35
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Human epigenetics and microbiome: the potential for a revolution in both research areas by integrative studies

      editorial
      * , 1
      Future Science OA
      Future Science Ltd
      epigenetics, health and disease, microbiome

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A simple PubMed search confirms the intuitive thinking that human epigenetics and human microbiome research have received considerable attention in the recent years (13,082 and 28,446 hits, respectively). It is therefore astonishing that the two fields are very rarely studied together (118 results for ‘human epigenetics microbiome’ search, but only 3 actual research articles among literature reviews and opinions articles), while the need for interdisciplinary studies is often called for. It makes perfect sense that the host-associated microbiome may be the ultimate environmental trigger for epigenetic processes, because of its spatial location and ability to convert environmental and diet-derived compounds before they reach human cells. It is already known that the dynamic nature of human epigenetics is a significant hurdle for clinical translation of basic research, and the human microbiome, which is both personalized and dynamic, is probably an additional significant confounder. So why try to correlate epigenetics & microbiome? First, as we reported last year [1], the extent to which epigenetic mechanisms modulate healthy or commensal microbes is virtually unknown, let alone how those processes are influenced and what influence they can have on their surrounding environment. This means that potentially any association with health and disease that have been suggested in the recent years [2,3] may have to be revisited through the microbiome epigenome perspective [4]. Understandably, investigators in microbiome research tend to consider microbes’ metabolic properties as relatively stable, as illustrated by the popular use of bioinformatics algorithms (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States [PICRUST]) that supposedly infer metabolic properties based on taxonomic (based on phylogenetic markers) profiles [5]. While the inherent limitations of such an approach are generally well discussed, the fact that epigenetic regulation of gene expression in the vast majority of human microbiome members is unknown represents another major limitation that has so far been ignored. Second, human microbiome is remarkably personalized [6]; thereby its influence on host epigenetic processes will be too. To put it simply into an ideal model, if two completely identical organisms were to be subjected to the exact same environmental conditions, their epigenetic regulation could still be divergent because of different metabolic activities of their personal microbiomes. Finally, there is still little understanding on how human microbiome modulates host epigenetic processes directly or indirectly. And the few popular paradigms, such as the heavily cited histone deacetylation in human colonocytes by microbially derived butyrate [7], probably still need to be refined. Challenges & potential directions for integrative human microbiome & epigenetic research Now, combining human epigenetic and microbiome research is surely easier said than done. While the most commonly used molecular methods (e.g., high-throughput sequencing, PCR and associated techniques) clearly overlap between the fields [8–10], data analysis and interpretation are already significantly different. However, the critical point is how to choose appropriate models and sample types, and I will focus on the digestive tract to illustrate the methodological issues. While overwhelmingly dominant in gut microbiome studies, stool samples are well known to be somewhat inappropriate representation of the actual gut microbiome. Biopsies, on the other hand, are better proxy of the microbial ecosystems surrounding epithelial cells, but not of the overall colonic ecosystem (not to mention that their sampling is inherently restricted to medical needs). Subsequently, it would appear that a more realistic approach would be to first determine baseline knowledge from different models. From the microbiome members’ perspective, there is already some knowledge about genomic fine-tuning in the model human gut symbiont Escherichia coli due to environmental conditions (inside vs outside digestive tract) [11,12]. It is very likely that epigenetics actually play an even greater role into maximizing microbial populations’ fitness to the ever-changing gut (micro)ecosystems. Therefore, there is a need to determine the amount of potential epigenetic control on (initially) abundant gut microbiome members. One recent report can be used as a blueprint for such projects. Leonard et al. performed a full methylome analysis of two strains of Bacteroides dorei isolated from two different stool samples [4], and found an outstanding difference in the number of methylation sites present in the two virtually similar genomes. To obtain a preliminary view on methylation potential, one approach would be to obtain methylome from a large collection of isolates obtained from a single stool sample. From such a baseline, it would then become possible to start hypothesis-based studies. While linking host epigenetic profiles with gut microbiome profiles has been attempted in a few previous studies [13,14], this kind of approach should probably be limited to cases where a relatively clear-cut segregation power has been identified in either the epigenetic or the microbiome profile. And even in those cases, the dynamic nature of the microbiome (particularly, in the gut) could become a strong confounder, as for example, stool sampling events cannot be standardized and scheduled to fit the scientific objectives of research investigators. Another option we are currently attempting in the swine model is to directly sample adjacent luminal content for gut microbiome analyses and colonic mucosa for epigenetic profiling. While this approach should arguably reduce confounding factors, it would have to be primarily applied to animal models, since human colonic biopsy sampling is relatively uncommon, and may not even allow for studying both microbiome and host epigenetics. There has also been extensive research into chemical compounds that could serve as epigenetic drugs [15]. In that context, metabolomics, the study of all metabolites resulting from microbial metabolism found across the human body [16], should definitely be considered more often. Indeed, molecules of interest may readily be produced in situ by specific microbes; and molecules of interest may actually be degraded or inactivated by microbial activity, as shown previously for a popular cardiac disease drug [17]. In addition, the metabolome clearly represents another parameter shaping epithelial cells surrounding environment. Since gut microbiome and gut and urine metabolome appear to correlate well, enough that the urine metabolome can be used as a proxy [18], it is possible that stool and/or urine would actually be the appropriate parameter to combine with host epigenetics profiling-based studies. Conclusion To summarize, there is interest and rationale to consider the human microbiome as a novel and crucial parameter in clinical epigenetic research. It is rather evident that attempting to perform large exploratory studies may be too ambitious based on our current knowledge. A reductionist approach is advised to explore both epigenetic controlling of the human microbiome, and how human microbiome and metabolome can modulate human epigenetic regulation.

