leur protection contre le risque améliorée et seront en faveur d'un traitement de l'inondation à l'échelle du bassin versant. Mais elle peut sembler non acceptable pour les habitants d'une zone rurale qui voient leur territoire devenir inondable, et vont plutôt oeuvrer pour une approche du problème en termes de solidarité nationale (J. Guerrin, G. Bouleau, Fr. Grelot, in : Journal of Hydrology, 2014). Néanmoins, la lecture de l'échelle par les intérêts contradictoires en présence se heurte à la difficulté de les identifier. En effet, les acteurs n'ont pas tous une vision stratégique de l'échelle à laquelle les problèmes doivent être définis. Par ailleurs, des acteurs aux intérêts divergents peuvent légitimer une même échelle d'action publique. Ainsi, la définition des problèmes va-t-elle aussi dépendre des représentations des acteurs, c'est-à-dire de ce qu'ils croient. Dans le même exemple, le positionnement d'acteurs en faveur du traitement du problème inondation à l'échelle nationale peut s'expliquer également par l'importance que ceux-ci confèrent aux principes d'égalité territoriale et de solidarité nationale. En fin de compte, si l'efficacité de l'action publique semble être favorisée par certaines échelles de gestion, dans une vision fonctionnaliste, celle-ci rencontre des limites étant donnée la complexité des problèmes. Il ne faut pas oublier qu'une bonne échelle de gestion varie dans le temps et l'espace, et selon les acteurs interrogés, en fonction de leurs intérêts et de leurs représentations. Plusieurs échelles pertinentes peuvent même coexister en fonction des problèmes et des solutions envisagées. Dès lors, le choix de l'échelle de gestion n'est pas un choix scientifique, mais éminemment politique. Il pourrait de ce fait être l'objet d'un débat démocratique, à organiser à la (ou aux) bonne(s) échelle(s)…

Joana Guerrin


Emmanouil Angelakis

Muhammad Yasir

Dipankar Bachar

Esam I Azhar

Jean-Christophe Lagier

Fehmida Bibi

Asif A Jiman-Fatani

Maha Alawi

Marwan A Bakarman

Catherine Robert

Didier Raoult
email: didier.raoult@gmail.com

Gut microbiome and dietary patterns in different Saudi populations and monkeys

Host genetics, environment, lifestyle and proximity between hosts strongly influence the composition of the gut microbiome. To investigate the association of dietary variables with the gut microbiota, we used 16S rDNA sequencing to test the fecal microbiome of Bedouins and urban Saudis and we compared it to the gut microbiome of baboons living in close contact with Bedouins and eating their leftovers. We also analyzed fermented dairy products commonly consumed by Bedouins in order to investigate their impact on the gut microbiome of this population. We found that the gut microbiomes of westernized urban Saudis had significantly lower richness and biodiversity than the traditional Bedouin population. The gut microbiomes of baboons were more similar to that of Bedouins compared to urban Saudis, probably due the dietary overlap between baboons and Bedouins. Moreover, we found clusters that were compositionally similar to clusters identified in humans and baboons, characterized by differences in Acinetobacter, Turicibacter and Collinsella. The fermented food presented significantly more bacteria genera common to the gut microbiome of Bedouins compared to urban Saudis. These results support the hypothesis that dietary habits influence the composition of the gut microbiome.

 .

Saudi Arabia is considered one of the most rapidly growing economies in the world, where eating habits have completely changed in recent years. As a result, urban Saudis have drastically changed their lifestyle and food habits, with a very limited variety of foods and an absence of fruit and vegetables 13 . In contrast, rural Bedouins

