Open access
Research Article
3 September 2019

Gut Microbiota Has a Widespread and Modifiable Effect on Host Gene Regulation

ABSTRACT

Variation in gut microbiome is associated with wellness and disease in humans, and yet the molecular mechanisms by which this variation affects the host are not well understood. A likely mechanism is that of changing gene regulation in interfacing host epithelial cells. Here, we treated colonic epithelial cells with live microbiota from five healthy individuals and quantified induced changes in transcriptional regulation and chromatin accessibility in host cells. We identified over 5,000 host genes that change expression, including 588 distinct associations between specific taxa and host genes. The taxa with the strongest influence on gene expression alter the response of genes associated with complex traits. Using ATAC-seq, we showed that a subset of these changes in gene expression are associated with changes in host chromatin accessibility and transcription factor binding induced by exposure to gut microbiota. We then created a manipulated microbial community with titrated doses of Collinsella, demonstrating that manipulation of the composition of the microbiome under both natural and controlled conditions leads to distinct and predictable gene expression profiles in host cells. Taken together, our results suggest that specific microbes play an important role in regulating expression of individual host genes involved in human complex traits. The ability to fine-tune the expression of host genes by manipulating the microbiome suggests future therapeutic routes.
IMPORTANCE The composition of the gut microbiome has been associated with various aspects of human health, but the mechanism of this interaction is still unclear. We utilized a cellular system to characterize the effect of the microbiome on human gene expression. We showed that some of these changes in expression may be mediated by changes in chromatin accessibility. Furthermore, we validate the role of a specific microbe and show that changes in its abundance can modify the host gene expression response. These results show an important role of gut microbiota in regulating host gene expression and suggest that manipulation of microbiome composition could be useful in future therapies.

INTRODUCTION

The microbial community in the human digestive tract, the gut microbiota, is highly complex and also displays strong variation across individuals (13). Variability in gut microbiota composition is related to many factors, including medication, diet, and genetics (416). The gut microbiota has a variety of functions within the host, such as metabolism of certain compounds (1720), and its composition is correlated with several diseases, such as Crohn’s disease and colorectal cancer (2126). In mice, certain microbial communities can lead to changes in the host’s weight and overall health, suggesting that there is a reciprocal effect between the host and the gut microbiota (27). Recent work in mice has explored host gene expression and gene regulation in response to microbiome exposure (2830). These studies suggested that the microbiome can induce both epigenetic changes and binding of specific transcription factors (28, 29, 3133). In humans, work has been done to correlate microbiota composition, cellular response, and phenotype, thus illuminating the interplay between microbe and host. For example, studies have shown that expression and RNA splicing changes correlate with changes in the gut microbiota in vivo (3437). However, in humans, it is challenging to perform large-scale studies and to account for environmental effects, such as host diet, health status, and medication use.
Recently, we described an in vitro approach based on human epithelial cells inoculated with live microbial communities (38) that is well suited to the study of the effects of the microbiome on human gene regulation. In short, we cultured human colonocytes under hypoxic conditions in order to recapitulate the gut environment. We then exposed the colonocytes to live gut microbiotas derived from human fecal samples under hypoxic conditions and measured the levels of response of host cells via transcriptome sequencing (RNA-seq). Using this technique, we identified differentially expressed (DE) genes that were enriched among genes associated with microbiome-related diseases, such as obesity (27, 39), and among genes that were differentially expressed in germ-free mice exposed to gut microbiota (28). The advantage of this system is that we can study human cellular response to gut microbiota in a highly scalable way.
Here we sought to use this in vitro system to determine the extent to which and the mechanism by which interindividual variations in microbiome composition drive differences in gene expression in the host cells. In order to assess the mechanism of cellular response to microbiotas, we considered chromatin accessibility via ATAC-seq (40), which utilizes the Tn5 transposase for fragmentation and tagging of accessible DNA. Similar to DNase-seq, ATAC-seq can capture open chromatin regions, which are regions accessible to transcription factor (TF) binding. Changes in chromatin accessibility can modulate transcription factor binding and thereby influence gene expression (33, 41). The specific TFs binding in regions of open chromatin can be identified through the motif sequence model and footprinting analysis of ATAC-seq data. We also sought to determine if specific microbial taxa drive gene expression variation. These open issues are crucial for understanding the causal role of the microbiome in host physiology and for designing targeted therapies revolving around interventions in the gut microbiome.

RESULTS

Exposure to microbiota influences host gene expression.

To determine the impact of variation in the gut microbiota on host cells, we treated human colonic epithelial cells (HCoEpiC) with live gut microbiota extracted from 5 healthy, unrelated human individuals (Fig. 1A; see also Fig. S1A to D in the supplemental material). The composition of these samples is representative of that other healthy gut microbiome samples from the Human Microbiome Project (Fig. S1E) (1, 42). We then separately assessed changes in gene expression and microbial composition following 1, 2, and 4 h of exposure. The overall changes in gene expression among microbiome treatments and controls are clustered first by time point (by principal-component analysis and by hierarchical clustering as shown in Fig. 1B [see also Table S1 in the supplemental material]), where the strongest response (3,240 genes across any of the five microbiota samples) occurred at 2 h following exposure (see equation 3 in Text S1 in the supplemental material; Benjamini-Hochberg false-discovery rate [BH FDR] < 10%, |log2fold change [FC]| > 0.25). Among these, we identified 669 transcripts (188 genes) that were differentially expressed in all five treatments following 2 h of treatment (Fig. 1C; see also 1-h and 4-h comparisons in Fig. S1F and G). To identify genes whose expression levels changed consistently across the treatments at each time point, we removed the individual effect from the model and considered the 5 microbiota treatments as replicates. Two representative genes, PDLIM5 and DSE, that were found to be differentially expressed at each time point are shown in Fig. 1D. Notably, the results from analysis of those genes show that we were able to identify various expression patterns through this model as long as the 5 microbiota treatments led to the same response at a given time point (Fig. S1H and I; see also Table S1). These 5,413 genes with shared expression changes were enriched for genes that function in protein translation, as well as those associated with the cell surface, such as in adherens junctions (BH FDR < 10−6) (Fig. S1J; see also equation 2 in Text S1), suggesting a biological function that may relate to the host cell’s interaction with the microbiota.
FIG 1
FIG 1 Gene expression changes in colonocytes treated with microbiota from five unrelated individuals. (A) Study design. Human colonocytes were inoculated separately with five microbiota samples from unrelated individuals. (B) Heat map of gene expression changes induced at each time point by the individual microbiota samples. Purple denotes an increase in gene expression (green shows a reduction) compared to the gene expression in the control (colonocytes cultured alone). Only genes that were differentially expressed in at least one sample are shown. LogFC, log fold changes. (C) Comparison of levels of transcripts differentially expressed at 2 h across the five treatments for the five individual microbiota samples (Ind.1 to Ind.5). The blue bars to the left show the total number of differentially expressed transcripts in the given set. The gray vertical bars show the number of transcripts that are in the set denoted below them. Sets with a single dark gray circle show the number of differentially expressed transcripts unique to that sample. (D) Examples of genes (PDLIM5 [left panel] and DSE [right panel]) whose changes in expression were consistent across treatments with the five different microbiotas. Changes in expression (y axis) are shown as log2 fold change compared to control. (E) Examples of genes (WNT7A [left panel] and SPARC [right panel]) whose changes in expression were significantly different across treatments with the five microbiota samples.
Each microbiota sample was derived from a different individual with a unique diet and genetic makeup. Therefore, we expect that the microbial composition and diversity of all samples differ. Examining the uncultured microbiomes, we found variability in their microbial composition and diversity (Simpson’s index values ranged between 0.94 and 0.98). In contrast, we found that the microbial communities in the cultured microbiomes did not change dramatically over time and that the microbiomes maintained their individual-specific composition characteristics during culture (Fig. S1A to C). Next, we considered how the human colonocytes influence microbial composition, and we found that most taxa were unaffected by the presence of human cells, while 13 taxa (of 112 tested) showed various levels of abundance dependent on the presence of host cells (likelihood ratio test; BH FDR < 10%) (examples are shown in Fig. S2A and B, table shown in Fig. S2C). In order to determine how the compositions of the microbiota samples influenced host gene expression differently, we utilized a likelihood ratio test to compare models that included or excluded the individual microbiota effect. This test is able to incorporate gene expression changes over time and to compare the trajectories of expression in response to the 5 different microbiota treatments. In this way, we identified 409 genes (1,484 transcripts) (likelihood ratio test; BH FDR < 10%; see Table S1) with expression patterns that significantly differed in response to the five microbiota samples. Two examples of representative genes with differing gene expression patterns are WNT7A and SPARC (Fig. 1E). These examples demonstrate that no two microbiota samples induce the same response in the genes identified by the likelihood ratio test and further showed that the genes had different responses to the same treatment. These data demonstrate that the host and the microbiota influence each other and that interindividual variation in the microbiome can lead to different gene expression responses in interacting host cells.

