High AMY1 copy number (CN) is associated with higher levels of Porphyromonas in saliva

Host genetic variation influences microbiome composition. While studies have focused on associations between the gut microbiome and specific alleles, gene copy number (CN) also varies. We relate microbiome diversity to CN variation of the AMY1 locus, which encodes salivary amylase, facilitating starch digestion. After imputing AMY1-CN for ∼1,000 subjects, we identified taxa differentiating fecal microbiomes of high and low AMY1-CN hosts. In a month-long diet intervention study, we show that diet standardization drove gut microbiome convergence, and AMY1-CN correlated with oral and gut microbiome composition and function. The microbiomes of low-AMY1-CN subjects had enhanced capacity to break down complex carbohydrates. High-AMY1-CN subjects had higher levels of salivary Porphyromonas; their gut microbiota had increased abundance of resistant starch-degrading microbes, produced higher levels of short-chain fatty acids, and drove higher adiposity when transferred to germ-free mice. This study establishes AMY1-CN as a genetic factor associated with microbiome composition and function.

Here, we address how AMY1-CN relates to the diversity and function of the gut microbiomes of healthy individuals with normal BMIs. Using an existing dataset of genotyped individuals with associated fecal microbiome diversity data, we identified taxa that discriminated high and low AMY1-CN individuals. We then conducted an intervention study by screening >100 volunteers for AMY1-CN and recruiting 25 participants into a 1-month longitudinal study in which diet was standardized for 2 weeks. We used 16S rRNA gene sequence analysis to assess the effects of host AMY1-CN and diet intervention on oral and fecal microbiomes. In addition, we obtained a functional characterization of fecal microbiomes through (1) deep metagenomic sequencing, (2) short-chain fatty acid (SCFA) measures, and (3) fecal transfers to germ-free mice. Together, results support an association between host AMY1-CN and microbiome diversity and function.

Because of its link to carbohydrate digestion, AMY1-CN has been investigated for associations with BMI and metabolism. Results of these studies vary, with low AMY1-CN associated with high BMI in some populations (), but not others (). Discrepancies may be methodological () or due to variation in starch intake between individuals (). Whether the gut microbiome, which is known to impact host metabolism (), responds to host AMY1-CN remains to be ascertained.

Dietary starch intake modifies the relation between copy number variation in the salivary amylase gene and BMI.

Structural forms of the human amylase locus and their relationships to SNPs, haplotypes and obesity.

Complex copy number variation of amy1 does not associate with obesity in two East Asian cohorts.

Structural forms of the human amylase locus and their relationships to SNPs, haplotypes and obesity.

Beneficial effect of a high number of copies of salivary amylase AMY1 gene on obesity risk in Mexican children.

Low copy number of the AMY1 locus is associated with early-onset female obesity in Finland.

Complex carbohydrates, a broad category of polysaccharides that includes starch, first encounter amylase in the mouth, then in the small intestine (SI), where pancreatic amylase is added to the chyme and the liberated sugars are absorbed. SI uptake of sugars liberated by host enzymes yields more energy to the host than uptake in the large intestine (LI) of microbial fermentation products (). Host-microbial competition for starch may have driven selection for duplications at the amylase locus. Indeed, amylase supplementation to farm animals enhances starch digestibility and promotes weight gain (). Similarly, humans with a high AMY1-CN (AMY1H), who produce high levels of salivary amylase, should derive more energy from the same carbohydrate-rich diet than those with a low AMY1-CN (AMY1L). Compared to AMY1H, AMY1L individuals might be expected to harbor gut microbiomes with a greater capacity for breakdown of complex carbohydrates, compensating for the lower levels of host amylase.

Effects of exogenous enzyme supplementation to corn- and soybean meal-based or complex diets on growth performance, nutrient digestibility, and blood metabolites in growing pigs.

The effect of damaged starch, amylolytic enzymes, and proteolytic enzymes on the utilisation of cereals by chickens.