regularly consume vegetables, fruit and homemade fermented dairy products. As a result, because of the cultural, behavioral and ecological environment, we hypothesized that rural Bedouins harbor different microbiome profiles than those previously described in urban Saudis [START_REF] Yasir | Comparison of the gut microbiota of people in France and Saudi Arabia[END_REF] . To test this hypothesis, we used high-throughput 16S ribosomal RNA (rRNA) gene amplicon sequencing to characterize their gut microbiota, and we compared it to those of urban Saudis. Moreover, to test the impact of specific dietary sources on the gut microbiome, we tested homemade fermented dairy products commonly consumed by Bedouins. In addition, the impact of proximity on the gut microbiome for several mammals has been previously reported, including chimpanzees 4, wild apes [START_REF] Moeller | Rapid changes in the gut microbiome during human evolution[END_REF] , dogs [START_REF] Song | Cohabiting family members share microbiota with one another and with their dogs[END_REF] , and gorillas [START_REF] Moeller | Sympatric chimpanzees and gorillas harbor convergent gut microbial communities[END_REF] . This may be explained by a dietary overlap between the animal populations. Bedouins live in close contact with baboons, and there is a dietary overlap between human and primates. Specifically, primates commonly consume the leftover food of Bedouins (Supplementary Figure 1). To reinforce our hypothesis about the impact of food on the gut microbiome, we also tested the fecal microbiome of baboons living in sympatry with Bedouins.

Results

Composition of the gut microbiota of humans. We sequenced the V3-V4 region of 16S rDNA in fecal samples from 18 individuals from Jeddah living urban lifestyles and 10 Bedouins living rural lifestyles. The analysis of the high quality trimmed reads revealed that the gut microbiota of urban Saudis contained sequences from 8 different bacterial divisions/phyla, and most of the sequences belonged to Firmicutes and Proteobacteria (Fig. 1, Supplementary Table 1). The gut microbiota of Bedouins contained sequences from 10 different bacterial divisions/phyla, and most of the sequences belonged to Firmicutes and Actinobacteria. The relative abundance Bacteroidetes were significantly more common in the stools of urban Saudis compared to Bedouins (p = 0.01). Indeed, the relative abundance of Bacteroides has been associated positively in the past with diets rich in animal fat and protein [START_REF] Wu | Linking long-term dietary patterns with gut microbial enterotypes[END_REF] . The relative abundance of Verrucomicrobia were significantly more common in Bedouins compared to urban Saudis (p = 0.05). Finally, sequences of Spirochaetae and particulary Treponema berlinense and Treponema succinifaciens were only present in the gut microbiome of Bedouins. In the gut microbiome of urban Saudis, we detected 129 different genera whereas Bedouins had 157 different genera. All humans shared a core set of bacterial genera that was recovered from a majority of individuals from every sampled population. Indeed, we detected 74 genera in > 50% of urban Saudis and 66 genera in > 50% of Bedouins (Supplementary Figure 2). We then investigated the distribution of aerobic and facultative anaerobic genera residing in the gut microbiome of these groups using the taxonomic classification provided by 16S amplicon analysis. The difference in anaerobic genus counts revealed that urban Saudis had 78 different genera and Bedouins 76 different genera. As a result, significantly more different anaerobic genera existed in the gut microbiome of urban Saudis compared to Bedouins (Chi-square test; p = 0.04) but urban Saudis presented significantly more relative abundance of anaerobic genera compared to Bedouins (p < 0.001) (Supplementary Figure 3).

Impact of Fermented food on gut microbiome of humans.

Diet plays a critical role in the gut microbiota [START_REF] Yasir | Comparison of the gut microbiota of people in France and Saudi Arabia[END_REF][START_REF] Angelakis | The relationship between gut microbiota and weight gain in humans[END_REF] , and we analyzed 6 fermented foods, called Lohoh in the local language, commonly consumed by Bedouins, in order to determine their impact on the gut microbiome of Bedouins. For their preparation, pearl millet flour is mixed with water in the ratio of 1:2 to make a dough (ajeen) and is incubated at 30 °C for 24 hours. Usually by this time the dough has a good consistency and sour taste. The dough was fermented by adding 5% inoculate (starter) from previously fermented dough to start each subsequent batch.

