Hadza Prevotella require diet-derived microbiota-accessible carbohydrates to persist in mice

SUMMARY

Industrialization has transformed the gut microbiota, reducing the prevalence of Prevotella relative to Bacteroides. Here, we isolate Bacteroides and Prevotella strains from the microbiota of Hadza hunter-gatherers in Tanzania, a population with high levels of Prevotella. We demonstrate that plant-derived microbiota-accessible carbohydrates (MACs) are required for persistence of Prevotella copri but not Bacteroides thetaiotaomicron in vivo. Differences in carbohydrate metabolism gene content, expression, and in vitro growth reveal that Hadza Prevotella strains specialize in degrading plant carbohydrates, while Hadza Bacteroides isolates use both plant and host-derived carbohydrates, a difference mirrored in Bacteroides from non-Hadza populations. When competing directly, P. copri requires plant-derived MACs to maintain colonization in the presence of B. thetaiotaomicron, as a no-MAC diet eliminates P. copri colonization. Prevotella’s reliance on plant-derived MACs and Bacteroides’ ability to use host mucus carbohydrates could explain the reduced prevalence of Prevotella in populations consuming a low-MAC, industrialized diet.

In brief

Gellman et al. present a set of Bacteroides and Prevotella isolates from the Hadza microbiota. The results of whole-genome sequencing, gnotobiotic mouse models, and RNA sequencing show that P. copri relies on the presence of dietary microbiota-accessible carbohydrates (MACs) to persist in the gut microbiota.

Graphical Abstract

INTRODUCTION

The industrialized lifestyle is defined by the consumption of highly processed foods, high rates of antibiotic administration, cesarean section births, sanitation of the living environment, and reduced contact with animals and soil, all of which can affect the human gut microbiota.¹ Certain taxa are influenced by industrialization; i.e., they are prevalent and abundant in non-industrialized populations and diminished or absent in industrialized populations, or vice versa.^2–8 The microbiota of 1,000- to 2,000 year-old North American paleofeces is more similar to the modern non-industrialized than industrialized gut.⁹ The industrialized microbiota appears to be a product of both microbial extinction, as once-dominant taxa disappear, and expansion of less-dominant or new taxa.¹⁰

The industrialized diet differs drastically from non-industrialized diets, including a reduced amount of microbiota-accessible carbohydrates (MACs), a major metabolic input for microbes in the distal gastrointestinal tract.^10–12 Some gut-resident microbes use host mucin, which is heavily glycosylated, as a carbon source, depending on the availability of dietary MACs.^13–17 Shifts in dietary MACs alter microbial relative abundances and may increase inflammation and susceptibility to intestinal pathogens.^14,18,19 Taxa are lost due to a lack of dietary MACs over generations in a mouse model²⁰ and in humans as they immigrate to the US.⁷

As human populations adopt an industrialized lifestyle, the prevalence of Prevotella decreases and that of Bacteroides increases.^2,3,21 These genera are both members of the Bacteroidota phylum, are known to colonize mammalian hosts, and make up a significant fraction of the human gut microbiome.^22–24 While Bacteroides are well studied, Prevotella species remain understudied with few tools available for mechanistic investigation.^25–28 Both genera harbor well-documented carbohydrate utilization capabilities, encoded in carbohydrate active enzymes (CAZymes), often organized into polysaccharide utilization loci (PULs).^29–31 Characterization of intestinal Prevotella species have been limited by challenges with colonization, particularly mono-colonization of germ-free mice. Here, we overcome these barriers to establish a causal link between diet and Prevotella copri abundance in a gnotobiotic mouse model.

The decreased prevalence of Prevotella in industrial populations is likely linked to a decline in relative abundance within individual microbiotas.³² Decreased abundance of bacterial taxa in individuals reduces the likelihood of transmission from mother to infant.^1,5 When compounded over generations, decreased abundance can result in population-level decline in prevalence and eventually taxa loss or extinction.⁷ The factors driving the decline in Prevotella and the increase in Bacteroides during industrialization remain elusive. The abundance and prevalence of specific strains of P. copri, the dominant Prevotella species in the human gut, vary among populations based on host lifestyle, particularly diet.^33,34 Here, we use gnotobiotic mice to investigate the role of diet in sustaining Prevotella and Bacteroides colonization; we demonstrate that dietary MACs play a key role in controlling the abundances of Bacteroides and Prevotella.

RESULTS

Bacteroides and Prevotella genomes from the Hadza microbiota vary in prevalence across lifestyle

To compare Prevotella and Bacteroides from non-industrialized lifestyle populations, we isolated and sequenced six Bacteroides strains and seven Prevotella strains from stool samples collected from 13 Hadza individuals. Single-isolate genomes were assembled using both MiSeq-generated short reads (146 bp) and nanopore-generated long reads (10–100 kb) (Table 1).

Table 1.

Bacterial strains used

Strain name	Genus	Species	Strain	Origin specific	Origin	Genome size (bp)	Genome size (Mb)	Number of genes	Number of contigs
Bt H-2209	Bacteroides	thetaiotaomicron	H-2209	Hadza	human feces	6,119,319	6.119319	4,722	1
Bt H-2622	Bacteroides	thetaiotaomicron	H-2622	Hadza	human feces	6,037,034	6.037034	4,686	1
Bc H-1617	Bacteroides	caccae	H-1617	Hadza	human feces	5,112,756	5.112756	4,360	1
Bf H-2631	Bacteroides	fragilis	H-2631	Hadza	human feces	4,877,774	4.877774	4,622	3
Bo H-1813	Bacteroides	ovatus	H-1813	Hadza	human feces	6,715,646	6.715646	5,536	4
Bo H-2495	Bacteroides	ovatus	H-2495	Hadza	human feces	6,789,892	6.789892	5,086	1
Bt VPI 5482	Bacteroides	thetaiotaomicron	VPI 5482	reference	human feces, unknown nationality	6,260,000	6.26	5,108	1
Bc ATCC 43185	Bacteroides	caccae	ATCC 43185	reference	human feces, Texas	5,280,000	5.28	4,399	1
Bf NCTC 9343	Bacteroides	fragilis	NCTC 9343	reference	human appendix abscess, UK	5,190,000	5.19	4,194	2
Bo ATCC 8483	Bacteroides	ovatus	ATCC 8483	reference	human feces, unknown nationality	6,470,000	6.47	4,896	1
Pc DSM 18205	Prevotella	copri	DSM 18205	reference	human feces, Japan	3,510,000	3.51	2,968	1
Pc H-2379	Prevotella	copri	H-2379	Hadza	human feces	4,350,632	4.350632	3,611	1
Pc H-2383	Prevotella	copri	H-2383	Hadza	human feces	4,506,031	4.506031	3,836	1
Pc H-2446	Prevotella	copri	H-2446	Hadza	human feces	4,057,255	4.057255	3,383	3
Pc H-2477	Prevotella	copri	H-2477	Hadza	human feces	4,111,062	4.111062	3,405	1
Pc H-2489	Prevotella	copri	H-2489	Hadza	human feces	4,115,122	4.115122	3,563	1
Pc H-2497	Prevotella	copri	H-2497	Hadza	human feces	4,081,238	4.081238	3,548	1
Pc H-2632	Prevotella	copri	H-2632	Hadza	human feces	3850424	3.850424	3251	4
Pc N-01	Prevotella	copri	N-01	non-Hadza	human feces, USA	4,057,390	4.05739	3,880	1
Pc YF2	Prevotella	copri	YF2	reference	unknown	3,860,000	3.86	3,060	2