Specific microbes influence unique host genes.

We hypothesized that the differences in gene expression responses to each microbiome could be attributed to specific microbiota features, such as the abundance of specific taxa. For this reason, we used DESeq2 to study the association between host gene expression (147,555 transcripts) and the abundance of microbial taxa (62 taxa that passed filtering criteria; see Materials and Methods and Text S1) at the time of treatment. Across all possible associations (9,125,927 tests), we identified 588 transcript-by-taxon pairs with a significant association (see equation 5 in Text S1; BH FDR = 10%) (see also Table S1), corresponding to 121 host genes with changes in expression associated with the abundance of 46 taxa. Among those taxa, 35 were associated with the expression of two or more host genes (see equation 5 in Text S1; BH FDR = 10%), suggesting that a single microbe may affect the regulation of many genes and the presence of a trans regulatory mechanism by which microbes may influence host organism traits.
Of the 121 host genes whose expression was found to be associated with abundance of microbial taxa, 70 genes (219 transcripts) were also found to be differentially expressed in comparisons of microbiota treatment to control and formed two clusters with distinct functions (Fig. 2A and D; examples are shown in Fig. 2B and C). Genes in the first cluster were found to be positively correlated with genera Ruminococcus, Coprococcus, and Streptococcus and to have functions in cell junction assembly (GeneTrail enrichment analysis BH FDR < 10−5) (Fig. 2E), while those in the second cluster were found to be positively correlated with microbial genera that included Odoribacter, Blautia, and Collinsella and to function in protein targeting of the endoplasmic reticulum (GeneTrail enrichment analysis BH FDR < 10−7) (Fig. 2F). Our analyses of the abundances of the microbes in the clusters showed them to be highly correlated, further suggesting that the microbial community as a whole may act on host gene expression (Fig. S2D and E).
FIG 2
FIG 2 Abundance of microbiome taxa is associated with specific host gene expression changes. (A) Heat map of microbiota taxa and colonocyte gene expression correlation (Spearman’s ρ). Rows correspond to 28 microbiota taxa (which include all OTUs within the taxa if collapsed to genus level), and columns correspond to 219 transcripts (70 genes) that had at least one significant association (likelihood ratio test [LRT]). Taxa and transcripts are each clustered via hierarchical clustering, showing two major groups indicated by a different shade of red (taxa)/blue (transcripts). (B and C) Examples (MMP14 [B] and CAPRIN2 [C]) of significant association (BH FDR = 7% for both genes) between host gene expression (fragments per kilobase per million [FPKM] quantile normalized) and baseline abundance of specific taxa. (CO), control samples. (D) Network of associations between taxa and genes from the heat map. Nodes in blue denote genes, while nodes in red denote microbial taxa. Color shading indicates clusters of genes or taxa as defined in the heat map. Black edges indicate a positive correlation, while light gray indicates a negative correlation. (E and F) Gene ontology enrichment for cluster 1 (E) and cluster 2 (F). ER, endoplasmic reticulum; SRP, signal recognition particle.
We then focused on microbial taxa associated with changes in expression for a large number of genes, since these microbes are more likely to impact host traits. To test this hypothesis, we focused on the six microbial genera that were associated with the largest number of host genes (at least 30 host genes at P values of <3.5 × 10−5 [see equation 5 in Text S1]): Odoribacter, Streptococcus, Blautia, Thiovirga, Thiothrix, and Collinsella. Indeed, all of those taxa, except for Blautia, showed expression changes in genes that were between 2.7-fold and 3.6-fold enriched (compared to a background of expressed genes) for those associated with complex traits from the genome-wide association study (GWAS) catalog (Fisher’s exact test P value < 0.005) (Fig. 3A; see also Table S2) (43). Moreover, we identified 21 genes that were associated with traits already linked to the gut microbiome, including colorectal cancer (21, 26), obesity (27, 39), and inflammatory bowel disorder (IBD) (4446). Genes associated with the genus Collinsella were found to be 3.4-fold enriched for those associated with a trait in GWAS (15 of 30 genes; Fisher’s exact test P value = 0.001) (Fig. 3A). Previous studies revealed that the abundance of Collinsella is correlated with several diseases, including colorectal cancer (47), Type 2 Diabetes (48), and irritable bowel syndrome (49). Interestingly, we identified a gene, GLTP, that is involved in glycolipid transfer and that has been associated with metabolic syndrome (50). Based on the transcriptome-wide association study (TWAS) analysis reported in Metaxcan (5153) (https://imlab.shinyapps.io/metaxcan_a_1/), higher expression of GLTP is associated with decreased levels of high-density lipoprotein (HDL) (P value = 3.1 × 10−7), which is a factor contributing to metabolic syndrome. In our data, the abundance of the genus Collinsella was found to be positively associated with GLTP expression (see equation 5 in Text S1; BH FDR = 12.6%). Additionally, GLTP was shown expressed in the colon according to the GTEx data (average number of transcripts per million [TPM] = 47 in transverse colon and 39 in sigmoid colon). These data suggest an association between GLTP and metabolic syndrome that may be modulated by exposure to Collinsella. These data also suggest that specific microorganisms, and not simply general exposure to the entire gut microbiota, can lead to changes in the expression levels of many genes. Furthermore, these results support the hypothesis that variations in the abundances of members of the microbiota may influence complex traits.
FIG 3
FIG 3 Manipulation of the microbial community induces predictable gene expression changes in the host. (A) Genes associated with particular microbiota (BH FDR < 20%) were enriched for complex traits. Microbiota were chosen if they had at least 30 genes associated via the genus-based model for gene expression. The shade of the point indicates the −log(adjusted P value) from the enrichment test, while the size of the point and the x-axis values give the fold enrichment. (B) Scatterplot of the effect of Collinsella abundance from the five microbiome samples (x axis) and effect of Collinsella aerofaciens from the spike-in validation experiment (y axis). Plotted are log2 fold changes normalized by the standard error. Blue points denote transcripts that were DE only in the genus-based model that included the abundance of Collinsella. The red points and line highlight the transcripts that were DE only in the spike-in experiment. Purple points denote transcripts that were DE in both the genus-based model and the spike-in experiment. Gray points denote transcripts that were not differentially expressed in either the gene-based model with abundance of Collinsella or in the spike-in experiment. There was a correlation across all points (linear regression P value < 10−20, Spearman’s ρ = 0.29) and across transcripts differentially expressed only in the spike-in experiment (linear regression P value < 10−20, Spearman’s ρ = 0.46). (C to E) Examples (MMP14 [C], UBE2E3 [D], and GTF3A [E]) of significant association (DESeq2 BH FDR = 9%, 6%, and 7%, respectively) between host gene expression (FPKM quantile normalized) and abundance of Collinsella aerofaciens from spike-in validation experiment (linear regression P value = 0.05, 0.02, and 0.004 with Pearson’s r = –0.64, 0.73, and 0.81, respectively).