          Most cited references14

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model

          Symbiotic gut microorganisms (microbiome) interact closely with the mammalian host's metabolism and are important determinants of human health. Here, we decipher the complex metabolic effects of microbial manipulation, by comparing germfree mice colonized by a human baby flora (HBF) or a normal flora to conventional mice. We perform parallel microbiological profiling, metabolic profiling by 1H nuclear magnetic resonance of liver, plasma, urine and ileal flushes, and targeted profiling of bile acids by ultra performance liquid chromatography–mass spectrometry and short-chain fatty acids in cecum by GC-FID. Top-down multivariate analysis of metabolic profiles reveals a significant association of specific metabotypes with the resident microbiome. We derive a transgenomic graph model showing that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize lipids: HBF mice present higher ileal concentrations of tauro-conjugated bile acids, reduced plasma levels of lipoproteins but higher hepatic triglyceride content associated with depletion of glutathione. These data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Genome sequencing of environmental Escherichia coli expands understanding of the ecology and speciation of the model bacterial species.

            Defining bacterial species remains a challenging problem even for the model bacterium Escherichia coli and has major practical consequences for reliable diagnosis of infectious disease agents and regulations for transport and possession of organisms of economic importance. E. coli traditionally is thought to live within the gastrointestinal tract of humans and other warm-blooded animals and not to survive for extended periods outside its host; this understanding is the basis for its widespread use as a fecal contamination indicator. Here, we report the genome sequences of nine environmentally adapted strains that are phenotypically and taxonomically indistinguishable from typical E. coli (commensal or pathogenic). We find, however, that the commensal genomes encode for more functions that are important for fitness in the human gut, do not exchange genetic material with their environmental counterparts, and hence do not evolve according to the recently proposed fragmented speciation model. These findings are consistent with a more stringent and ecologic definition for bacterial species than the current definition and provide means to start replacing traditional approaches of defining distinctive phenotypes for new species with omics-based procedures. They also have important implications for reliable diagnosis and regulation of pathogenic E. coli and for the coliform cell-counting test.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Gut Microbiota as an Epigenetic Regulator: Pilot Study Based on Whole-Genome Methylation Analysis

