Uniform bacterial genetic diversity along the gut

Domain: biology
Published: 2026-03-17
Editorial outcome: minor_revision

Abstract
While environmental gradients are known to result in heterogeneous distributions of bacterial species along the gastrointestinal tract, the spatial distribution of genetic diversity within these species remains poorly understood. Because bacterial genetic variants influence host traits like inflammation and metabolism, understanding their distribution is critical. Here, we analyze ~30 common gut commensals in germ-free mice colonized with the same healthy human stool. Unexpectedly, we find that while species composition varied significantly across gut regions, genetic diversity within species remained remarkably uniform. This uniformity is driven by similar strain frequencies along the gut lumen, indicating that genetically divergent strains can coexist without spatial segregation. Furthermore, ~60 evolutionary adaptations arising within the mice tend to sweep globally throughout the gut, showing little region-specificity. We observe similar dynamics in conventional mice and humans, suggesting that uniform bacterial genetic diversity is a conserved, robust feature of mammalian gut ecosystems.

Introduction
The spatial organization of microbiota along the gut plays a critical role in microbiome function1. Previous research in humans and mice has established that different regions of the gut, such as the small and large intestines, harbor distinct assemblages of bacterial species2,3, and that disruption of this organization is associated with disease4. Spatial organization in the microbiome is thought to arise from distinct niches imposed by environmental and physiological gradients along the gut3,5,6. However, it remains unknown whether such gradients drive similar spatial organization of within-species bacterial genetic diversity, such as by determining the occupancy of genetically distinct strains or individual genetic variants along the gut. Understanding spatial organization at this level is crucial, as bacterial genetic diversity drives traits that support proper microbiome function and are associated with multiple host-relevant phenotypes7, including ability to digest food8, local inflammation in specific regions of the gut9, antibiotic resistance10, metabolic capabilities8,11–14, and pathogen resistance15.

There are several ecological and evolutionary mechanisms that could result in either i) spatially segregated or ii) well-mixed genetic variation along the gut (Figure 1A). Spatial segregation could arise because environmental gradients select for different strains and mutations along the gut, co-colonizing strains occupy distinct physical niches to reduce competition, or slow migration rates limiting the spread of genetic variation. Contrastingly, spatial uniformity could arise because adaptive variants are globally adaptive along the gut, co-colonizing strains occupy distinct non-physical (e.g., metabolic) niches, or rapid migration rates.

Previous studies have sought to examine the spatial distribution of strains and individual nucleotide variants. One study looking at a small number of commensal bacterial species found that different strains can coexist along the human gut16, but did not determine whether strain frequencies of these co-colonizing strains were variable in different gut regions or whether coexistence was a general phenomenon. Other recent work found that single nucleotide variants (SNVs) can arise locally along the gut for a few bacterial species16,17, though these studies did not demonstrate that spatially specific evolutionary changes were adaptive or common across bacterial species or host. In another case, a uropathogenic strain of Enterococcus gallinarum evolved adaptive mutations specific to the mucosa, which caused inflammation9. However, it is unclear how commonly adaptive mutations spatially segregate along the longitudinal axis of the gastrointestinal (GI) tract (i.e., from small to large intestine), particularly among commensal microbes not facing any strong selective perturbations such as antibiotics. Here we will test the hypothesis that strains and their adaptations may also display spatial segregation, similar to the spatial heterogeneity observed at the species level.

To date, characterizing the spatial structure of genetic diversity along the mammalian gut has been challenging because of the invasive nature of directly sampling the GI tract, the limited bacterial biomass obtained from such attempts, and because of the difficulties in culturing a broad diversity of taxa found natively in the human gut microbiome. However, these challenges can be circumvented using a combination of experimental and statistical approaches. First, the use of a gnotobiotic mouse model for studying the gut microbiome enables the study of human gut-specific species of bacteria in vivo in a controlled manner as a single inoculum can be administered to multiple mice, allowing us to measure variability across replicates18–21. Second, the use of mice overcomes the hurdle of invasive sampling in humans as the full gut contents can be extracted after sacrifice and then processed for sequencing. Third, the use of shotgun metagenomic sequencing enables the characterization of genetic diversity across a broad panel of bacterial taxa, including those undetectable using culture-based methods22. Finally, recent statistical innovations in phasing genotypes of individual strains from shotgun metagenomic data enables the ability to quantify strain frequencies and their evolutionary modifications23–25.

In this study, we capitalize on these advances to assess the distribution of strains and evolutionary modifications along the gut. Specifically, we analyzed the frequencies of bacterial strains and the evolutionary modifications they carry within luminal samples obtained from along the GI tracts of germ-free mice eight weeks after they were inoculated with the microbiome of a single healthy human (Figure 1B). We previously analyzed this same humanized mouse dataset with 16S rRNA sequencing of luminal contents and found significant shifts in abundance of taxonomic family members along the gut3. By using metagenomic sequencing to analyze the same samples at a deeper level, we were able to estimate strain frequencies and identify evolutionary modifications. We report that the frequency of strains and evolutionary modifications found in most bacterial species was unexpectedly uniform across the gut. Confirming that this uniformity extends to other natural microbiomes, we applied the same analyses to (i) a cohort of conventional mice with their own naturally acquired, host-adapted microbiomes and (ii) a cohort of healthy humans whose luminal contents from multiple gut regions were sampled using ingestible capsule devices swallowed over several days. In all systems, we observed spatial uniformity of genetic diversity along the GI tract, suggesting that this pattern may be a common feature of multiple mammalian gut microbiomes. These results further imply that species abundance differences along the gut may play a greater role than genetic differences in responding to environmental gradients along the gut, and that this could be a robust feature of gut microbial ecology across both mouse and human hosts.

Results
To investigate the effect of gut region on microbial strain diversity, we performed shotgun sequencing on microbiomes from eight mice that were orally gavaged germ-free Swiss-Webster mice with a single stool sample (hereafter referred to as the inoculum) from a healthy human donor in a previous study3 (Figure 1B). Luminal contents were collected from five intestinal regions in each mouse: the duodenum, jejunum, ileum, cecum, and colon. DNA was extracted and metagenomic sequencing was performed on each of the five mouse intestinal samples as well as the original human inoculum (Supplementary Data 1, Methods).

To infer species abundances and SNV frequencies, we mapped shotgun reads to a database of reference genomes26 for the most prevalent and abundant bacterial species present in these mice (Figure 1B) (see Methods for more details on the bioinformatic pipeline used). Summaries of species and genetic-level diversity have been previously quantified from stool with similar approaches (e.g., Nayfach et al., 2016)26, but here we investigate this diversity along the gut.

Bacterial species and functional diversity differ along the gut
First, we examined whether bacterial species diversity, measured using alpha diversity (the Shannon index), varied along the tract of the gut. We hypothesized the large intestine would have higher alpha diversity compared to other regions of the gut, as previous studies in humans27 and mice19,20 have found that species diversity is highest in the colon due to lower flow rates and the relatively high abundance of host- and food-derived complex polysaccharides5,6,28. As expected, gut region was a major driver of alpha diversity, with the large intestine having significantly higher average alpha diversity than the small intestine (fold change in average diversity between large and small intestine per mouse = 1.22; two-sided Wilcoxon signed-rank test, p = 0.0156) (Figure 2A).

Next, we hypothesized that the relative abundance of individual bacterial community members differ along the gut, as factors such as oxygen availability, antimicrobial peptides, and carbon-source favor different dominant taxa in the mammalian small and large intestines, respectively1,2,29,30. To quantify shifts in community composition along the gut, we examined the relative abundance of bacterial families, expecting to see saccharolytic, obligate anaerobic taxa dominate in the large intestine and rapidly dividing facultative anaerobes dominate in the small intestine2,6,26,28,29. Supporting our prediction, several families in the order Bacteroidales—an order of anaerobic bacteria comprising many species which are involved in breaking down complex sugars in the colon31—were enriched in the large intestine (Figure 2B, Supplementary Figure 1), including the families Bacteroidaceae (median log2 fold change per mouse = 0.338; two-sided Wilcoxon signed-rank test, p = 0.0143), Porphyromonadaceae (median log2 fold change per mouse = 8.58×10−2; two-sided Wilcoxon signed-rank test, p = 0.0143), and Rikenellaceae (median log2 fold change per mouse = 6.23×10−3; two-sided Wilcoxon signed-rank test, p = 0.0209). Meanwhile, the family Lactobacillaceae, which is known to colonize the human small intestine with the aid of unique mucus-binding proteins (MUBs) which adhere to the small intestinal mucosal layer29, was enriched in the small intestine relative to the large intestine (median log2 fold change per mouse = -0.121; two-sided Wilcoxon signed-rank test, p = 0.0225) (Supplementary Figure 1). These enrichments in the small intestine confirm that despite human stool more closely resembling the community composition of the human large intestine30, inoculation with a human fecal sample is sufficient to support colonization by small intestine-associated taxa. Together, these results are in line with previous work showing that the composition of microbial taxa differs along the gut3,28.

Lastly, to investigate community-wide functional diversity along the gut, using HUMAnN332 we mapped metagenomic reads to gene families and MetaCyc pathways. Across all samples, HUMAnN3 identified 808,204 gene families corresponding to 470 MetaCyc pathways, which were subsequently aggregated at the community level by summing read counts across taxa. These pathway profiles were then analyzed using MaAsLin333 to test for differences between the small intestine and large intestine. The model incorporated gut location, cage, and read count as fixed effects and used subject-level stratification for paired comparisons—revealing 80 pathways significantly associated with gut location (q < 0.05). Among these, 33 pathways showed highly significant associations (q < 0.001), and their coefficients are shown in Supplementary Figure 2 (Supplementary Data 2). The same families we found to be enriched in the large intestine were also the source of many of the pathways most significantly associated with the large intestine, namely Bacteroidaceae (25 pathways) and Rikenellaceae (14 pathways). Meanwhile, a family enriched in the small intestine, Lactobacillaceae, was the source of 7 pathways significantly associated with the small intestine.

Bacterial genetic diversity along the gut
Having confirmed that taxonomic diversity varies spatially along the gut, we next investigated whether the same was true of within-species genetic diversity. First, we quantified the distribution of nucleotide diversity (𝜋) for the 30 most abundant species detected in the inoculum and mice (Methods) (Supplementary Figure 3, Supplementary Data 3). Across these species, nucleotide diversity measured in the inoculum was highly variable, ranging from 2.2 ×10−4 in Odoribacter splanchnicus to 1.2 ×10−2 in Bacteroides vulgatus, spanning two orders of magnitude (Figure 3A). High values of 𝜋 (> 10−3) are generally inconsistent with the colonization and subsequent diversification of a single strain during a host’s lifetime, and instead support the presence of two or more strains that diverged long before colonization23. Meanwhile, low values of 𝜋 are more consistent with colonization by a single strain23. Thus, the large range of 𝜋 values observed across species indicates that in some instances, there are multiple co-colonizing strains of the same species, and in other instances there is only one strain.

Average nucleotide diversity values in the mice were positively correlated with nucleotide diversity in the inoculum (Spearman 𝜌 = 0.845, p = 7.55×10−7) (Figure 3A, Supplementary Figure 4). However, despite this overall similarity, a range of nucleotide diversity values was observed within individual mice, with some mice having levels comparable to those seen in the inoculum and others having substantially lower values, suggesting variation in lineage structure across mice. Specifically, for the species with the highest values of nucleotide diversity in the inoculum (𝜋 > 1×10−3), values across mice were often bimodally distributed. For example, 𝜋 values for Bacteroides vulgatus were either all clustered around 10−2 in some mice or around 10−3 and lower for other mice (Figure 3A). This variation suggests that in some mice, multiple strains colonized, and in other mice, a single strain colonized even when multiple were present in the inoculum. However, 𝜋 never spanned an order of magnitude within a single mouse, potentially implying more similar strain colonization dynamics along the gut within mice than across mice.

To understand if nucleotide diversity differed between regions of the gut, we measured F , or fixation index, between the small and large intestine for each species. F is a distance metric where high values indicate large genetic differentiation between two samples39,40. We hypothesized that F would be elevated between gut regions within hosts, either due to evolutionary changes accruing or variable strain frequencies. Contrary to this expectation, we observed that F between the small and large intestine of the same host was on average low for the 30 species examined (0.119), especially relative to the average F between mice at the same gut region (0.225) (Figure 3B, Supplementary Data 4). This difference was more pronounced in species with high nucleotide diversity in the inoculum (𝜋 > 10−3) (two-sided Wilcoxon rank sum test, p = 7.7×10−6) compared to species with low diversity (𝜋 ≤ 10−3) (two-sided Wilcoxon rank sum test, p = 0.024).

Species with high nucleotide diversity in the inoculum may have larger differences in F within versus between mice than species with low nucleotide diversity in the inoculum because the former exhibits more variable strain colonization dynamics. Specifically, when multiple strains are present in the inoculum, there is a potential for different combinations of strains to colonize different mice, leading to higher F and differences in 𝜋 between mice. Within mice, F tends to be low and 𝜋 tends to be uniform for these species, suggesting that similar combinations of strains are found all along the gut tract of the same host. Meanwhile, when there is only one strain present in the inoculum, the same strain should colonize all mice and gut regions. This is consistent with the observed low values of F and more uniform values of 𝜋 both within and between mice for these latter species. In the next section, we further investigate whether similar combinations of strains are found along the gut tract when nucleotide diversity is high.

Strain frequencies are uniform along the gut within hosts
We next asked whether the identities and frequencies of strains colonizing different parts of the gut of a given mouse are the same or different when multiple strains are present in the inoculum. Variation in occupancy or frequency along the gut would suggest that physical niche partitioning is important in maintaining multiple conspecific strains in the same host that might otherwise compete in the same physical niche, or that distinct environmental pressures along the gut select for different strains.