Validation of changes in host gene expression due to specific microbiota.

To validate and further demonstrate the effect of specific microbes on host gene expression, we treated colonocytes with a microbiota sample without any detectable Collinsella aerofaciens and supplemented it with titrated abundances (0.01%, 0.1%, 1%, and 10%) of this bacterium relative to the whole-microbiota sample. We used RNA-seq to study the resulting changes in gene expression and identified 1,570 genes whose expression levels had changed (see equation 6 in Text S1; BH FDR = 10%) (Table S1; see also Fig. S2F) in a manner dependent on the abundance of Collinsella aerofaciens. Examining changes in gene expression associated with Collinsella abundance in the five microbiota treatments, we found that the effects of Collinsella in the two experiments were correlated (Fig. 3B). We validate 19 of the 29 genes originally identified (Fisher’s exact test P value = 0.0002, odds ratio [OR] = 4.1) (see equation 5 in Text S1; BH FDR = 20%), including GLTP and MMP14 (see equation 5 in Text S1; BH FDR = 7% in Fig. 2B) (see also equation 6 in Text S1; BH FDR = 9% in Fig. 3C), demonstrating that the presence of Collinsella is sufficient for the occurrence of changes in the expression of these genes. Interestingly, we also found a correlation between the effect of Collinsella in the spike-in experiment and the effect of several other microbes from our genus-based model on host gene expression. Specifically, we found positive correlations of effects on gene expression between the Collinsella spike-in experiment and other microbes in the cluster that contained Collinsella (Fig. 2A; Spearman’s ρ = 0.25 to 0.4) and negative correlations for microbes in the cluster that did not contain Collinsella (Spearman’s ρ = –0.27 to –0.32). These results underline the importance of considering the entire microbial community rather than individual microbial species in studying host-microbiome interactions. That the expression levels of a large number of genes changed in this experiment could have been due to several factors, including the increase in the power of the analysis that resulted from the use of a larger number of samples. These 1,570 genes were found to be enriched for those associated with complex traits in the GWAS data compared to a background of all expressed genes (Fisher’s exact test P value = 10−10, OR = 1.5) and to be specifically enriched for genes associated with HDL cholesterol (Fisher’s exact test, Bonferroni-corrected P value = 0.018, OR = 2.75). Other DE genes were found to be associated with additional traits relevant to the microbiome, such as body mass index (e.g., see GTF3A in Fig. 3E) and obesity and celiac disease (e.g., see UBE2E3 in Fig. 3D). The results of this spike-in experiment showed that host gene expression can be modulated by changing the abundance of a single bacterial species within the microbiome.

Changes in chromatin accessibility and transcription factor binding following microbiota exposure.

In order to investigate the regulatory mechanism whereby the microbiome induces changes in host gene expression, we performed ATAC-seq in colonocytes inoculated for 2 h with each of the five microbiota samples and were able to identify ATAC-seq peaks at the transcription start site (TSS) of expressed genes (Fig. S3E). We then used DESeq2 to characterize regions with differential levels of chromatin accessibility. We identified only a limited number of regions that were differentially accessible across the five microbiome treatments with two technical replicates (n = 234) (Fig. S3A). Nevertheless, we found enrichment of differentially accessible regions at 2 h within 50 kb of genes differentially expressed at 4 h (Fig. 4A; see also Table S3) (Fisher’s exact test P value = 3.96 × 10−4, OR = 2.13). We note that this enrichment consisted of 24 differentially accessible chromatin regions within 50 kb of 32 differentially expressed genes and so still represented a minority of the differentially expressed genes. Interestingly, we did not find the same enrichment when we examined genes differentially expressed at 1 and 2 h following exposure to gut microbiota (Fisher’s exact test P value > 0.15), suggesting that the changes in chromatin accessibility that we identified occurred first (at 2 h) and then led to subsequent changes in gene expression by 4 h postinoculation.
FIG 4
FIG 4 The microbiome induces changes in gene expression through changes in chromatin accessibility. A QQ (quantile-quantile) plot of P values from the analysis of consistent differential levels of gene expression in colonocytes treated with five microbiota samples for 4 h. The blue points indicate the P values for transcripts that were within 50 kb of a differentially accessible region identified through ATAC-seq after 2 h of treatment. The gray points indicate transcripts that were not within 50 kb of a differentially accessible region. (B) ATAC-seq profile centered on the 300-bp window (with 1,000 bp on either side) that was differentially accessible following 2 h of treatment with the microbiome (log2 fold change [FC] = 0.47, BH FDR = 12%). This region was found 4,795 bp distant from the differentially expressed TPM4 gene. (C) RNA-seq profile of TPM4 (ENST00000586833) (and of 20,000 bp in either direction), which was differentially expressed following 4 h of treatment with the microbiome (log2 FC = 0.29, BH FDR = 1.9%). The gene model is shown at the bottom. Panels B and C show the summation of reads at each position across the five biological replicates and the two technical replicates.
Notably, the directions of the changes in chromatin accessibility and in gene expression were concordant, which indicates that opening of the chromatin resulted in upregulation of gene expression, as expected (Fig. S3B). One interesting example is the TPM4 gene, which is a gene that binds actin filaments and whose role in the regulation of the cytoskeleton may impact the host cell response to the microbiome. TPM4 is regulated by the microbiome through changes in chromatin accessibility and showed increased expression at 4 h following treatment with the five microbiota samples (Fig. 4B and C; see also Fig. S3C and D). We identified the chromatin change at a region located at 4,800 bp upstream of the transcription start site of TPM4.
To identify transcription factors that may mediate the response to the microbiome, we performed footprinting analysis of the ATAC-seq data using CENTIPEDE (54). We identified 397 factors that were active under the treatment and control conditions, for a total of 36,565,954 and 28,922,773 footprints, respectively. We identified 40 motifs with footprints in differentially accessible regions, including factors that bind the ELF5, SOX2, and HMGA2 motifs (Table S4). Interestingly, using a stratified FDR approach to identify differential levels of chromatin accessibility for regions that contained footprints for active factors, we identified a larger number of differentially accessible regions (209 regions; FDR = 10%; Table S5).
We next investigated how specific transcription factors may be involved in modulating gene expression responses to the microbiome that may not require changes in chromatin accessibility. In this case, we conducted an enrichment analysis using a logistic model to predict which genes would be found to be differentially expressed, using the footprint counts for each of the 397 active factors within 50 kb of the gene transcription start site. At a FDR of 10%, we detected 110 factors that had footprints enriched in genes that were more likely to be differentially expressed after treatment, including motifs such as AP1, RELB, NκFB, STAT1, STAT3, and HNF4A (Table S6). Many of these factors are involved in immune response pathways, inflammation pathways, and chemokine signaling and may not require chromatin remodeling to activate their target genes to elucidate a response after microbiome treatment.