              Observation The human gut microbiota is mainly composed of the phyla Bacteroidetes, Firmicutes, and Actinobacteria, with other phyla, such as Proteobacteria, as a smaller but important component. Small shifts in microbial composition may modify energy intake in a way that leads to weight gain and, later, to insulin insensitivity. For instance, the abundances of Bacteroidetes and Firmicutes can be highly biased in cases of obesity (1), diabetes (2), and cardiovascular risk factors (3). The molecular mechanism controlling these metabolic alterations has been related to effects on glucose absorption, generation of fatty acids, hepatic lipogenesis, and deposition of triglycerides in adipocytes (4). We hypothesized that host-microbe interaction during the critical and sensitive period of pregnancy may determine the risk of developing obesity with comorbidities. Recent reports suggest that the microbiota and its metabolites influence genomic reprogramming (5 – 7). For example, Faecalibacterium prausnitzii and Eubacterium rectale/Roseburia spp., which belong to the Firmicutes, are major contributors of butyrate, which regulates gene expression by histone modifications (5). Lipopolysaccharide (LPS) is another microbial factor that may have a significant role in the epigenetic regulation of immune and intestinal cells (8). LPS, widely recognized as an inflammatory molecule, acts as risk factor for cardiovascular diseases (9). There are various molecules of microbial origin that are in complex interplay with host metabolism and physiology. However, to our knowledge, there are no studies in human subjects that have correlated the microbiota and epigenetic modifications. As a first step, we compared the fecal microbiota composition to blood DNA methylation patterns in 8 subjects (body mass index of ≤25). Previously, we have shown that microbiota alteration during pregnancy has a significant impact on host metabolism (10). Analysis of the fecal microbiota at different stages of pregnancy indicated that the bacterial diversity in the first trimester of pregnancy is similar to that of normal nonpregnant individuals (10). In the present study, we have identified an association that suggests a role of the predominant microbiota in epigenomic regulation. Thus, the following questions were asked: is the abundance of Bacteroidetes, Firmicutes, and/or Proteobacteria associated with differences in methylation pattern, and what are the major pathways associated epigenetically with patterns of microbial community structure? Selection of subjects. Eight pregnant women were selected from a cohort of 91 subjects previously described elsewhere (10). We selected mothers based on the relative abundances of the predominant phyla as previously reported (10). The HighBact group (n = 4) exhibited a predominance of the Bacteroidetes (P = 0.017) and Proteobacteria (P = 0.013) phyla, whereas Firmicutes were predominant in the HighFirm group (n = 4) mothers (P = 0.020) (Fig. 1). Previously, it has been shown that Bacteroidetes and Firmicutes constitute the dominant core of gut microbiota, and a Firmicutes-dominant microbiota has been implicated in the development of overweight, obesity, and metabolic syndrome (11 – 13). Additionally, the dietary and health characteristics of selected mothers in each group were similar, i.e., statistically insignificant (see Table S1 in the supplemental material). FIG 1  Categorization of mothers into group HighBact and group HighFirm was based on their dominant bacterial phyla. Box plots show the relative abundance (%) of the three major bacterial phyla, Bacteroidetes, Firmicutes, and Proteobacteria. There is a statistically significant difference between the groups (P < 0.05) using t test analysis. Correlation between DNA methylation profile and microbiota composition. The two groups showed distinct methylation profiles (blood samples were collected 6 months after delivery), as illustrated by the clustering analysis in Fig. 2A. With a fold change cutoff of 1.5 and P value of 0.05, the promoters of a total of 568 genes were more methylated and the promoter of 245 genes were less methylated in mothers with higher levels of Firmicutes (HighFirm group) than in mothers with higher levels of Bacteroidetes and Proteobacteria (HighBact group) (see Table S2 in the supplemental material). FIG 2  Association of gut microbiota with DNA methylation. (A) Clustering analysis of DNA methylome data revealed a clear correlation between the whole-blood epigenetic profile and the composition of the gut microbial population of the mothers with a predominance of either Bacteroidetes and Proteobacteria (group HighBact) or Firmicutes (group HighFirm). Green indicates decreased and red indicates increased gene promoter methylation in group HighFirm compared to the promoter methylation in group HighBact. (B) Based on Pathway Analysis (Ingenuity Systems), the gut microbial composition affects the DNA methylation status of genes primarily linked to cardiac diseases, with associations to lipid metabolism, inflammatory response, and obesity. The affected genes linked to the particular functional or metabolic syndrome displayed in the network had fold changes of ≥3 in the promoter DNA methylation status between the HighBact group and the HighFirm group. The symbols and their colors are defined in Table S4 in the supplemental material. Gut microbiota composition associates with promoter DNA methylation status of genes associated with lipid metabolism, obesity, and inflammation. Pathway analysis revealed that the most significant functional network altered in the HighBact group was linked to cardiovascular diseases, together with gene expression and cell morphology functions (score of 43; Fisher exact test, P = 1 × 10−43) (see Table S2 in the supplemental material). In addition, differentially methylated genes were enriched in other functional networks, including the inflammatory response, metabolic pathways, and diseases like cancer, mostly affecting the gastrointestinal system (312 molecules, P < 0.05). As the Bacteroidetes/Firmicutes ratio was associated with obesity-related comorbidities, the cardiovascular disease risk network was further expanded, and associations with lipid metabolism (72 