Short-term dietary fiber interventions produce consistent gut microbiome responses across studies

Background The composition of the human gut microbiome varies tremendously among individuals, making the effects of dietary or treatment interventions difficult to detect and characterize. The consumption of fiber is important for gut health, yet the specific effects of increased fiber intake on the gut microbiome vary across studies. The variation in study outcomes might be due to inter-individual (or inter-population) variation or to the details of the interventions including the types of fiber, length of study, size of cohort, and molecular approaches. Thus, to identify consistent fiber-induced responses in the gut microbiome of healthy individuals, we re-analyzed 16S rRNA sequencing data from 21 dietary fiber interventions from 12 human studies, which included 2564 fecal samples from 538 subjects across all interventions. Results Short-term increases in dietary fiber consumption resulted in highly consistent gut microbiome responses across studies. Increased fiber consumption explained an average of 1.5% of compositional variation (versus 82% of variation attributed to the individual), reduced alpha diversity, and resulted in phylogenetically conserved responses in relative abundances among bacterial taxa. Additionally, we identified bacterial clades, at approximately the genus level, that were highly consistent in their response (increasing or decreasing in their relative abundance) to dietary fiber interventions across the studies. Conclusions Our study is an example of the power of synthesizing and reanalyzing microbiome data from many intervention studies. Despite high inter-individual variation of the composition of the human gut microbiome, dietary fiber interventions cause a consistent response both in the degree of change as well as the particular taxa that respond to increased fiber.


BACKGROUND
Dietary bers are carbohydrates that resist digestion by the small intestine and have a positive health impact on humans (1).High-ber diets are associated with health bene ts such as increased nutrient absorption, production of bene cial metabolites, improved immune responses, and amelioration of various diseases including obesity, diabetes, allergies, and others (2)(3)(4)(5)(6).To understand the in uence of dietary ber on the gut microbiota, researchers have performed dietary interventions using a variety of ber compounds on humans (7)(8)(9).
Experiments that increase ber intake in humans often result in shifts in the composition of the gut microbiome.For example, ber interventions including inulin and gala-oligosaccharides often report an increase of Bi dobacterium and Lactobacillus taxa in the gut, genera known as lactic acid producers and carbohydrate degraders (10)(11)(12).In addition, plant-based diets (with high ber content) enrich for the genera Ruminococcus and Prevotella, which degrade and ferment complex dietary carbohydrates (13)(14)(15)(16)(17)(18).However, while some bacterial responses seem to be consistent across ber interventions, other studies report contradictory trends (8,19,20).For instance, Tian et al. 2021 found no increases in the taxa mentioned above, but instead observed a decrease in Ruminococcus, and Whisner and colleagues (2018) found that Ruminococcus were more abundant in a group of college students that consumed low ber foods (20).Such contradictions are not necessarily surprising, as comparing results across any type of microbiome intervention comes with at least three challenges.The rst obstacle is heterogeneity in study design and technical approaches.For ber interventions in particular, studies vary in the types of ber compounds used, intervention lengths, and population sizes.Moreover, differences in molecular approaches and in downstream bioinformatic pipelines add technical variation to the characterization of microbiome composition that potentially obscures biological patterns across studies.
A second challenge is the high inter-individual variation of gut microbiome composition.This variation can be due to many factors such as host genetics, diet, medical conditions, pet ownership, and stool consistency just to mention some (21,22).Such differences make comparing microbiome responses across individuals di cult, let alone across studies.Not only does the starting, pre-intervention composition of the gut microbiome vary widely between individuals, but many operational taxonomic units (OTUs) are not shared among individuals within a study.As a result, the variation in gut composition explained by an intervention will be typically small relative to interindividual variation and thus, may be di cult to detect and characterize.Finally, comparing taxa across studies can be di cult.Not only can OTUs of bacterial sequences be de ned differently across studies (e.g., at different cutoffs such as 100%, 99%, and 97% sequence similarity), but the results are often summarized at different taxonomic levels.For instance, some studies may report changes in relative abundance in terms of phyla (e.g., Actinobacteria), whereas others by family or genus (e.g., Bi dobacteriaceae/Bi dobacterium).Moreover, the consistency of responses of ner-scale taxa within a reported taxonomic level is often unclear as an individual's gut typically contains several strains and/or species within the same genus (23).For instance, healthy adults can harbor up to 6 species of Bi dobacterium at any one time (24,25).However, most studies report only the most responsive OTUs and/or changes in relative abundances lumped at a broader taxonomic level.Thus, variation in the responses of ner-scale taxa (and in their distribution among individuals) might contribute to inconsistent results among intervention studies.
Although some of the above-mentioned discrepancies cannot be modi ed for past studies (e.g., study design and sequencing processes), there are avenues to improve comparisons of past results across interventions.One approach is to reanalyze the data in a consistent manner and then use phylogenetic information to organize the biological variation.Speci cally, the raw data (e.g., 16S rRNA sequencing reads) can be uniformly processed using similar bioinformatic pipelines, threshold parameters, and statistical analyses.Then, phylogenetic placement of the sequences can be used to precisely compare compositional shifts across studies.Furthermore, this approach can shed light on the phylogenetic depth of the response to the intervention (26, 27).If lineages within a clade respond in a similar (positive or negative) manner to an intervention, then this phylogenetic signal provides a hypothesis about how microbiome composition may respond in other human populations, even if the ne-scale taxonomic composition (i.e., the precise OTUs) among populations is highly divergent.
Here, we took this analytical approach to investigate the consistency of ber-induced changes in the gut microbiome of healthy individuals by re-analyzing 16S rRNA sequencing data from 21 dietary ber interventions.We hypothesized that short-term increases in ber intake would result in consistent changes in microbiome composition even though many aspects of the ber interventions varied, including the type and amount of ber and the duration of the intervention.To test this hypothesis, we assessed three features of each intervention: 1) changes in bacterial alpha-diversity after the ber intervention, 2) the amount of compositional variation (beta-diversity) explained by the ber intervention relative to that of between individuals, and 3) taxa responses in a phylogenetic context to identify consistent ber-responding clades.