DISCUSSION

The gut microbiota has recently been reported to be associated with several different diseases and disorders (21, 26, 27, 39, 4446). However, the mechanism of action is not well understood, and we know little about how microbiome risk factors interact with the host genes that have been linked to the same disorders. Here, we investigated how interindividual variations in microbiome composition affect host gene regulation. Previous work in mice has shown that transplantation with microbiomes derived from mice with different phenotypes, e.g., obese versus lean, can lead to differences in organismal phenotypes (39). In contrast, our work focuses on variable responses associated with microbiomes derived from healthy humans. We note that the in vitro system has several limitations: for example, the presence of a low level (5%) of oxygen may be deleterious to obligate anaerobes. Moreover, the combination of the inclusion of a single cell type and the short duration of treatment (up to 4 h) does not recapitulate in vivo systems where microbes and host cells of different types interact for longer time periods. Nevertheless, this is a well-controlled system that is not susceptible to many of the biases that are found in other model systems and that allows characterization of the direct effect of microbial communities and isolates on gene regulation in host cells. We illustrate that while there were thousands of genes that showed consistent responses induced by the five microbiome treatments, there were several hundred that responded differently to each microbiome. Furthermore, some of the differences in response can be understood as having resulted from the variations in the microbiome compositions of the five samples. Though we focus on healthy microbiomes, our results highlight the interactions of particular microbes and host genes that may play roles in various host traits. For example, we identified hundreds of genes that responded to particular microbes in a predictable way, such as the GLTP gene, whose response was correlated with the presence of Collinsella. Our results suggest that Collinsella may impact complex traits through regulation of host gene expression. While we closely examined the effect of the presence of Collinsella on host gene expression, there were many other relevant microbes that may also play a role in host gene regulation. For example, the abundance of Prevotella, which has been associated with colorectal carcinoma (55) and ileal Crohn’s disease (56), induces changes in the expression of 6 genes (BH FDR = 10%). Further studies performed with a wider panel of microbiome samples will allow increased power to detect more genes that are differentially expressed across microbiome treatments.
One consideration in our analysis of the effect of Collinsella on host gene expression is that our treatments consisted of healthy microbiota samples spiked with Collinsella. This was done in order to better replicate natural variation in gut microbiota composition and microbial interaction in the human gut. Microbes are constantly in competition for particular niches in the gut, and so we do not expect that increases in Collinsella levels in an organism would occur alone but would instead likely affect the levels of other microbes. However, future work using treatments of Collinsella alone may identify genes whose expression levels change as direct consequences of Collinsella abundance rather than as a result of overall changes in microbial environment induced by manipulation of Collinsella abundance.
Our study of chromatin accessibility changes after 2 h of microbiome exposure in 12 samples identified a limited number of differentially accessible regions. These results are similar to those reported from previous work in conventionalized mice that did not show broad changes in chromatin accessibility compared to germ-free mice and could have been a consequence of limited statistical power, incorrect time point for detection, or short duration of the treatment (28, 29). In our study, the regions that were detected as significantly differentially accessible were enriched near genes that changed their expression following treatment with a consistent direction of effect; and yet the changes in chromatin accessibility were small and accounted for only a limited number of genes that were differentially expressed after treatment. Many of the changes in gene expression could still be mediated by transcription factor binding that does not affect chromatin accessibility, as footprints for key transcription factors related to immune responses and inflammation were found to be enriched in genes that showed a response to the microbiome. Other mechanisms may also explain the observed changes in gene expression. For example, others have observed that RNA processing in the host changes in response to exposure to bacteria (35, 5759). Future studies could analyze whether the gut microbiota may modulate RNA processing directly and thereby induce a transcriptional response. In this study, we identified specific transcription factors that may play a key role in mediating the gene regulatory response to the microbiome. The recently developed CUT&RUN technique (60) could be used in the future to specifically investigate whether the microbiota induces binding of specific TFs through the use of the footprinting data we have generated to identify potential candidates. It is unlikely that changes in methylation could happen in the short time frame analyzed here and could induce the observed gene expression changes. However, a longer time course would allow investigation of methylation changes as potential mechanisms for long-term gene regulatory changes induced by the microbiome. It has been shown that epigenetic changes and changes in TF binding can be induced by the gut microbiome (31, 32), and our results confirm that they may also be among the mechanisms in human colonic epithelial cells by which the microbiota drives host gene expression.

Conclusion.

Our results suggest that specific microbes in the microbiome may be important in regulating host gene expression in the gut and that microbes can induce changes in the expression levels of a large number of genes. Furthermore, our data support the hypothesis that the microbiome induces changes in the levels of expression of host genes that are involved in complex traits. While the mechanism of response is as yet unclear for most genes, we identified a small subset of genes whose response to microbiome exposure may be regulated by changes in chromatin accessibility. Finally, our work suggests a molecular mechanism by which supplementation of the microbiome can influence human health through changes in host cellular response. We have shown that by manipulating microbiome composition by supplementation of a single microbe, we were able to influence the host cell regulatory response in a predictable way. This work and future research will help to determine which microbes may be most beneficial in interventional therapy to improve one’s health.

MATERIALS AND METHODS

Extended materials and methods.

Extended materials and methods can be found in Text S1 in the supplemental material.

Cell culture and treatment.

Experiments were conducted using primary human colonic epithelial cells (HCoEpiC; lot 9810), which we also term “colonocytes” (ScienCell catalog no. 2950). Five fecal microbiota samples were purchased from OpenBiome as live microbial suspensions in 12.5% glycerol. On the day of the experiment, medium was removed from the colonocytes and fresh antibiotic-free medium was added, followed by inoculation with microbial extract for a final microbial ratio of 10:1 (microbe/colonocyte) in each well. Additional wells containing only colonocytes were also cultured for use as controls. Control wells also received fresh antibiotic-free media when the treatment wells were being treated. While the live microbial suspension was purchased in 12.5% glycerol, the microbiota sample in the culture plate was present only in traces (0.014%); therefore, no glycerol was added to the controls. This experimental protocol was described previously (38).
After 1, 2, or 4 h, the wells were scraped on ice and the contents were pelleted and washed with cold phosphate-buffered saline (PBS) and then resuspended in lysis buffer (Dynabeads mRNA Direct kit) and stored at –80°C until extraction of colonocyte RNA was performed. Each microbiota sample was used to treat colonocytes three times in total (once for each time point). At each time point, three wells of control (untreated) colonocytes were collected as technical replicates. To identify the genes that responded to the microbiota at each time point, the five microbiota samples were treated as biological replicates (see equation 2 in Text S1). To identify the genes that responded to a specific microbiota sample, we considered the technical replicates of each microbiome collected at 3 different time points (see equation 3 in Text S1). Further details can be found below and in the supplemental material.

Collinsella spike-in experiment.

Collinsella aerofaciens was purchased from ATCC (catalog no. 25986) and grown in reinforced clostridial medium (BD Biosciences; catalog no. 218081), following the manufacturer’s protocol, under anaerobic conditions. We verified that we were utilizing Collinsella aerofaciens by extracting the DNA by the use of a PowerSoil kit as described below. We then PCR amplified the 16S region using primers specific to Collinsella aerofaciens (61) (see Fig. S3F in the supplemental material).
Cell culture conditions for this experiment were the same as those described above. On the day of treatment, solutions were made with the microbiota sample (from individual 4) at 10:1 compared to the number of colonocytes and the C. aerofaciens inoculum was spiked into the microbiota sample at the following 4 dilutions: 10%, 1%, 0.1%, and 0.01%. Additional wells containing only colonocytes and colonocytes with the microbiota sample (0% Collinsella aerofaciens) were cultured as controls on the same 12-well plate. Each treatment was performed in duplicate.
Following 2 h of culturing, the wells were scraped on ice and the contents were pelleted and washed with cold PBS, resuspended in lysis buffer (Dynabeads mRNA Direct kit), and stored at –80°C until extraction of colonocyte RNA was performed (as described below).