CNV of the AMY1 gene, encoding the salivary amylase enzyme, is considered one of the strongest signals of recent natural selection on human populations (). Salivary amylase hydrolyzes alpha bonds of starch and glycogen, beginning the process of starch degradation in the mouth. AMY1-CN is positively correlated with oral amylase activity (). A shift to greater starch consumption during the agronomic transition of the Neolithic period likely selected for the duplications observed within the AMY1 locus (). Today, the mean AMY1-CN is reported higher in populations with an agrarian background compared to hunter-gatherers (). Across genetic backgrounds, human AMY1-CN ranges from 2 to 24 ().

Complex copy number variation of amy1 does not associate with obesity in two East Asian cohorts.

Structural forms of the human amylase locus and their relationships to SNPs, haplotypes and obesity.

Host genotype has recently emerged as a significant factor in shaping the relative abundance of specific members of the human gut microbiota (). Heritable gut microbes, whose abundances are influenced by host genotype, have been identified, and genome-wide association studies (GWASs) have linked specific gene variants to members or functions of the gut microbiome (). In addition to single nucleotide polymorphism (SNP) differences between individuals, another important aspect of human genetic variation is the copy number (CN) of genes. Gene duplications resulting in increased CN provide a rapid means of adaptation to environmental change (). Copy-number variation (CNV) in genes accounts for more genomic variability than SNPs () and significantly influences gene expression (). This important aspect of genetic variability likely affects microbiome differences between individuals, but links between the CNV of specific human genes and the microbiome remain to be elucidated.

ABO antigen and secretor statuses are not associated with gut microbiota composition in 1,500 twins.

The effect of heritability and host genetics on the gut microbiota and metabolic syndrome.

The relationship between the human genome and microbiome comes into view.

To gauge differences in function for AMY1H and AMY1L gut microbiomes, we inoculated fecal samples obtained from AMY1H and AMY1L donors, sampled at 5 TPs, into 96 male Swiss-Webster 4- to 6-week-old germ-free mice fed a polysaccharide-rich chow ad libitum and single-caged post-transfer. Adiposity was assessed by DEXA after 4–6 weeks. Across all mice, we observed a significantly higher body fat percentage for recipients of the AMY1H compared to the AMY1L microbiomes (linear mixed model; p = 0.026). Post hoc pairwise comparisons revealed that TPs 3, 7, and 10 showed a significantly higher final adiposity for the AMY1H compared to the AMY1L treatment groups (Tukey’s honest significant difference [HSD] test adjusted p < 0.05), whereas TPs 4 and 9 did not (after controlling for weight on the day of inoculation and length of the experiment; Figures 7 E–7I). Food intake was not significantly different between high versus low AMY1-CN donor groups, and there were no differences in intestinal inflammation (measured by Lipocalin 2 at the end of the experiments using samples from TPs 3 and 10; data not shown). Thus, the AMY1H microbiomes generally drove higher adiposity gains that were unrelated to food intake and metabolic inflammation.

Fecal Transplants from AMY1H Donors into Germ-Free Mice Promote Greater Adiposity Than Those from AMY1L Donors

Next, we performed a similar analysis in which we asked if SCFAs could be used to predict subjects with high or low mean SAA measurements. We determined high and low SAA groups by performing k-means clustering for all 25 subjects; all observations from the same subject were labeled with the subject’s SAA group. We achieved an accuracy of 83.61% with an MCC of 64.46% and an AUC of 85.03% ( Figure 7 C). The total concentration of SCFAs was the most informative element for discriminating the high and the low SAA groups, followed by the concentrations of butyrate, valerate, propionate, and acetate ( Figure 7 D). Using a linear mixed model that included SAA group as a covariate, we confirmed that the concentrations of the SCFAs were higher in subjects with high SAA (adjusted p values: total SCFA concentration = 4.7 × 10, acetate = 6.5 × 10, propionate = 6.5 × 10, and butyrate = 4.7 × 10; valerate, isovalerate, heptanoate, and hexanoate were not significantly different). Assuming equal uptake of SCFAs in the colon across subjects, these results suggest that the higher the host SAA, the greater the SCFA production in the colon. Given that SAA can vary from day to day for a given individual, the observation that SAA is a better predictor of SCFAs than AMY1-CN indicates that the microbiome’s metabolic output is sensitive to daily SAA variation.