We found that the food contained approximately 3 million 16S rRNA gene sequence reads, contained sequences from 11 different bacterial divisions/phyla, and most of the sequences belonged to Firmicutes and Proteobacteria (Fig. 1, Supplementary Table 1). We found 110 different genera, and most of the sequences belonged to Halomonas, Lactobacillus and Shewanella. In humans, 64 genera present in food also existed in the gut microbiome of both Bedouins and urban Saudis, whereas 3 genera present in food only existed in urban Saudis and 33 genera present in food only existed in the gut microbiome of Bedouins (Fig. 2, Supplementary Table 2). Composition of the gut microbiota of baboons. We also sequenced 34 stools from baboons living in contact with Bedouins. The analysis of the high quality trimmed reads revealed that the gut microbiota of baboons contained sequences from 18 different bacterial divisions/phyla, and most of the sequences belonged to Actinobacteria, followed by Firmicutes (Fig. 1, Supplementary Table 1). Fusobacteria were only present in the stools of Bedouins and baboons. The relative abundance of Bacteroidetes were significantly more common in the stools of baboons compared to Bedouins (p = 0.02). Baboons presented significantly less relative abundance of Actinobacteria compared to Bedouins and urban Saudis (p = 0.002 and p > 0.001 respectively) and significantly less relative abundance of Verrucomicrobia compared to Bedouins (p = 0.008).

In the gut microbiome of baboons we detected 237 different genera (75 anaerobic) and 72 genera were presented in the gut microbiome of > 50% baboons. Moreover we detected 32 genera that existed in the gut microbiome of > 50% of both humans and baboons (Supplementary Figure 2). In animals, 12 genera present in food existed only in the gut microbiome of baboons, whereas 93 genera existed in the gut microbiome of both Bedouins and baboons. Significantly less different anaerobic genera existed in the gut microbiome of baboons compared to urban Saudis (Chi-square test; p = 0.0001), whereas Bedouins presented significantly more different anaerobic genera compared to baboons (Chi-square test; p = 0.001). Finally, baboons presented significantly less relative abundance of anaerobic genera compared to urban Saudi and Bedouin groups (p < 0.001 and p < 0.001 respectively) (Supplementary Figure 3).

Gut microbiota proximity among humans and baboons.

Overall, 147 different genera from all sequence reads were identified, and based on genus-level Bray-Curtis dissimilarity (BCD), the microbiomes of baboons were more different than human microbiomes (p < 0.001) (Fig. 3). Moreover, Principle Coordinate Analysis (PCoA) of the overall composition of the genera communities between the groups revealed that the microbiome of baboons is closer to that of Bedouins than that of urban Saudis (Supplementary Figure 4). Comparing baboon and human microbiomes allowed us to infer the compositional changes that are most parsimoniously explained by the present-day variation among the microbiomes of human populations. The differences between microbiomes of urban Saudis and baboons was particularly evident in the first two principal coordinate axes of the pairwise beta diversities among samples at both the genus and 97% operational taxonomic unit (OTU).

The rarefaction with the measure chao1 shows that the samples from the baboon community are richer and more diverse than the other 3 groups (Supplementary Figure 5). Moreover, the microbial richness, estimated by the Chao1 index, and biodiversity, assessed by a nonparametric Shannon index for comparison among the groups, revealed that the gut microbiomes of baboons had significantly higher richness and biodiversity than both urban Saudis and Bedouins (Supplementary Figure 6). Moreover, we found that the gut microbiomes of westernized urban Saudis had significantly lower richness and biodiversity than the traditional Bedouin population.

Enterotype-Like Clusters among humans and baboons.