The taxonomy of these newly isolated strains was evaluated using the Genome Taxonomy Database (GTDB) version r207 (Figure 1A). All Bacteroides isolates belong to known species: Bacteroides ovatus, Bacteroides thetaiotaomicron, Bacteroides caccae, and Bacteroides fragilis. Three of our Prevotella isolates belong to named species Prevotella sp015074785 and Prevotella sp900551275, while the remaining five isolates are novel species according to GTDB. To verify this finding, we created a phylogenetic tree with our Prevotella isolates, representatives of the most closely related representative species in GTDB, and all P. copri representative genomes in GTDB (Figure S1A; Table S1). We observe that our isolated genomes have approximately the same phylogenetic distance to the closest representative genomes as the representative genomes have to one another, supporting their characterization as novel species. Apart from GTDB, there have been other efforts to characterize the extensive genomic diversity of the Prevotella genus.³⁴ Of the four proposed P. copri subgroups possessing >10% inter-clade genetic divergence, all eight Hadza Prevotella strains recovered in this study belong to clade A (Figure S1B).

Figure 1. Hadza Bacteroides and Prevotella strains are related to previously sequenced isolates and vary in prevalence across populations.

(A) Phylogenetic tree of Prevotella and Bacteroides genomes. Isolates from this study (red) and genomes from GenBank (black), strains used later in this study (bold).

(B) Prevotella and Bacteroides subspecies prevalence across foragers (Hadza and Chepang), agriculturalists (Rau, Raj, Tharu), or industrialized (California). Population size for each group is denoted as n. Prevalence defined as percentage of gut metagenomes from a population (column) in which a particular strain (row) is detected. Gray triangles indicate whole genomes isolated in this study, aligned with (A).

To understand the prevalence of these genomes across human populations, we compared Prevotella and Bacteroides prevalence among Hadza adults and infants, four populations from Nepal living on a lifestyle gradient including foraging (Chepang), recent agriculturalist (Raute, Raji), longer term agriculturalist (Tharu), and industrial lifestyle populations (California) (Figure 1B; Table S2). We chose these groups due to their varied lifestyles and the exceptional metagenomic sequencing depth achieved, averaging 23 Gbp per sample.^3,4 Prevotella genomes are rare in or absent from the industrialized populations, while they are more prevalent and abundant in the Hadza and Nepali samples. Conversely, nearly all Bacteroides genomes, including those isolated from the Hadza, are more prevalent in the California samples. The clear lifestyle shift associated with Bacteroides and Prevotella prevalence leads to the question of what aspects of the industrial lifestyle have driven these changes.

Dietary MACs are necessary for P. copri persistence

While many factors differentiate the industrial and non-industrial lifestyles, diet serves as the top candidate for driving microbiota alterations.¹⁰ The Hadza diet is rich in dietary MACs from foraged tubers, berries, and baobab.³⁵ In contrast, the industrialized diet is typified by high caloric intake and foods rich in fat and low in MACs.³⁶ We wondered whether diet alone could affect the ability of Hadza Bacteroides and Prevotella to colonize mice. Germ-free (GF) mice were colonized with either Hadza B. thetaiotaomicron (Bt) H-2622, or Hadza P. copri (Pc) H-2477. Mice were maintained on a high-MAC diet for 7 days and then switched to either a diet devoid of MACs (no MAC), a high-fat/low-MAC diet (Western), or maintained on the high-MAC diet for 7 days (Figure 2A). Bt H-2622 colonization density (10⁹ colony-forming units [CFU]/mL in feces) at baseline on the high-MAC diet was maintained in all three diet conditions (Figure 2B). Pc H-2477 colonized to a lower degree on the high-MAC diet (10⁷ CFU/mL on day 0) and declined drastically following the change to the Western or no-MAC diet, with no fecal CFUs detectable 7 days post diet switch (Figure 2C). The lack of detectable Pc H-2477 in the absence of MACs was particularly striking given the absence of competition from other microbes in this mono-associated state. To our knowledge, this is the first example of a strain’s apparent eradication in a mono-associated state due to a diet change. Two other P. copri strains (Hadza Pc H-2497 and a non-Hadza strain isolated from an individual of African origin Pc N-01) are also lost in vivo in the absence of dietary MACs (Figures S2A and S2B), indicating that survival of P. copri in vivo depends on the presence of dietary MACs.

Figure 2. Bt and Pc colonization differ in diet-dependent manner.

(A) Schematic of gnotobiotic experiments.

(B and C) Fecal density of Bt H-2622 (B), and Pc H-2477 (C) in monocolonized mice (n = 4/group for high-MAC and Western diets, n = 5/group for no-MAC diet) fed different diets (multiple Mann-Whitney tests, *p < 0.05, **p < 0.01). Representative experiments, repeated twice. Dashed line denotes limit of detection (LOD = 500 CFU/mL). Error bars indicate standard error of the mean (SEM).

(D) Proportion of genes upregulated in vivo in cecal contents of monocolonized mice on high-MAC diet on day seven of the experiment shown in (A), compared to gene expression in culture with PYG in late exponential phase. Genes organized by functional categories (Rapid Annotation using Subsystem Technology, RAST).

(E) Proportion of predicted substrate categories of upregulated CAZymes in cecal contents of monocolonized under high-MAC conditions on day seven of the experiment shown in (A).

