Skip to content
naulon
Get started

No retraction, correction or concern found when last checked on Oct 24, 2026.

Epigenetic Regulation at the Interplay Between Gut Microbiota and Host Metabolism

Joan Miro-Blanch, Oscar Yanes

Frontiers in Genetics · 2019 · CC BY

doi:10.3389/fgene.2019.00638

Abstract

Gut microbiota communities have coevolved for millions of years in a symbiotic relationship with their mammalian hosts. Elucidating and understanding the molecular mechanisms by which microbiota interacts with its host and how this contributes to the homeostasis of the host is crucial. One of these molecular relationships is the so-called chemical crosstalk between microbiota and host metabolisms, including the poorly explored epigenetic regulation of host tissues by the metabolic activity of gut microbiota in response to changes in diet. DNA methylation and histone modifications are epigenetic marks partly regulated by enzymes such as methylases and acetylases, whose activity depend on host and microbiota metabolites that act as substrates and cofactors for these reactions. However, providing a complete mechanistic description of the regulatory interactions between both metabolisms and the impact on the expression of host genes through an epigenetic modulation, remains elusive. This article presents our perspective on how metabolomic, metagenomic, transcriptomic, and epigenomic data can be used to investigate the "microbiota-nutrient metabolism-epigenetics axis." We also discuss the implications and opportunities this knowledge may have for basic and applied science, such as the impact on the way we structure future research, understand, and prevent diseases like type 2 diabetes or obesity.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 24, 2026. Paragraph numbers are ours, so an answer can point at one.

Crosstalk Between Host and Gut Microbiota Metabolisms

The human body co-habit with a diverse community of symbiotic microorganisms and their set of genes, collectively known as the microbiome. The acquisition of the initial microbiome is a dynamic rather than a static process during early life. A recent estimation of the number of bacterial cells over human cells in our body has reduced the ratio from 10:1 to a 1.3:1. This implies a similar number of bacterial and human cells in and on the human body. However, the human microbiome encodes for at least 100 times more genes than our genome. Therefore, the corresponding higher functionality of bacterial genes is a key aspect to understand existing metabolic interactions between the host and its microbiota.

The microbiota helps their hosts to digest dietary fiber; produces some important neurotransmitters, hormones, and vitamins; helps in training the host immune system; and protects against pathogens, among many other functions. However, unbalanced microbiota can also cause disease. Some common diseases in western societies such as obesity and type 2 diabetes are associated with shifts in the relative abundance of gut bacteria composition and functionality, compared to the ones observed in healthy individuals. The cause of the microbiota imbalance (dysbiosis) of unhealthy individuals across age and geography has been mainly correlated with dietary habits. Therefore, the so-called chemical crosstalk between the microbiota and its host has tangible consequences for the physiological state of the host. However, the molecular mechanisms by which microbiota chemically interacts with host cells and regulate gene expression remain largely unknown. In this regard, the role that certain host-microbiota derivate metabolites may exert on epigenetic events at the DNA, RNA, and histone level needs to be further investigated.

The “Microbiota–Nutrient Metabolism–Host Epigenetic” Axis

Differences in the microbiota or epigenome in two genetically identical organisms, such as same-sex inbred mice or monozygous twins, can create differences in susceptibilities to diseases including obesity and type 2 diabetes. As with gut microbiota, new studies have demonstrated that epigenetic events are highly dynamic, changing in response to nutrient availability or physical exercise. DNA methyltransferases, DNA hydroxylases, histone acetyltransferases, histone deacetylases, histone methyltransferases, and histone demethylases are enzymes responsible for adding or removing these dynamic epigenetic modifications. In this regard, endogenous metabolites can regulate gene expression through epigenetic events in host cells. For instance, histone deacetylation regulated by sirtuin family deacetylases is regulated by the NAD+/NADH ratio, acetyl-CoA, O-acetyl-ADP-ribose, and nicotinamide. Whether gut microbiota metabolism is regulating the concentration and/or activity of endogenously produced metabolites by the host remains largely unexplored, and it is only recently that an increasing number of researchers have started to investigate this possibility. have demonstrated that microbial colonization regulates global histone acetylation and methylation in multiple host tissues in a diet-dependent manner.

Short-chain fatty acids (SCFAs) are exclusively produced by the microbial fermentation of dietary carbohydrates, and their abundances are regulated by the composition of the microbiota. Importantly, SCFAs, particularly butyrate and acetate, produced by the microbiota, inhibit histone deacetylases ( Figure 1 ). Increased levels of histone acetylation promote decondensation and relaxation of chromatin, supporting a more transcriptionally active state of chromatin.