RNA library preparation from colonocytes.

Polyadenylated mRNAs were isolated from thawed cell lysates by the use of a Dynabeads mRNA Direct kit (Ambion) following the manufacturer’s instructions. RNA-seq libraries were prepared using a protocol modified from the NEBNext Ultradirectional (NEB) library preparation protocol to use barcodes from Bioo Scientific added by ligation, as described previously (62). The libraries were then pooled and sequenced on two lanes of an Illumina NextSeq 500 system in the Luca/Pique-Regi laboratory using high-output kits for 75 cycles to obtain paired-end (PE) reads for an average of over 40 million total reads per sample.

RNA sequencing and alignment.

Reads were aligned to the hg19 human reference genome using STAR (63) (https://github.com/alexdobin/STAR/releases, version STAR_2.4.0h1) and the Ensemble reference transcriptome (version 75). We further removed reads with a quality score of <10 (equating to reads mapped to multiple locations) and removed duplicate reads using samtools rmdup (http://github.com/samtools/).

Differential gene expression analysis.

To identify differentially expressed (DE) genes, we used DESeq2 (64) (R version 3.2.1, DESeq2 version 1.8.1). Gene annotations from Ensembl version 75 were used, and transcripts with fewer than 20 reads in total were discarded. coverageBed was utilized to count reads with -s to account for strandedness and -split for BED12 input. The counts for 28,107 genes (147,555 transcripts) were then utilized in DESeq2 with several models to determine changes in gene expression under various conditions. A gene was considered DE if at least one of its transcripts was DE. In order to identify genes whose level of expression changed at each time point following coculturing, we used each microbiota treatment as a replicate. With this model, we identified 1,835 genes whose level of expression changed after 1 h (70% of the genes showed increased expression levels), 4,099 genes whose level of expression changed after 2 h (53% of the genes showed increased expression levels), and 1,197 genes whose level of expression changed after 4 h (56% of the genes showed increased expression levels) (BH FDR < 10%, |logFC| > 0.25) (Fig. S1H and I).
In order to identify genes that were differentially expressed at a given time point after coculturing with a specific microbiota sample, we used an alternative model which allows analysis of different effects of each microbiome. With this model, 1,131 genes showed changes in expression levels after 1 h with any of the 5 samples, 3,240 after 2 h, and 1,060 after 4 h (BH FDR < 10%, |logFC| > 0.25 (Fig. 1B).
We next used the likelihood ratio test that is a part of DESeq2 to compare the 2 models described above in order to identify genes whose expression changes over time are determined by the individual from which the microbiota sample was taken. In this way, we identified 409 genes at a BH FDR of <10%.
In order to identify components of the microbiota samples that affect gene expression, we used a model that included the baseline abundance of each taxon as measured in the microbiota samples prior to coculturing with the human cells (after all of the samples had been rarified to the sample with the lowest read count of 141,000). For this analysis, we considered the 62 most abundant taxa (>0.1% of the total number of reads in at least one of the five samples) and all genes expressed in the colonocytes, for a total of 9,125,927 tests. We identified 588 significant taxon-gene associations (BH FDR < 10%), comprising of 46 taxa and 121 genes (see Table S1 in the supplemental material).
Finally, we analyzed the validation experiment performed with the spike-in of Collinsella aerofaciens. In order to identify genes that were differentially expressed because of the presence of Collinsella aerofaciens, we used the model that included increasing abundances of Collinsella aerofaciens. We identified 1,570 genes that showed changes in expression levels (BH FDR = 10%) (Table S1; see also Fig. S2F) depending on the abundance of Collinsella aerofaciens.

16S rRNA gene sequencing and analysis of the microbiome.

Half of the volume of each culturing well and the full volume of wells with microbiota samples cultured alone were used for extraction of microbial DNA by the use of a PowerSoil kit (Mo Bio Laboratories) as directed, with a few modifications (found in the supplemental material). Microbial DNA was also extracted from the uncultured microbial samples. 16S rRNA gene amplification and sequencing were performed at the University of Minnesota Genomics Center (UMGC), as described previously by Burns et al. (21).
The trimmed 16S rRNA gene sequence pairs were quality filtered (q score of >20, using QIIME 1.8.0), resulting in 1.41, 1.06, and 1.53 million high-quality reads for sample replicates 1, 2, and 3, respectively (65, 66). Operational taxonomic units (OTUs) were picked using the closed reference algorithm against the Greengenes database (August 2013 release) (21, 6567). The resulting OTU table was analyzed to determine microbial community diversity using QIIME scripts and rarefaction to 141,000 reads.
We verified that the fecal samples that we utilized were similar to other healthy samples by comparing the OTUs detected to data from the Human Microbiome Project (1, 42). 16S V4 OTU and HMP V1V3 OTU tables (https://www.hmpdacc.org/HMQCP/, final OTU table) were run through QIIME’s summarize_taxa.py algorithm and consolidated at the L3 class level.

Determining effect of colonocytes on microbiota composition.

OTUs were collapsed to the genus level using scripts in QIIME 1.9.1. In total, 292 taxa were detected across all samples and treatments. After performing filtering on the data representing the relative abundances of each taxon, we focused on 112 taxa. We used the most recent version of QIIME throughout our analysis, and the version was updated while we were working on the project. The change in version does not affect the results (running summarize_taxa.py and alpha_diversity.py from the two versions of QIIME on the OTU table produced exactly the same output tables).
To assess how each taxon changed in response to culturing with colonocytes, we ran two linear models (including or excluding the effect of colonocytes) and compared the goodness-of-fit data using a likelihood ratio test. The model yielded 13/112 taxa that changed significantly due to treatment, with a BH FDR of <10% (Fig. S2C).

ATAC-seq in colonocytes exposed to gut microbiota.

Colonocytes were treated as described above. We treated colonocytes in a six-well plate with one of each of the five microbiota samples; as a control, one additional well was left untreated and yet was maintained under the same culturing conditions. This setup was used in duplicate, leading to the use of a total of 12 wells for the experiment. Thus, in total, the experiment consisted of five biological replicates, performed with two technical replicates each. Following 2 h of exposure to the microbiome, cells were collected by scraping the plate on ice. The contents of each well were counted to enable the removal of 50,000 cells to be used for ATAC-seq. We followed a previously reported protocol (40) to lyse 50,000 cells and prepare ATAC-seq libraries, with the exceptions that we used an Illumina Nextera index kit (catalog no. 15055290) in the PCR enrichment step and the cells were not lysed with 0.1% IGEPAL CA-630 before addition of the transposase to begin the ATAC-seq protocol. Individual library fragment distributions were assessed on an Agilent Bioanalyzer, and pooling proportions were determined using a quantitative PCR (qPCR) library quantification kit (Kapa Biosystems). Library pools were run on an Illumina NextSeq 500 desktop sequencer in the Luca/Pique-Regi laboratory. Barcoded libraries of ATAC-seq samples were pooled and sequenced in multiple sequencing runs for 130 million 38-bp PE reads (maximum, 159 million; minimum, 105 million). One sample, derived from treatment of the colonocytes with microbiota from individual 3, was removed from later analysis due to low sequencing coverage (63 million reads).
Reads were aligned to the hg19 human reference genome using HISAT2 (68) (https://github.com/, version hisat2-2.0.4) and the Ensemble reference transcriptome (hg19) with the options HISAT2 -x <genome> -1 <fastq_R1.gz> -2 <fastq_R2.gz>, where <genome> represents the location of the genome file and <fastqs_R1.gz> and <fastqs_R2.gz> represent the fastq files.
The multiple-sequencing runs were merged for each sample using samtools (version 2.25.0). We further removed reads with a quality score of <10 (equating to reads mapped to multiple locations) and removed duplicate reads using samtools rmdup (http://github.com/samtools/).