Study inclusion criteria
The search for studies has been previously described in the Data Description paper by Rodriguez et al. 2023 (28).Brie y, we performed a keyword search of published literature on May 9th 2020, through the PubMed search engine (keywords: dietary, ber, and microbiome) under the Best Match algorithm recommended by PubMed.The search yielded 977 abstract hits from 2010 to 2020 (https://pubmed.ncbi.nlm.nih.gov/).We also searched through all the records available in the database of open-source microbial management site Qiita (29) on April 7th, 2020 and found 528 microbiome studies including human and animal studies (https://qiita.ucsd.edu).From both sources, each abstract was carefully read to select studies with ber interventions in healthy humans that included 16S rRNA amplicon sequencing data from fecal microbial communities (n = 34).We excluded studies in animals and unhealthy humans.Corresponding authors and rst authors were contacted up to 4 times requesting their sequencing data and metadata when not publicly available.We were able to obtain 16S rRNA amplicon sequencing and their corresponding metadata from 12 studies and within these, 5 conducted diet interventions with different types of dietary bers and/or food sources (Table 1, Table 2).When this was the case, the sequence data in each study were divided by the ber intervention, resulting in a total of 21 intervention experiments (Table 3).For example, if one study conducted separate interventions with inulin and psyllium, the dataset was divided into two.We named each of the interventions as: Last name of the rst author in the publication, followed by the year the study was published, continuing with the region of the 16S rRNA bacterial gene that was ampli ed, with the addition of the ber used in the study (e.g., Baxter_2019_V4_potato).In total, we analyzed 16S rRNA data from 2564 fecal samples derived from 538 subjects across all study interventions (Table 3).Table 3 Summary of the samples included and of the alpha-and beta-diversity results by ber intervention.We note the number of samples and subjects per intervention and the rarefaction depth used for the normalization of each dataset for alpha-and beta-diversity analysis.The alpha-diversity column represents the results of the comparison between two timepoints (before vs after ber intervention) for both Shannon and Simpson indices; the arrow direction represents an increase (upward) or decrease (downward) in alpha diversity after the intervention, and when indicated the arrow applies to only the Shannon index.