To measure the gene expression employed by Hadza Pc and Bt in vivo, we analyzed transcriptional profiling data from cecal contents of mice monocolonized with either Pc H-2477 or Bt H-2622 fed a high-MAC diet relative to in vitro growth in peptone yeast glucose broth (PYG). Both Bt H-2622 and Pc H-2477 upregulate a large number of genes in vivo under high-MAC diet conditions. Despite the fact that 18% and 13% of genes in the Bt H-2622 and Pc H-2477 genomes, respectively, encode for predicted carbohydrate utilization proteins, 86% (in Bt H-2622) and 65% (in Pc H-2477) of genes upregulated in vivo relative to in vitro encode for carbohydrate utilization (p < 4e–12 for Bt, p < 5e–13 for Pc, Fisher’s exact test), indicating that carbohydrate utilization is the major metabolic function of these organisms in vivo (Figure 2D).

A comparison of glycosidic linkage-breaking CAZymes, glycoside hydrolases (GHs) and polysaccharide lyases (PLs), reveals that both Bt H-2622 and Pc H-2477 upregulate more CAZymes in vivo on the high-MAC diet than in vitro (Pc, 71/6 CAZymes significantly expressed in vivo/in vitro; Bt, 244/55 CAZymes significantly expressed in vivo/in vitro) (Figures S2C and S2D). However, Bt H-2622 upregulates a higher proportion of GHs and PLs devoted to animal-derived carbohydrate utilization relative to Pc H-2477 (Figure 2E). Specifically, in vivo under high-MAC diet conditions, Bt H-2622 upregulates eight of 22 encoded mucus-targeted GHs (three out of 10 GH18; five out of 12 GH20), whereas Pc H-2477 encodes no GH18s and only one GH20, which is not upregulated in the high-MAC diet condition (Figures 2E, S2C, and S2D). In addition to targeting mucus carbohydrates, Bt H-2622 also upregulates 40 of its 97 plant-targeting GHs and PLs, whereas Pc H-2477 upregulates all 38 of its plant-targeting GHs and PLs in the high-MAC diet (Figure 2E).

On the no-MAC diet relative to the in vitro condition, Bt H-2622 upregulates two additional GH20s (along with the eight other mucin CAZymes upregulated on the high-MAC diet) as well as 27 plant-targeting GHs and PLs (Figures 2E and S2E). In other words, under high-MAC diet conditions, Bt upregulates CAZymes associated with plant, animal, and other carbohydrates equivalently (48 animal, 45 other, 46 plant), whereas, under the no-MAC diet condition, Bt upregulates a larger number and proportion of CAZymes associated with the utilization of animal associated carbohydrates (51 animal, 27 other, 35 plant). Since Pc H-2477 does not colonize mice fed the no-MAC diet, this condition was not profiled. When comparing the no-MAC diet to the high-MAC diet, Bt H-2622 differentially upregulates only three GHs, two of which degrade mucin (GH18) (Figure S2F).¹⁷

Taken together, these data indicate that, in vivo, Bt H-2622 relies on both mucus and plant-derived carbohydrates. When plant carbohydrates are eliminated from the diet, Bt H-2622 further upregulates mucus-degrading machinery, whereas Pc H-2477’s minimal mucus-degrading capacity renders it incapable of sustaining colonization in the absence of MACs.

Carbohydrate degradation capacity differences between Hadza Bacteroides and Prevotella mirrors industrialized strains

Hadza Pc and Bacteroides isolates have a similar number and predicted function of GHs and PLs to reference strains of the corresponding species (Table S3; Figure 3). Unsupervised clustering of GHs and PLs reveals that the Hadza strains cluster with their type strain counterparts, in keeping with the genetic similarity between genomes of the same species; the sets of CAZymes in each genome are most similar to the CAZymes found in genomes assigned to the same species (Figure 3A). When comparing the total number of GHs and PLs encoded within the Hadza strains to non-Hadza strains, we found similar total numbers of these genes and distribution of substrate specificity between strains of the same species (Figures 3B and 3C). Comparisons of Hadza Pc CAZymes are limited by the limited number of annotated Pc genomes in the CAZy database (only two exist at the time of this publication). We have now added seven more Pc genomes, and, as more Pc genomes are published, more variation in CAZyme repertoire may be uncovered.

Figure 3. Hadza Bacteroides and Prevotella differ in distribution of GHs and PLs.

(A) Number of GHs and PLs per genome indicated by CAZy family (rows). CAZymes shown appear at least once in any of the genomes analyzed. Hierarchical clustering via complete-linkage clustering method.

(B) Number of GHs and PLs normalized to genome size (Mb), colored by predicted substrate.

(C) Proportion of GHs and PLs in each genome colored by predicted substrate.

(D) Number of mucin-degrading GH18 and GH20 genes per genome.

While Hadza Bacteroides and Prevotella strains mirror the carbohydrate-degrading capacity of their non-Hadza counterparts, large differences exist between the Bacteroides and Prevotella strains. The Bacteroides encode more GHs and PLs than Prevotella strains even when corrected for genome size (251/21 average GH/PL in Bacteroides; 101/5 in Prevotella; Welch two-sample t test, p = 0.0056) (Table S3; Figure 3B). The proportion of Bacteroides GHs and PLs that are predicted to target plant carbohydrates or animal carbohydrates are equivalent (average 34% and 37%, respectively), whereas the Prevotella-encoded carbohydrate degradation is biased toward plant over animal carbohydrates (average 44% and 19%, respectively) (Figure 3C). The Bacteroides also encode a greater breadth of GH and PL families (averaging 68 CAZyme families per genome), while Pc isolates average 40 CAZy families per genome (Figure 3A), consistent with previously reported distributions for industrial-lifestyle-derived Bacteroides and Prevotella strains.³¹ The two genera also differ in their predicted mucin-degradation capacity (Figure S3; Wilcoxon test, p = 3e–4). CAZyme families GH18 and GH20 target carbohydrates found within the intestinal mucus lining.³⁷ All Hadza Bacteroides isolates harbor 11–14 GH20 and 1–13 GH18 CAZymes; however, the Hadza Prevotella isolates contain only one or two GH20s and only one isolate, Pc H-2497, contains a single GH18 (Figure 3D; Wilcoxon test, p = 4e–4).