To understand whether frequencies of co-colonizing strains of the same species vary along the gut, we inferred strain frequencies for the 14 species with levels of nucleotide diversity > 10−3 in the inoculum, as these samples had sufficiently high enough diversity to potentially harbor at least two strains (Figure 3). To infer strain frequencies, we used an algorithm previously applied to metagenomic time series data24,25 (Methods). For the species Alistipes shahii, Bacteroides ovatus, Coprococcus comes, and Faecalibacterium prausnitzii, only one strain was inferred to be present within the mouse samples. For the rest of the species, we inferred that two strains were present (Figure 3, Supplementary Figure 5, Supplementary Data 5). We next asked whether the frequencies of strains for species with two strains were uniform or variable along the gut. Application of the strain phasing algorithm revealed that frequencies of co-colonizing strains were relatively uniform across gut regions within hosts for most bacterial species and mice, contrary to the expectation that their frequencies may differ due to niche partitioning or environmental selection. For example, for the species Blautia wexlerae, the two strains were able to coexist at roughly 40:60% frequency along all five regions of the gut in mouse 8, and for the species Adlercreutzia equilofaciens, the two strains were able to coexist at roughly 20:80% frequency in all parts of the gut in mice 4 and 5 (Figure 4A). To understand whether co-colonizing strains generally displayed uniform frequencies along the gut, we calculated change in major strain frequency (Δ𝑓) between mouse samples for 7 species that were present in multiple mice, cages, and gut regions (Methods). Across the seven species, median change in frequency between gut regions within a host was only 9.36×10−3, much smaller than the median change in frequency we observed across mice within the same cage (3.49×10−2) or between mice in different cage (3.09×10−1) (Figure 4B, Supplementary Data 6).

To confirm that strain frequencies were generally more homogeneous within versus between hosts for all species in our dataset, we performed a series of ANOVA tests to estimate the proportion of variance in major strain frequencies attributable to gut region (“gut region”), mouse identity (“mouse”), and cage identity (“cage”) (Supplementary Figure 6). Consistent with the observations of uniformity in genetic variation along the gut, gut region explained the lowest variance for all seven bacterial species (average variance explained = 2.70%), suggesting that gut region is less important in explaining strain frequencies than mouse identity or cage identity. By contrast, mouse and cage explained substantially greater variation (average combined variance explained = 90.0%). These observations support the idea that strain frequencies along the gut were generally uniform across species and reveal that multiple strains were able to coexist in the same region of the gut. Furthermore, they imply some ecological mechanism other than spatial segregation must be responsible for permitting strain coexistence within the same host.

Social transmission of bacteria, through behaviors like coprophagy14,41, may also facilitate between-host spread of strains, resulting in similar strain frequencies between mice in the same cage. Supporting this notion, strain frequencies were more similar among cohoused mice compared to mice housed in different cages, indicating that migration of strains between mice plays a role in homogenizing frequencies of strains. For example, for the species A. equilofaciens, which was present in multiple mice and cages, the within-cage variance of strain frequencies measured in the jejunum ranged from 1.65 × 10−6 in cage 3 to 1.29 × 10−3 in cage 2, whereas the between-cage variance was at least two orders of magnitude greater, 1.18×10−1 (Supplementary Figure 7, Supplementary Data 7). These results align with previous studies that have found coprophagic behavior in mice can facilitate the spread of strains and adaptive variants in Bacteroides thetaiotaomicron42 and Escherichia coli14,41 between co-housed mice, enforcing similar strain frequencies between mice14. However, not all species have similar within-cage strain frequencies (e.g., B. uniformis, Figure 4), suggesting barriers to between-host migration in these cases. This species-specific migration effect is reflected in the fact that “cage” explains most of the variance in strain frequency for four species, but very little variance in the remaining three species (Supplementary Figure 6).

Together, these results indicate that co-colonizing strains do not need to occupy physically distinct niches to colonize the same host. To the contrary, strain frequencies tended to be relatively uniform along the gut. This uniformity may be driven by rapid migration rates between gut regions, which also likely results in greater similarity in strain frequencies among mice of the same cage versus different cages due to social transmission.

Evolutionary changes are not localized and spread throughout the gut
Even if bacterial strain frequencies are relatively homogeneous along the gut, it is possible that individual genetic variants within species are locally restricted along the gut, potentially due to local adaptation. To assess the possibility of local adaptation, we asked if any SNVs have extreme frequency differences between gut regions, or if they too are homogeneously distributed. SNV frequency changes between gut regions could arise within a host as a consequence of (i) evolutionary changes (e.g., adaptation), (ii) fluctuations in frequencies of genetically distinct strains, or (iii) sampling error. Here, we were interested in distinguishing SNV frequency changes arising as a consequence of evolution from those arising from strain fluctuations or sampling error. To distinguish SNV frequency changes arising due to evolution as opposed to strain fluctuations, we inferred the genotype of one of the strains present in the sample, or ‘quasi-phased’ the genotype23 and then tracked SNV frequency changes on the background of that genotype between pairs of samples. Doing so allowed us to detect new mutations modifying a resident lineage as opposed to alleles rising to high frequency due to strain fluctuations. To distinguish SNV frequency changes due to evolution (e.g., adaptation) versus sampling error, we considered only large allele frequency changes (𝑓 ≤ 0.2 in one sample to 𝑓 ≥ 0.8 in another sample) between samples with a median read coverage of 20 or more and at sites with a minimum read coverage of 20 or more in both samples, which are unlikely to arise due to sampling error or drift23,43. Previously, we showed that the genotype of the dominant lineage in samples with a single, high-frequency strain can be confidently quasi-phased23, and that evolutionary changes with large allele frequency changes can subsequently be detected (Methods).

Using this method, we identified 366 quasi-phaseable genomes from 62 bacterial species present in the intestinal contents of the eight mice and the inoculum (Supplementary Figure 3). Next, we detected SNV frequency changes between pairs of quasi-phaseable genomes. These pairs were between the inoculum and any mouse (10 bacterial species), different gut regions within mice (33 bacterial species), and gut regions between mice (28 bacterial species). To avoid comparing distantly diverged lineages that were not descended from the same inoculum strain, we only considered pairs with ≤20 SNV frequency changes. We observed a total of 65 unique SNVs experiencing extreme allele frequency changes across all possible pairs of quasi-phaseable genomes in our dataset (Figure 5A, Supplementary Figure 3, Supplementary Figure 8, Supplementary Data 8).

By having an inoculum to compare to, we can ascertain if a lack of SNV differences along the gut is due to the no evolutionary changes accruing, or the global spread of an evolutionary change throughout the gut. We observed 11 SNV frequency changes rising from low frequency in the inoculum to high frequency in mice in 4 species out of the 10 with at least one inoculum-mouse sample quasi-phaseable genome pair (Figure 5A, Supplementary Figure 8, Supplementary Data 8). The rapid timescales over which these SNVs rose to high frequency are consistent with these SNV frequency changes potentially representing adaptations occurring during the host colonization process. Only one of these SNVs, detected in Coprococcus sp., showed spatial differentiation in a single host (SNV frequency of 0 in jejunum versus 0.935 in cecum of mouse 1) (Figure 5A, Supplementary Data 8), indicating that advantageous SNVs tend to spread along the gut. Since not all SNVs observed within mice had sufficient coverage to be analyzed in the inoculum, we next examined SNV frequency changes arising between gut regions. Supporting the observation that SNVs spread along the gut, we observed only 4 unique within-host SNV frequency changes in 3 out of the 33 species, all between the small intestine and large intestine, including the SNV in Coprococcus sp. (Supplementary Data 8). By contrast, we observed 57 unique between-host SNV frequency changes in 15 of the 28 species (Figure 5A, Supplementary Figure 8, Supplementary Data 8). The rate of SNV frequency changes observed between hosts was significantly higher than that within hosts (bootstrapped 95% confidence interval for between-host SNV changes is 1.54 ×10−7 differences/bp - 4.42 ×10−7 differences/bp; bootstrapped 95% confidence interval for within-host SNV changes is 0 differences/bp - 6.62×10−8 differences/bp) (Figure 5B). The low rate of SNV frequency changes within mice relative to between-host and inoculum-mouse SNV frequency changes rates suggests that potentially advantageous SNVs overwhelmingly transmit along the gut within hosts.

To understand whether SNVs spreading along the gut showed additional signals of being adaptive beyond extreme allele frequency changes on short time scales, we asked if the same SNV changes arise in multiple mice, as parallelism is a hallmark of adaptation44–48. Adaptive variation can arise in multiple mice via independent recurrent de novo mutation or the parallel rise in frequency of standing genetic variation (SGV) already present in the inoculum48. Of the 21 unique SNVs that were observed to undergo an extreme allele frequency change between any pair of samples and had sufficient coverage in the inoculum (minimum of 20 reads), 15 had non-zero frequency in the inoculum, indicating that SGV is an important source of genetic variation in these mice (Figure 5A, Supplementary Figure 8, Supplementary Data 8). Of these, five nonsynonymous and one synonymous SNVs were observed to undergo parallel sweeps from low frequency in the inoculum to high frequency in multiple independent cages. One of these parallel SNVs was in P. distasonis in a gene encoding a TonB-dependent transporter, which rose from low frequency in the inoculum (𝑓 = 0.057) to high frequency in multiple mice in all three cages (ranging from an average 𝑓 = 0.319 in mouse 4 to an average 𝑓 = 1.00 in mouse 6), consistent with parallelism (Figure 5A, Supplementary Data 8). TonB-dependent transporters are ubiquitous outer membrane-associated proteins involved in nutrient transport49 with structural variation having been found to be associated with niche partitioning by different Bacteroides taxa50. Moreover, TonB-dependent transporters have been found to be a common target of adaptation in the human gut microbiome47,51,52. Notably, all the SNVs observed to undergo parallelism were not found to be differentiated along the gut within hosts. This fact would seem to suggest that even when SNVs are adaptive, they are either broadly adaptive to the entire gut, or alternatively have high migration rates that outweighs local selection pressures, perhaps due to mixing and peristalsis along the gut53.

Genetic homogeneity is generalizable to the gut of conventional mice and healthy humans
Having established that uniform strain frequencies and spread of evolutionary adaptations along the gut is a common feature in humanized mice, we next investigated whether these same patterns also arise in hosts with established native microbiota, specifically conventional mice and healthy humans.

We first analyzed a cohort of nine conventional mice that were cohoused until weaning at three weeks of age before being housed into three cages of three mice each. At six weeks of age, we sampled and sequenced luminal contents from the same five gut regions that were previously examined in humanized mouse guts (Methods). Large intestinal samples harbored higher alpha diversity and a different family-level composition than small intestinal samples (Methods) (Supplementary Figure 9), confirming that the expected spatial organization does exist at higher taxonomic levels along the guts of the conventional mice.

Further replicating our findings from humanized mice, we found that in conventional mice, co-colonizing strains of the same species exhibited largely uniform frequencies across gut regions. This pattern contrasts with the substantial variation in strain frequencies between mice, even if co-housed (Figure 6A, Supplementary Figure 10, Supplementary Data 9). Among the ten bacterial species with co-colonizing strains that were present in multiple mice, cages, and gut regions, median change in frequency Δ𝑓 between gut segments within hosts was only 2.21×10−2, an order of magnitude smaller than the median frequency changes observed between mice in the same cage (2.16×10−1) and in different cages (1.42×10−1) (Figure 6B, Supplementary Data 10). Corroborating these results, gut region explained the least variation (average variance explained = 0.75%), whereas mouse identity explained the most variation (average variance explained = 78.39%) (Supplementary Figure 11). Cage explained a moderate amount of variation (average variance explained = 19.97%). These results indicate that uniform frequencies of strains along the gut is not unique to humanized mice and may be a common feature of mouse microbiota.

Additionally, we did not observe evolutionary changes accruing between gut regions within hosts even while changes arose between hosts at a rate of 1.03×10−6 changes/bp (Figure 6C, Supplementary Data 11). These results indicate that any evolutionary changes arising within hosts sweep globally throughout the gut.

Next, to determine whether spatially uniform patterns of genetic diversity are a feature of human guts as well, we analyzed previously published metagenomic data54 collected using ingestible sampling capsules designed to sample luminal contents from four successive regions of the gastrointestinal tract: pyloric sphincter to the upper small intestine, upper to mid-small intestine, mid- to lower small intestine and the lower small intestine into the ascending colon. In this dataset, 15 healthy adult subjects ingested four capsule types twice daily for as long as a week. We hypothesized that strain frequencies in humans might vary more along the gut than in mice, given the greater length and compartmentalization of the human intestine, as well as the potential for host-specific niche adaptation over long residence times25,27. Contrary to this expectation, strain frequencies were generally homogeneous not only along the gut but also across timepoints. For example, the frequency of the major strain of Ruminococcus obeum in Subject 11 fluctuated around a mean of 72% with a variance of 0.4% across gut locations and timepoints (Figure 6D, Supplementary Data 12). More generally, across the 25 species 𝑥 host pairs with co-colonizing strains in multiple timepoints and device types, the median change in major strain frequency Δ𝑓 between gut regions within the same timepoint was only 3.79×10−2. This was the same order of magnitude as the median change observed between consecutive timepoints (2.92×10−2), or even between the first and last observed timepoint for a species 𝑥 host pair (3.55×10−2) (Figure 6E, Supplementary Data 13). These results indicate that strain frequency tends to be uniform along the human gut as well as across hourly and daily timescales.

While the majority of species 𝑥 host pairs never showed a temporal or spatial strain fluctuation in strain frequency exceeding 25%, there were several outlier species 𝑥 host that harbored strains undergoing large frequency temporal shifts, and to a lesser degree, spatial shifts. However, these fluctuations tended to be transient. For example, the frequency of the major strain of Burkholderiales bacterium in Subject 6 fluctuated between a minimum of ~10% and maximum of ~95% within and across timepoints, but displayed no consistency in the direction of change across either space or time (Supplementary Figure 12). We observed this in other species 𝑥 host pairs as well, illustrating that strain fluctuations that do occur along the gut and over time are short-lived, consistent with previous work showing that stably co-colonizing strains can fluctuate in frequency around a carrying capacity over longer timescales25.

We next asked whether extreme SNV frequency changes arising as a consequence of within-host evolution were spatially segregated. To do so, we identified SNV frequency changes between distinct regions of the gut for each of the 56 host 𝑥 species pairs (representing 35 species detected in 11 subjects) with enough data in at least two capsules for a given time point. Out of the 56 pairs, only 7 of these (12.5% of species x host pairs) had detectable SNV frequency changes between gut regions within a single timepoint (Supplementary Data 14), indicating that spatial segregation of SNVs is a feature in only a minority of samples.