Alteration of chromatin state is a possible mechanism by which gut microbiota induces host immune maturation. Recognition of a “self” antigen should not only be limited to mammalian host antigens, but also symbiotic microbiota antigens forming part of the whole human ecosystem in a healthy state. The human leukocyte antigen (HLA) or major histocompatibility complex (MHC) gene system encodes many antigen-presenting proteins, which are essential to recognize and distinguish “self” from “non-self” antigens. Colonization of germ-free mice has demonstrated the capacity of microbiota-specific species to activate MHC class II genes. However, little is known about the immunomodulatory effect of microbial metabolites. A poorly explored possibility is that epigenetically relevant metabolites such as SCFA, highly influenced by microbiota composition, would regulate MHC gene expression by coordinating activity of enzymes that acetylate and methylate histones and DNA allowing chromatin accessibility.

Regulation of DNA and histone methylation may be driven by complex microbiota–host metabolism interactions involving S-adenosyl methionine (SAM), derived from the essential amino acid methionine through diet. Folate plays an essential role by re-methylating homocysteine to methionine ( Figure 1 ), thereby ensuring the provision of SAM. In this regard, enzymes that are depleted in obese microbiomes are frequently involved in cofactor and vitamin metabolism, including the production of cobalamin (vitamin B12), pyridoxal phosphate (the active form of vitamin B6), tetrahydrofolate, and folate. Taken together, dysbiosis of microbiota can influence SAM levels and, as a result, alter the methylation status of DNA and histones. Whether dysbiosis of microbiota can alter α-ketoglutarate and succinate levels in specific peripheral host tissues, and regulate the rate of DNA demethylation, is a plausible but little explored possibility. Ten-eleven translocation (TET) enzymes are a key family of DNA and histone demethylases that use α-ketoglutarate as co-substrate. However, due to the structural similarity with α-ketoglutarate, Tets are susceptible to competitive inhibition by fumarate and succinate, causing an increase in histone and DNA methylation levels.

Another modification that could be regulated by microbial metabolism is histone phosphorylation. In response to a low ATP/AMP ratio indicative of energy status, the AMP-activated protein kinase (AMPK) can translocate to chromatin and phosphorylate histone H2B. Changes in AMPK activity have been reported in obesity, type 2 diabetes, metabolic syndrome, and cardiovascular disease. Interestingly, germ-free mice were resistant to obesity and insulin resistance that develop after consuming a Western-style, high-fat, and sugar-rich diet. The persistently lean phenotype of germ-free animals was associated with increased skeletal muscle and liver levels of phosphorylated AMPK. It is also tempting to speculate that phosphotransferase systems (PTS) overrepresented in the Western diet microbiomes could have an impact on this histone modification.

In short, dysbiosis and reduction of the microbiota diversity can potentially alter the levels of nutrients and metabolites that can potentially act as regulators of DNA methylation and histone modifications either by directly inhibiting enzymes that catalyze the processes, or by altering the availability of substrates necessary for the enzymatic reactions.

Holobionts, Multifactorial Diseases, and Omic Technologies

Hosts and their microbiota have a very intimate relationship and should be considered as a single biological and evolutionary unit, termed holobiont. In this regard, we could arguably talk about holo–genome, –transcriptome, –proteome, or –metabolome, referring to the combination of both, host and host microbiota molecular layers or modules of information at the DNA, RNA, protein, or metabolite level, respectively ( Figure 2 ). To investigate the dynamics of the holobiont ecosystem network, multi-omic approaches bring unprecedented advantages. Diseases such as obesity and diabetes are known to be multifactorial, and the collection of several -omic data ( Table 1 ) from the same holobiont specimen, may provide a detailed molecular description and new mechanistic insights of how dietary nutrients and gut microbiota metabolism can regulate the host phenotype through gene expression and epigenetic and metabolic regulation.

To study a complex metabolic disorder such as familial type 1 diabetes mellitus (T1D), used a combination of host genomics and proteomics together with metagenomics, metatranscriptomics, and metaproteomics to demonstrate a pronounced family membership effect in the structuration and functionality of the microbiomes. They observed a correlation between certain human pancreatic enzymes and the expression of specific microbial genes involved in key T1D metabolic transformations. used a combination of metabolomic, proteomics of histones, transcriptomic, and metagenomic techniques in conventional and germ-free mice, to demonstrate how SCFAs produced by the microbiota, or supplemented exogenously to germ-free mice, regulate histone post-translational modifications (PTMs). Comparing histone PTMs and transcriptional profiles between conventional, germ-free mice and germ-free mice supplemented with SCFAs, Krautkramer et al. concluded that SCFAs alone are partially causative for histone PTMs. In the same direction, used a combination of metagenomics, transcriptomics, epigenomics, and metabolomics together with imaging electron microscopy, to identify a diurnal rhythmicity in the microbial biogeography, metabolic profile, and metagenomic functionality as critical orchestrators of host epigenetic marks and gene expression. Thaiss et al. have demonstrated that host epigenetic and transcriptional circadian oscillations are partially dependent on environmental signals such as microbiome metabolite dynamics in the intestines and that peripheral organs “sense and adapt” to this circadian metabolite rhythms in a similar manner.