To test for the presence of enterotypes in baboons, we performed a multidimensional cluster analysis and Principal Component Analysis (PCA) by employing the same clustering and cluster validation methods that Arumugam et al. [START_REF] Arumugam | Enterotypes of the human gut microbiome[END_REF] used to identify the human enterotypes (Supplementary Figures 7 and8). As in chimpanzees [START_REF] Moeller | Chimpanzees and humans harbour compositionally similar gut enterotypes[END_REF] , our analyses revealed that the gut microbiome of baboons based on their genus-level compositions into three distinct clusters (i.e., enterotypes) that do not significantly correlate with host age, genealogy or gender (Fig. 4A). The bacterial taxa identified by class analysis as contributing most significantly to each cluster were Acinetobacter in enterotype 3 baboons, Turicibacter in enterotype 2 baboons, and Collinsella in enterotype 1 baboons (Fig. 4B). For humans, the bacterial taxa Shewanella and Vibrio are significantly overrepresented in enterotype 3, Bifidobacterium in enterotype 2 and Collinsella in enterotype 1. Bacterial taxa Collinsella contributes significantly in both human and baboon enterotype 1. Despite the overall congruence between the human and baboon enterotypes, we found differences in the prevalence of several bacterial genera (Table 1). We found a broad correspondence between the baboon and human enterotypes, although several bacterial genera that were overrepresented in a baboon enterotype were not overrepresented in any of the human enterotypes. As a result, Coprococcus and Marvinbryantia were represented in baboon enterotype 1, Turicibacter, Pedobacter and Vasilyevaea were represented in baboon enterotype 2, and Acinetobacter, Paenibacillus, Arthrobacter, Pectobacterium, Desemzia and Pseudomonas were represented in baboon enterotype 3, but these genera did not contribute significantly to the human enterotypes (Tables 1.2 and Supplementary Table 2).

Discussion

In this study, the comparison of the gut microbiomes of populations of humans and baboons, as well as dairy products commonly consumed by Bedouins, provides us with important information about the impact of dietary habits on gut microbiomes. In particular, we have demonstrated that the similarity in the microbiota of cohabiting individuals extends beyond human-to-human relationships and to animal-to-human relationships. We also provide evidence that baboons possess enterotypes that are compositionally similar to those observed within human populations, and that food has an important impact on the composition of the gut microbiome. All our samples were collected under similar conditions and they were kept frozen under sterile conditions at -80 °C, eliminating the possibility of contamination. In addition, before analyses, we verified that all of our samples had a good DNA load, and 16S rRNA sequencing-based studies targeting the V3-V4 region have been commonly used for the determination of the gut microbiome. Finally, our analysis for the presence of enterotypes was previously validated in humans [START_REF] Arumugam | Enterotypes of the human gut microbiome[END_REF] and in animals [START_REF] Moeller | Chimpanzees and humans harbour compositionally similar gut enterotypes[END_REF] . Previous reports have indicated that Western populations have less microbial richness than non-Western populations [START_REF] De Filippo | Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa[END_REF] . Our analyses of microbial richness yielded similar results, as Bedouins regularly consumed vegetables, fruit, chicken, dairy products, fermented food and rice. Vegetables and fruit were part of the daily diet in Bedouin populations compared to urban Saudis, who reported vegetable and fruit consumption only 1-2 times per week. Indeed, the urban population in Saudi Arabia has shifted away from traditional food to Western diets [START_REF] Washi | Poor diet quality and food habits are related to impaired nutritional status in 13-to 18-year-old adolescents in Jeddah[END_REF] . Most of the participants reported that they consumed a lot of snacks and fast foods, such as shawarma, hamburger, pizza and fried chicken. Regular consumption of junk-food snacks, eating away from home and abundant use of carbonated beverages in the Saudi population has already been reported [START_REF] Washi | Poor diet quality and food habits are related to impaired nutritional status in 13-to 18-year-old adolescents in Jeddah[END_REF] . A high-fiber diet has been associated with an enrichment of the microbiome 5 , and differences associated with diet were found in the gut microbiota of different populations [START_REF] Schnorr | Gut microbiome of the Hadza hunter-gatherers[END_REF][START_REF] Yatsunenko | Human gut microbiome viewed across age and geography[END_REF][START_REF] Escobar | The gut microbiota of Colombians differs from that of Americans, Europeans and Asians[END_REF] . Moreover, the gut microbiota can rapidly respond to an altered diet, potentially facilitating the diversity of human dietary lifestyles [START_REF] David | Diet rapidly and reproducibly alters the human gut microbiome[END_REF][START_REF] Cotillard | Dietary intervention impact on gut microbial gene richness[END_REF] . It was previously proposed that microbial diversity in the human microbiomes has decreased during human evolution [START_REF] Moeller | Chimpanzees and humans harbour compositionally similar gut enterotypes[END_REF] , and that recent lifestyle changes in humans have depleted the human microbiome of microbial diversity that was present in ancestors living in the wild [START_REF] Blaser | What are the consequences of the disappearing human microbiota?[END_REF] .