Finally, we investigated whether the SNV changes arising along the gut are observable over multiple time points, or whether they are transient. Persistence would suggest that environmental gradients maintain spatial segregation of genetic variation along the gut over time. Of the seven host 𝑥 species pairs with spatial differences, four had sufficient temporal and spatial data (at least two timepoints with at least two device types per timepoint). In three of four host 𝑥 species pairs, spatial differences did not persist over multiple time points, with allele frequencies often reverting to their original state (Supplementary Figure 13, Supplementary Data 14). These results imply that sustained extreme allele frequency changes along the gut are rare.

Methods
Experimental design
All animal experiments were performed in accordance with the University of British Columbia Animal Care Committee (protocols A19-0078 and A23-R0115) and in direct accordance with the guidelines of the Canadian Council of Animal Care (PCCAC). Swiss-Webster mice, bred in-house, were housed in Ehret cages for the duration of the study, in a 12/12 light cycle, with temperature between 68°F to 79°F and humidity between 30% to 70%. All mouse experiments and breeding were conducted in the Centre for Disease Modelling rodent facility at the University of British Columbia, Canada. Human samples were collected in accordance with the University of British Columbia Office of Research Ethics (protocol H21-02464). Human subjects provided written informed consent for use of their samples and data in this study.

For the humanized mouse study, we performed shotgun sequencing on the luminal contents of mice we previously analyzed with 16S rRNA sequencing3. Swiss-Webster male and female mice at nine weeks of age were used and were provided with an autoclaved standard diet (Purina LabDiet 5K67). Briefly, eight germ-free Swiss-Webster mice were orally gavaged with the same human stool sample (the inoculum) from a healthy adult human previously labeled TL13. This set up is akin to previous studies conducting controlled mouse studies with human inoculums18–21. The eight mice were housed in three different cages according to sex and assigned treatment in the following configurations: cage 1 had mice 1-3 (sex male), cage 2 had mice 4 and 5 (sex male), and cage 3 had mice 6-8 (sex female). Given that human microbiota can rapidly colonize the mouse in a matter of days to weeks, we allowed for a total of eight weeks to colonize and equilibrate in the mice18–21, allowing for stabilization of community structure and recovery following oral gavage. This period included six weeks on a standard rodent diet (LabDiet 5k67), followed by two weeks in which mice in cages 2 and 3 were exposed to a fiber-rich diet of 30% guar gum (TestDiet 5BSE) while those in cage 1 remained on the same chow. With this experimental set up, we were able to ascertain replicability and generalizability of the genetic homogeneity observed along the gut in three independent cages with varying diets and sexes, as well as contrast within-host, across-host, and across-cage dynamics (Figure 1).

Eight weeks after inoculation, mice were sacrificed using carbon dioxide with secondary cervical dislocation, and luminal contents were collected from five intestinal regions: the duodenum, jejunum, ileum, cecum, and colon. The intestines were carefully sectioned using a razor blade into the corresponding regions and the contents were squeezed into 1.5 mL-microcentrifuge tubes. DNA extraction was performed on luminal content samples and the original inoculum (TL1) from 96-well plates using the DNeasy PowerSoil Pro Kit (Catalog number: 47016). For more information on the approach used, see the methods section of Ng et al., 20233.

Conventional mice microbiomes were obtained from nine male Swiss-Webster mice from the same litter and with their own microbiomes at six weeks of age. Mice were cohoused until three weeks of age, at which point they were separated into three cages (three mice per cage). They were provided with an autoclaved standard diet (Purina LabDiet 5K67) for the duration of the six weeks. At six weeks, mice were sacrificed and luminal contents were collected from the same five intestinal regions as with the humanized mice (duodenum, jejunum, ileum, cecum, and colon), and DNA extraction was performed using an identical approach.

Metagenomic Sequencing
Metagenomic sequencing was performed on luminal content samples from humanized and conventional mouse cohorts and the inoculum using a NovaSeq with 150 bp paired end reads. Humanized mouse samples were sequenced to a depth ranging from 23M to 136M reads (median = 33M reads), and the inoculum was sequenced to a depth of 119M reads. Samples from mice 7, 8 and the inoculum were sequenced in a separate batch than from samples from mice 1-6 at the highest end of the range of sequencing depths. Conventional mouse samples were sequenced to a depth ranging from 39M to 75M reads (median = 45M reads) (Supplementary Data 1).

Estimation of species abundances, SNVs, and CNVs
For the humanized mice and data from Shalon et al., 202354, we used the Metagenomic Intra-species Diversity Analysis System 1 (MIDAS 1; version 1.2, downloaded November 24, 2021)26 to calculate relative species abundances, gene copy number variants (CNVs), and single nucleotide variants (SNVs). MIDAS 1 calculated these values by mapping reads against a database of genomes representing 5,952 bacterial species26,76 found in the human gut microbiome.

The conventional mice microbiomes harbored bacteria native to mice and thus was not well-suited to the MIDAS 1 database used to process the humanized mouse and human microbiomes, as the reference genomes in the database corresponded only to human-associated bacterial species resulting in fewer reads being mapped. We therefore used MIDAS 3 (version 3.0.1; downloaded August 5, 2024) to construct a mouse- specific database using isolate and metagenome assembled genome (MAG) reference genomes from the Mouse Gastrointestinal Bacteria Catalogue (MGBC)77 using a standard approach78. MIDAS 3 was subsequently used to map reads to this database and calculate relative species abundances, CNVs, and SNVs.

While the outputs from both pipelines were generated with permissive parameters, as described below, we subsequently applied several stringent post-processing filters to rule out metagenomic artifacts and to accurately estimate species, CNVs, and SNVs. The post processing pipeline we use is also briefly described below and reflects the pipeline developed in Garud et al., 201923.

Estimating species relative abundance
For humanized mouse and Shalon et al., 2023 data, MIDAS 1 was used to estimate species presence and abundance by mapping reads to 15 single-copy marker genes from 5,952 representative species26,76. Reads were mapped with default MIDAS 1 settings. Species with a median single copy marker gene coverage ≥3 reads were marked as present within a sample. These read coverages were then used by MIDAS 1 to compute species relative abundance. For conventional mouse data, MIDAS 3 was used estimated species presence and abundance in a similar manner, this time to 15 single-copy marker genes from 1,093 representative species77 in the custom-built mouse-specific database. Species were marked as present if they had a median marker coverage ≥ 2 reads, per the recommendation of MIDAS 3 developers78,79.

Quantifying pathway relative abundance
To quantify changes in pathway abundances along the gut, we used HUMAnN 3 (Beghini et al., 2021) version 3.9. HUMAnN3 was downloaded on August 11, 2025 and run with default settings using the CHOCOPhlAn pangenome database version v201901_v31 and the UniRef90-DIAMOND 2019-01 protein database obtained through the HUMAnN3 download utility both on August 11, 2025. UniRef gene families were mapped to KEGGS Ortholog (KO) groups using the HUMAnN3-provided mapping file map_ko_uniref90.txt.gz (downloaded December 5, 2025). Gene family and pathway read counts were summed across all taxa to generate community-wide functional profiles. Pathway abundance was automatically normalized in the MaAsLin3 analysis described below via the “total sum scaling” setting.

To identify pathways differing between the upper and lower gut, we analyzed the raw pathway count matrix using the MaAsLin333 R package version 1.2.0. MaAsLin3 was downloaded on December 7, 2025 and run with the default parameters on the raw pathway count matrix (minimum feature abundance of 0.0001, minimum prevalence of 0.1, and default FDR correction). MaAsLin3 fits separate multivariate models per pathway. Abundance associations were assessed using linear regression applied to log₂-transformed, total-sum-scaled (TSS) relative abundances in samples with nonzero feature counts; p-values reflect a two-sided t-test of each coefficient against the median coefficient across features (median comparison) to account for compositionality. All models included intestinal position, cage, and read counts as fixed-effect covariates stratified by subject ID. Reported q-values are false discovery rate (FDR)-adjusted p-values computed using the Benjamini-Hochberg method. Significance threshold: q < 0.05. P-values were adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR) procedure, as implemented in MaAsLin3, and pathways with q < 0.05 were considered significant.

Quantifying SNV frequencies
We estimated SNV frequencies for each species in each sample. Downstream, these frequencies were used to estimate nucleotide diversity, identify quasi-phaseable samples, and quantify SNV frequency differences between pairs of quasi-phaseable samples. To estimate SNV frequencies in humanized mouse and Shalon et al., 2023 samples, reads were mapped to representative reference genomes using default MIDAS 1 mapping thresholds and arguments23. SNV frequencies were estimated in conventional mouse data using MIDAS 3, with mapping thresholds and arguments set to match MIDAS 1 defaults.

To obtain accurate estimates of SNV frequencies, we imposed coverage requirements. MIDAS 1 and 3 report the read coverage D for each site in the reference genome for each sample. We excluded any sample with a mean D < 5X across all protein coding sites, as was done in Garud et al., 2019. We further excluded from analysis species non-zero coverage at ≥ 40% of reference sites within a sample. Additionally, to minimize the possibility of any mapping errors, we implemented two controls. First, as described below and as was done in Garud et al., 201923, we exclude genes with copy number greater than 3 in at least one sample in our study, as they were potential candidates for read stealing. Second, we exclude individual sites with a coverage value < 0.3𝐷̅ or > 3𝐷̅ where 𝐷̅ is mean genome-wide coverage, as these sites had abnormal coverages outside the range of expectations for single copy genes.

Finally, SNV frequency changes were identified only if median coverage values between two samples from the same mouse were within a factor of three of each other, as we expect that coverage should be relatively constant within a mouse. Below we describe additional coverage filters that were imposed for various calculations, including SNV frequency changes.

Annotating SNVs as synonymous versus nonsynonymous
MIDAS 1 annotated SNVs as synonymous and nonsynonymous based on the reading frames of genes as annotated in the PATRIC database80, and MIDAS 3 annotated SNVs as synonymous and nonsynonymous based on annotations from Prokka81.

Quantifying gene copy number
We estimate copy number variation (CNV) to exclude any genes from further analysis that may be potential candidates for read stealing or donating. To estimate CNVs in humanized mice and Shalon et al., 2023 data, reads were mapped to pangenomes using default MIDAS 1 mapping thresholds and arguments23. CNVs were estimated in conventional mouse data using MIDAS 3 with mapping thresholds and arguments set to match MIDAS 1 defaults.

To minimize read mapping errors, we identified genes with abnormally high gene copy number values, which may be potentially indicative of erroneous read recruitment82. MIDAS 1 and 3 estimates CNVs by mapping reads to a pangenome consisting of several sequenced isolates belonging to the same species. Average gene coverage was calculated as the number of reads mapping to that gene normalized by gene length. Both versions of MIDAS compute copy number, 𝑐, as the ratio between a gene’s average coverage and the median coverage of a set of 15 single-copy marker genes belong to that species. Genes with a copy number 𝑐 > 3 in at least one sample in our study were excluded during post-processing, as they were potential candidates for stealing. To exclude any additional genes that may suffer from erroneous read recruitment, we further filtered out any genes with a copy number 𝑐 > 3 in the human samples analyzed in Garud et al., 201923. Finally, any genes sharing ≥ 95% average nucleotide identity with any other gene across species boundaries as identified in a set of “blacklisted” genes in Garud et al., 2019 (section A.iv)23 were excluded to further prevent erroneous read mapping.

Calculating alpha and beta diversity of gut samples
Alpha diversity was computed for humanized and conventional mouse data using the Shannon diversity index as implemented by the Vegan package in R83 (Figure 2A, Supplementary Figure 9A). To assess if Shannon diversity between gut regions was significantly different, we used a paired Wilcoxon signed-rank test comparing pairs of samples from one region versus the other from the same mouse. To calculate fold change in alpha diversity between the small and large intestines, alpha diversity was averaged across small intestinal regions (duodenum, jejunum, and ileum) and large intestinal regions (cecum and colon), and the per mouse fold change was calculated as the ratio of the averaged large intestinal and small intestinal alpha diversity values. The fold change values presented in the main text represent the average of these per-mouse estimates.

To produce the stacked bar plots in Figure 2B and Supplementary Figure 9B, species abundances within taxonomic families were summed. Species with less than 0.1% abundance were excluded. To test for differential abundance of specific families between the small and large intestines of humanized mice, relative abundances for each family were averaged across the small intestine (duodenum, jejunum, and ileum) and large intestine (cecum and colon) in each host, the wilcox.test() in R was used to apply a paired Wilcoxon signed-rank test to compare average large and small intestinal relative abundances (paired by mouse) for each species, generating per-species estimates for the median log2 fold change in mean relative abundance between large and small intestinal samples in the same mouse, as well as p-values and 95% confidence intervals to assess significance (Supplementary Figure 1).

Calculating nucleotide diversity
For every humanized mouse and sample and the inoculum, we estimated 𝜋, a population-level metric of nucleotide diversity that represents the probability of two randomly sampled genomes from the population have different alleles at a base pair. To do so, we used the formula for nucleotide diversity applied in Schloissnig et al., 201384, which accounts for total read counts:
𝜋(𝑆,𝐺) = 1/|𝐺| ∑_{𝑖=1}^{|𝐺|} ∑_{𝐵1∈𝐴𝑇𝐺𝐶} ∑_{𝐵2∈𝐴𝑇𝐺𝐶\𝐵1} (𝑥_{𝑖,𝐵1} * 𝑥_{𝑖,𝐵2}) / (𝐷_𝑖 * (𝐷_𝑖 - 1)) (1)
Here, 𝑆 is the sample; 𝐺 is the genome of the focal species, with |𝐺| being its size; 𝑖 is the locus in the genome; 𝐵1 and 𝐵2 are the reference and alternative alleles, respectively; 𝑥_{𝑖,𝐵𝑗} is the number of reads with allele 𝐵𝑗 at locus 𝑖; and 𝐷_𝑖 is the total read depth at locus 𝑖. This formula is an extension of a previously proposed 𝜋 estimator on next generation sequencing data devised by Begun et al., 200785. Sites with read coverage less than four were excluded from 𝜋 calculations, such that |𝐺| is equivalent to the total number of sites with adequate coverage rather than the total size of the genome.