Transcription factor binding footprints detection with CENTIPEDE.

To detect which transcription factor motifs had footprints under each set of conditions, we adapted CENTIPEDE (54) to use the fragment length information contained in ATAC-seq in the footprint model and to jointly use in parallel the treatment conditions and control conditions in order to ensure that the same footprint shape would be used for the same motifs under both sets of conditions.
As described for CENTIPEDE, we needed to start from candidate binding sites for a given motif model. For each transcription factor, we scanned the entire human genome (hg19) for matches to its DNA recognition motif using position weight matrix (PWM) models from TRANSFAC and JASPAR as previously described (54). Then, for each candidate location l, we collected all the ATAC-seq fragments which were partitioned into four binding groups on the basis of the fragment length as follows: (i) 39 to 99 bp, (ii) 100 to 139 bp, (iii) 140 to 179 bp, and (iv) 180 to 250 bp. For each fragment, the two Tn5 insertion sites were calculated as the position 4 bp after the 5′ end in the 5′ to 3′ direction. Then, for each candidate motif, a matrix (X) was constructed to count Tn5 insertion events; each row represented a sequence match to a motif in the genome (a motif instance) and each column a specific cleavage site at the relative base pair and orientation with respect to the motif instance. We built a matrix [ {Xl}l=14] for each fragment length bin, each using a window a half-size S value of 150 bp, resulting in (2 × S + W) × 2 columns, where W represents the length of the motif in base pairs.
Finally, we fitted the CENTIPEDE model in a subset of instances to determine which motifs have active binding (i.e., show footprints) with a Z value of >5 as described previously (69). The statistical significance was assessed by calculating a Z-score corresponding to the PWM effect in the prior probability in the CENTIPEDE’s logistic hierarchical prior. Then we used CENTIPEDE and motif instances with posterior probabilities higher than 0.99 to denote locations where the transcription factors are bound.

Identification of differentially accessible regions following inoculation with microbiota.

To identify differentially accessible regions, we used DESeq2 (64) (R version 3.2.1, DESeq2 version 1.8.1). We separated the genome into 300-bp regions, and coverageBed was used to count reads in these regions. A total of 65,000 regions with >0.25 reads per million were then utilized in DESeq2 using the following model:
Gene expressiontreatmentYjn=tβjtMMtn
(1)
where Yjn represents the internal DEseq mean accessibility parameter for region j and experiment n, Mn is the treatment indicator (control or microbiome), and the βjtM parameter is the microbiome effect. With this model, we identified 234 regions that change in the host following exposure to the gut microbiota, with BH FDR of <20%. These regions were then compared to gene annotations from Ensembl version 75 to identify those that were within 50 kb of a transcription start site. We found enrichment for differentially accessible regions close to DE genes at 4 h (P value = 3.96 × 10−4, OR = 2.13) (Fig. 4A). In order to identify transcription factors which are important for response to microbiome exposure, we calculated enrichment scores for each active motif in regions of differentially accessible chromatin. A total of 40 motifs were found to have been significantly enriched or depleted (nominal P value < 0.05; see Table S4). Because we identified factors which were enriched or depleted for differentially accessible regions, we calculated a stratified FDR for regions tested for differential levels of chromatin accessibility by motif (70). For each transcription factor motif, we used the footprinting results from CENTIPEDE to classify subsets of regions containing that footprint from the DESeq2 test for differential chromatin accessibility. We then adjusted the FDR using the Benjamini-Hochberg procedure (71) for each motif separately. Combining the results from all motifs, we identified 209 regions displaying levels of differential chromatin accessibility with an FDR of <10%.
To identify transcription factor footprints enriched in differentially expressed genes, we used a logistic model for each of the 397 active factors within 50 kb upstream and downstream of each gene transcription start site as follows:
log(Prob. gene DE/Prob. gene not DE)=β0+β1(footprint count near gene)
(2)
We performed Benjamini-Hochberg multiple-test correction and defined significant factors as corresponding to FDR values of <10%.

ACKNOWLEDGMENTS

We thank members of the Luca/Pique-Regi group and members of the Blekhman laboratory for helpful discussions and comments.
This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health (R01GM109215 to F.L. and R.P.-R., R35GM128716 to R.B.).

Supplemental Material

File (msystems.00323-18-s0001.pdf)
File (msystems.00323-18-sf001.pdf)
File (msystems.00323-18-sf002.pdf)
File (msystems.00323-18-sf003.pdf)
File (msystems.00323-18-st001.txt)
File (msystems.00323-18-st002.txt)
File (msystems.00323-18-st003.txt)
File (msystems.00323-18-st004.txt)
File (msystems.00323-18-st005.txt)
File (msystems.00323-18-st006.txt)
ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse.