In this work, we found that the gut microbiome of Bedouins was closer to that of baboons than that of urban Saudis. This may possibly be explained by a dietary overlap with Bedouins. In fact, baboons rely on the leftover food from the local population dumped by the municipality or the food shared by people with them. People bring food like khubus (a type of local bread) and fruit for baboons. Moreover, baboons raid local farms and crops for their food. This is reinforced by the fact that we found that food affects the gut microbiome, because we found significantly more bacteria genera present in fermented food in the microbiome of Bedouins than in urban Saudis who do not consume these products. Indeed, the average consumption of fermented food and dairy products per week is much higher in the Bedouins compared to urban Saudis, and it is estimated that Bedouins consume these fermented foods on average 5-8 times per week. Moreover, the types of dietary pattern are almost the same in Bedouin populations. However, a limitation of our study was that we did not measure the amount of each food consumed among the populations. Moreover the use of bread to avoid baboon's aggression did not modify their gut microbiota as stools were collected the same time. Baboons and Bedouins presented almost the same bacteria genera present in fermented food in their gut microbiome, indicating that the effect of dietary choices is a very important factor affecting the gut microbiome.

As previously reported for humans [START_REF] Arumugam | Enterotypes of the human gut microbiome[END_REF][START_REF] Wu | Linking long-term dietary patterns with gut microbial enterotypes[END_REF] and animals [START_REF] Moeller | Chimpanzees and humans harbour compositionally similar gut enterotypes[END_REF][START_REF] Wang | Dietary history contributes to enterotype-like clustering and functional metagenomic content in the intestinal microbiome of wild mice[END_REF] , we have demonstrated the existence of three enterotypes in the human and baboon gut microbiome. Long-term diets were previously correlated with enterotype status, where individuals with greater animal fat and protein intake were more likely to present the Bacteroides-dominated enterotype compared with those with more carbohydrate intake, associated with a Prevotella-dominated enterotype [START_REF] Wu | Linking long-term dietary patterns with gut microbial enterotypes[END_REF][START_REF] Wang | Dietary history contributes to enterotype-like clustering and functional metagenomic content in the intestinal microbiome of wild mice[END_REF] . Moeller et al. [START_REF] Moeller | Chimpanzees and humans harbour compositionally similar gut enterotypes[END_REF] found that chimpanzees similarly presented three enterotypes, contributed by Faecalibacterium, Lachnospiraceae and Bulleidia respectively. In contrast, the enterotypes of baboons were contributed by Acinetobacter, Turicibacter and Collinsella. Despite marked differences among the microbiomes of humans and baboons, we found that there is a set of bacterial taxa shared across host populations. Gender has been previously correlated with modifications of the gut microbiota [START_REF] Chen | Impact of demographics on human gut microbial diversity in a US Midwest population[END_REF] , but a limitation of our study was that, due to ethical reasons, we tested only male individuals from Jeddah living urban lifestyles. Although the ecological/cultural divergence between humans can explain the differences among their microbiomes, the relative roles of genetic divergence between host species in generating the differences between their microbiomes remain unclear.

In conclusion, we have shown that baboons possess enterotypes that are compositionally similar to those observed in human populations, and we confirmed that Western diet populations have less microbial richness than traditional populations. The dietary overlap between baboons and Bedouins probably explains why these populations present similarities in their gut microbiome compared to urban individuals. Moreover, the fact that the fermented food presented significantly more bacteria genera common to the gut microbiome of Bedouins compared to urban Saudis shows the impact of diet on the gut microbiome. We point out the importance of more intensive research in the future to understand the impact of diet-microbiome interactions.