Samples from mice 7 and 8 were sequenced to a substantially higher depth compared to samples from mice 1 through 6 (Supplementary Data 1), resulting in elevated levels of coverage per bp for bacterial species in mice 7 and 8. As a consequence, there was an increased opportunity to detect higher levels of nucleotide diversity in mice 7 and 8 merely due to systematic differences in coverage. To be able to compare nucleotide diversity levels between mice, we applied a random down sampling per nucleotide site such that the depth of mice 7 and 8 matched the median coverage of mice 1 through 6.

Distributions of 𝜋 in Figure 3A were plotted for the 30 species which met the following criteria: minimum coverage of four reads in the inoculum and at least three mouse samples representing at least two different mouse hosts. Species were only considered in samples in which at least 500,000 nucleotides met the minimum coverage threshold.

Calculating F_ST
𝜋 estimates were used to compute genome-wide 𝐹_𝑆𝑇 as a quotient of the mean genome-wide 𝜋 subtracted from 1:
𝐹_𝑆𝑇(𝑆1,𝑆2,𝐺) = 1 - (𝜋_𝑤𝑖𝑡ℎ𝑖𝑛(𝑆1,𝐺) + 𝜋_𝑤𝑖𝑡ℎ𝑖𝑛(𝑆2,𝐺))/2 / 𝜋_𝑏𝑒𝑡𝑤𝑒𝑒𝑛(𝑆1,𝑆2,𝐺) (2)
Where 𝜋_𝑤𝑖𝑡ℎ𝑖𝑛 is given by the formula 1, and pairwise 𝜋_𝑏𝑒𝑡𝑤𝑒𝑒𝑛 is given by the following formula also derived in Schloissnig et al., 2013:
𝜋_𝑏𝑒𝑡𝑤𝑒𝑒𝑛(𝑆1,𝑆2,𝐺) = 1/|𝐺| ∑_{𝑖=1}^{|𝐺|} ∑_{𝐵1∈𝐴𝑇𝐺𝐶} ∑_{𝐵2∈𝐴𝑇𝐺𝐶\𝐵1} (𝑥_{𝑖,𝐵1,𝑆1} * 𝑥_{𝑖,𝐵2,𝑆2}) / (𝐷_{𝑖,𝑆1} * 𝐷_{𝑖,𝑆2}) (3)
Where 𝑘 in 𝑆_𝑘 designates sample 1 or 2, respectively. We used ileum and colon to represent the small and large intestine, respectively because those regions had the highest mean read coverage across all samples in each gut region. In order to accurately estimate allele frequencies, each site was only included in the 𝐹_𝑆𝑇 calculation if a minimum of 4 reads was observed at that site for both samples being compared.

Inferring strain frequencies
Previous work has shown that hosts tend to be colonized by multiple genetically distinct strains (typically 1-4) of the same species23,86. When levels of recombination between strains within a host is sufficiently low, these strains may co-exist as genetically distinct subpopulations24. Typically, two randomly drawn strains from different hosts harbor on order 10^3 - 10^4 SNV changes23,47,84,87, and two strains residing within a host are expected to be similarly diverged23. We leverage this expected divergence between strains to distinguish between the strain genotypes and infer their overall frequencies.

Inferring strain genotypes and frequencies from short read sequencing data is a difficult problem because linkage between co-segregating SNVs residing on the same strain backbone are destroyed by the fact that they are unlikely to reside on the same short read. Even perfectly linked SNVs may be difficult to assign to the correct strain given the potential for sampling noise. However, recent work has shown that strain inference is feasible when there are many samples collected from the same hosts, as alleles belonging to the same strain will display highly correlated allele frequencies across samples24,25,65,88–90.

We leverage recent work by Roodgar et al., 202124 and extended by Wolff et al., 202325 to infer strain identity and frequency in samples from all three datasets analyzed in this study. The papers cited above inferred strain genotypes and frequencies from temporal data. Here, we use spatial data both within mice and across mice, and a mixture of spatial and temporal data within human hosts sampled in Shalon et al., 2023 to infer strain genotypes and frequencies within samples. Because humanized mice were inoculated with the same human sample and conventional mice shared homogenized microbiomes due to co-housing, we inferred strain frequency jointly across all mouse samples belonging to the same dataset, reasoning that mice from the same dataset all shared the same subset of strains. For Shalon et al., 2023 data, we inferred strain frequency jointly across all samples belonging to the same host, likewise reasoning that these samples carried the same subset of strains.

Briefly, the approach clusters SNVs segregating on the same strain background by detecting correlations between SNVs with highly similar allele frequencies across samples. The degree of correlation between a pair of SNVs 𝑖 and 𝑗 is measured by distance metric 𝑑 between allele frequency trajectories 𝑓̂_𝑖 and 𝑓̂_𝑗:
𝑑(𝑓̂_𝑖,𝑓̂_𝑗) = ∑_{𝑠=1}^{𝑆} (2 * (𝐷_{𝑖𝑠} + 𝐷_{𝑗𝑠}) * (𝑓̂_{𝑖𝑠} - 𝑓̂_{𝑗𝑠})^2) / ((𝑓̂_{𝑖𝑠} + 𝑓̂_{𝑗𝑠}) * (1 - 𝑓̂_{𝑖𝑠} + 1 - 𝑓̂_{𝑗𝑠})) (4)
where 𝑆 is the number of samples, 𝑓̂_{𝑖𝑠} and 𝑓̂_{𝑗𝑠} are the frequency of the alleles in a sample 𝑠, and 𝐷_{𝑖𝑠} and 𝐷_{𝑗𝑠} represent the read depth of the alleles in sample 𝑠. SNVs that are in perfect linkage with one another are expected to have a 𝑑 = 0.

To ensure that high-quality and informative sites were included in our stain inference procedure, we only considered loci that had a read coverage of 𝐷 ≥ 10 and were polymorphic in at least 20% of samples in which the species was detected. Filtering out non-polymorphic loci prevented the algorithm from generating clusters at allele frequencies of 1 or 0 that reflect low frequency variation within a strain population as oppose to actual genetically distinct strains.

Detecting correlated clusters of SNVs belonging to the same strain background can be confounded without knowing if the alternative or reference allele belongs to one strain versus another. To address this issue, as done in Roodgar et al., 202124, we set 𝑑 between a pair of loci to the minimum of 𝑑(𝑓̂_𝑖,𝑓̂_𝑗) and 𝑑(𝑓̂_𝑖, 1-𝑓̂_𝑗) to ensure that polarization differences (i.e., calling an allele frequency 0.2 instead of 0.8, based on the reference allele used in the reference genome) between loci did not prevent them from being clustered together.

To identify SNVs linked on the same strain background, we applied a greedy, network-based algorithm to extract large cluster SNVs with highly correlated allele frequencies across samples. The algorithm begins by forming a network in which nodes represent SNVs, with pairs SNVs connected by an edge if they have a distance 𝑑 < 3.5, which represents a maximum distance threshold below which pairs of SNVs are likely to be linked on the same strain background, as demonstrated in Roodgar et al., 202124. Next, the algorithm identifies a focal SNV as the SNV with the maximum number of connections (i.e., edges) and extracts all polymorphic SNVs connected to it. This cluster of SNVs was designated as a strain if it included at least 10^3 SNVs, as this represents the lower bound for the typical number of alleles that are expected to segregate between genetically distinct strains23,47,84,87. Note that SNVs connected to the focal SNV were only extracted if they were also connected to at least 25% of other SNVs within a cluster. SNVs included in the initial cluster were removed from the overall set of variable sites, and the clustering process was repeated until no additional clusters of at least 10^3 SNVs were identified. Strain frequencies were then inferred as the mean SNV allele frequencies in each cluster (Supplementary Figure 14). However, despite requiring strain clusters to have a minimum of 10^3 SNVs, not all SNVs met our coverage requirement of 𝐷 ≥ 10 across all samples. Therefore, we only report strain frequencies for samples in which every cluster has at least 100 high coverage SNVs (𝐷 ≥ 10) to ensure that the inferred strain frequencies were well-supported.

95% confidence intervals for strain frequencies in each sample were generated using a stationary bootstrapping approach91. Specifically, 1,000 bootstrap samples each consisting of 100 SNVs were drawn with replacement from each strain cluster within each sample, and subsequently the mean of the subsampled SNV frequencies in each bootstrap sample was computed as strain frequency. The upper and lower bounds of the 95% confidence interval was calculated as the 2.5 and 97.5 percentile of the bootstrapped strain frequency distribution.

In addition to the filters used by Wolff et al., 2023, we required minor alleles to have a read support of at least four reads to prevent rare mutations and sequencing errors from erroneously giving rise to spurious low-frequency clusters inferred from conventional and humanize mouse data. For strain frequency profiles in Shalon et al., 2023, we filtered out spurious low-frequency clusters and lowly abundant strains using a different approach by requiring all strains rise above a frequency of 5% in at least one sample to be included in downstream analyses.

We visually inspected all strain frequency profiles and either applied manual modifications to the algorithm or excluded from downstream analysis all profiles which violated one of the following expectations. In a scenario when only one strain is present, we expect zero clusters to be detected. In a scenario in which two strains are present, we expect only one cluster, representing the alleles segregating between the two strains. The relative frequencies of the two strains can be inferred as 𝑓̄ and 1−𝑓̄, where 𝑓̄ is the mean frequency of the clustered SNVs. In a scenario in which three strains are present, we expect three clusters the frequencies of which roughly sum to 1 in each sample, representing the SNV segregating between each of the three pairs of strains. This second scenario did not occur in our humanized or conventional mouse data. In the Shalon et al., 2023 data, the strain frequency inference pipeline did at times yield a maximum of three strain clusters. However, manual inspection revealed that these clusters tended to sum to an overall frequency significantly greater or less than 1 in some samples, suggesting that the strain frequency pipeline was failing to accurately capture all strains or spuriously inferring too many. As a result, we excluded species x host pairs with three strain clusters and limited our analysis to species x host pairs that had two strains. At times, the strain inference algorithm outputs two clusters, which is inconsistent with a scenario of either 1, 2 or 3 strains colonizing and instead may be arising from high variance in allele frequency. This happened once in the humanized mouse data with B. uniformis, the strain inference algorithm inferred two clusters (Supplementary Figure 15A). It was visually apparent in this case that the two clusters likely represented the same group of SNVs segregating between two strains’ genetic backgrounds because of their highly correlated trajectories, but were separated into two distinct clusters due to there being a large variance in individual SNV frequency. In this singular instance, we reclustered by using the mean SNV frequency of all SNVs in both clusters as a focal SNV (Supplementary Figure 15B) and extracting all other SNVs connected to it by an edge in the network (i.e., with 𝑑 < 3.5). This method produced a single cluster. The algorithm was able to infer strain frequencies for all other species analyzed in this study from the humanized and conventional mouse datasets without the need for ad hoc modifications to the pipeline. For several species x host pairs in the Shalon et al., 2023 data, the strain inference algorithm yielded two clusters. These species x host pairs were excluded from downstream analysis of strain frequency in favor of species x host pairs with more unambiguous strain phasing results, all of which had two strains (i.e., a single cluster).

When applying this algorithm to the humanized mouse data, it in some instances inferred only single strain for a bacterial species across all samples despite the inoculum displaying high nucleotide diversity, suggestive of multiple strains being present in the inoculum. This phenomenon may occur when applying the algorithm to a collection of samples in which a strain is present in only one sample (e.g., the inoculum) but absent in all others (e.g., the mouse samples). As a result, the algorithm would be unable to detect correlations between the SNVs private to a single sample and therefore fail to detect that strain. We identified when this might be happening in the humanized mouse data by looking for species in which only a single strain was inferred (i.e., the algorithm produced no clusters), but which had at least 1,000 SNVs in the inoculum that were polymorphic and had a read support of four reads for the minor allele. When this scenario occurred, we assumed two strains were present in the inoculum and inferred the strain frequency 𝑓_{𝑠𝑡𝑟𝑎𝑖𝑛 1,𝑖𝑛𝑜𝑐𝑢𝑙𝑢𝑚} of the first strain as the mean of the allele frequencies of these polymorphic SNVs in the inoculum, and the strain frequency 𝑓_{𝑠𝑡𝑟𝑎𝑖𝑛 2,𝑖𝑛𝑜𝑐𝑢𝑙𝑢𝑚} of the second strain as 1−𝑓_{𝑠𝑡𝑟𝑎𝑖𝑛 1,𝑖𝑛𝑜𝑐𝑢𝑙𝑢𝑚}. We generated 95% confidence intervals by applying the same stationary bootstrapping approach to the polymorphic SNVs identified in the inoculum.

Calculating change in major strain frequency Δ𝑓 between samples
The change in major strain frequency Δ𝑓 was calculated as the magnitude of the difference in frequencies of one of the strains between two samples. Because all samples with co-colonizing strain analyzed in this study harbored two strains, Δ𝑓 was the same regardless of whether strain 1 or 2 was used to calculate the frequency change. For the humanized and conventional mouse data, we calculated Δ𝑓 between samples for co-colonizing species present in at least at least two mice, at least two cages, and at least two gut regions. When visualizing the distribution of Δ𝑓 values, we categorized pairwise comparisons as being “within host”, “between host, within cage”, and “between host, between cage”. For each species, we selected a single “within host” pair per mouse, a single “between host, within cage” pair per cage, and a single “between host, between cage” pair for the three possible cage-wise comparisons. For “within host” comparison within each host, we selected the pair of samples that maximized the distance between the two sampling sites, breaking ties by selecting the pair of samples with the sample that is furthest down the gut (i.e., closest to the colon). For between host comparisons (both within and between cage), we selected the pair of samples that represented the same gut region or two gut regions proximal to one another along the gut, breaking ties by selecting pairs of samples with the sample that is furthest down the gut (i.e., closest to the colon). For “between host, between cage” comparisons, we ensured that no mouse was involved in more than one cross-cage comparison to eliminate redundancy. For Shalon et al., 2023 data, we calculated Δ𝑓 between samples belonging to co-colonized species 𝑥 host pairs if the species were present in at least two timepoints and at least two device types. When visualizing the distribution of Δ𝑓 values, we categorized pairwise comparisons as being “within timepoint, between gut region”, “between consecutive timepoints”, and “between host first and last timepoint”. For each species 𝑥 host pair, we selected a single “within timepoint” pair per timepoint, a single “between consecutive timepoints” pair for each consecutive pair of timepoints, and a single “between host first and last timepoint” pair. For “within timepoint” comparisons within each timepoint, we selected the sample pair that maximized the distance between device types (i.e., biasing towards device type 1 versus device type 4 comparisons, if available), breaking ties by selecting the sample pair with the maximum device type number (i.e., biasing towards pairs with device type 4). For between timepoint comparisons (both consecutive and first and last timepoints), the sample pair that minimized the distance between device types and was furthest along the gut was selected.