genes), inflammatory response (85 altered genes), and obesity (23 altered genes) were found (Fig. 2B). Consistent with these results, the gene SCD5, which had the greatest difference between the two groups (fold change, 6.239; P = 0.00005), encodes primate-specific stearoyl-coenzyme A (CoA) desaturase, which has a key function in the catalysis of monounsaturated fatty acids from saturated fatty acids. The promoter region of SCD5 was more methylated in the HighFirm group and had an undetectable methylation in the HighBact group. LPS (P = 0.00208) was one of the upstream regulators of genes identified in the network (see Table S3), which further strengthens the role of microbial molecules in epigenetic modifications. Some of the epigenetically regulated genes include the genes encoding USF1 (P = 0.00805), ACOT7 (P = 0.035), ASAH2 (P = 0.0367), TAC1 (P = 0.00972), and LMNA (P = 0.03081). USF1 is one of the key regulators of fatty acid synthase (FAS) and is also a key enzyme in lipogenesis (14). USF1 and LMNA have also been linked with the onset of coronary heart disease (15, 16). The expression of ACOT and microRNAs 103/107 was also found to be upregulated in obese rats and mice, respectively (17, 18). Similarly, gut microbiota or its metabolites are directly linked to obesity and associated metabolic pathways. However, the association with epigenetic regulation of these genes should be further confirmed by quantitative PCR (qPCR) and in vitro experiments. These findings are consistent with previous studies, which have linked higher levels of Firmicutes to the development of overweight, obesity, higher energy extraction, and metabolic functions, including lipid metabolism. Additionally, deviant gut microbiota composition could also be one of the risk factors which may contribute to metabolic syndrome. Our findings are novel, but due to the small sample size, larger studies and interventions, and possibly animal experiments also, are required to assess the mechanisms. Nonetheless, this approach is intriguing and could offer a new basis for prevention and treatment strategies involving the gut microbiota and its impact on long-term genomic modifications. Microbial phylum comparisons. All the OTU tables were retrieved from our earlier study (10).The percentages of relative abundance for all phyla were used to compare the mothers (divided into two groups). Statistical package SPSS was used for the t test and to make box plots of the percentages of relative abundance. DNA methylome analysis. The DNA methylome analysis was carried out from 5 µg of genomic DNA that was extracted from EDTA blood with a QIAamp DNA blood maxikit (Qiagen) and fragmented into an average size of 150 bp with a Covaris S2 sonicator. Methylated DNA was enriched with a MethylMiner methylated DNA enrichment kit (Invitrogen) by following the high-salt (2 M NaCl), single-elution workflow. The sequencing libraries were prepared from 500 ng of enriched DNA with a SOLiD fragment library construction kit (Life Technologies), and the SOLiD fragment library barcoding kit module 1-16 (Life Technologies) was used for multiplexing. The libraries were purified (AMPure XP beads; Agencourt) and size selected (150 to 300 bp) from 1% agarose gels (QIAquick gel extraction kit; Qiagen). The bead preparation was carried out according to the SOLiD 4 System Templated Bead Preparation Guide. The SOLiDEZ bead system was used for automated templated bead preparation. The libraries were sequenced with a SOLiD 4 or SOLiD 5500XL sequencer (Life Technologies) by using 50-bp chemistry. Methylation sequencing data analysis. The raw sequence data were mapped to hg19 reference genome sequences with Life Technologies Bioscope (version 2.0) software using the default parameters, yielding on average 56.7 M mapped reads per sample (standard deviation, 14.26 M reads). The read counts for proximal promoters (region between 1,000 bp upstream and 500 bp downstream from the transcription start site, coordinates derived from RefSeq gene annotations) were calculated using bedtools (version 2.17.0). Statistical analysis for comparing differentially methylated promoters between sample groups was carried out using R/Bioconductor limma package on TMM-normalized and voom-transformed count values as suggested in the limma manual (19, 20). The promoters with absolute fold changes above 2 and P values below 0.05 were listed as significantly differentially methylated. SUPPLEMENTAL MATERIAL Table S1 Diet and health characteristics of mothers divided into two groups, HighBact and HighFirm. Statistical analysis was carried out using the Mann-Whitney U test in the SPSS package. Table S1, DOC file, 0.1 MB. Table S2 Differentially methylated promoters compared between two groups of mothers, HighBact and HighFirm. Table S2, XLS file, 0.3 MB. Table S3 Upstream regulators of the differentially methylated genes shown in Fig. 2B Table S3, XLS file, 0.1 MB. Table S4 Key to molecule shapes and colors in Ingenuity Pathway Analysis. Table S4, PDF file, 2.1 MB.
                Bookmark

                Author and article information

                Journal
                Future Sci OA
                Future Sci OA
                FSO
                Future Science OA
                Future Science Ltd (London, UK )
                2056-5623
                August 2017
                09 June 2017
                : 3
                : 3
                : FSO207
                Affiliations
                [1 ]Department of Food Science, University of Arkansas, Fayetteville, USA
                Author notes
                *Author for correspondence: Tel.: +1 479 575 6822; fgcarbon@ 123456uark.edu
                Article
                10.4155/fsoa-2017-0046
                5583657
                7bd6d674-c807-4265-b8ed-ddbbb17217bf
                © Franck Carbonero

                This work is licensed under a Creative Commons Attribution 4.0 License

                History
                : 10 April 2017
                : 13 April 2017
                Categories
                Editorial

                epigenetics,health and disease,microbiome
                epigenetics, health and disease, microbiome

                Comments

                Comment on this article