Online Materials. Subject selection criteria. This study protocol was approved by the Ethics Committee of King Abdul Aziz University under agreement No. (014-CEGMR-2-ETH-P), and methods were carried out in accordance with the approved guidelines. We tested only male volunteers from Jeddah living urban lifestyles and 8 male and 2 female Bedouins living rural lifestyles. The data (date of birth, weight, height, antibiotic use, dietary pattern and significant changes in diet) were recorded using a standardized questionnaire. Exclusion criteria included individuals under 18 years of age, a past history of colon cancer, inflammatory bowel disease, acute or chronic diarrhea in the previous 8 weeks and treatment with an antibiotic in the 3 months before fecal sampling. All patients gave written informed consent. We also collected baboon stool samples from the Taif region (21°26′ N 40°21′ E) located in the western province of Makkah. It is estimated that approximately 2,000 baboons live in this area and feed on garbage. Indeed, the baboons have become dependent on humans for food. No experimentation was conducted on baboons, as fecal samples were collected from the soil. No other permit was required, as this research was non-invasive work, and the collection of the samples did not disrupt the wild fauna. We fed baboons with bread to avoid their aggression and followed the herd of adult baboons for sample collection. Only little bread was used per herd of the baboon while stool samples were collected at the same time. We followed a different herd at seven different locations in that area to maximize samples from different baboons. We collected fresh stool samples with sterile spoons, avoiding the soil particles. Troops of baboons consisted of both male and female baboons; however, gender information was not possible to be collected for the sampled baboon. All stool samples were stored at -80 °C.

In addition, we collected different types of millet-fermented food called Lohoh that is commonly consumed by Bedouins. This food is prepared from different types of millet flour, such as Pearl millet flour, Injera millet flour and whole grain flour. These samples were collected in a sterile container from the Bedouin people at the time of stool sample collection and were stored at -80 °C.

Extraction of DNA from stool samples and 16S rRNA sequencing using MiSeQ technology. Fecal DNA was extracted from samples using the NucleoSpin ® Tissue Mini Kit (Macherey Nagel, Hoerdt, France) according to a previously described protocol [START_REF] Zoetendal | Isolation of RNA from bacterial samples of the human gastrointestinal tract[END_REF] . Samples were sequenced for 16S rRNA sequencing using MiSeq technology as described by the manufacturer's Guide 15044223-B from the MiSeq procedure. Briefly, PCR amplified templates from genomic DNA using the surrounding conserved region V3-V4 primers with overhang adapters ( Fw Ov Ad _341F T CG TCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG;ReOvAd_ 785RGTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC). Samples were amplified individually for the 16S "V3-V4" regions by Taq Phusion and visualized on the Caliper LabchipII device by a DNA 1K LabChip. After purification on AMPure beads, the concentrations were measured using high sensitivity Qubit technology. Using a subsequent limited cycle PCR on 1 ng of each PCR product, Illumina sequencing adapters and dual-index barcodes were added to each amplicon. After purification on AMPure beads, the libraries were then normalized according to the Nextera XT protocol. The 96 multiplexed samples were pooled into a single library for sequencing on the MiSeq. The pooled library containing indexed amplicons was loaded onto the reagent cartridge and then onto the instrument along with the flow cell. Automated cluster generation and paired-end sequencing with dual index reads was performed in a single 39-hour run in a 2 × 250 bp. On the instrument, the global cluster density and the global passed filter per flowcell were generated. MiSeq Reporter software determined the percentage of indexing and cluster passed filter (PF) for each amplicon or library. The raw data were configured in fastq files for R1 and R2 reads.