Performing ANOVA of major strain frequencies
To quantify the variance in major strain frequency across samples, we applied an ANOVA approach. In the humanized mouse dataset, the major strain was designated as the strain at frequency 𝑓 > 0.5 in the inoculum. In conventional mouse and Shalon et al., 2023 data, the major strain was designated as the strain at frequency 𝑓 > 0.5 in a majority of samples. The centered log ratio (CLR) transformation was applied to major strain frequency in all samples in which that species was detected, a commonly used approach to remove compositional artifacts when applying non-compositional approaches such as ANOVA92,93. For each species, we built an ANOVA model using the Stats package in R. In the humanized and conventional mouse data, the model was specified with CLR-transformed major strain frequency as the dependent variable and cage, mouse, and gut region as the independent categorical variables, and was applied jointly to all samples belonging to the same study. Variance explained was calculated as the sum of squares between groups corresponding to each variable divided by the sum of squares total. We applied several prevalence requirements to ensure that the amount of variance explained by each dependent variable was properly quantified. In humanized and conventional mouse data, we only performed ANOVA on species detected across at least two cages (with the species detectable in at least two mice in those cages), at least two mice (with the species detectable in at least two gut regions in those mice), and two gut regions (with the species detectable in these same gut regions in at least two mice).

Identifying quasi-phaseable lineages
SNV frequency differences between samples can arise due to evolution, or due to shifts in the frequency of co-colonizing strains, which are likely to harbor thousands of differing nucleotides between them. To distinguish between SNV frequency differences that arise due to evolution versus those arising from strain frequency shifts, we identified quasi-phaseable lineages using an approach developed in Garud et al., 201923. By inferring quasi-phaseable lineages, we then were able to confidently infer evolutionary changes.

Briefly, the approach works by identifying samples in which a particular species has a single dominant strain at 𝑓 ≥ 0.8, hereafter referred to as a quasi-phaseable sample. In such samples, the allele corresponding to the dominant strain can be inferred with statistical confidence. Specifically, the alleles with frequency 𝑓 ≥ 0.8 at genomic loci with coverage 𝐷 ≥ 20 are assigned to the quasi-phaseable sample. Only samples with a median coverage of 20X for the species of interest are processed through the quasi-phaseable pipeline. Employing such stringent coverage and allele frequency thresholds ensures that the incorrect allele is not inferred, as it is statistically improbable that sampling error would give rise to the incorrect allele23.

We made an important alteration to the quasi-phaseable pipeline in its application to the humanized and conventional mouse data relative to its application in Garud et al., 201923. Normally, the quasi-phaseable pipeline designates a sample as having a quasi-phaseable strain if the number of intermediate frequency SNVs (i.e., SNVs with frequency 0.2 < 𝑓 < 0.8) which a species harbors within the sample are 10% or less than the number of SNV differences that are typically observed for that species between independent hosts. This heuristic arises from the assumption that independent hosts should harbor genetically distinct strains that have diverged over long evolutionary timescales, and is born out by empirical evidence23. The 10% threshold is calibrated independently for each species, as genetically distinct strains of one species are not necessarily expected to show the same level of nucleotide differentiation as genetically distinct strains of a different species. In previous applications of the quasi-phaseable pipeline, the number of SNV differences between independent hosts has been assessed from the dataset used in those studies. For example, Garud et al., 201923 studied evolutionary dynamics within 693 unique individuals sampled as a part of the human microbiome project94,95, the TwinsUK registry96, a cohort of Chinese subjects97, and four additional young twins98. Because most hosts included in this compiled dataset were unrelated (and in some cases, on different continents), the assumption that their resident strains were distantly diverged was a reasonable one. By contrast, the humanized and conventional mice are expected to have low between-host strain diversity, either because they were inoculated with the stool from a single human host or because their microbiomes were homogenized early on in development through co-housing. As a result, we could not accurately quantify the number of differences that are expected between two unrelated hosts, making it impossible to detect quasi-phaseable strains using this dataset alone. We took two different approaches to overcome this issue in humanized and conventional mice, respectively. In humanized mice, we calculated the expected number of between-host SNV differences for each focal species based on the aforementioned data used in Garud et al., 201923, which we had previously processed through MIDAS 1. Because this resource was not available for the conventional mouse data processed through MIDAS 3, we designated samples as quasi-phaseable if intermediate frequency sites (i.e., SNVs with frequency 0.2 < 𝑓 < 0.8) occurred at a rate of 1×10−4 per bp or less, consistent with rates observed in quasi-phaseable samples from humanized mice. Results published in this study (Figure 3A) and elsewhere23 suggest that this is a highly conservative threshold for the maximum amount of intermediate frequency diversity we would expect to see if only a single strain were present within a host.

Inferring SNV frequency changes
To detect SNV frequency changes arising as a result of evolution, we follow the approach in Garud et al., 2019. A SNV frequency change is defined as an allele with low frequency in one quasi-phaseable sample (𝑓 ≤ 0.2) and high frequency in another quasi-phaseable sample (𝑓 ≥ 0.8). SNV differences were only considered at loci that had a coverage 𝐷 ≥ 20 in both samples being compared. An extreme allele frequency change of this magnitude and this minimum depth is statistically unlikely to arise between quasi-phaseable strains due to sampling error or drift given large population sizes and short timescales considered in this study23,43. Pairs of samples harboring ≤ 20 SNV differences were considered to have the same inoculum strain evolving over short timescales (e.g., the eight-week timescale considered in this study), as larger amounts of SNV differences, such as 𝒪(10^3) or greater are inconsistent with within-host rates of diversification47,82,84,87. Consequently, pairs of quasi-phaseable strains with > 20 SNV differences were excluded from this evolutionary analysis. For more information, see the Supplementary Information for Garud et al., 201923.

Bootstrapping confidence intervals for SNV difference rates
To measure whether there were significant differences in the number of SNV frequency changes occurring between samples within hosts, between hosts, and between the inoculum and mouse hosts in the humanized mouse data, we employed a bootstrap approach to compute confidence intervals for the rate of SNV difference between quasi-phaseable pairs of samples with the same dominant strain. In 1,000 bootstrap iterations, we sampled 100 quasi-phaseable sample pairs (not necessarily of the same species) with replacement from each comparison category: within host, between host, and between inoculum and host. For the within-cage comparison category, we only considered quasi-phaseable pairs from different hosts. We computed the actual rate of SNV frequency changes for a particular comparison category as the total number of SNVs displaying extreme allele frequency changes across all sampled quasi-phaseable pairs divided by the total number of high coverage loci across all sampled quasi-phaseable pairs. We calculated 95% confidence intervals using the cumulative distribution function (CDF) of the overall SNV change difference rate in each comparison category generated from the 1,000 bootstrap iterations, taking the 2.5 and 97.5 percentile as the upper and lower bound, respectively (Figure 5B). We applied an identical approach for assessing whether SNV frequency change rates were significantly different between samples from the same host or different hosts in the conventional mouse data (Figure 6C).

Statistics & Reproducibility
No statistical method was used to predetermine sample size for the humanized mice and conventional mice study. Mice were assigned to cages based on sex, and the investigators were not blinded to allocation during experiments. The statistical analyses conducted for each section are described in the corresponding text. No data were excluded from these analyses. For the analyses of Shalon et al. data, no samples were excluded from these analyses.

Data Availability
The raw metagenomics sequencing reads data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1230553. The processed count tables, associated metadata, and other data tables used to produce figures are available in Supplementary Information.