REFERENCES

1.
Human Microbiome Project Consortium. 2012. Structure, function and diversity of the healthy human microbiome. Nature 486:207–214.
2.
Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. 2012. Diversity, stability and resilience of the human gut microbiota. Nature 489:220–230.
3.
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Dore J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J, et al. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464:59–65.
4.
Blekhman R, Goodrich JK, Huang K, Sun Q, Bukowski R, Bell JT, Spector TD, Keinan A, Ley RE, Gevers D, Clark AG. 2015. Host genetic variation impacts microbiome composition across human body sites. Genome Biol 16:191.
5.
Bolnick DI, Snowberg LK, Hirsch PE, Lauber CL, Org E, Parks B, Lusis AJ, Knight R, Caporaso JG, Svanback R. 2014. Individual diet has sex-dependent effects on vertebrate gut microbiota. Nat Commun 5:4500.
6.
Dethlefsen L, Relman DA. 2011. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U S A 108(Suppl 1):4554–4561.
7.
Dominianni C, Sinha R, Goedert JJ, Pei Z, Yang L, Hayes RB, Ahn J. 2015. Sex, body mass index, and dietary fiber intake influence the human gut microbiome. PLoS One 10:e0124599.
8.
Jernberg C, Lofmark S, Edlund C, Jansson JK. 2010. Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology 156:3216–3223.
9.
Lee S, Sung J, Lee J, Ko G. 2011. Comparison of the gut microbiotas of healthy adult twins living in South Korea and the United States. Appl Environ Microbiol 77:7433–7437.
10.
Makivuokko H, Tiihonen K, Tynkkynen S, Paulin L, Rautonen N. 2010. The effect of age and non-steroidal anti-inflammatory drugs on human intestinal microbiota composition. Br J Nutr 103:227–234.
11.
Maurice CF, Haiser HJ, Turnbaugh PJ. 2013. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 152:39–50.
12.
Seto CT, Jeraldo P, Orenstein R, Chia N, DiBaise JK. 2014. Prolonged use of a proton pump inhibitor reduces microbial diversity: implications for Clostridium difficile susceptibility. Microbiome 2:42.
13.
Tims S, Derom C, Jonkers DM, Vlietinck R, Saris WH, Kleerebezem M, de Vos WM, Zoetendal EG. 2013. Microbiota conservation and BMI signatures in adult monozygotic twins. ISME J 7:707–717.
14.
Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP, Egholm M, Henrissat B, Heath AC, Knight R, Gordon JI. 2009. A core gut microbiome in obese and lean twins. Nature 457:480–484.
15.
Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. 2009. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 1:6ra14.
16.
Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, Magris M, Hidalgo G, Baldassano RN, Anokhin AP, Heath AC, Warner B, Reeder J, Kuczynski J, Caporaso JG, Lozupone CA, Lauber C, Clemente JC, Knights D, Knight R, Gordon JI. 2012. Human gut microbiome viewed across age and geography. Nature 486:222–227.
17.
Backhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. 2005. Host-bacterial mutualism in the human intestine. Science 307:1915–1920.
18.
Fujimura KE, Sitarik AR, Havstad S, Lin DL, Levan S, Fadrosh D, Panzer AR, LaMere B, Rackaityte E, Lukacs NW, Wegienka G, Boushey HA, Ownby DR, Zoratti EM, Levin AM, Johnson CC, Lynch SV. 2016. Neonatal gut microbiota associates with childhood multisensitized atopy and T cell differentiation. Nat Med 22:1187–1191.
19.
Hirata SI, Kunisawa J. 8 July 2017, posting date. Gut microbiome, metabolome, and allergic diseases. Allergol Int.
20.
Rowland I, Gibson G, Heinken A, Scott K, Swann J, Thiele I, Tuohy K. 2017. Gut microbiota functions: metabolism of nutrients and other food components. Eur J Nutr.
21.
Burns MB, Lynch J, Starr TK, Knights D, Blekhman R. 2015. Virulence genes are a signature of the microbiome in the colorectal tumor microenvironment. Genome Med 7:55.
22.
Gevers D, Kugathasan S, Denson LA, Vazquez-Baeza Y, Van Treuren W, Ren B, Schwager E, Knights D, Song SJ, Yassour M, Morgan XC, Kostic AD, Luo C, Gonzalez A, McDonald D, Haberman Y, Walters T, Baker S, Rosh J, Stephens M, Heyman M, Markowitz J, Baldassano R, Griffiths A, Sylvester F, Mack D, Kim S, Crandall W, Hyams J, Huttenhower C, Knight R, Xavier RJ. 2014. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15:382–392.
23.
Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK, Al-Soud WA, Sørensen SJ, Hansen LH, Jakobsen M. 2010. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5:e9085.
24.
Ott SJ, Musfeldt M, Wenderoth DF, Hampe J, Brant O, Folsch UR, Timmis KN, Schreiber S. 2004. Reduction in diversity of the colonic mucosa associated bacterial microflora in patients with active inflammatory bowel disease. Gut 53:685–693.
25.
Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C, Hardt PD. 2010. Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring) 18:190–195.
26.
Sinha R, Ahn J, Sampson JN, Shi J, Yu G, Xiong X, Hayes RB, Goedert JJ. 2016. Fecal microbiota, fecal metabolome, and colorectal cancer interrelations. PLoS One 11:e0152126.
27.
Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, Beaumont M, Van Treuren W, Knight R, Bell JT, Spector TD, Clark AG, Ley RE. 2014. Human genetics shape the gut microbiome. Cell 159:789–799.
28.
Camp JG, Frank CL, Lickwar CR, Guturu H, Rube T, Wenger AM, Chen J, Bejerano G, Crawford GE, Rawls JF. 2014. Microbiota modulate transcription in the intestinal epithelium without remodeling the accessible chromatin landscape. Genome Res 24:1504–1516.
29.
Davison JM, Lickwar CR, Song L, Breton G, Crawford GE, Rawls JF. 2017. Microbiota regulate intestinal epithelial gene expression by suppressing the transcription factor Hepatocyte nuclear factor 4 alpha. Genome Res 27:1195–1206.
30.
Donohoe DR, Garge N, Zhang X, Sun W, O'Connell TM, Bunger MK, Bultman SJ. 2011. The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab 13:517–526.
31.
Krautkramer KA, Kreznar JH, Romano KA, Vivas EI, Barrett-Wilt GA, Rabaglia ME, Keller MP, Attie AD, Rey FE, Denu JM. 2016. Diet-Microbiota interactions mediate global epigenetic programming in multiple host tissues. Mol Cell 64:982–992.
32.
Qin Y, Roberts JD, Grimm SA, Lih FB, Deterding LJ, Li R, Chrysovergis K, Wade PA. 2018. An obesity-associated gut microbiome reprograms the intestinal epigenome and leads to altered colonic gene expression. Genome Biol 19:7.
33.
Semenkovich NP, Planer JD, Ahern PP, Griffin NW, Lin CY, Gordon JI. 2016. Impact of the gut microbiota on enhancer accessibility in gut intraepithelial lymphocytes. Proc Natl Acad Sci U S A 113:14805–14810.
34.
Haberman Y, Tickle TL, Dexheimer PJ, Kim MO, Tang D, Karns R, Baldassano RN, Noe JD, Rosh J, Markowitz J, Heyman MB, Griffiths AM, Crandall WV, Mack DR, Baker SS, Huttenhower C, Keljo DJ, Hyams JS, Kugathasan S, Walters TD, Aronow B, Xavier RJ, Gevers D, Denson LA. 2014. Pediatric Crohn disease patients exhibit specific ileal transcriptome and microbiome signature. J Clin Invest 124:3617–3633.
35.
Hasler R, Sheibani-Tezerji R, Sinha A, Barann M, Rehman A, Esser D, Aden K, Knecht C, Brandt B, Nikolaus S, Schauble S, Kaleta C, Franke A, Fretter C, Muller W, Hutt MT, Krawczak M, Schreiber S, Rosenstiel P. 2017. Uncoupling of mucosal gene regulation, mRNA splicing and adherent microbiota signatures in inflammatory bowel disease. Gut 66:2087–2097.
36.
Lepage P, Hasler R, Spehlmann ME, Rehman A, Zvirbliene A, Begun A, Ott S, Kupcinskas L, Dore J, Raedler A, Schreiber S. 2011. Twin study indicates loss of interaction between microbiota and mucosa of patients with ulcerative colitis. Gastroenterology 141:227–236.
37.
Morgan XC, Kabakchiev B, Waldron L, Tyler AD, Tickle TL, Milgrom R, Stempak JM, Gevers D, Xavier RJ, Silverberg MS, Huttenhower C. 2015. Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease. Genome Biol 16:67.
38.
Richards AL, Burns MB, Alazizi A, Barreiro LB, Pique-Regi R, Blekhman R, Luca F. 2016. Genetic and transcriptional analysis of human host response to healthy gut microbiota. mSystems 1:e00067-16.
39.
Turnbaugh PJ, Backhed F, Fulton L, Gordon JI. 2008. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3:213–223.
40.
Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. 2013. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10:1213–1218.
41.
Ackermann AM, Wang Z, Schug J, Naji A, Kaestner KH. 2016. Integration of ATAC-seq and RNA-seq identifies human alpha cell and beta cell signature genes. Mol Metab 5:233–244.
42.
Human Microbiome Project Consortium. 2012. A framework for human microbiome research. Nature 486:7402215–7402221.
43.
Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, Klemm A, Flicek P, Manolio T, Hindorff L, Parkinson H. 2014. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42:D1001–D1006.
44.
Bibiloni R, Mangold M, Madsen KL, Fedorak RN, Tannock GW. 2006. The bacteriology of biopsies differs between newly diagnosed, untreated, Crohn’s disease and ulcerative colitis patients. J Med Microbiol 55:1141–1149.
45.
Frank DN, St Amand AL, Feldman RA, Boedeker EC, Harpaz N, Pace NR. 2007. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc Natl Acad Sci U S A 104:13780–13785.
46.
Knights D, Silverberg MS, Weersma RK, Gevers D, Dijkstra G, Huang H, Tyler AD, van Sommeren S, Imhann F, Stempak JM, Huang H, Vangay P, Al-Ghalith GA, Russell C, Sauk J, Knight J, Daly MJ, Huttenhower C, Xavier RJ. 2014. Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med 6:107.
47.
Moore WE, Moore LH. 1995. Intestinal floras of populations that have a high risk of colon cancer. Appl Environ Microbiol 61:3202–3207.
48.
Candela M, Biagi E, Soverini M, Consolandi C, Quercia S, Severgnini M, Peano C, Turroni S, Rampelli S, Pozzilli P, Pianesi M, Fallucca F, Brigidi P. 2016. Modulation of gut microbiota dysbioses in type 2 diabetic patients by macrobiotic Ma-Pi 2 diet. Br J Nutr 116:80–93.
49.
Kassinen A, Krogius-Kurikka L, Makivuokko H, Rinttila T, Paulin L, Corander J, Malinen E, Apajalahti J, Palva A. 2007. The fecal microbiota of irritable bowel syndrome patients differs significantly from that of healthy subjects. Gastroenterology 133:24–33.
50.
Zabaneh D, Balding DJ. 2010. A genome-wide association study of the metabolic syndrome in Indian Asian men. PLoS One 5:e11961.
51.
Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM, Torstenson ES, Shah KP, Garcia T, Edwards TL, Stahl EA, Huckins LM, Nicolae DL, Cox NJ, Im HK. 2018. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun 9:1825.
52.
Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, Eyler AE, Denny JC, Nicolae DL, Cox NJ, Im HK. 2015. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 47:1091–1098.
53.
Wheeler HE, Shah KP, Brenner J, Garcia T, Aquino-Michaels K, Cox NJ, Nicolae DL, Im HK. 2016. Survey of the heritability and sparse architecture of gene expression traits across human tissues. PLoS Genet 12:e1006423.
54.
Pique-Regi R, Degner JF, Pai AA, Gaffney DJ, Gilad Y, Pritchard JK. 2011. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res 21:447–455.
55.
Sobhani I, Tap J, Roudot-Thoraval F, Roperch JP, Letulle S, Langella P, Corthier G, Tran Van Nhieu J, Furet JP. 2011. Microbial dysbiosis in colorectal cancer (CRC) patients. PLoS One 6:e16393.
56.
Manichanh C, Rigottier G, Bonnaud E, Gloux K, Pelletier E, Frangeul L, Nalin R, Jarrin C, Chardon P, Marteau P, Roca J, Dore J. 2006. Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach. Gut 55:205–211.
57.
Kalam H, Fontana MF, Kumar D. 2017. Alternate splicing of transcripts shape macrophage response to Mycobacterium tuberculosis infection. PLoS Pathog 13:e1006236.
58.
Kalam H, Singh K, Chauhan K, Fontana MF, Kumar D. 2018. Alternate splicing of transcripts upon Mycobacterium tuberculosis infection impacts the expression of functional protein domains. IUBMB Life 70:845–854.
59.
Pai AA, Baharian G, Page Sabourin A, Brinkworth JF, Nedelec Y, Foley JW, Grenier JC, Siddle KJ, Dumaine A, Yotova V, Johnson ZP, Lanford RE, Burge CB, Barreiro LB. 2016. Widespread shortening of 3’ untranslated regions and increased exon inclusion are evolutionarily conserved features of innate immune responses to infection. PLoS Genet 12:e1006338.
60.
Skene PJ, Henikoff S. 16 January 2017, posting date. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. Elife.
61.
Kageyama A, Sakamoto M, Benno Y. 2000. Rapid identification and quantification of Collinsella aerofaciens using PCR. FEMS Microbiol Lett 183:43–47.
62.
Moyerbrailean GA, Davis GO, Harvey CT, Watza D, Wen X, Pique-Regi R, Luca F. 2015. A high-throughput RNA-seq approach to profile transcriptional responses. Sci Rep 5:14976.
63.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. 2013. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21.
64.
Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550.
65.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336.
66.
Navas-Molina JA, Peralta-Sanchez JM, Gonzalez A, McMurdie PJ, Vazquez-Baeza Y, Xu Z, Ursell LK, Lauber C, Zhou H, Song SJ, Huntley J, Ackermann GL, Berg-Lyons D, Holmes S, Caporaso JG, Knight R. 2013. Advancing our understanding of the human microbiome using QIIME. Methods Enzymol 531:371–444.
67.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072.
68.
Kim D, Langmead B, Salzberg SL. 2015. HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360.
69.
Moyerbrailean GA, Kalita CA, Harvey CT, Wen X, Luca F, Pique-Regi R. 2016. Which genetics variants in DNase-Seq footprints are more likely to alter binding? PLoS Genet 12:e1005875.
70.
Sun L, Craiu RV, Paterson AD, Bull SB. 2006. Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies. Genet Epidemiol 30:519–530.
71.
Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57:289–300.
72.
Friedman J, Alm EJ. 2012. Inferring correlation networks from genomic survey data. PLoS Comput Biol 8:e1002687.
73.
Joly Beauparlant C, Lamaze FC, Deschenes A, Samb R, Lemacon A, Belleau P, Bilodeau S, Droit A. 2016. Metagene profiles analyses reveal regulatory element’s factor-specific recruitment patterns. PLoS Comput Biol 12:e1004751.