Data processing: Filtering the reads, dereplication and clustering. The paired end reads of corresponding raw fastq files were assembled into contigs by using FLASH [START_REF] Magoc | FLASH: fast length adjustment of short reads to improve genome assemblies[END_REF] , which gave a total of 14,428,091 assembled sequences. The high quality sequences were then selected for the next steps of analysis by considering only those sequences which contain both the primers (forward and reverse). In the following filtering steps the sequences containing N were removed. Sequences with a length shorter than 200 nts were removed and the sequences longer than 500 nts were trimmed. Both the forward and reverse primers were also removed from each of the sequences. An additional filtering step was applied to remove the chimeric sequences by using chimera slayer from QIIME [START_REF] Caporaso | QIIME allows analysis of high-throughput community sequencing data[END_REF] . After these filtering steps, a total of 13,085,792 sequences remained. Strict dereplication (clustering of duplicate sequences) was performed on these filtered sequences, and they were then sorted by decreasing abundance [START_REF] Guillou | The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy[END_REF] . Next, clustering was performed with 97% identity and the OTUs (representative sequence of each cluster) were extracted [START_REF] Guillou | The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy[END_REF] . These OTUs represented a total of 11,989,797 sequences. The above filtering steps and OTU extractions were performed in QIIME [START_REF] Caporaso | QIIME allows analysis of high-throughput community sequencing data[END_REF] . All the raw sequences of fastq files have been submitted to EMBL-EBI [START_REF] Cochrane | EMBL Nucleotide Sequence Database: developments in 2005[END_REF] with the accession number PRJEB9815.

Building the reference database. We downloaded the Silva SSU and LSU database [START_REF] Quast | The SILVA ribosomal RNA gene database project: improved data processing and web-based tools[END_REF] of release 119 from the Silva website and a local database of predicted amplicon sequences was built from it, after extracting the Siva reference sequences containing both the forward and reverse primers and by allowing 3 differences between each primer and the sequence [START_REF] Terrat | Molecular biomass and MetaTaxogenomic assessment of soil microbial communities as influenced by soil DNA extraction procedure[END_REF] . Lastly, we had our local reference database containing a total of 456,714 well-annotated sequences.

Taxonomic Assignments. The OTUs were then searched against our reference database Silva 199 by using blast [START_REF] Altschul | Basic local alignment search tool[END_REF] . The 100 best matches above 80% identity (similarity) with each of the OTUs were extracted from the reference database and were sorted with respect to their decreasing percentage of similarity. Thus, the best hits with the highest percentage of similarity (by also considering all the hits within 0.5% similarity of the best hits) with the OTUs were then considered for taxonomic assignments, and taxonomy to the lowest rank was obtained by applying majority voting [START_REF] Guillou | The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy[END_REF] .

Database of obligate anaerobes. We conducted a bacterial oxygen tolerance database based on the literature (available online at http://www.mediterranee-infection.com/article.php?laref= 374). Each phylotype was assigned as 'obligate anaerobe' , 'aerotolerant' or 'unknown' according to oxygen tolerance.

Statistical Analysis. We calculated the richness and biodiversity index of the OTUs by using the QIIME software package [START_REF] Schloss | Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities[END_REF] . We estimated richness using the Chao1 index and diversity by the non-parametric Shannon formula [START_REF] Chao | Nonparametric estimation of Shannon's index of diversity when there are unseen species in sample[END_REF] . Non-parametric Kruskal-Wallis along with Mann-Whitney analyses were performed to identify significantly different bacterial taxa in the study participants. Linear discriminant analysis was performed using Lefse [START_REF] Segata | Metagenomic biomarker discovery and explanation[END_REF] with normalized option. We used QIIME for rarefaction and Principal Coordinate Analysis [START_REF] Caporaso | QIIME allows analysis of high-throughput community sequencing data[END_REF] . For PCoA, first we normalized the data at 20,000 sequences for each sample and calculated the weighted-unifrac distance [START_REF] Caporaso | QIIME allows analysis of high-throughput community sequencing data[END_REF] . We performed the Adonis 36 test using this weighted-unifrac distance. PCoA was plotted using the weighted-unifrac distance [START_REF] Caporaso | QIIME allows analysis of high-throughput community sequencing data[END_REF] . We performed enterotype analyses (Supplementary Figures 7 and8) by employing the PAM clustering and silhouette cluster validation technique, as described in the paper of Arumugam et al. [START_REF] Arumugam | Enterotypes of the human gut microbiome[END_REF] using the R statistical software package. For enterotype analysis, the Jensen-Shannon Distance (JSD) distance of genus abundance was used for clustering [START_REF] Arumugam | Enterotypes of the human gut microbiome[END_REF] , the Calinski-Harabasz (CH) Index 37 was used to assess the optimal number of clusters and the Silhouette coefficient [START_REF] Rousseeuw | Silhouettes: A graphical aid to the interpretation and validation of cluster analysis[END_REF] was used for cluster validation. At the final step of enterotype analysis, the Principal Component Analysis (PCA) and Between Class Analysis (BCA) were performed and the results were plotted [START_REF] Arumugam | Enterotypes of the human gut microbiome[END_REF] . Other statistical analyses were performed using SPSS (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ © The Author(s) 2016