As an assessment of microbial metabolic output, we measured levels of SCFAs in stool samples collected at all TPs. We used machine learning to assess whether SCFAs levels were predictive of AMY1-CN groups or SAA. Using the AMY1H′ and AMY1L′ group assignations, we trained a random forest model with 80% of the SCFA measures to predict the AMY1 group to which the remaining 20% belonged. The model achieved an accuracy of 70.37% with an MCC of 40.74% and an AUC of 77.8% ( Figure S7 ). In agreement with AMY1-CN being positively correlated with SAA, there was a trend for SCFA concentrations to be higher in the AMY1H′ (CN > 6) group.

To directly assess functional capacity for carbohydrate degradation, we used hidden Markov models from dbCAN to identify carbohydrate-active enzymes (CAZymes), which include the following enzyme classes: glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE), carbohydrate-binding modules (CBM), S-layer homology modules of the cellulosome (SLH), and auxiliary activities (AA) (). We then used a linear mixed model to assess differences in the abundances of each of the 7 CAZyme classes between AMY1H and AMY1L groups. We observed a higher number of read counts for GH and PL classes in AMY1L individuals (post hoc analysis; G, p = 0.0054; PL, p = 0.030; Figures 7 A and 7B ), and a similar trend for AA and CE classes ( Figure S6 ). These enzyme classes are involved in the breakdown of complex carbohydrates; their enrichment in AMY1L individuals is consistent with the notion that a greater proportion of complex carbohydrates reaches the distal gut in AMY1L individuals.

(E–I) Boxplots of the adiposity measure normalized by baseline weight on the day of inoculation of formerly germ-free mice after humanization with stool collected from the study participants at 5 time points: (E) TP3; (F) TP4; (G) TP7; (H) TP9; (I) TP10.Tukey’s HSD adjusted p < 0.05. See also Figures S6 and S7

(D) The features used by the random forest model for the classification of the test samples are shown in decreasing order of importance given by the mean decrease in Gini index. SCFA is the total of all SCFAs. But, butyrate; Val, valerate, Pro, propionate; Ace, acetate; Iso, isovalerate; Hex, hexanoate; Hep, heptanoate.

(C) We used machine learning to assess whether SCFA levels were predictive of SAA. Subjects were clustered according to their salivary amylase activity in two groups (SAA-H and SAA-L) by k-means clustering (n = 25). Shown here is the receiver operating characteristic (ROC) curve of a random forest model used to predict the SAA group using the SCFA measurements in a test dataset.

(A and B) Boxplots of the read counts in the AMY1H and AMY1L groups for each of the significantly different carbohydrate-active enzyme classes: glycoside hydrolases (GH; A) and polysaccharide lyases (PL; B) (n = 20).

Despite the convergence of AMY1H and AMY1L microbiomes over the standardized diet period, the two groups could be differentiated by their functional gene content. We used the statistical software DESeq2 to identify gene families with differential abundances between high and low AMY1 groups at each time point. We identified 481 gene families with significantly different read counts at one or more TPs between AMY1H and AMY1L groups ( Figure 6 A). Notably, 39% of the 481 gene families were taxonomically assigned to Bacteroides dorei and were more abundant in the AMY1H group at multiple TPs, in accordance with our 16S rRNA gene diversity results ( Figures 5 B and 6 A). Eight other species of Bacteroides were identified, but only Bacteroides cellulosilyticus was enriched in the AMY1L group. In accordance with the results of the 16S rRNA gene diversity analysis, reads for gene families mapping to Ruminococcus were enriched in the AMY1H group ( Figures 5 B and 6 A). Sequences mapping to gene families from Prevotella copri were also more abundant in AMY1H, but almost exclusively at TP 3, consistent with the 16S rRNA gene diversity data. Together, these data support a relative enrichment in taxa responsible for resistant starch breakdown in the AMY1H microbiomes.