The CAZyme contents of Hadza Bacteroides and Prevotella isolates are similar to their non-Hadza counterparts. Hadza Bacteroides isolates contain both more GHs and PLs overall as well as broader substrate-degrading capabilities that include both plant- and animal-derived carbohydrates relative to the Hadza Prevotella isolates. This difference between the Hadza Bacteroides and Prevotella strains is similar to that seen in non-Hadza strains and industrial lifestyle microbiotas, suggesting that the Prevotella niche is more reliant upon plant carbohydrates compared to Bacteroides.^38,39

Dietary MACs are sufficient to maintain Pc colonization in the presence of Bt

To test whether Hadza Bacteroides and Prevotella isolates differ in their ability to use plant- and mucus-derived carbohydrates, we cultured Hadza and type strain Bacteroides and Pc isolates in media containing the plant carbohydrate inulin, porcine gastric mucin glycans, porcine intestinal heparin, or fructose as the sole carbon source. There is a range of ability to utilize inulin across the strains, consistent with previous work (Figure 4A).⁴⁰ Growth in the presence of mucin, however, is divided by genera; most Bacteroides isolates grow well on mucin, but the P. copri isolates do not (Figure 4A). These data are consistent with the lack of mucin-degrading capacity within the Pc genomes and the loss of Pc colonization in vivo when the host is the major carbohydrate source.

Figure 4. Reintroduction of MACs is sufficient to maintain P. copri colonization.

(A) Normalized maximum optical density 600 (OD₆₀₀) of Bacteroides and Prevotella isolates grown in Yeast Casitone Fatty Acids broth (YCFA) with a single added carbohydrate for 24 h.

(B) Fecal CFUs of Pc H-2477 in monocolonized mice fed a no-MAC-g or inulin-g diet (mean + SEM, n = 5 mice per group, multiple Mann-Whitney tests, *p ≤ 0.05, **p < 0.01). Dashed line denotes LOD = 500 CFU/mL. Error bars indicate SEM. Representative experiment, repeated three times.

(C) Schematic of bicolonization with Pc H-2477 and Bt H-2622.

(D and E) qPCR index of DNA of Pc H-2477 (D) and Bt H-2622 (E) quantified from fecal samples from bicolonized mice (mean + SEM, n = 5 mice per group; multiple t tests, *p ≤ 0.05, **p < 0.01). Dashed line denotes LOD = 10⁻⁷ index. Error bars indicate SEM. Shaded bars indicate administration of high-MAC diet. Representative experiment, repeated twice.

To determine whether the lack of diet-derived MACs is responsible for the loss of Pc H-2477 colonization we observed in the high-fat/low-MAC Western diet and no MAC diet (Figure 2C), we fed mice monocolonized with Pc H-2477 a high-MAC diet and then switched to either a custom diet containing 34% inulin by weight as the sole fermentable carbohydrate to match MAC content of the high-MAC diet (custom diets use gelatin as a binding agent and are noted by a “-g”; inulin-g) or a no-MAC diet (no-MAC-g).⁴¹ The no-MAC-g diet did not sustain Pc H-2477 colonization, with the strain becoming undetectable within 3 days (Figure 4B). However, Pc H-2477 maintained colonization in the presence of the inulin-g diet to levels similar to those observed in the high-MAC diet (Figures 2C and 4B), consistent with the requirement of MACs for Pc H-2477 colonization in vivo.

We were curious how dietary MACs affect the relative abundance of Pc and Bt in mice when colonized together. GF mice were co-colonized with Pc H-2477 and Bt H-2622 and fed a high-MAC diet for 7 days and then either maintained on the high-MAC diet, switched to the no-MAC-g diet, or switched to the inulin-g diet for 2 weeks, followed by a 1-week period in which all mice consumed the high-MAC diet (Figure 4C). Prior to the diet switch (day 0), mice harbored both Pc H-2477 and Bt H-2622 with Pc abundance significantly lower than Bt (Pc index = 1.9e–4; Bt index = 9e–4; unpaired t test, p = 0.002; n = 15) (Figures 4D and 4E). However, 7 days after the switch to the no-MAC-g diet, Pc H-2477 was no longer detectable, whereas Bt H-2622 colonization remained the same. The switch to the inulin-g diet resulted in a less dramatic decrease, with Pc still detectable after 7 days but not after 14 days, indicating that inulin provided support to Pc beyond the no-MAC diet (Figure 4D). Bt colonization remained stable on the inulin-g diet, with a small but significant increase in abundance on day 14 relative to the high-MAC condition (Figure 4E). When mice were returned to the high-MAC diet on day 14, those fed the inulin-g diet regained relative abundance of Pc H-2477 equivalent to that of baseline and to mice fed the high-MAC diet throughout the experiment. However, in mice switched to the high-MAC diet from the no-MAC-g diet, Pc H-2477 DNA remained undetectable. Bt levels stayed constant in the no-MAC diet condition, with a small decrease on day 21 relative to high-MAC-fed mice (Figure 4E). These data are consistent with the requirement of dietary MACs for Pc colonization in the presence of Bt and indicate that the variety of carbohydrates in the high-MAC diet (derived from wheat, corn, oats, and alfalfa) better supports Pc colonization relative to a single-MAC diet (inulin). Given that Bt’s consumption of inulin is minimal in vivo (Figure 4A), it was unexpected that Pc colonization decreased in the inulin-g diet condition (Figure 4D). It is possible that Pc may face competition from Bt for the host-sourced carbohydrates it can access (Figure 2E) or that Bt colonization may alter the environment such that Pc abundance is affected in the inulin-g diet but not the high-MAC diet (Figure 2E). Our data further indicate that prolonged absence of MACs restricts the ability of Pc to regain abundance when dietary MACs are reintroduced.

DISCUSSION

The tradeoff between a microbiota dominated by Bacteroides or Prevotella based on host lifestyle has been well described, but its basis is not well understood.^8,42

Here, we demonstrate that Hadza isolates of Bacteroides and Prevotella do not differ dramatically from their non-Hazda counterparts in terms of genome-wide average nucleotide identity and carbohydrate utilization, suggesting that differences in their relative abundance and prevalence across lifestyle is not due to an inherent property of the population-specific strains themselves but to differences in their environments. Furthermore, we demonstrate that MACs are crucial for Prevotella to maintain colonization: even as the sole microbe, Prevotella is eradicated when dietary MACs are removed. Bacteroides species, however, can maintain colonization in the absence of dietary MACs due to their ability to use both plant- and host-derived carbohydrates, enabling continued colonization in low-MAC industrialized diets. Our data demonstrate that, in the presence of dietary MACs in gnotobiotic models, Hadza Bacteroides and Prevotella can coexist, as is seen in the Hadza microbiota. However, removal of dietary MACs results in a precipitous decline in Prevotella, which does not recover when MACs are reintroduced. The presence of a single MAC, inulin, in the diet was sufficient to maintain an intermediate level of colonization that then rebounded when a more complete palate of MACs was available. These data are reminiscent of the seasonal pattern of Prevotella abundance in the Hadza, which cycles in abundance with the seasonality of their diet.