Code Availability
All necessary metadata, as well as the source code for the sequencing pipeline, downstream analyses, and figure generation, are available on GitHub (https://github.com/garudlab/Wasney-Briscoe/).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Wasney, Briscoe et al, aimed to explore the distribution of strains and evolutionary modifications along\nthe gut and “the ecological and evolutionary processes that give rise to this organization”. For this, they inoculated 8 germ-\nfree mice with human stool sample from a single healthy volunteer. After eight weeks, they collected luminal contents of five\nintestinal regions, subjected them to metagenomic sequencing and analyzed species abundances and strain and SNV\nfrequencies. They answered the following questions:\nHow is the microbiota different along the gut?\nThey find a different composition of gut microbiota along the gut.\nDoes within-species genetic diversity vary along the gut?\nNucleotide diversity is lower within mice than between mice\nDo strain frequencies vary along the gut?\nMouse and cage identity are more important for this than gut region\nHow are genetic variants distributed along the gut?\nMore SNV frequency changes between hosts than within hosts.\nMajor points:\n1.\nHow valid is their experimental system to answer the underlying question? The authors want to address how genetic\ndiversity within bacterial species distribute along the gut and “the ecological and evolutionary processes that give rise to this\norganization” (line 25).\nFirst, they do not analyze ecological and evolutionary processes, only bacterial composition, strain diversity and genetic\ndiversity. Evolutionary ecology studies how evolutionary and ecological processes influence each other. Factors like\nresource competition and environmental shifts drive evolutionary change. Using human microbiota in mice makes it hard to\ndraw conclusions about eco-evolutionary adaptation in mice. To assess co-evolution with the mouse gut, it's essential to use\na species-specific microbiota [1].\nSecond, they use a very artificial system by colonizing germ-free mice with human stool. As the microbiota is species-\nspecific, it would be more suitable to use mouse microbiota in this system (as mentioned by the authors in the discussion).\nTherefore, the authors need to perform the same experiment with transplanting mouse microbiota or frame their conclusions in a way that is\nmore applicable to their specific experimental setting.\nAlso, they use stool to colonize the whole gastrointestinal tract. How suitable is this? And how well does the colonization\nwork? E.g. Lactobacillaceae seem to have a very low abundance in the inoculum but are present at high abundance in the\nsmall intestine of some mice.\n2.\nThe authors should think about a way to display the data in Figure 3 in a way that is better understandable and that\nunderlines their point of a higher nucleotide diversity between mice than within mice. Also, it is not clear whether they plot\neach gut region or separate by small intestine/large intestine.\n3.\nHow well can these results be transferred to humans? As the authors mention in the discussion, mice have a coprophagic\nbehavior and thus re-inoculate themselves with colon material while humans do not do this. E.g. a mutation occurs in the\ncolon and would – in humans – usually not reach the upper gastrointestinal tract, which would happen in mice due to\ncoprophagic behavior.\n4.\nThe authors present only sequencing data, without any functional analyses such as metabolomics, which could reveal\nregion-specific activity along the gastrointestinal tract. It is possible that strains and genetic mutations are evenly distributed\nthroughout the gut (Line 33-34) which does not necessarily means that the function is also the same.\nMinor points:\nLine 70: Incomplete sentence, verb is missing\nLine 90-91: How long does it take for human microbiota to fully colonize the mouse gastrointestinal tract?\nLines 112–113: Why were outbred mice (Swiss Webester mice) used? According to the metaorganism theory [2], both the\nhost genome and microbiome shape the organism's phenotype. Using outbred mice introduces genetic variability—so how\ncan the study rule out that host genetics influenced the gut microbiome?\nLine 147-151 and 154-155: The authors should show the data that corresponds to this.\nLine 161: The PCoA also shows a segregation by cage/chow. The authors should comment on this\nLine 182: Please show the data that support that “Diversity measured from the inoculum was highly correlated with diversity\nmeasured from the mouse gut averaged for each species”.\nLine 269: “For example, for the species A. equilofaciens, which was present in multiple mice and cages, the within-cage\nvariance of strain frequencies measured in the jejunum ranged from 1.65 × 10-6 in cage 3 to 1.29 × 10-3 in cage 2, whereas\nthe between-cage variance was at least two orders of magnitude greater 1.18 × 10-1 (Figure 4).” Where do I see this?\nLine 338: What are the 11 SNV frequency changes they are referring to? Which one showed spatial differentiation? Which\nare the 4 unique within-host SNV frequency changes? What are the 57 unique between-host SNV frequency changes?\nLine 489: “nine” instead of “nice”?\nThe study uses 9-week-old mice for inoculation (Line 489). However, this overlooks ontogenetic effects—key developmental\nfactors that influence microbial adaptation to specific gut regions. To account for this, mice should be inoculated with the\nhuman microbiota before breeding, and microbial composition should be analyzed in the F1 generation.\nLine 496: We appreciate, that the authors want to “ascertain replicability and generalizability”. However, the different diet\nseems to make a huge difference (see PCoA and Ng et al., mBio (2023)). So, it would only be fair to include sex and diet as\nseparate variables.\nLine 704: Supplementary Figure 6 instead of Supplementary Figure 5?\nReferences:\n1.\nChung, H., et al., Gut Immune Maturation Depends on Colonization with a Host-Specific Microbiota. Cell, 2012. 149(7): p.\n1578-1593.\n2.\nStappenbeck, T.S. and H.W. Virgin, Accounting for reciprocal host-microbiome interactions in experimental science. Nature,\n2016. 534(7606): p. 191-9.", "reviewer_2": "The study by Wasney et al. provides a detailed gut biogeographical analysis of strain-level diversity in a humanised\ngnotobiotic mouse model. As stated by the authors, the main motivation was to “characterise how genetic diversity varies\nalong the gut”. Overall, the study is careful, thoughtful, and precise, and the manuscript is clear and well written. However,\nthe experimental design and scope of the sampling and controls is unfortunately quite limited, and perhaps best put by the\nweaknesses outlined in the Discussion by the authors themselves. If the main motivation was indeed to characterise how\ngenetic diversity varies along the gut, the design and sampling would arguably be more thorough. And while the analysis\nwas carefully conducted and there are some noteworthy observations, I do question how much these limited observations\nshould really be generalized. The picture is simply too premature.\nHere are some more specifics.\n1. As stated by the authors themselves, things might look very different if they had included native mouse microbiomes as a\ncontrol / comparison group. As it stands, statements like page 14, lines 364-367:\n“This fact would seem to suggest that even when SNVs are adaptive, they are either broadly adaptive to the entire gut, or\nalternatively have high migration rates that outweighs local selection pressures, perhaps due to mixing and peristalsis along\nthe gut”\nCould for example be attributed to adaptations to universal aspects of the drastically new mouse host gut environment.\nWithout mouse samples as both controls and a necessary group comparison, it really limits what can be inferred here. I\nwould recommend to gavage native mouse stool(s) into germ free mice as an appropriate control/comparison.\n2. Only a single host inoculum was used. There could also be peculiarities of any given single sample. The conclusions\nwould be much stronger if e.g. these general patterns could be observed using more than one inoculum.\n3. Related to 2., it seems as if there is very high level of replication, i.e. eight recipients for just a single donor. However, it\nonly becomes apparent in the methods section that there is a “cryptic experiment” within this experiment, as the “mice in\ncages 2 and 3 were exposed to a fiber-rich diet of 30% guar gum for two weeks, while the rest continued on the standard\ndiet.” It is argued that this added to the generalizability of the study, but more human and mouse donor material would have\narguably offered more genuine generalizability. Further, without longitudinal sampling, it is not clear whether this 2 week\nintervention had an effect on the results or not.\nThus, in summary, it regretfully appears that the design reflects a re-purposing of samples from a very unrelated previous\nstudy (ref 3), rather than making a dedicated attempt at addressing the actual stated topic.\nAdditional questions:\nLines 337-339, are any of the 4 unique within-host SNV frequency changes between the upper and lower GI tract?\nLines 339-341, is there any additional higher level evidence of parallelism among the 57 unique between-host SNV\nfrequency changes, e.g. at the level of KEGG pathways of the genes involved?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "Response to Reviewers\nReviewer #1 (Remarks to the Author)\nIn this manuscript, Wasney, Briscoe et al, aimed to explore the distribution of strains and\nevolutionary modifications along the gut and “the ecological and evolutionary processes that\ngive rise to this organization”. For this, they inoculated 8 germ-free mice with human stool\nsample from a single healthy volunteer. After eight weeks, they collected luminal contents of five\nintestinal regions, subjected them to metagenomic sequencing and analyzed species\nabundances and strain and SNV frequencies. They answered the following questions:\nHow is the microbiota different along the gut?\nThey find a different composition of gut microbiota along the gut.\nDoes within-species genetic diversity vary along the gut?\nNucleotide diversity is lower within mice than between mice\nDo strain frequencies vary along the gut?\nMouse and cage identity are more important for this than gut region\nHow are genetic variants distributed along the gut?\nMore SNV frequency changes between hosts than within hosts.\nMajor points:\n1.\nHow valid is their experimental system to answer the underlying question? The authors want to\naddress how genetic diversity within bacterial species distribute along the gut and “the\necological and evolutionary processes that give rise to this organization” (line 25).\nFirst, they do not analyze ecological and evolutionary processes, only bacterial composition,\nstrain diversity and genetic diversity. Evolutionary ecology studies how evolutionary and\necological processes influence each other. Factors like resource competition and environmental\nshifts drive evolutionary change. Using human microbiota in mice makes it hard to draw\nconclusions about eco-evolutionary adaptation in mice. To assess co-evolution with the mouse\ngut, it's essential to use a species-specific microbiota [1].\nWe thank the reviewer for their comments. First, to clarify our objectives, we do not attempt to\nstudy how evolution and ecology influence each other and agree with the reviewer that factors\nlike resource competition and environmental shifts would potentially be important for such\ninquiries. To improve clarity on the objectives of our work, we have removed the mention “the\necological and evolutionary processes that give rise to this organization” and instead state “thus,\n1\nto be able to understand how the microbiome functions at a mechanistic level, it is essential to\nunderstand how genetic diversity is organized along the gut” (Lines 19-20).\nSecond, regarding the suitability of the microbiota used here, as we will explain further below, a\nmajor point in our revision was to analyze i) mice with their native, conventional microbiota, as\nwell as ii) humans that have ingested a capsule that can sample luminal contents. In repeating\nour experiment, we found that the same patterns of uniform strain frequency and spread of\nevolutionary adaptations along the gut hold in native microbiomes and across different animal\nhosts. While this study does not aim to characterize co-evolution between mice and their native\ngut microbiota per se, we believe this addition does address confounders arising from not using\na native host-microbiome pair. The results are addressed in the response to comment below\nand in Figure 6.\nSecond, they use a very artificial system by colonizing germ-free mice with human stool. As the\nmicrobiota is species-specific, it would be more suitable to use mouse microbiota in this system\n(as mentioned by the authors in the discussion). Therefore, the authors need to perform the\nsame experiment with transplanting mouse microbiota or frame their conclusions in a way that is\nmore applicable to their specific experimental setting.\nWe thank the reviewers for this important suggestion. To address this suggestion we repeated\nthe experiment in conventional mice harboring native mouse microbes to confirm that the\npatterns seen in our study are not due to incompatibility effects between host and microbe.\nIn this experiment, we analyze a cohort of nine fully conventional mice that were cohoused until\nweaning before being housed into three cages of three mice each. At six weeks of age, we\nsampled and sequenced luminal contents from the same five gut regions that were previously\nsampled in humanized mouse guts.\nWe describe our results in lines 418-434 of the paper:\n“Further replicating our findings from humanized mice, we found that in conventional\nmice, co-colonizing strains of the same species exhibited largely uniform frequencies\nacross gut regions. This pattern contrasts with the substantial variation in strain\nfrequencies between mice, even if co-housed (Figure 6A, Supplementary Figure 10).\nAmong the nine bacterial species with co-colonizing strains that were present in multiple\nmice, cages, and gut regions, median change in frequency Δf between gut segments\nwithin hosts was only 2.21×10-2, much smaller than the median frequency changes\nobserved between mice in the same cage (2.16×10-1) and in different cages (1.42×10-1)\n(Figure 6B). Corroborating these results, gut region explained the least variation\n(average variance explained = 0.75%), whereas mouse identity explained the most\nvariation (average variance explained = 78.39%) (Supplementary Figure 11). Cage\nexplained a moderate amount of variation (average variance explained = 19.97%).\nThese results indicate that uniform frequencies of strains along the gut is not unique to\nhumanized mice and may be a common feature of mouse microbiota.\nwithin hosts even while changes arose between hosts at a rate of 1.03×10-6 changes/bp\n(Figure 6C). These results indicate that any evolutionary changes arising within hosts\nsweep globally throughout the gut.”\nWe show Figure 6A-C in Figure R1 below:\nFigure R1 (Figure 6A-C in the manuscript). Uniform genetic variation along the gut in\nconventional mice and healthy humans. (A) Strain frequencies for two co-colonizing strains\nof an Oscillospiriceae species long the guts of nine conventional mice housed across three\ncages. Error bars represent bootstrapped 95% confidence intervals. Mice 1-3 were co-housed in\ncage 1, mice 4-6 in cage 2, and mice 7-9 in cage 3, as indicated by the light and dark purple\nboxes. Absence of a strain frequency bar indicates insufficient coverage or species absence in a\ngiven sample. For additional strain frequency plots corresponding to species analyzed in (B),\nsee Supplementary Figure 10. (B) Change in strain frequency Δf was calculated for seven\nspecies present in multiple mice, cages, and gut regions. (Methods). Each dot represents the\nchange in major strain frequency between a pair of samples from within the same host (red),\nbetween hosts in the same cage (light purple), and between hosts in the different cages (dark\npurple). (C) Comparison of the rate of SNV frequency changes within and between mice.\nColored in red are the observed SNV frequency change rates aggregated across species.\nBars represent bootstrapped 95% CIs.\nhumans. We analyzed previously published metagenomic data collected from 15 people over\nmultiple time points using ingestible capsules designed to sample luminal contents from four\nsuccessive regions of the gastrointestinal tract: pyloric sphincter to the upper small intestine\n(device type 1), upper to mid-small intestine (device type 2), mid- to lower small intestine (device\ntype 3) and the lower small intestine into the ascending colon (device type 4) (Shalon et al.,\n2023).\nWe found that in humans, the frequency of co-colonizing strains generally remained\nhomogenous along the four sampled regions of the gut. In some cases, we observed short-lived\nchanges in strain frequency with time but these changes did not persist to the next time point.\nWe also observed a paucity of SNV frequency differences along the gut, and those that we did\ndetect were transient from one timepoint to the next, suggesting they arose through rapid,\nstochastic fluctuations rather than environmental selection along the gut. We describe these\nresults in lines 435-478, which correspond to Figure 6D-E (Figure R3 in the response) and\nSupplementary figure 12 and 15.\nOur results suggest spatial uniformity in genetic diversity may be a robust feature of mammalian\ngut microbiota. Importantly, the humanized mouse data is still crucial for the message of our\nwork. By having an inoculum to compare to, we can ascertain if a lack of SNV differences along\nthe gut is due to the absence of evolutionary changes accruing, or the global spread of an\nevolutionary change throughout the gut. Additionally, the replication across mice and cages\nallows us to understand the statistical robustness of our results. Humanized mouse models are\nstandard models in human microbiome studies, and have been leveraged in several landmark\nstudies to understand microbiome dynamics during bacterial colonization and community\nassembly (Seedorf et al., 2014; Johansson et al., 2015), as well as in the context of human\ninfectious and inflammatory diseases (Sonnenberg et al., 2016) and obesity (Turnbaugh et al.,\n2009). For this reason, humanized mice serve as an important and more tightly controlled\nexperimental model with which to explore the spatial organization of genetic diversity in the\nmammalian gut. We now cite these papers in the methods and have clarified our reasoning in\nthe main text (lines 67-76).\nAlso, they use stool to colonize the whole gastrointestinal tract. How suitable is this? And how\nwell does the colonization work? E.g. Lactobacillaceae seem to have a very low abundance in\nthe inoculum but are present at high abundance in the small intestine of some mice.