Sequencing processing
To compare the sequences directly across studies, we obtained the raw sequencing reads for each study and processed them in a similar manner.First, we assessed the quality of the 16S rRNA sequencing data using FastQC software version 0.11.8 (30).The sequencing reads were cleaned from poor quality sequences using the Fastp program version 0.20.0 (31).The cleaned sequences were imported into the QIIME2 platform version 2020.11.1 (32), and primers were removed using Cutadapt plugin (33) when necessary.We then denoised the reads using DADA2 plugin (34), obtaining an OTU table with exact sequence variants (ESVs) depicting the number of reads per sample for each taxonomic unit.
Next, the taxonomic classi cation of the reads was also performed in the QIIME2 platform by training the SILVA version 132_99_16S (35) and the Genome Taxonomy Database (GTDB) version bac120_ssu_reps_r95 (36) databases to each respective study based on the primers that were originally used.The SILVA database was used to remove chloroplast and mitochondrial DNA.Then, the cleaned reads were assigned to a nal taxonomic group using the GTDB trained database.Only reads classi ed to the phylum level and beyond were kept in the OTU tables.All processed datasets described have been deposited to Figshare, https://doi.org/10.6084/m9.gshare.21295352,except for Vandeputte and colleagues (37), whose raw 16S rRNA data can be accessed through the European Genotyping Agency (EGAS00001002173).

Bacterial community composition responses to individual ber interventions
For the analysis of individual ber interventions, we used the forward reads (for uniformity) from all the studies and imported the data into R (version 4.0.2) for rarefaction to normalize for sequencing depth before the alpha-and beta-diversity analyses.We calculated rare ed OTU tables through randomized sampling sequences without replacement for 1000 iterations, using the highest sequencing depth possible for each dataset (Table 3).Although there is some controversy about the best method to standardize for sequencing depth, recent work comparing standardization techniques concluded that rarefaction provides a robust method for microbiome data (38).
For each study, we only used samples from the ber intervention treatments and excluded samples from other treatments (e.g., drugs or maltodextrin-controls).
We tested for differences in alpha diversity (Shannon and Simpson indices) before and after ber interventions using the rare ed OTU tables via vegan package, version 2.6-2, and paired-t tests in R, version 4.0.2.When multiple timepoints were collected before and after the ber intervention, we used only two timepoints (the earliest timepoint before, and the latest sample after, the intervention) to allow for paired analyses.
To test differences in bacterial community composition (beta-diversity), we ran permutational multivariate analysis of variance (PERMANOVA) on Bray-Curtis dissimilarity matrices including all timepoints available for each study.To construct these matrices, we averaged dissimilarity matrices created from rare ed and square-root transformed (to minimize the in uence of the most abundant taxa) OTU tables (1000 iterations) (39).The PERMANOVA formula used in the R vegan package was: adonis2.(bray.dist.matrix~ subject_id + ber, data = metadata, method= "bray", by= "term", permutations = 999); where ber speci es whether the sample was collected pre or post intervention.Thus, a signi cant main effect of ber indicates that the ber intervention altered microbiome composition in a consistent way, and a signi cant main effect of subject_id, that individuals have a distinct microbiome composition.Note that although we expect microbiome composition to vary over time within an individual for reasons unrelated to the ber intervention (40), such temporal variability would not produce a signi cant result for timepoint as the changes are not likely to be consistent across individuals (41).