All together, these data are consistent with the model that, prior to industrialization, human microbiotas harbored both Bacteroides and Prevotella species. As diets shifted from high-MAC foraged foods to low-MAC industrially produced foods, abundance and prevalence of Prevotella diminished to the point of extinction in some individuals.⁴ Prevotella has been associated with beneficial health states, including improved glucose metabolism and increased resistance to malnutrition.^43,44 However, Prevotella species have also been linked to negative outcomes, including rheumatoid arthritis and insulin sensitivity.^45,46 Strain-level variation, differences in other members of the microbiota (i.e., context-specific effects), and differences in host immune status could account for these contradictions. While a high-MAC diet is broadly beneficial to overall health, whether the presence of Prevotella affects these benefits is unknown.^47–50 Additionally, how the loss of Prevotella and increased abundance of Bacteroides within the industrialized microbiota affects human physiology remains an important question.

Limitations of the study

Prior to this study, very few human-derived isolates of Pc were available for study, making comparisons of the strains isolated here to existing strains limited. As more isolates of Pc become available, it will be important to update the comparisons performed in this study. Additionally, while we demonstrate that Pc colonization decreases in response to a no-MAC diet, this colonization decline occurs so rapidly that we were not able to capture transcriptional data under this diet condition. This dataset would have been a useful comparison to Pc colonized mice on the high-MAC diet and Bt-colonized mice on the no-MAC diet. Furthermore, our study measured the effect of diet on Pc colonization, which was profound, but it is possible that there are other factors outside of diet that are important regulators of Pc colonization in vivo.

STAR★METHODS

RESOURCE AVAILABILITY

Lead contact

All information and requests for further resources should be directed to and will be fulfilled by the Lead Contact, Erica Sonnenburg, erica.sonnenburg@stanford.edu.

Materials availability

This study did not generate new unique reagents.

Data and code availability

Raw data files for WGS and RNAseq can be found at Zenodo: https://doi.org/10.5281/zenodo.7651179. Code used to generate the figures and additional data can be found at Zenodo: https://doi.org/10.5281/zenodo.8339517. Isolate genomes will be available at NCBI: PRJNA1015720 upon publication. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Bacterial culture

Bacteria not isolated in this study were purchased from DSMZ (P. copri DSM 18205), or ATCC (all other reference strains). Glycerol stocks were struck out on Brain Heart Infusion agar with 10% defibrinated horse blood (BHIBA) and incubated anaerobically for 24–48 h at 37°C. All growth and culturing of Bacteroides and Prevotella strains were performed anaerobically in a Coy anaerobic chamber containing 87% N2, 10% CO2, and 3% H2.

Mouse husbandry

All mouse experiments were performed in accordance with the Stanford Institutional Animal Care and Use Committee. Mice were maintained on a 12-h light/dark cycle at 20.5 °C at ambient humidity, fed ad libitum, and maintained in flexible film gnotobiotic isolators for the duration of all experiments (Class Biologically Clean). Swiss-Webster mice were used for gnotobiotic experiments and the sterility of germ-free mice was verified by 16S PCR amplification and anaerobic culture of feces. Sample sizes were chosen on the basis of litter numbers and controlled for sex and age within experiments. Researchers were unblinded during sample collection.⁷⁹

Statement on work with indigenous communities

In order to acquire scientific knowledge that accurately represents all human populations, rather than only reflecting and benefiting those in industrialized nations, it is necessary to involve indigenous populations in research in a legal, ethical, and non-exploitative manner.^25,80 Here, we isolated live bacterial strains from anonymized fecal samples collected from Hadza hunter-gatherers in 2013/2014.^4,6,81 Samples were collected with permission from the Tanzanian government, National Institute of Medical Research (MR/53i 100/83, NIMR/HQ/R.8a/Vol.IX/1542), the Tanzania Commission for Science and Technology, and with aid from Tanzanian scientists. A material transfer agreement with the National Institute for Medical Research in Tanzania specifies that collected samples are solely to be used for academic purposes. For more information on the consent practices followed, and our ongoing work to communicate the results of these projects to the Hadza, please see Carter et al.⁴ and Olm et al.⁵

METHOD DETAILS

Strain isolation from fecal samples

Samples for strain isolation were chosen from the samples reported previously based on the 16S abundance of either Bacteroides or Prevotella genera.⁶ All isolations were performed under anaerobic conditions on YCFA agar with 5% glucose or baobab powder. 1μL of frozen feces was struck onto a single agar plate. Visible colonies from the initial plates were identified via colony PCR using bacterial 16S primers, and re-plated onto BBE and LKV plates (Anaerobe Systems). PCR products were purified using the QIAquick PCR Purification Kit (Qiagen), and sequenced via Sanger sequencing at Elim Biopharm. The resulting sequences were identified using nucleotideBLAST.⁸² Colonies that were predicted to share >95% identity with a Bacteroides or Prevotella species were re-struck two additional times on either BBE or LKV plates, respectively, to ensure a pure culture. Glycerol stocks were made by growing a liquid culture of a single colony overnight in PYG, and then mixing at a 1:1 ratio with a 50% glycerol, 50% PBS solution.

Whole genome sequencing

Genomic DNA was extracted from single-isolate cultures grown for 24 h using a MasterPure Gram Positive DNA Purification Kit. Long-read sequencing was performed using a Nanopore MinION (flow cell FLO-MIN106, Ligation Sequencing Kit SQK-LSK109; Barcoding Kit EXP-NBD104) and short read sequencing was performed using an Illumina MiSeq. Nanopore basecalling was performed with Guppy version 3.4.2m using the command “guppy_basecaller -r -i raw_fast5/–flowcell $flowcell –kit $kit -x auto –compress_ fastq –gpu_runners_per_device 8 -q 0 –chunks_per_runner 4096”. Short read sequence quality was assessed using Fastqc with the command “fastqc –nogroup -q”, and adapters were trimmed with BBTools using the command “bbduk.sh -Xmx2g -eoom ref = adapters, phix threads = 8 ktrim = r k = 23 mink = 11 edist = 2 entropy = 0.05 tpe tbo qtrim = rl minlength = 100 trimq = 30 pigz = t unpigz = t samplerate = 0.25.” If there was more than 100x coverage of the genome, reads were normalized using the command “bbnorm.sh target = 100 min = 2”. Hybrid assembly of the short and long reads was performed using SPAdes with the command “spades.py –careful –cov-cutoff auto -k 21,33,55,77,99,127”.⁸³ RagOUT was used for chromosome-level scaffolding using either the matched reference genome of the same species for Bacteroides (Table 1), or Pc H-2477 for Prevotella.⁵² Assembly quality was assessed with Quast.⁸⁴ Gene annotation was performed using RASTtk.⁵³