\nSeveral landmark studies (Turnbaugh et al., 2009; Seedorf et al., 2014; Johansson et al., 2015;\nSonnenberg et al., 2016) have used stool for inoculation of humanized mice. Additionally, due to\nthe coprophagic nature of mice, it is not unusual for stool-associated microbes to pass through\nand potentially colonize the whole GI tract. One limitation of using stool as an inoculum is that\nsmall intestinal oxygen intolerant microbes, such as Lactobacillus, could be missing from these\nsamples. We address this concern by evaluating the abundance of Lactobacillus in the small\nintestine in our study. As expected, we observed Lactobacillus to have high abundance in the\nsmall intestine and lower abundance in the large intestine.\nsmall intestine in the species section (lines 146-153):\n“Meanwhile, the family Lactobacillaceae, which is known to colonize the human small\nintestine with the aid of unique mucus-binding proteins (MUBs) which adhere to the\nsmall intestinal mucosal layer28, was enriched in the small intestine relative to the large\nintestine (median log2 fold change per mouse = -0.121; Wilcoxon signed-rank test,\np-value = 0.0225) (Supplementary Figure 1). These enrichments in the small intestine\nconfirm that despite human stool more closely resembling the community composition of\nthe human large intestine29, inoculation with a human fecal sample is sufficient to support\ncolonization by small intestine-associated taxa.”\nNote that Supplementary Figure 1 is included as Figure R4 in this response.\n2. The authors should think about a way to display the data in Figure 3 in a way that is better\nunderstandable and that underlines their point of a higher nucleotide diversity between mice\nthan within mice. Also, it is not clear whether they plot each gut region or separate by small\nintestine/large intestine.\nWe thank the reviewer for these comments and the suggestions have improved the paper. To\naddress the reviewers comments, Figure 3 has been modified in the following ways:\n● First, to more directly compute genetic differentiation between samples, an additional\nfigure (Figure 2B) has been added to show fixation index F , or genetic distance,\nST\ncalculated between samples within the same host vs different hosts. This statistic was\nuseful for demonstrating that subpopulation genetic differentiation is smaller within hosts\nthan between hosts, particularly for species with high nucleotide diversity in the\ninoculum, indicating that the frequencies and identities of colonizing strains are more\nsimilar within than between hosts.\n● Additionally, in Figure 2A, different gut segments have now been given different shapes\nto make it clear that we are plotting all gut segments as opposed to just small versus\nlarge intestine. In the figure caption we make clear that all gut segments are plotted.\nThe text has been updated as follows (lines 202-211):\n“To understand if nucleotide diversity differed between regions of the gut, we measured\nF , or fixation index, between the small and large intestine for each species. F is a\nST ST\ndistance metric where high values indicate large genetic differentiation between two\nsamples. We hypothesized that F would be elevated between gut regions within hosts,\nST\neither due to evolutionary changes accruing or variable strain frequencies. Contrary to\nthis expectation, we observed that F between the small and large intestine of the same\nST\nhost was on average low for the 30 species examined (0.119), especially relative to the\naverage F between mice at the same gut region (0.225) (Figure 3B). This difference\nST\nwas more pronounced in species with high nucleotide diversity in the inoculum (π>10-3)\n5\n(Wilcoxon rank sum test, p-value = 2.3×10-7) compared to species with low diversity\n(π≤10-3) (Wilcoxon rank sum test, p-value=0.043).”\nWe include Figure 3B as Figure R2 below:\nFigure R2 (Figure 3 in the manuscript). Genetic diversity measured in 30 gut commensal\nspecies. (A) Nucleotide diversity (π) estimates for the 30 most abundant and prevalent species\n(Methods). Each dot represents nucleotide diversity (π) in a specific gut region of an individual\nmouse. Dot color corresponds to the mouse, while dot shape indicates the gut region. Red\n6\nasterisks denote nucleotide diversity measured in the inoculum for that species. Species names\nin light red correspond to those species with π> 1×10-3 in the inoculum. (B) Fixation index (F )\nST\nfor between all pairs of mouse samples for the 30 species analyzed in panel (A). Each data\npoint represents a single species for a given pair of samples, with points colored according to\nwhether the sample comparison is within host (grey) or between hosts (black). Data points are\nfurther separated based on nucleotide diversity of the species in the inoculum. Two separate\nWilcoxon rank sum tests were performed comparing the distributions of F within versus\nST\nbetween hosts for species with π>10-3 and species with π≤10-3, respectively (* indicates p ≤\n0.05; **** indicates p ≤ 0.0001).\n3.\nHow well can these results be transferred to humans? As the authors mention in the discussion,\nmice have a coprophagic behavior and thus re-inoculate themselves with colon material while\nhumans do not do this. E.g. a mutation occurs in the colon and would – in humans – usually not\nreach the upper gastrointestinal tract, which would happen in mice due to coprophagic behavior.\nWe analyzed data from Shalon et al., 2023 to assess if these results are applicable to humans.\nWe found that strain frequency was uniform across gut regions and timepoints. Moreover, there\nwere few SNV frequency differences along the gut, and those that we did detect tended to be\ntransient, suggesting they arose through rapid and potentially stochastic temporal fluctuations\nrather than environmental selection. We have added the following to the text in lines 435-478:\n“Next, to determine whether spatially uniform patterns of genetic diversity are a feature\nof human guts as well, we analyzed previously published metagenomic data54 collected\nusing ingestible sampling capsules designed to sample luminal contents from four\nsuccessive regions of the gastrointestinal tract: pyloric sphincter to the upper small\nintestine, upper to mid-small intestine, mid- to lower small intestine and the lower small\nintestine into the ascending colon. In this dataset, 15 healthy adult subjects ingested four\ncapsule types twice daily for as long as a week.\nWe hypothesized that strain frequencies in humans might vary more along the gut than\nin mice, given the greater length and compartmentalization of the human intestine, as\nwell as the potential for host-specific niche adaptation over long residence times25,27.\nContrary to this expectation, strain frequencies were generally homogeneous not only\nalong the gut but also across timepoints. For example, the frequency of the major strain\nof Ruminococcus obeum in Subject 11 fluctuated around a mean of 72% with a variance\nof 0.4% across gut locations and timepoints (Figure 6D). More generally, across the 25\nspecies x host pairs with co-colonizing strains in multiple timepoints and device types,\nthe median change in major strain frequency Δf between gut regions within the same\ntimepoint was only 3.79×10-2. This was the same order of magnitude as the median\nchange observed between consecutive timepoints (2.92×10-2), or even between the first\nand last observed timepoint for a species x host pair (3.55×10-2) (Figure 6E). These\nresults indicate that strain frequency tends to be uniform along the human gut as well as\nacross hourly and daily timescales.\nin strain frequency exceeding 25%, there were several outlier species x host that\nharbored strains undergoing large frequency temporal shifts, and to a lesser degree,\nspatial shifts. However, these fluctuations tended to be transient. For example, the\nfrequency of the major strain of Burkholder of Burkholderiales bacterium in Subject 6 fluctuated\nbetween a minimum of ~10% and maximum of ~95% within and across timepoints,\ndisplaying no consistency in the direction of change across space or time\n(Supplementary Figure 12). We observed this in other species x host pairs as well,\nillustrating that strain fluctuations that do occur along the gut and over time are\nshort-lived, consistent with previous work showing that stably co-colonizing strains can\nfluctuate in frequency around a carrying capacity over longer timescales25.\nWe next asked whether extreme SNV frequency changes arising as a consequence of\nwithin-host evolution were spatially segregated. To do so, we identified SNV frequency\nchanges between distinct regions of the gut for each of the 56 host x species pairs\n(representing 35 species detected in 11 subjects) with enough data in at least two\ncapsules for a given time point. Out of the 56 pairs, only 7 of these (12.5% of species x\nhost pairs) had detectable SNV frequency changes between gut regions within a single\ntimepoint (Supplementary Table 6), indicating that spatial segregation of SNVs is a\nfeature in only a minority of samples.\nFinally, we investigated whether the SNV changes arising along the gut are observable\nover multiple time points, or whether they are transient. Persistence would suggest that\nenvironmental gradients maintain spatial segregation of genetic variation along the gut\nover time. Of the seven host x species pairs with spatial differences, four had sufficient\ntemporal and spatial data (at least two timepoints with at least two device types per\ntimepoint). In three of four host x species pairs, spatial differences did not persist over\nmultiple time points, with allele frequencies often reverting to their original state\n(Supplementary Figure 13, Supplementary Table 6). These results imply that\nsustained extreme allele frequency changes along the gut are rare.”\nFigure 6D and E is included below as Figure R3 to demonstrate the spatial uniformity in strain\nfrequency that we observed in the Shalon et al., 2023 data set. These results corroborate that\nthe findings of our humanized mouse study can offer insight into the human microbiome.\nstrains of Ruminococcus obeum were inferred from capsule devices that sampled luminal\ncontents along the gut of a healthy human subject (subject 11) across six timepoints. Device\ntype 1 targeted the pyloric sphincter to the upper small intestine, device type 2 targeted the\nupper to mid-small intestine, device type 3 targeted the mid- to lower small intestine, and device\ntype 4 targeted the lower small intestine into the ascending colon54. Error bars represent\nbootstrapped 95% confidence intervals. Timepoints represent the time at which capsules were\nswallowed, with days being relative to the first timepoint plotted and times of day being\ncoarsened into “morning” (before 12 pm PST), “afternoon” (after 12pm and before 8 pm PST),\nand “evening” (after 8 pm PST) bins. For additional strain frequency plots corresponding to\nspecies analyzed in (E), see Supplementary Figure 12. (E) Change in strain frequency Δf was\ncalculated between samples belonging to 25 species x host pairs with co-colonizing strains\npresent in multiple timepoints and device types. (Methods). Each dot represents the change in\nmajor strain frequency between samples from different device types swallowed during the same\ntimepoint, (red), collected during consecutive timepoints (light purple), and collected during the\nfirst and last timepoint (dark purple). Strain frequency change over the course of the entire\nsampling period never exceeded changes occurring between consecutive timepoints,\nsuggesting that hourly or daily shifts in strain frequency are transient.\nAdditionally, we include Supplementary Figure 13 to illustrate the transience of local SNV\nchanges along the guts of humans.\nmetabolomics, which could reveal region-specific activity along the gastrointestinal tract. It is\npossible that strains and genetic mutations are evenly distributed throughout the gut (Line\n33-34) which does not necessarily means that the function is also the same.\nWe thank the reviewer for this comment. We performed additional functional profiling on the\nmetagenomic data from humanized mice to directly assess whether region-specific functional\ndifferences exist along the gastrointestinal tract. Using HUMAnN3 (Beghini et al., 2021), we\nexamined the community level abundance of MetaCyc pathways and then applied MaAsLin3\n(Nickols et al., 2024) to modeling abundance as a function of gut region, cage, and read depth\nas fixed effects with subject-level stratification for paired comparisons. This analysis revealed\nfunctional differentiation between the small and large intestine. In total, 80 pathways were\nsignificantly associated with gut location (q < 0.05), including 33 pathways with highly significant\nassociations (q < 0.001; Supplementary Figure 2, Supplementary Table 2). When examining\nthe taxonomic source of these pathways, the results were consistent with our findings for\nspecies diversity as the same families enriched in the large intestine—Bacteroidaceae and\nRikenellaceae—were also the primary contributors to the pathways most strongly associated\nwith the large intestine. Conversely, Lactobacillaceae, which was enriched in the small intestine,\nalso contributed 8 pathways significantly associated with the small intestine and none\nassociated with the large intestine. Together, these results demonstrate that even though\nstrain-level and gene-level diversity is relatively uniform along the gut, functional activity is not.\nInstead, we observe clear region-specific functional signatures that align with the species\ncompositional differences we report. These new analyses suggest that uniform genetic diversity\ndoes not imply uniform functional potential.\nWe now report these results in lines 155-168 of the paper.\nMinor points:\nLine 70: Incomplete sentence, verb is missing\nThank you for this correction, we have updated the text.\nLine 90-91: How long does it take for human microbiota to fully colonize the mouse\ngastrointestinal tract?\nHuman microbiota samples can rapidly colonize the mouse gastrointestinal tract, with studies\nshowing that transplanted human fecal communities establish within gnotobiotic mice in 3 to 7\ndays (Seedorf et al., 2014). A different study found that mice gavaged with conventional mouse\nmicrobiota required about 2 weeks to develop a bacterial composition resembling the donor\nmicrobiota, with immune-associated changes appearing at week 4 and full intestinal mucus\nlayer formation by week 6 (Johansson et al., 2015). We therefore allowed a total of eight weeks\n10\nfor the microbiota and host responses to equilibrate before sampling. We now cite these papers\nin the methods.\nLines 112–113: Why were outbred mice (Swiss Webester mice) used? According to the\nmetaorganism theory [2], both the host genome and microbiome shape the organism's\nphenotype. Using outbred mice introduces genetic variability—so how can the study rule out\nthat host genetics influenced the gut microbiome?\nWe believe the consistent gut colonization patterns seen across genetically distinct mice as well\nas unrelated humans is a strength of this study, as it allows us to rule out any kind of spatial\npatterns being attributed to host genotype.\nLine 147-151 and 154-155: The authors should show the data that corresponds to this.\nWe have added Figure R4 as Supplementary Figure 1 in our manuscript to visually illustrate\nthe median log2 fold change in family relative abundance when comparing the large versus\nsmall intestine in the same host (Methods). Positive values indicate enrichment in the large\nintestine, while negative values indicate enrichment in the small intestine. We now reference\nSupplementary Figure 1 in the portion of the results section describing differential taxonomic\nabundance along the guts of humanized mice (lines 139-150), and describe how these log2 fold\nchange values are calculated in the methods section (lines 745-754).\nabundance along the gut. A paired Wilcoxon signed-rank test was used to estimate median\nlog2 fold change between paired family relative abundance values corresponding to the large\nand small intestine of the same mice (Methods). Taxa labels represent Order; Family, and are\nordered from highest to lowest log2 fold change. Positive log2 fold change values indicate\nfamilies that are enriched in the large intestine, whereas negative log2 fold change values\nindicate families that are enriched in the small intestine.\nthis\nIn our study design, diet and cage were necessarily confounded due to standard animal\nhusbandry practices, which require co-housing of mice by diet to reduce stress given they are\nsocial animals. As such, the PCoA plot, created using species abundances, did reveal\nsegregation by cage and diet, though largely driven by diet, consistent with previous literature\nshowing that diet can significantly influence microbial community composition (Turnbaugh et al.,\n2009). However, at the strain and evolutionary level, there was no effect of diet on the pattern of\nspatial uniformity observed along the gut. As such, since there was no contribution of diet to our\nmain conclusions, we chose to remove this plot from the manuscript. We appreciate the\nreviewer’s attention to this point and have noted the potential influence of cage and chow in the\nmethods section.\nLine 182: Please show the data that support that “Diversity measured from the inoculum was\nhighly correlated with diversity measured from the mouse gut averaged for each species”.\nWe have now included Supplementary Figure 3 (Figure R6) to show the positive correlation\nbetween nucleotide diversity in the inoculum and average nucleotide diversity observed across\nthe mouse samples:\nNucleotide diversity (π) observed in the inoculum (x axis) and average nucleotide diversity\nobserved across mouse samples (y axis) is visualized for the 30 most abundant and prevalent\nspecies in the humanized mouse cohort (Methods). Error bars represent the standard deviation\nof π values observed in mouse samples. The dashed grey line represents the identity line (y =\nx).\nLine 269: “For example, for the species A. equilofaciens, which was present in multiple mice\nand cages, the within-cage variance of strain frequencies measured in the jejunum ranged from\n1.65 × 10-6 in cage 3 to 1.29 × 10-3 in cage 2, whereas the between-cage variance was at least\ntwo orders of magnitude greater 1.18 × 10-1 (Figure 4).” Where do I see this?\nWe have added a new Supplementary Table 4 to our supplement that supplies the variance\nstatistics for between cage and within cage comparisons and have updated the text accordingly\nin Line 286.\nAdditionally, we have added Supplementary Figure 7 showing the comparison of between\ncage variance and within cage variance, included below as Figure R6.\nand between cages. Variance in major strain frequency was measured for samples belonging to\nthe same gut region in different mice within the same cage (“Within cage,” dark orange) and\nbetween different cages (“Between cage,” light orange). Within cage bars represent the average\nwithin cage variance across the three cages and five gut regions, while between cage bars\nrepresent between cage variance averaged across the five gut regions.