We compared the metabolic potentials of the gut microbiomes for AMY1H and AMY1L groups using metagenomes generated for all subjects sampled at 6 TPs (3, 4, 6, 7, 9, and 10). We sub-sampled 20 million paired-end reads per sample to normalize sequencing depth and used the HMP Unified Metabolic Analysis Network (HUMAnN2) pipeline to classify shotgun metagenomic reads into gene families ( Figure 6 A). Using non-parametric bootstrapping with 1,000 permutations, we determined that Bray-Curtis distances calculated from gene family counts decreased in mean values between AMY1H and AMY1L individuals during the diet period relative to the pre-diet period ( Figure 6 B). The number of gene families significantly enriched in the AMY1H group was also lower after TP 4 ( Figure 6 D), and the number of gene families significantly different between AMY1 groups was lower after TP 4 regardless of taxonomy or function ( Figures 6 C and 6E). We also observed a spike of differentially abundant genes associated with mobile elements at the start of the diet provision period (TP 3 to TP 4), which is consistent with nutritional stress-induced activation of prophages, lytic bacteriophages, and horizontal gene transfer (). Thus, the diet standardization drove the convergence of metabolic functions as well as composition.

(C) Number of significantly enriched gene families that could be grouped by function.

(A) Heat map displaying each of 481 gene families with abundances differing between AMY1H and AMY1L at one or more of six different TPs (n = 20). The heat map is sorted by taxonomy, annotation group, and gene family. Each concentric circle in the heat map corresponds to a TP. Gene family abundances with significant differences between AMY1 groups were identified using DESeq2, and the log 2 fold difference between AMY1H relative to AMY1L is depicted in the heat map. Higher abundances of gene families in the AMY1H group are colored yellow, while those higher in AMY1L are colored blue. Only gene families with a BH-adjusted p < 0.01 and that were assigned taxonomy are shown.

We assessed whether diet standardization resulted in more similar microbiomes (i.e., reduced beta diversity) for AMY1H and AMY1L groups by comparing samples before, during, and after the standardized diet. Non-parametric bootstrap confidence intervals (CIs) for the differences in the weighted and unweighted UniFrac distances between AMY1H and AMY1L groups for each pair of diet periods indicated that diet standardization did not induce convergence of oral microbiomes between AMY1H and AMY1L subjects but did so for the gut microbiomes ( Figures S5 B and S5C).

We also identified taxa that differentiated AMY1H and AMY1L groups at each TP (using Harvest): 7 of the 11 discriminatory OTUs were members of the Ruminococcaceae family, and all but one were elevated in the AMY1H compared to the AMY1L group ( Figure 5 B; Table S6 ). As observed in the oral microbiota, the Harvest analysis showed that discriminatory OTUs were consistently enriched in the same AMY1-CN category. Members of the Ruminococcaceae have been linked to resistant starch degradation: enrichment of Ruminococcaceae OTUs in the AMY1H is consistent with reduced availability of starches susceptible to host amylase degradation in the distal gut of the AMY1H host. OTUs classified to the Ruminococcus genus were also enriched in the 994 subjects (above) with predicted high host AMY1-CN.

In contrast to oral microbiomes, the alpha diversity of fecal microbiomes was generally similar between AMY1-CN groups (using Chao 1, Observed Species, and Faith’s PD metrics; Figure 4 A). Beta diversity was unrelated to host AMY1-CN ( Figures 4 B and S4 A), with some clustering by subject ( Figure S4 B). Using a random forest analysis, the prediction performed on the 20% of the samples reserved as a testing dataset produced an accuracy of 98.21% with an MCC of 96.04% and an AUC of 97.36%. After performing a feature selection process, we identified OTUs discriminating between AMY1H and AMY1L as belonging to Ruminococcaceae (Ruminococcus and Oscillospira) and Lachnospiraceae (Blautia, Dorea, and Roseburia; Figure 5 A; Table S6 ). Similarly, when all 25 subjects were reclassified into low and high groups based on k-means clustering (AMY1M included, as above), a similar set of discriminatory OTUs was observed ( Figure S5 ).