Clustering genomes into subspecies

All public Bacteroides and Prevotella genomes of “Scaffold” quality or better were downloaded from NCBI GenBank on 5/15/2023 using the program ncbi-genome-download (https://github.com/kblin/ncbi-genome-download). The commands used were “ncbi-genome-download –genera Bacteroides –section GenBank –formats fasta –assembly-levels complete, chromosome,scaffold bacteria” and “ncbi-genome-download –genera Prevotella –section GenBank –formats fasta –assembly-levels complete, chromosome,scaffold bacteria” This resulted in a total of 888 and 1894 genomes of Prevotella and Bacteroides, respectively.

Public genomes were clustered along with the isolate genomes recovered in the study using dRep v3.2.1⁵⁴ using the command “dRep dereplicate –S_algorithm fastANI -sa 0.98”. The 98% ANI threshold was chosen manually based on a histogram of reported ANI values (Figure S4A). Representative genomes were chosen using dRep’s default scoring system with the following adjustments: isolates sequenced in this study were given an additional 100 points, isolate genomes used in this study were given an additional 80 points, public genomes marked as “representative genome” in Refseq were given an additional 60 points, and public genomes of “Complete Genome” and “Chromosome” quality were given an additional 40 and 20 points, respectively.

Evaluating subspecies prevalence and phylogenetic analysis

All metagenomic reads were downloaded from Carter et al.⁴ Metagenomic reads were mapped to Prevotella and Bacteroides subspecies representative genomes using Bowtie2 with default settings⁵⁵ (command “bowtie2 -x $index −1 $r1 −2 $r2 | samtools sort -o $output.bam), and the resulting .bam files were profiled using coverM as implemented through inStrain with default settings⁵⁶ (command “inStrain profile $bam $fasta -s $stb –coverm”). Genomes detected with ≥65% genome breadth were considered “present” in a metagenome. This threshold was chosen based on manual inspection of a genome breadth histogram (Figure S4B).

The prevalence of each genome in each population was calculated as the percentage of metagenomes in which the genome was detected. Phylogenetic trees were made for Bacteroides and Prevotella subspecies representative genomes detected in at least one metagenome using GToTree v1.5.36 with the command “GToTree -H Bacteria -T IQ-TREE”. One outgroup from a different genus was included in each tree. Tree leaves were labeled based on GTDB taxonomy release r207⁵⁹ Trees were visualized using iTol.⁶⁰ Figure S1A includes GTDB representative genomes for i) all species “copri” in their species name, ii) all Prevotella species of isolates recovered in this study, and iii) the closest representative genome (according to GTDB) for all isolate genomes recovered in this study. For Figure S1B, 10 genomes from each clade were randomly chosen to include in the tree. Prevotella stercorea was included as an outgroup.

CAZyme annotation

CAZyme annotations were performed for each isolate. An additional 20 strains of Prevotella copri available at NCBI, with variable assembly levels, were annotated as well for comparative purpose, with the isolates and two model strains. All amino acid sequences were first compared to the full-length sequences stored in the CAZy database (Sept. 2021)⁶¹ using BlastP (version 2.3.0+).⁶² Queries obtaining 100% coverage, >50% sequence identity and E-value ≤10⁻⁶ were automatically annotated with the same domain composition as the closest reference homolog. All remaining sequences were subject to human curation to verify the presence of each putative module. During this process, the curator could rely on (i) bioinformatics tools, including BLAST against libraries on either full-length protein, modules only or characterized modules only, and HMMER version 3.1⁶³ against in-house built models for each CAZy (sub)family; (ii) human expertise on the appropriate coverage, sequence identity and E-value thresholds which vary across (sub)families, and ultimately on the verification of the catalytic amino acid conservation. Hierarchical clustering of isolates’ CAZyme repertoires was performed using ComplexHeatmap.⁷⁵ Predicted substrate assignment was compiled from previously published works.^6,14

In vitro polysaccharide growth assays

Glycerol stocks were struck out on Brain Heart Infusion plates with 10% defibrinated horse blood and incubated anaerobically for 24 h at 37°C. Isolates were passaged overnight in BHI-S (Bacteroides), and YCFA-G (Prevotella). After 16h, cultures were diluted 1:50 for Bacteroides and 1:10 for Prevotella into 200uL of culture media in a clear, flat bottomed 96-well plate. Growth media was composed of a YCFA background, plus 0.5% carbohydrate, with the exception of inulin, which was added at a 1.5% concentration. OD600 was measured every 15 min for 48h using a BioTek Epoch2 plate reader, with 30 s of shaking prior to each reading. Normalized OD was calculated for each carbohydrate condition by subtracting the average blank OD600 from the raw OD600 for each isolate grown in the corresponding polysaccharide. Maximum OD was calculated as the highest normalized OD in the first 24h period.

Colonization and enumeration of gnotobiotic mice

For colonization with B. thetaiotaomicron H-2622, mice were gavaged with 300uL of a 3mL liquid culture grown for 16h in BHI-S. For colonization with P. copri, mice were gavaged with 300uL of a 3mL liquid culture grown for 16h in YCFAC, in which was suspended 10–15 lawns (~1 per mouse) of P. copri grown on BHIBA for 48 h. For Prevotella colonization, food was removed from mouse cages and bedding was changed 12h before gavage. Before the gavage of Prevotella, mice were gavaged with 300uL of 10% sodium bicarbonate in water. Food was returned 2h post-gavage. For bicolonization experiments, mice were first colonized with Pc H-2477, then gavaged with Bt H-2622 7 days later. Bicolonization was allowed to stabilize for 5–7 days before the diet switch.

To measure bacterial density, feces were collected from individual mice. Two biological replicates of 1 μL feces were resuspended in 200 μL sterile PBS, serially diluted 1:10 in sterile PBS using a 96-well tissue culture plate, and 3 technical replicates of 2μL of each dilution were plated on BHIBA. CFUs were counted after 36h anaerobic growth at 37 °C.