Phylogenetic responses to dietary ber
To conduct an in-depth phylogenetic analysis, we next considered only studies (8/12) that shared the V4 region of the 16S rRNA gene and re-processed their sequences to compare speci c OTUs between studies including all their ber interventions (26,42).When available, we merged the forward and reverse V4 reads using BBmerge from BBMap Tools version 38.95 (43).Then, we extracted the same V4 region across the 8 studies with Cutadapt version 3.5 using the V4 primer sequences (forward:GTGYCAGCMGCCGCGGTAA; reverse:GGACTACNVGGGTWTCTAAT) from the Earth Microbiome Project (44).To ensure that the sequences were properly extracted (e.g., read size = 250bp), we visualized them using Geneious prime (version 2020.2.4; https://www.geneious.com/),FastQC version 0.11.9 and summarized the results with Multiqc, version 1.11.Then, the extracted reads (250 bp) were imported into QIIME2 (version 2020.11) as a single artifact.The q2-vsearch plugin in QIIME2 was used to dereplicate the sequences and cluster them at 97% identity.Because our goal was to make in-depth phylogenetic comparisons across studies, we used 97% dereplication identity rather than ESVs in order to simplify the complexity of the gut bacterial responses across studies using different collection and sequencing methods.Based on previous research (26), a ner-scale assignment of OTUs (ESVs) results in too few overlaps in OTUs among the studies making it di cult to make comparisons across interventions.Finally, we ltered the OTU table by removing OTUs with low abundance (< 10 summed across all samples) and/or those in less than 3 samples based on the assumption that these may not represent real biological sequences but rather are sequencing errors or PCR chimeras.We assigned taxonomy as described above for each individual study using the V4 primer sequences from the Earth Microbiome Project.The merged data were then divided into OTU tables for each study.Finally, to focus on taxa distributed widely among individuals, we excluded OTUs that were present in less than 50% of the samples per study.
To perform a standard differential abundance analysis of the OTUs, we rst used Phyloseq version 1.34.0 (45) to convert the data to the standard phyloseq-class data object to be used in DESeq2.For each study, we used the non-rare ed data in DESeq2 to 1) normalize the data and 2) calculate the log 2 -fold ratio of the normalized OTU abundances to identify OTUs signi cantly affected by ber treatment (p adjusted < 0.05) with log 2 -fold change cutoff 0 and > |0.58| (1.5-fold change).We then averaged the log 2 -fold change responses across studies.OTUs with a log 2 -fold change higher than zero were considered to be positive responding taxa, whereas the OTUs with a negative log 2 -fold change were considered negatively responding taxa.
To assess the phylogenetic conservation of ber responses, we selected only widespread OTUs (present in ≥ 3 studies) to ensure that the response trends were not driven by just one or two studies.We aligned these sequences using the Biostrings version 2.58.0 and DECIPHER version 2.18 (46) packages to create a neighbor-joining (NJ) tree using phangorn version 2.5.3 package (47).The positive and negative responding taxa were assigned a 1 and a 0 respectively.We then ran ConsenTRAIT, with percent shared trait cutoff of 0.9, using the castor package version 1.3.5 (48) to identify consensus clades, clades that respond to ber intervention in the same direction across studies and to calculate the average depth (τ D ) of the conserved clades from the NJ phylogenetic tree we created.We used an NJ tree for the consenTRAIT analysis because the genetic scale of NJ trees roughly represents sequence dissimilarity and to compare trait depth to similar analyses (26,27).Previous studies have also found that ConsenTRAIT results are robust regardless of phylogenetic reconstruction method (26, 27).To corroborate this, we built a Maximum Likelihood (ML) tree with 100 bootstrap replications with RAxML v8.2.12, using the GTR + Gamma distribution model at the CIPRES science gateway (49) and found a high correlation between both the trees (NJ vs ML)(Mantel statistic r = 0.935, p < 0.001, method = spearman, 999 permutations).Finally, we conducted a similar analysis for each individual study (building an NJ phylogenetic tree using all OTUs present and running ConsenTRAIT) to con rm that the cross-study results were not starkly different when using all OTUs within a study.