Information & Contributors

Information

Published In

cover image mSystems
mSystems
Volume 4Number 529 October 2019
eLocator: 10.1128/msystems.00323-18
Editor: Elizabeth A. Grice, University of Pennsylvania

History

Received: 2 January 2019
Accepted: 22 July 2019
Published online: 3 September 2019

Keywords

  1. microbiome
  2. gene expression
  3. genomics

Contributors

Authors

Allison L. Richards
Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
Amanda L. Muehlbauer
Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, Minnesota, USA
Department of Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, Minnesota, USA
Adnan Alazizi
Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
Michael B. Burns
Department of Biology, Loyola University Chicago, Chicago, Illinois, USA
Anthony Findley
Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
Francesco Messina
Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
Trevor J. Gould
Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, Minnesota, USA
Department of Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, Minnesota, USA
Camilla Cascardo
Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
Roger Pique-Regi
Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, USA
Ran Blekhman
Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, Minnesota, USA
Department of Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, Minnesota, USA
Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, USA
Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, USA

Editor

Elizabeth A. Grice
Editor
University of Pennsylvania

Notes

Address correspondence to Roger Pique-Regi, [email protected], Ran Blekhman, [email protected], or Francesca Luca, [email protected].

Metrics & Citations

Metrics

Note:

  • For recently published articles, the TOTAL download count will appear as zero until a new month starts.
  • There is a 3- to 4-day delay in article usage, so article usage will not appear immediately after publication.
  • Citation counts come from the Crossref Cited by service.

Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. For an editable text file, please select Medlars format which will download as a .txt file. Simply select your manager software from the list below and click Download.

View Options

Figures and Media

Figures

Media

Tables

Share

Share

Share the article link

Share with email

Email a colleague

Share on social media

American Society for Microbiology ("ASM") is committed to maintaining your confidence and trust with respect to the information we collect from you on websites owned and operated by ASM ("ASM Web Sites") and other sources. This Privacy Policy sets forth the information we collect about you, how we use this information and the choices you have about how we use such information.
FIND OUT MORE about the privacy policy