Figure 1 .

 1 Figure 1. The relative abundance of the gut microbiota phyla among the groups tested.

Figure 2 .

 2 Figure 2. Network of bacterial genera present in fermented food and in the gut microbiome of urban Saudis and Bedouins.

Figure 3 .

 3 Figure 3. Diminished diversity in human and baboon gut microbiomes across populations. Mean numbers of observed bacterial genera per individual in baboons and in human populations at a sequencing depth of 20,000 reads. Error bars correspond to 95% CIs, and asterisks denote significant differences at p < 0.001.

Figure 4 .

 4 Figure 4. Identification of baboon enterotypes. (A) Assortment of gut microbial communities into enterotypes in baboons and humans. Shown are BCA visualizations of enterotypes (colored ellipses), as identified by PAM clustering, with black dots representing abundance distributions of bacterial genera from an individual host and numbered white rectangles marking the center of each enterotype. Panel (right) showing human gut enterotypes modified from Arumugam et al. 3 . Bacterial taxa uniquely overrepresented in the corresponding baboon and human enterotypes are listed. (B) Relative abundance of the three bacterial taxa that are principally responsible for the separation of baboon enterotypes. Shown are means, ranges and first and third quartiles.

Table 1 . Frequencies of bacterial taxa overrepresented within each baboon enterotype.

 1

			Frequency in
	Taxa in Baboons	Enterotype1	Enterotype2	Enterotype3
	Enterotype 1
	Collinsella	> 0.0001	> 0.0001	0.02
	Coprococcus	> 0.0001	0.0005	0.01
	Blautia	0.0006	0.002	0.08
	Marvinbryantia	> 0.0001	0.0002	0.01
	Enterotype 2
	Turicibacter	> 0.0001	0.005	0.0003
	Pedobacter	0.0004	0.02	0.0002
	Vasilyevaea	> 0.0001	0.001	0
	Enterotype 3
	Acinetobacter	0.2	0.03	0.04
	Paenibacillus	0.008	0.0007	0.0002
	Arthrobacter	0.12	0.07	0.003
	Pectobacterium	0.002	> 0.0001	> 0.0001
	Desemzia	0.002	> 0.0001	0.0001
	Pseudomonas	0.02	0.001	0.001
			Frequency in
	Taxa in Humans	Enterotype1 Enterotype2 Enterotype3
	Enterotype 1
	Pseudobutyrivibrio	0.06	0.003	0.003
	Collinsella	0.05	0.01	0.03
	Papillibacter	> 0.0001	0	> 0.0001
	Geobacillus	> 0.0001	> 0.0001	> 0.0001
	Enterotype 2
	Bifidobacterium	0.06	0,1	0.04
	Blautia	0.05	0.1	0.02
	Streptococcus	0.005	0.07	0.02
	Enterotype 3
	Shewanella	0	0	0.003
	Vibrio	0	0	> 0.0001
	Brochothrix	0	0	> 0.0001

Table 2 . Frequencies of bacterial taxa overrepresented within each human enterotype.

 2

Scientific RepoRts | 6:32191 | DOI: 10.1038/srep32191

Acknowledgements

This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (3-140-1434-HiCi).

Author Contributions

Additional Information