RESULTS
We screened over 1,500 abstracts of published literature and obtained data for 21 ber diet interventions (from 12 studies) performed in healthy humans, for a total of 2,564 samples from 538 subjects (Table 2).The duration of interventions ranged from 3 days to 84 days (Mdn = 15.5 days; SD = 21.3 days; Table 2) with a minimum of two fecal collection timepoints (before and after the diet intervention) but some collected up to 8 times.The types of bers also varied across ber interventions, with starches derived from potato being the most common ber intervention used (Table 2).

Alpha-diversity responses
Short-term increases in dietary ber consumption resulted in a highly consistent alpha-diversity response across studies.Five interventions showed a signi cant decline in bacterial alpha-diversity with both indices (paired-t-test p < 0.05; Table 3).Additionally, in 20 out of 21 interventions, alpha-diversity tended to decrease with at least one of the two alpha diversity metrics, Shannon and Simpson, (Fig. 1).

Beta-diversity responses
Increased ber intake also had a consistent effect on gut microbiome beta-diversity in healthy humans.As expected, inter-individual variation in microbiome composition was high.Microbiome composition differed signi cantly among individuals in every study, and on average, explained 82% of the compositional variation observed (PERMANOVA: p < 0.05; Table 3).Despite this variability, in 14 out of 21 studies, a signi cant effect of the ber intervention on microbiome composition was still detected.Further, the different interventions explained a relatively small but consistent amount of microbiome variation across studies, ranging from 0.2-4.6%, for an average of 1.5% of compositional variation (PERMANOVA: p < 0.05; Table 3).

Phylogenetic responses
To detect speci c taxa (OTUs) and broader phylogenetic clades that consistently shifted after ber interventions across studies, we reanalyzed a subset of the interventions that ampli ed the same 16S rRNA region.After averaging the log 2 -fold change responses for the widespread OTUs, we identi ed 5 bacterial OTUs that displayed signi cant, highly positive responses to ber interventions (log 2 -fold change > 1).The positive responding taxa belonged to the families Bi dobacteriaceae (three from Bi dobacterium genus, phylum Actinobacteria), Burkholderiaceae (one from Sutterella genus, Proteobacteria), and Ruminococcaceae (one from Faecalibacterium genus, phylum Firmicutes).Among these taxa, OTUs belonging to the Bi dobacteriaceae family had the highest positive response to ber with an average of 1.3 positive log 2 -fold change, followed by Burkholderiaceae and Ruminococcaceae with 1.2 and 1.1 log 2 -fold change, respectively.We also identi ed 8 bacterial taxa that showed a highly negative response to ber treatment (log 2 -fold change < -1.0).These taxa all fell within the class Clostridia (phylum Firmicutes) and belonged to the following families: CAG-508 (three from UMGS1994, CAG-354, and unidenti ed genus), Lachnospiraceae (one from Mediterraneibacter and three from unidenti ed genus), and Ruminococcaceae (one from Negativacillus genus).The OTUs belonging to the Lachnospiraceae family had the strongest negative log 2 -fold change with an average of -1.4,followed by CAG-508 and Ruminococcaceae with − 1.2 and − 1.1 log 2 -fold change, respectively (Fig. 2).
We next identi ed broader phylogenetic clades whose response to the ber intervention was conserved and calculated the average phylogenetic depth (τ D ) of conservation.The three most predominant phyla in the phylogenetic tree were Firmicutes, Bacteroidota, and Actinobacteriota.Bacterial responses, positive and negative, to ber treatment were signi cantly conserved with an average phylogenetic depth, τ D , of 0.019 and a 0.020 16S rRNA distance, respectively (permutation test; p < 0.05, Fig. 3).However, not all groups within a phylum responded in the same manner.For example, not all Actinobacteriota responded positively.Further, these patterns held within the individual interventions.The depth at which the ber responses were conserved was greater than expected given a randomized distribution (P < 0.05) for all studies except Liu_2017_V4 (Table 4).On average, the degree of conservation for positively responding clades was of τ D = 0.021 (n = 6 signi cant interventions) and for negatively responding clades was τ D = 0.019 (n = 4 signi cant interventions) (Table 4).