In vivo competition assays

Feces were collected from individual mice. Genomic DNA was extracted from 2 biological replicates of fecal pellets using the DNeasy PowerLyzer PowerSoil kit (Qiagen). Concentration of Pc and Bt DNA was assessed using species-specific qPCR primers (Key Resources Table). qPCR was performed using the Brilliant III, Ultra Fast SYBR Green QPCR Master Mix and a Bio Rad CFX thermocycler. Genomic DNA from Bt H-2622 and Pc H-2477 were used to generate a standard curve for each primer pair. The standard curves were used to calculate the absolute quantity of Bt or Pc DNA in the sample. The efficiency value (E) for each primer pair was calculated as 10^(1/−slope) of log10(DNA input) against Ct value. qPCR index was calculated using this equation: E^−Ct primer pair.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Biological samples
Human fecal samples from Hadza people	Smits et al., 20176	N/A
Critical commercial assays
MasterPure Gram Positive DNA Purification Kit	LGC Biosearch Technologies	Cat#NC9197506
QIAquick PCR Purification Kit	Qiagen	Cat#28106
MinION Flowcell FLO-MIN106	Nanopore	Kit: SQK-LSK109 Barcode kit: EXP-NBD104
MiSeq	Illumina	https://www.illumina.com/systems/sequencing-platforms/miseq.html
Epoch2 Microplate Reader	BioTek	https://www.biotek.com/products/detection-microplate-readers/epoch-2-microplate-spectrophotometer/
DNeasy PowerLyzer PowerSoil	Qiagen	Cat#12855–50
Brilliant III, Ultra Fast SYBR Green QPCR Master Mix	Agilent	Cat#600883
CFX Connect Real-Time PCR Detection System	Bio-Rad	Cat#1855201
RNeasy PowerMicrobiome Kit	Qiagen	Cat#26000–50
RiboMinus™ Transcriptome Isolation Kit, bacteria	Invitrogen	Cat#K155004
TruSeq® Stranded Total RNA Library Prep Human/Mouse/Rat	Illumina	Cat#20020597
NovaSeq SP Flow Cell	Illumina	NovaSeq 6000 System
Deposited data
Hadza 16S sequencing	Smits et al., 20176	N/A
Metagenomic reads: Hadza, Nepal, and California populations	Carter et al., 20234	N/A
Whole genome sequences: Bacteroides and Prevotella isolates	this study	NCBI BioProject PRJNA1015720: http://www.ncbi.nlm.nih.gov/bioproject/1015720
RNAseq data	this study	Zenodo: https://doi.org/10.5281/zenodo.7651179
Bacteroides and Prevotella reference genomes	GenBank	NCBI
Experimental models: Organisms/strains
P. copri isolates	this study	N/A
Bacteroides sp. isolates	this study	N/A
Prevotella copri DSM 18205	DSMZ	Cat#DSM 18205
Bacteroides ovatus ATCC 8483	ATCC	Cat#8483
Bacteroides thetaiotaomicron VPI-5482	ATCC	Cat#29148
Bacteroides fragilis NCTC 9343	ATCC	Cat#25285
Bacteroides caccae ATCC 43185	ATCC	Cat#43185
Mouse: Swiss Webster, germ free	Taconic	Cat#SW-F GF
Oligonucleotides
P. copri forward primer: hpc_gyrB_03_F1	this study	CACCCACACCATGTAAACCGCCAG
P. copri reverse primer: hpc_gyrB_03_R	this study	TGTACCGACATCGAAGTTACCATCAACGAAG
B. thetaiotaomicron forward primer: HBT05_03F	this study	GCAGGCACGGGCAGTATCAGTATCG
B. thetaiotaomicron reverse primer: HBT05_03R	this study	CGCCACGGATAGGCAGACATTTGTCA
Bacterial 16S forward primer: 16S rRNA 515F	Parada et al., 199851	5’-GTGYCAGCMGCCGCGGTAA-3’
Bacterial 16S reverse primer: 16S rRNA 806R	Parada et al., 199851	5’-GGACTACHVGGGTWTCTAAT-3’
Software and algorithms
FastQC	Babraham Bioinformatics	https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
BBTools	Joint Genome Institute	https://jgi.doe.gov/data-and-tools/software-tools/bbtools/
SPAdes	Center for Algorithmic Biotechnology	https://cab.spbu.ru/software/spades/
RagOUT	Kolmogorov et al., 201852	https://github.com/fenderglass/Ragout
Quast	Center for Algorithmic Biotechnology	https://quast.sourceforge.net/index.html
RASTtk	Brettin et al., 201553	https://rast.nmpdr.org/
dRep(v3.2.1)	Olm et al., 201754	https://github.com/MrOlm/drep
Bowtie2	Langmeade and Salzberg, 201255	https://bowtie-bio.sourceforge.net/bowtie2/index.shtml
inStrain	Olm et al., 202156	https://github.com/MrOlm/inStrain
scipy.cluster.hierarchy	Virtanen et al., 202057	https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html
GToTree (version 1.5.36)	Lee, MD, 201958	https://github.com/AstrobioMike/GToTree/tree/V1.5.36
GTDB	Chaumeil et al., 202059	https://gtdb.ecogenomic.org/
iTol	Letunic and Bork, 202160	https://itol.embl.de/
CAZy	Drula et al., 202261	http://www.cazy.org/
BlastP (version 2.3.0+)	Camacho et al., 200962	https://blast.ncbi.nlm.nih.gov/Blast.cgi? PAGE_TYPE = BlastDocs&DOC_TYPE = Download
HMMER (version 3.1)	Mistry et al., 201363	http://hmmer.org/download.html
Multiqc (version 1.14)	Ewels et al., 201664	https://multiqc.info/
Trimmomatic (version 0.39)	Bolger et al., 201465	http://www.usadellab.org/cms/?page= trimmomatic
HiSAT2 (version 2.2.0)	Kim et al., 201966	http://daehwankimlab.github.io/hisat2/
SAMtools (version 1.16.1)	Danecek et al., 202167	http://www.htslib.org/
StringTie (version 2.1.3)	Shumate et al., 202268	http://ccb.jhu.edu/software/stringtie/index.shtml?t=manual
DESeq2 (version 1.38.3)	Love et al., 201469	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
R (version 4.2.2)	R Core Team	https://www.r-project.org/
tidyverse (version 1.3.2)	Wickham et al., 201970	https://www.tidyverse.org/
RStudio (version 1.4)	R Core Team	https://www.rstudio.com/
stringr (version 1.5.0)	Wickham, 202271	https://stringr.tidyverse.org
MetBrewer (version 0.2.0)	Blake Mills	https://github.com/BlakeRMills/MetBrewer
RColorBrewer (version 1.1–3)	Erich Neuwirth	https://cran.r-project.org/web/packages/RColorBrewer/index.html
cowplot (version 1.1.1)	Claus Wilke	https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html
readxl (version 1.4.1)	Wickham and Bryan, 202372	https://readxl.tidyverse.org/
svglite (version 2.1.1)	Wickham, 202373	https://svglite.r-lib.org/
circlize (version 0.4.15)	Gu et al., 201474	https://jokergoo.github.io/circlize_book/book/
ComplexHeatmap (version 2.14.0)	Gu, 202275	https://jokergoo.github.io/ComplexHeatmap-reference/book/
dendsort version 0.3.4	Sakai et al., 201476	https://cran.rstudio.com/web/packages/dendsort/index.html
dendextend version 1.16.0	Tal Galili	https://www.rdocumentation.org/packages/dendextend/versions/1.16.0#how-to-cite-the-dendextend-package
ggplot2 version 3.4.0	Wickham, 201677	https://cran.r-project.org/web/packages/ggplot2/index.html
seriation version 1.4.1	Hahsler et al., 200878	https://www.rdocumentation.org/packages/seriation/versions/1.4.1
Prism 9 for macOS	GraphPad Software	https://www.graphpad.com/guides/prism/latest/user-guide/citing_graphpad_prism.htm
Other
Heparin sodium salt (from porcine intestinal mucosa)	Sigma	H3393–50KU
D-Glucose, Anhydrous	Alfa-aesar	aaa16828–0e
D-Fructose 99%	Alfa-aesar	AAA17718–30
Inulin (chicory)	Beneo	Orafti®HP
KAIBAE Premium Baobab Fruit Powder	Amazon	N/A
Mucin from porcine stomach, Type III, bound sialic acid 0.5–1.5%, partially purified powder	Sigma	M1778–100G
Bacteroides Bile Esculin (BBE) Agar Plates	Anaerobe Systems	AS-144
Laked Brucella Blood Agar w/Kanamycin and Vancomycin (LKV) Plates	Anaerobe Systems	AS-142
Brain Heart Infusion Agar (BHI)	BD Diagnostics	DF0418177
Defibrinated horse blood	Hemostat	DHB500
96-Well, Cell Culture-Treated, Flat-Bottom Microplate	Falcon	Cat#353072
Yeast Casitone Fatty Acids Broth with Carbohydrates - YCFAC Broth	Anaerobe Systems	AS-680
No MAC diet: Teklad custom diet, Glucose Only Carb (93G, Irrad)	Envigo	TD.150689
Western diet: Teklad custom diet, adjusted fat diet	Envigo	TD.96132
High MAC chow: LabDiet® JL Rat and Mouse/ Auto 6F	LabDiet	5K67
AIN-93G Basal Mix (CHO, Cellulose Free)	Envigo	TD.200788
Gelatin, bovine	Sigma	G9391