(B) Ribbon plot showing the OTUs identified using HARVEST that distinguish AMY1H and AMY1L groups at each time point. See legend for Figure 3 . In addition, samples collected at the time points in bold print were also subjected to shotgun metagenomics analysis. See also Figure S5 and Tables S1 and S5

(A) OTUs, identified using machine learning, that distinguish AMY1H and AMY1L gut microbiomes. See legend for Figure 3

Gut Microbiomes Do Not Differ in Overall Composition between AMY1-CN Groups

Figure 4 Gut Microbiomes Do Not Differ in Overall Composition between AMY1-CN Groups

To gain a time-resolved view into the taxa driving differences for subjects at the extremes of the AMY1-CN gradient, we compared AMY1H and AMY1L groups (AMY1M excluded) at each TP using Harvest. This analysis revealed 9 OTUs with significantly different mean relative abundances between AMY1H and AMY1L ( Figure 3 B); none exhibited different variances. As observed for the machine learning analysis, OTUs that discriminated the AMY1H and AMY1L groups belonged to the genera Prevotella, Haemophilus, Neisseria, and Porphyromonas ( Figure 3 B; Table S5 ). These patterns highlight that the same OTUs are consistently elevated over time in either AMY1H or AMY1L.

We searched for OTUs that distinguished AMY1H and AMY1L categories (AMY1M excluded) using a machine learning technique (random forest; STAR Methods ). A model trained on 80% of the samples produced an accuracy of 97.67% with a Matthews correlation coefficient (MCC) of 94.92% and an area under the curve (AUC) of 96.66% once it was tested on the remaining 20% of the samples. We then used a feature selection process to identify the relevant features of the model. Among the top OTUs that most discriminated AMY1H and AMY1L groups (AMY1M excluded) were OTUs classified as Prevotella and Porphyromonas ( Figure 3 A; Table S5 ). In order to include all 25 participants, we reclassified the individuals into only two new AMY1 groups, AMY1H′ (CN > 6) and AMY1L′ (CN < 6), using k-means. Using this assignation, 13 subjects were in the AMY1H′ and 12 were in the AMY1L′. Performing machine learning using the AMY1H′ and AMY1L′ groups yielded similar results ( Figure S3 ).

(B) The mean relative abundances of the OTUs included in this ribbon plot were significantly different between AMY1H and AMY1L groups at one or more TPs using the statistical model Harvest (n = 20). Each ribbon corresponds to a single OTU with taxonomy indicated to the left, with unclassified abbreviated “U.” Taxonomy may be shared by several OTUs. The width of the ribbon at each TP shows the ratio of the mean OTU relative abundances between the AMY1-CN groups. If the ribbon is colored orange at a given TP, the AMY1H group has a higher mean relative abundance of the OTU; when purple, the AMY1L has a higher mean relative abundance. When the ribbon is colored gray, the Benjamini-Hochberg (BH) adjusted p ≥ 0.15. Lighter orange or purple corresponds to a BH adjusted p < 0.15; darker colors correspond to BH adjusted p < 0.05. The asterisk () denotes an OTU that was assigned taxonomy with higher resolution after performing a BLAST search using the representative sequence. See also Figure S3 and Table S5

(A) The OTUs shown here were identified using machine learning as distinguishing between AMY1H and AMY1L groups (n = 20). The length of the bar represents the magnitude of the mean decrease in Gini index and the orientation indicates the group in which the OTU is enriched.

We applied the UniFrac metrics to assess between-subject (beta) diversity. Principal coordinate analysis (PCoA) of unweighted UniFrac distance metrics revealed clustering of saliva microbiomes by subject and a trend for separation by AMY1-CN group across all subjects ( Figures 2 B, S2 B, and S2C). Together, these observations indicate that across the AMY1-CN gradient, a higher AMY1-CN is associated with greater richness of the microbiome without a significant shift in overall diversity.