Table 4
ConsenTRAIT results for individual studies.Number of OTUs is the number of taxa at 97% identity that were present after ltering, followed by the number of signi cantly responding OTUs and OTUs that signi cantly shifted at > |1.5| fold change.The positive and negative responding taxa columns correspond to the OTUs used to build the phylogenetic trees, which were found through DESeq2 with a log 2 -fold change higher than zero or below zero, respectively.Bold numbers represent that τ D values are signi cantly > 0 (p < 0.05).

DISCUSSION
Our re-analysis of bacterial 16S rRNA data from ber intervention studies in healthy humans demonstrates that short-term increases in ber consumption result in remarkably consistent responses in bacterial alpha-diversity, compositional variation (beta-diversity), and the relative abundance of speci c taxa despite a myriad of study differences, including the ber type and amount and experimental duration.Further, bacterial responses were phylogenetically conserved, allowing us to identify bacterial clades that generally increased or decreased across studies.Thus, even though individuals may vary in the speci c taxa (OTUs) that they carry, taxa within these clades tended to respond similarly across individuals and studies.
In line with previous work (11,14,24,(50)(51)(52), ber intake reduced alpha-diversity in 20 out of 21 studies, with this pattern being signi cant in 5 interventions.We therefore conclude that a sudden increase in ber intake generally decreases bacterial alpha-diversity.Previously, it has been suggested (50) that such a decline in alpha-diversity after a ber intervention could be due to the short-term nature of the interventions.Speci cally, short-term studies might capture only a transitional period, where bacteria that are not well adapted to the changing environment (e.g., decreased pH due to increased fermentation) are rapidly outcompeted by taxa that can quickly consume the newly-available carbohydrates.This reasoning suggests that over a longer time period, bacterial alpha-diversity might decline less (or perhaps even increase) as more slowly-growing ber consumers increase in relative abundance.In the studies analyzed here, the alpha-diversity response was not correlated with intervention length (Shannon's Spearman r=-0.156,p > 0.05; Simpson's Spearman r=-0.325,p > 0.05)), but we note that the studies ranged from only 3 to 84 days.Thus, longer studies are needed to investigate the effects of increased dietary ber on the long-term dynamics of gut microbiome alpha-diversity.
The changes in the overall variation in bacteria composition (beta-diversity) were also similar among studies.While ber intervention explained a relatively small amount of compositional variation compared to interindividual variability (1.5% versus 82%), a signi cant effect of the ber intervention on microbiome composition was detected in 14 out of 21 studies.Notably, the number of subjects in the non-signi cant studies included < 34 individuals (although some studies with less than that amount did nd signi cant effects), suggesting that the studies were statistically underpowered.In contrast, ber interventions with 40-50 individuals detected even small (0.2-0.7%) effects on microbiome composition.
The effects on overall bacterial beta-diversity were largely driven by changes in the relative abundance of well-known ber degrading taxa.OTUs belonging to the genus Bi dobacterium showed the strongest positive response across ber interventions (Fig. 2).Further, these responses were phylogenetically conserved, meaning that the response was not limited to particular strains of Bi dobacterium, but seems to be a response that is shared across the genus.Indeed, the genus has been previously found to increase in abundance following an increased ber intake (11,12,24).Bi dobacterium possess a high number of carbohydrate active enzymes (CAZymes) that allow the degradation of various plant carbohydrates (53)(54)(55) and thus its ability to respond to increased ber availability is not surprising.These ber degrading bacteria are thought to bene t health via production of short chain fatty acids [SCFAs] (56).Indeed, ber rich diets also associated with positive changes in SCFAs (57,58).