Mouse diets

The Inulin-g and No MAC-g diets were created using 32% AIN-93G Basal Mix (CHO, Cellulose Free) and 68% carbohydrates, to match the carbohydrate content of the No MAC diet (TD.150689). The Basal Mix and carbohydrate components were suspended in a mixture of water (1100mL per 250g package of Basal Mix) and 5% bovine gelatin as a binder. The carbohydrates (100% glucose, no MAC-g; 50% glucose and 50% inulin, Inulin-g) and gelatin were dissolved separately in MilliQ water and autoclaved. The gelatin mix and AIN-93G Basal Mix (CHO, Cellulose Free) (TD.200788) were added to the carbohydrate solution in a tissue culture hood, and the mix was allowed to solidify at 4°C. Diets are listed in the key resources table. After 1 week post-colonization, standard chow was removed and replaced with the desired test diet, and the bedding was changed. Gelatin chow was replaced every 3 days as the chow dried out.

RNAseq

RNA was extracted from mouse cecal contents and in vitro cultures using the RNeasy PowerMicrobiome Kit (Qiagen). Ribosomal RNA depletion was performed using the RiboMinus Transcriptome Isolation Kit (Invitrogen). A cDNA library was constructed using the TruSeq Stranded Total RNA Library Prep Human/Mouse/Rat kit. Sequencing was performed on a NovaSeq SP flow cell. Quality of raw reads was assessed with Multiqc using the command “multiqc”.⁶⁴ Adapters were trimmed using Trimmomatic and the command “trimmomatic PE ILLUMINACLIP -PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36”.⁶⁵ Reads were aligned to the Pc H-2477 and Bt H-2622 genomes using HiSAT2 commands “hisat2-build” to generate indexes, and “hisat2 -p 8 –dta -x” to align reads to the indexes.⁶⁶ SAMtools was used to generate .bam files with the commands “samtools sort -@ 8 -o” and “samtools index”.⁶⁷ Transcripts were assembled using the Stringtie commands “stringtie”, “stringtie-merge”, and “stringtie -e -B -p 11 -G”.⁶⁸ Differential expression was analyzed using DESeq2.⁶⁹

QUANTIFICATION AND STATISTICAL ANALYSIS

Quantification and statistical analyses were performed using R version 4.2.2 or GraphPad Prism version 9 (key resources table). The ComplexHeatmap and ggplot2 packages were used to create heatmap and barplot visualizations. Prism was used to generate graphs of fecal CFU and qPCR data, to calculate the mean and standard error values for these data, and to perform statistical analyses for these experiments. The number of samples per group (n) for each experiment is indicated either in the figure legend or within the figure itself. Two-tailed Mann-Whitney tests were used to compare the distributions of two unmatched groups without the assumption of normal distribution. Paired or unpaired t-tests were used to compare two normally distributed groups of paired or unpaired samples, respectively. The Welch two-sample t test was used to compare two normally distributed groups with different standard deviations. A Fisher’s exact test was used to determine the presence of nonrandom associations between two sets of categorical variables with a small sample size. The Wilcoxon test was used to determine the distinctness of the means of two groups of independent samples. Further details of statistical analyses for experiments can be found in the results section and figure legends.

Highlights.

• Bacteroides and Prevotella sp. are isolated, sequenced from Hadza fecal samples

• Bacteroides sp. encode more mucus degrading capacity than Prevotella sp.

• Unlike Bacteroides, Prevotella colonization requires dietary plant fiber

• Bacteroides outcompetes Prevotella in vivo on low plant fiber diet

INCLUSION AND DIVERSITY

We support inclusive, diverse, and equitable conduct of research.