Saliva samples were profiled for microbial community diversity by Illumina sequencing 16S rRNA gene PCR amplicons (V4 region; Table S2 ). Sequences were clustered into OTUs using a threshold of 97% pairwise sequence identity ( STAR Methods ). We observed that oral microbiome richness (alpha diversity) was correlated with AMY1-CN (using Chao 1, Observed Species, and Faith’s PD metrics p < 0.01, but not Shannon’s Index, which is also a measure of evenness) and was higher in AMY1H than AMY1L individuals (p = 0.011; Figure 2 A).

(B) Principal coordinate analysis (PCoA) of the unweighted UniFrac distances between samples collected from all 25 subjects throughout the study. The first two PCs are plotted. The percent variation explained by each PC is indicated on the axes. Samples are colored according to AMY1-CN group. See also Figure S2

We obtained saliva and stool samples at 12 time points (TPs; 3 per week). Salivary amylase activity (SAA) ranged between 10.2 and 527 units per mL of saliva, similar to previously reported ranges (). AMY1-CN correlated with SAA across all subjects at all TPs (linear mixed model; p = 2.1 × 10). SAA levels for the AMY1H were higher than for the AMY1L at all TPs (linear mixed model; p = 1.9 × 10 Figure 1 B) with AMY1M individuals intermediate ( Figure S2 A). Fecal amylase activity (FAA) was variable within and between subjects (0.6–1,120 Ugstool; Figure S1 D). Unlike SAA, FAA did not correlate with AMY1-CN. To further characterize FAA, we used an ELISA method ( STAR Methods ) to measure levels of host pancreatic amylase in stool samples at TPs 6 and 10. Across all 25 subjects, pancreatic amylase levels were highly correlated with FAA (Spearman’s rho = 0.80, p = 3.7 × 10for TP 6 and Spearman’s rho = 0.78, p = 6.3 × 10for TP 10; Figure S1 E), although AMY2-CN was not. This finding corroborates previous reports that FAA is largely pancreatic ().

To mitigate the effects of dietary differences between individuals on their microbiomes and to promote frequent starch consumption, during study weeks 2 and 3 we provided all participants with all meals and snacks. Participants ate from the same menu freely, occasionally supplemented it ( Table S4 ), and recorded their dietary intake in food diaries ( STAR Methods ). Based on dietary records, mean percentages of total carbohydrate, protein, and fat intake did not differ significantly between the AMY1H, AMY1M, and AMY1L groups, regardless of whether meals were consumed during (weeks 2–3) or outside (weeks 1 and 4) of the standardized diet period ( Figures S1 A–S1C). The intake of all three macronutrients differed between days (p < 1 × 10). However, the standardized diet period did not significantly impact mean intake of macronutrients.

We collected buccal swabs from 105 volunteers recruited on the Cornell University campus and measured their AMY1-CN by qPCR ( Figure 1 A; Table S2 Data S1 ), then selected 25 participants across the AMY1-CN distribution for further study. We confirmed the AMY1-CN of participants using alternate qPCR primers and digital PCR ( STAR Methods Table S3 ). Based on the results, 11 participants were assigned to a high group (CN > 8, designated AMY1H), 5 to a medium group (5 < CN < 8, designated AMY1M), and 9 to a low group (CN < 5, designated AMY1L). Neither BMI nor body fat percentage differed significantly between groups ( Table S3 ). CN of the gene for pancreatic amylase, AMY2, was positively correlated with AMY1-CN (Spearman’s rho = 0.79, p = 3 × 10 Table S3 ) and had a smaller range.

(B) Mean amylase activity per mL of saliva ± SEM at each time point for the 25 individuals in the AMY1-CN intervention groups. Measurements were performed in triplicate for both qPCR and salivary amylase activity.

(A) Diploid AMY1-CN distribution for 105 subjects, from which the 25 intervention subjects were selected, was obtained using qPCR with primer sequences previously reported ().