\nLine 338: What are the 11 SNV frequency changes they are referring to? Which one showed\nspatial differentiation? Which are the 4 unique within-host SNV frequency changes? What are\nthe 57 unique between-host SNV frequency changes?\nWe have updated Supplementary Table 5 (formerly Supplementary Table 3) to provide more\ndetail on the SNVs considered in this study, including the 11 mentioned here. Supplementary\nTable 5 is a table containing the allele frequencies of all SNVs that underwent an extreme allele\nfrequency shifts between a pair of samples, and includes indication of which sample comparison\nthe change arose in. For clarity, we have added a column “Observed in” that identifies what kind\nof comparison the SNV frequency change was identified in. For example, the 11 SNV frequency\nchanges mentioned in the text are referring to SNVs in which there was a large frequency shift\n15\nin the mice relative to the inoculum, so these can be identified by looking for SNVs in rows\nlabeled with “Inoculum” in the “Observed in” column. Of these 11, one SNV in Coprococcus sp.\nshowed a large rise in frequency from small intestine to large intestine (we have now noted in\nthe text that this SNV was found in Coprococcus).\nOverall, there were four SNVs in our dataset that showed an extreme allele frequency change\nalong the gut within a host, including the SNV in Coprococcus sp. along with one SNV in\nEnterococcus faecium and two in Ruminococcus sp. The extreme within-host changes were\nexclusively observed between small intestinal and large intestinal gut regions. Their exact\nfrequencies can be found by looking for SNVs labeled with “Within Host” in the “Observed in”\ncolumn in Supplementary Table 5. For additional clarity on these 4 SNVs, we have added a tab\nto Supplementary table 5 that describes the average frequencies of these SNVs in the small\nintestine (duodenum, jejunum, and ileum) and large intestine (cecum and colon) in the mouse in\nwhich the extreme frequency change was detected.\nThe frequencies of all SNVs detected in the humanized mouse microbiomes are visualized in\nFigure 5 and Supplementary Figure 8.\nLine 489: “nine” instead of “nice”?\nThank you for this correction, we have updated the text.\nThe study uses 9-week-old mice for inoculation (Line 489). However, this overlooks ontogenetic\neffects—key developmental factors that influence microbial adaptation to specific gut regions. To\naccount for this, mice should be inoculated with the human microbiota before breeding, and\nmicrobial composition should be analyzed in the F1 generation.\nWe thank the reviewer for raising this point. While we agree that ontogenetic factors can shape\nmicrobiome-host interactions, our study's primary focus was not on host development but rather\non the spatial distribution of microbial genetic diversity across the gut.\nTo mitigate concerns about adaptation timescales, our germ-free mice were colonized with\nhuman microbiota and equilibrated for eight weeks prior to analysis, a practice standard in the\nfield (Turnbaugh et al., 2009; Seedorf et al., 2014; Johansson et al., 2015; Sonnenberg et al.,\n2016). This equilibration period allowed for stabilization of community structure and recovery\nfollowing oral gavage.\nTo further address the reviewer’s concern and assess whether our findings were specific to\nhumanization, we performed an additional experiment using conventional mice with naturally\nacquired, host-adapted microbiomes. These mice were sourced from the same litter and\nco-housed until weaning to ensure shared microbial exposure and strain pools. At six weeks of\nage, we collected luminal contents from the same five gut segments sampled in our original\nstudy design. Remarkably, we again observed spatial uniformity in genetic diversity, suggesting\nthat this pattern is not an artifact of host-microbiome mismatch or colonization timing, but rather\na potentially generalizable feature of microbial diversity in the gut.\nHowever, the different diet seems to make a huge difference (see PCoA and Ng et al., mBio\n(2023)). So, it would only be fair to include sex and diet as separate variables.\nWe thank the reviewer for this important point. In our study design, sex, diet, and cage were\nnecessarily confounded due to standard animal husbandry practices, which require co-housing\nof mice by sex and by diet. This constraint made it statistically underpowered and biologically\nconfounded, preventing us from including these variables independently in our models.\nRather than focusing on specific contributions of sex or diet, which we acknowledge can\ninfluence the microbiome, our goal was to examine whether the pattern of spatial\nuniformity—the central finding of our study—persisted across multiple conditions. Importantly,\nwe observed that this spatial pattern holds true regardless of sex and diet. Moreover, we found\nthe same trend in conventional mice, all of whom were male and fed the same diet of standard\nrodent chow. This indicates that sex and diet are likely not driving the spatial signal we are\nattempting to highlight in this study across various dataset.\nWe appreciate the reviewer’s attention to this point and have noted the potential influence of\ncage and chow in the methods section (lines 607-610):\n“With this experimental set up, we were able to ascertain replicability and generalizability\nof the genetic homogeneity observed along the gut in three independent cages with\nvarying diets and sexes, as well as contrast within- and across-host dynamics (Figure\n1).”\nLine 704: Supplementary Figure 6 instead of Supplementary Figure 5?\nThank you for this correction, we have updated the text.\nReviewer #2 (Remarks to the Author)\nThe study by Wasney et al. provides a detailed gut biogeographical analysis of strain-level\ndiversity in a humanised gnotobiotic mouse model. As stated by the authors, the main\nmotivation was to “characterise how genetic diversity varies along the gut”. Overall, the study is\ncareful, thoughtful, and precise, and the manuscript is clear and well written. However, the\nexperimental design and scope of the sampling and controls is unfortunately quite limited, and\nperhaps best put by the weaknesses outlined in the Discussion by the authors themselves. If\nthe main motivation was indeed to characterise how genetic diversity varies along the gut, the\ndesign and sampling would arguably be more thorough. And while the analysis was carefully\nconducted and there are some noteworthy observations, I do question how much these limited\nobservations should really be generalized. The picture is simply too premature.\nHere are some more specifics.\n17\n1. As stated by the authors themselves, things might look very different if they had included\nnative mouse microbiomes as a control / comparison group. As it stands, statements like page\n14, lines 364-367:\n“This fact would seem to suggest that even when SNVs are adaptive, they are either broadly\nadaptive to the entire gut, or alternatively have high migration rates that outweighs local\nselection pressures, perhaps due to mixing and peristalsis along the gut”\nCould for example be attributed to adaptations to universal aspects of the drastically new mouse\nhost gut environment. Without mouse samples as both controls and a necessary group\ncomparison, it really limits what can be inferred here. I would recommend to gavage native\nmouse stool(s) into germ free mice as an appropriate control/comparison.\nWe thank the reviewer for these comments and have added two new major analyses to the\npaper to address them. First, we repeated the experiment in (i) conventional mice with native\ngut microbiota as well as (ii) analyzed humans whose luminal contents had been sampled over\nseveral spatial points. We found that spatial uniformity extends to these two systems as well.\nSpecifically, we observed spatial uniformity in strain frequencies along the gut, and zero local\ngenetic adaptations arising in distinct gut segments. These results indicate that the uniformity is\nnot an artifact of the humanized mouse system, and instead is a generalizable feature of\nconventional mouse microbiomes. For more details on the results stemming from our\nconventional mouse and healthy human cohorts, see Figure 6 (Figure R1, Figure R3),\nSupplementary Figure 9-13, as well as our response to reviewer 1’s comments on pages 4-6\nand 9-11.\n2. Only a single host inoculum was used. There could also be peculiarities of any given single\nsample. The conclusions would be much stronger if e.g. these general patterns could be\nobserved using more than one inoculum.\nBy observing the same patterns of uniformity in conventional mouse microbiomes and in healthy\nhumans, we can rule out that these results are an artifact of the peculiarities of any given\nsample.\n3. Related to 2., it seems as if there is very high level of replication, i.e. eight recipients for just a\nsingle donor. However, it only becomes apparent in the methods section that there is a “cryptic\nexperiment” within this experiment, as the “mice in cages 2 and 3 were exposed to a fiber-rich\ndiet of 30% guar gum for two weeks, while the rest continued on the standard diet.” It is argued\nthat this added to the generalizability of the study, but more human and mouse donor material\nwould have arguably offered more genuine generalizability. Further, without longitudinal\nsampling, it is not clear whether this 2 week intervention had an effect on the results or not.\nThus, in summary, it regretfully appears that the design reflects a re-purposing of samples from\na very unrelated previous study (ref 3), rather than making a dedicated attempt at addressing\nthe actual stated topic.\nconcerns about the generalizability of our claims by including results from 9 conventional mice\n(all the same sex and on a standard rodent diet) with natural microbiomes, as well as 15\nhumans of varying sexes and genetic backgrounds in our analysis.\nThe following descriptions of the conventional mouse experimental setup has been added to the\nmain text to accompany Figure 6A-C and Supplementary Figure 9 (Lines 407-417):\n“Having established that uniform strain frequencies and spread of evolutionary\nadaptations along the gut is a common feature in humanized mice, we next investigated\nwhether these same patterns also arise in hosts with established native microbiota,\nspecifically conventional mice and healthy humans.\nWe first analyzed a cohort of nine conventional mice that were cohoused until weaning\nat three weeks of age before being housed into three cages of three mice each. At six\nweeks of age, we sampled and sequenced luminal contents from the same five gut\nregions that were previously examined in humanized mouse guts (Methods). Large\nintestinal samples harbored higher alpha diversity and a different family-level\ncomposition than small intestinal samples (Methods) (Supplementary Figure 9),\nconfirming that the expected spatial organization does exist at higher taxonomic levels\nalong the guts of the conventional mice. ”\nFrom these 9 subjects, we inferred that strain frequencies remained generally consistent along\nthe gut. The general trend observed across species was quantified with ANOVA as described in\nFIgure 6A-B and the accompanying main text (lines 418-430):\n“Further replicating our findings from humanized mice, we found that in conventional\nmice, co-colonizing strains of the same species exhibited largely uniform frequencies\nacross gut regions. This pattern contrasts with the substantial variation in strain\nfrequencies between mice, even if co-housed (Figure 6A, Supplementary Figure 10).\nAmong the nine bacterial species with co-colonizing strains that were present in multiple\nmice, cages, and gut regions, median change in frequency Δf between gut segments\nwithin hosts was only 2.21×10-2, much smaller than the median frequency changes\nobserved between mice in the same cage (2.16×10-1) and in different cages (1.42×10-1)\n(Figure 6B). Corroborating these results, gut region explained the least variation\n(average variance explained = 0.75%), whereas mouse identity explained the most\nvariation (average variance explained = 78.39%) (Supplementary Figure 11). Cage\nexplained a moderate amount of variation (average variance explained = 19.97%).\nThese results indicate that uniform frequencies of strains along the gut is not unique to\nhumanized mice and may be a common feature of mouse microbiota.”\nNext, we asked whether SNV changes spatially segregated along the guts of conventional mice,\nunlike SNVs in our humanized mouse system. To the contrary, we found even fewer extreme\n19\nwithin-host SNV changes in our conventional mice than in our humanized mice, suggesting that\ngenetic variation in natural mice is also uniformly distributed along the gut. We added the\nfollowing passage in the manuscript to present these results (Lines 431-434).\n“Additionally, we did not observe evolutionary changes accruing between gut regions\nwithin hosts even while changes arose between hosts at a rate of 4.73×10-7 changes/bp\n(Figure 6C). These results indicate that any evolutionary changes arising within hosts\nsweep globally throughout the gut.”\nThe following descriptions of the human study (Shalon et al., 2023) in the main text\naccompanying Figure 6D-E are as follows (lines 435-453):\n“Next, to determine whether spatially uniform patterns of genetic diversity are a feature\nof human guts as well, we analyzed previously published metagenomic data54 collected\nusing ingestible sampling capsules designed to sample luminal contents from four\nsuccessive regions of the gastrointestinal tract: pyloric sphincter to the upper small\nintestine, upper to mid-small intestine, mid- to lower small intestine and the lower small\nintestine into the ascending colon. In this dataset, 15 healthy adult subjects ingested four\ncapsule types twice daily for as long as a week.\nWe hypothesized that strain frequencies in humans might vary more along the gut than\nin mice, given the greater length and compartmentalization of the human intestine, as\nwell as the potential for host-specific niche adaptation over long residence times25,27.\nContrary to this expectation, strain frequencies were generally homogeneous not only\nalong the gut but also across timepoints. For example, the frequency of the major strain\nof Ruminococcus obeum in Subject 11 fluctuated around a mean of 72% with a variance\nof 0.4% across gut locations and timepoints (Figure 6D). More generally, across the 25\nspecies x host pairs with co-colonizing strains in multiple timepoints and device types,\nthe median change in major strain frequency Δf between gut regions within the same\ntimepoint was only 3.79×10-2. This was the same order of magnitude as the median\nchange observed between consecutive timepoints (2.92×10-2), or even between the first\nand last observed timepoint for a species x host pair (3.55×10-2) (Figure 6E). These\nresults indicate that strain frequency tends to be uniform along the human gut as well as\nacross hourly and daily timescales.”\nWe then analyzed gut region-specific evolutionary changes in the human gut (Lines 464-478)\n“We next asked whether extreme SNV frequency changes arising as a consequence of\nwithin-host evolution were spatially segregated. To do so, we identified SNV frequency\nchanges between distinct regions of the gut for each of the 56 host x species pairs\n(representing 35 species detected in 11 subjects) with enough data in at least two\ncapsules for a given time point. Out of the 56 pairs, only 7 of these (12.5% of species x\nhost pairs) had detectable SNV frequency changes between gut regions within a single\n20\ntimepoint (Supplementary Table 6), indicating that spatial segregation of SNVs is a\nfeature in only a minority of samples.\nFinally, we investigated whether the SNV changes arising along the gut are observable\nover multiple time points, or whether they are transient. Persistence would suggest that\nenvironmental gradients maintain spatial segregation of genetic variation along the gut\nover time. Of the seven host x species pairs with spatial differences, four had sufficient\ntemporal and spatial data (at least two timepoints with at least two device types per\ntimepoint). In three of four host x species pairs, spatial differences did not persist over\nmultiple time points, with allele frequencies often reverting to their original state\n(Supplementary Figure 13, Supplementary Table 6). These results imply that\nsustained extreme allele frequency changes along the gut are rare.”\nFigure 6 has been included in this response as Figure R1 and Figure R3; Supplementary\nFigure 9-13, and Supplementary Table 6 can be found in the Supplementary Information file.\nWith these additional studies we find that the tendency for co-colonizing strains to maintain\nuniform frequencies along the gut is not just unique to humanized mice but can be generalized\nto conventional mice and native human microbiomes. Moreover, local SNV frequency changes\ndo not occur at all along the guts of conventional mice, and arise only transiently in humans,\nsuggesting they do not represent spatial differentiation along the gut driven by environmental\nselection.\nAdditional questions:\nLines 337-339, are any of the 4 unique within-host SNV frequency changes between the upper\nand lower GI tract?\nYes, for all 4 unique within-host SNV frequency changes, the frequency changed between the\nupper and lower GI tract in at least one of the mice. All SNV frequency changes are shown in\nSupplementary Table 5 along with a secondary sheet indicating the 4 changes that occurred\nbetween the small and large intestine. We also clarify this observation in the text as follows\n(lines 357-359): “we observed only 4 unique within-host SNV frequency changes in 3 out of the\n33 species, all between the small intestine and large intestine, including the SNV in\nCoprococcus sp. (Supplementary Table 4).”\nLines 339-341, is there any additional higher level evidence of parallelism among the 57 unique\nbetween-host SNV frequency changes, e.g. at the level of KEGG pathways of the genes\ninvolved?\nYes, we found evidence of parallelism among the between-host SNV frequency changes. In all,\nthese 57 observed changes occurred in 36 unique genes. Of those genes, 16 of them could be\nannotated with KEGG pathways in prokaryotes using an HMMER search with Kofam-KOALA\n(Aramaki et al. 2020). We found 8 of these genes involved in metabolism, 3 in genetic\ninformation processing, and 3 in signaling. Of the 8 metabolism genes, 3 were explicitly involved\nwith glycan biosynthesis and metabolism.\nReviewer #3 (Remarks to the Author)\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I would like to sincerely thank the authors for their additional efforts and for carefully addressing my questions and concerns.\nI am satisfied with the revised version of the manuscript and believe that it has improved substantially in terms of clarity,\nrigor, and overall quality.", "reviewer_2": "The authors have made considerable and sufficient efforts in addressing my previous concerns, well done!", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": ""}]

Decision letter