Our analysis also detected less appreciated, but similarly consistent, responses to ber intake.In particular, OTUs within the Sutterella genus increased signi cantly across the studies, although we could only identi ed one previous report of this response in a ber intervention of pregnant women suffering from hypertensive disorders (59).Sutterella species have been associated with health disorders such as autism and metabolic syndrome (60, 61), but are also present in healthy humans.Their ability to adhere to intestinal epithelial cells might indicate a positive relationship with its host (62), but further investigations into the role of this genus, particularly in terms of ber degradation, is warranted.
We also identi ed speci c taxa and clades that consistently decreased during the ber interventions.While a signi cant positive response would seem to indicate the use of ber as a carbon resource, it is less clear what a consistent negative response means.As mentioned above, one possibility is that increased ber degradation will change the gut environment (e.g., low pH due to increased fermentation) and some taxa might not compete as well in these conditions, hence decreasing their abundance.All negative responding taxa fell within the class Clostridia (phylum Firmicutes) with the Lachnospiraceae family showing the strongest negative response.While this family is typically found in the human gut microbiome and some members are main producers of SCFAs (63), studies have suggested that members of this family may be associated with certain diseases (64).However, out of the four responding taxa within this family, we were only able to identify the Mediterraneibacter genus.Previous research has found that Mediterraneibacter is associated with host obesity in women with polycystic ovary syndrome (65) and that is able to produce aldehyde alcohols which are considered harmful to the host (63), whereas its role in ber fermentation has not been described.Together, the identi cation of both positively and negatively responding clades provide candidates for investigating the mechanistic links between a ber-rich diet, the metabolic outputs of ber degradation, and intestinal health.
Finally, although microbial responses to ber interventions are thought to be highly individualized to the person (55,67), bacterial taxa that respond to ber interventions showed a phylogenetic signal.Speci cally, bacterial taxa that respond positively or negatively to ber intake exhibited a signi cant average phylogenetic depth of conservation (τ D = 0.019 and τ D = 0.020 ; p < 0.05; Fig. 3).Depth of conservation (τ D ) serves as a metric for predicting the distribution of functional traits in microorganisms (66).The depth of the ber response was similar to that previously found for nitrogen xation traits ( τ D = 0.018-0.020),but more deeply conserved than that of simple carbon utilization (τ D = 0.011) (27,66,68) and the ability to produce extracellular enzymes (τ D = 0.008-0.01)(69).Moreover, the average depth of bacterial responses to ber intervention displayed a relatively narrow range across studies (τ D = 0.014-0.028;Table 4).
These results come with certain limitations inherent to the use of 16S rRNA data and the re-analysis of publicly available data.First, phylogenetic trees built with 16S rRNA amplicon sequences are not as reliable as multi-locus trees (70); however, they are still useful to estimate the depth of the response to ber interventions and to compare this response with other traits that have been analyzed previously (26, 27, 66).Second, using publicly available data resulted in unequal sample sizes across ber interventions, hindering comparisons between ber types.In the future, studies that directly compare different bers would be useful to test whether there are ner scale differences in bacterial responses to particular ber types as different gut bacteria specialize on different types of bers (71)(72)(73).

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Table 1
Data repositories for individual ber intervention studies.

Table 2
Summary of datasets collected including ber type, grams of ber used, duration of the intervention, number of timepoints for fecal collections, number of subjects, and total number of fecal samples per study.The interventions column refers to the dietary ber interventions that we included in our analysis per study.
The beta-diversity columns show the variation explained by either subject or the ber interventions.ns = not signi cant; bold indicates p < 0.05.