We searched for microbial taxa that discriminate fecal microbiomes of subjects with high or low AMY1-CN in a genotyped population with available fecal microbiome data ( STAR Methods ). Available genotype data included 7 of the 10 SNPs that correlate with AMY1-CN (). For each of 994 subjects with normal BMIs and available microbiome data, we calculated the sum of the change in AMY1-CN values corresponding to each of their 7 alleles. We then selected the top and bottom 5% of the distribution (50 subjects at each extreme of predicted total difference in AMY1-CN). Using a bivariate model (hereafter referred to as “Harvest”) (), we identified 17 operational taxonomic units (OTUs) whose relative abundances discriminated high and low groups ( Table S1 ). Some of these OTUs were classified as Ruminococcus, Faecalibacterium prausnitzii, and Bacteroides. Members of the Ruminococcaceae family were prominent among the taxa enriched in fecal microbiomes obtained from subjects with predicted high AMY1-CN.

Structural forms of the human amylase locus and their relationships to SNPs, haplotypes and obesity.

Identification of Gut Microbiota that Discriminate between Hosts with High and Low Predicted AMY1-CN Genotypes

Discussion

Here, we show that variation in the CN of the human salivary amylase gene AMY1 influences the diversity and function of the human oral and gut microbiome. AMY1-CN shapes the carbohydrate milieu in the gut through its dose-dependent effect on salivary amylase production. We observed a strong effect on the composition of the oral microbiome and compositional and functional effects on the gut microbiome that are consistent with the type of complex carbohydrate depletion expected from the host genotype.

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Flint H.J. Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. Members of the Ruminococcaceae were enriched in gut microbiomes of ∼50 subjects with high predicted AMY1-CN and in the intervened population. Members of the Ruminococcaceae family have been reported to ferment resistant starch (). High AMY1-CN hosts may preferentially select for members of the Ruminococcaceae because of an enrichment of resistant starch in chyme.

Host AMY1-CN was also related to the functional capacity of the gut microbiome. Our metagenomic analysis revealed enrichment in the AMY1L gut microbiomes of two classes of carbohydrate-active enzymes involved in the breakdown of overall complex carbohydrates, GH, and polysaccharide lyases. These results suggest that for a given diet, the AMY1L distal gut microbiota may be presented with a greater load of complex carbohydrates in general than the AMY1H microbiota. However, as a result of greater average host SAA, resistant starch is a greater proportion of the complex carbohydrates in the AMY1H colon, and the corresponding fermenters are proportionally more abundant.

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Clifton P.M. Short-chain fatty acids and human colonic function: roles of resistant starch and nonstarch polysaccharides. We assessed functional output of AMY1H and AMY1L microbiomes with measures of SCFAs. SCFAs are fermentation products of distal gut microbiota, and their levels in stool are influenced both by production by the microbiome and uptake by the host. We observed that SCFAs in stool were associated more strongly with host SAA levels than with host AMY1-CN. Within an individual, SAA varies from day to day. Although AMY1-CN is a good predictor of average SAA, it may be a poor predictor of SAA on any given day. A better association of SCFAs with SAA than to AMY1-CN indicates that fecal SCFA pools reflect short-term fermentation dynamics in the gut that are affected by fluctuating SAA. Since microbiota known to ferment resistant starch (e.g., Ruminococcaceae) are enriched in the AMY1H subjects, the activity of these microbiota may be driving the higher levels of SCFAs in their stool ().

Another way we tested the functional capacity of the gut microbiomes was to transfer fecal microbiota to germ-free mice and assess adiposity differences for mice receiving microbiomes of AMY1H compared to those receiving AMY1L microbiomes. We transplanted human microbiomes 1:1 into mice and used 5 samples per subject.

The 5 transfer experiments are not exact replicates because we used five separate samples collected at different TPs from each donor, which takes into account daily variability in microbiomes. Overall, we observed a greater adiposity for mice recipients of microbiomes derived from the AMY1H donors. Thus, the daily fluctuation in the SAA and the microbiomes was reflected in the variance in functional output. Our findings suggest that the mice (AMY1-CN = 2) consumed a diet rich in complex carbohydrates that human AMY1H-conditioned microbiomes may have accessed better. Within their native human hosts, however, AMY1H microbiomes may not behave the same way since they are not decoupled from their high SAA environment, the way they are when transferred to germ-free mice.