Overall, multi-omic approaches will facilitate the structuration of future research, improve patient stratification toward a more personalized care, and open new avenues to evaluate the effectiveness of functional probiotics, functional foods, or nutritional interventions aimed at regulating host gene expression in health and disease.

Opportunities for Biomedical and Clinical Research

The development and improvement of new technologies and bioinformatic tools are advancing biomedical research at a fast pace. Multi-omic experiments are allowing researchers to obtain mechanistic insights on the human holobiont homeostasis, improving decision-making for next experiments to be performed. In a research context, extensive collection of -omic data will allow integration of information into modulable and controlled models of microbial communities. However, it is important to identify confounding factors in longitudinal -omic studies. Standardization of methods and techniques to reduce noise and bias in microbiome research will improve how we translate lab findings into the clinic.

Using germ-free or gnotobiotic mouse models provide a framework to manipulate the gut microbial composition in a controlled manner. These models can be used to study the chemical crosstalk between host and microbiota by uncovering specific epigenetic changes in host cells induced by colonization of specific bacterial strains. Colonizing gnotobiotic mice with single strains or small bacterial consortia, instead of whole fecal transplants, might bring more accurate information to understand specific molecular mechanisms and molecular pathways involved in the host epigenetic regulations. Microbiota-induced dysbiosis with antibiotics might provide a useful approach to validate findings in gnotobiotic models by partially mimicking the effects of absence of microbiota or a reduction in the microbial diversity. Interestingly, organoids might also provide a challenging but very useful in vitro culturing system to study host–microbiota interactions.

In a clinical context, gaining knowledge on the “microbiota–nutrient metabolism–host epigenetics axis” using multiple -omic approaches in combination with microbiota modulation therapies, has the potential to prevent and treat more efficiently metabolic diseases:

Fecal microbiota transplantation (FMT): The use of microbiota modulation therapies such as FMT to treat recurrent Clostridium difficile infections has been proved significantly more efficient than a vancomycin treatment. Recently, the potential of FMT as microbiome modulation technique for treating metabolic, neurological, and immunological disorders has been tested, improving these conditions by partially restoring microbiota diversity and functionality. Assessing long-term host epigenetic effects of FMT has to be further investigated.

Microbiota as drug target: The unique microbiome composition of each person is probably responsible for different susceptibilities to the same nutrient, pollutant, or drug treatment. Microbiota-derived metabolites can enter the bloodstream and interact with drug treatments, impacting the efficacy, toxicity, and clearance of the drug. Diagnosing or treating diseases using microbiota-targeted drugs, probiotics, and use of bacteriophages or engineered bacteria has recently re-emerged.

Nutritional intervention, probiotics, and prebiotics: The acute consumption of a cocktail of probiotics containing a selection of five strains of Lactobacillus and five strains of Enterococcus modulates the microbiome and enhances SCFAs production in human and mice. The consumption of some probiotics has proven to be beneficial and ameliorates stress felt in healthy women, improves insulin sensitivity, protects against infections, and helps to restore microbiota after distortion by antibiotics, among other benefits. Promoting SCFAs producing bacteria in the host gut by a nutritional intervention that increases fiber consumption may have an epigenetic effect in the host.

Conclusions and Perspective

The study of the “microbiota–nutrient metabolism–host epigenetic” axis has great potential to reveal the molecular mechanisms by which gut microbiota composition affects the expression of genes in their hosts. In a human holobiont context, this axis is relevant to understand, prevent, diagnose, and treat the existing epidemic of metabolic disorders such as type 2 diabetes and obesity. Microbiota is a key player in health outcomes due to the potential myriad of metabolites that can produce and interact with any cell of our body through systemic circulation. Those metabolites are coming from direct transformations of nutrients available in the gut microbiota or from secondary transformed host products. The link between epigenetic marks and gut microbes appears to be mediated by host-microbial metabolites that act as substrates and cofactors for key epigenetic enzymes in the host. A disruption in the composition of the gut microbiota may lead to unbalanced key metabolites that sequentially may impact epigenetic pathways and alter gene expression. The implementation of multi-omic approaches to study the human holobiont will facilitate the stratification of patients toward a personalized-oriented care, improving disease prevention, diagnostics, drug election, and treatment efficiency.

Data Availability

All datasets analyzed for this study are cited in the manuscript and/or the supplementary files.

Author Contributions

JM and OY designed, wrote, revised, and approved the submitted version of the manuscript.

Funding

This work was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 675610. OY thanks the following bodies for funding: Ministerio de Economia y Competitividad (MINECO) (BFU2017-87958-P) and the Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), an initiative of Instituto de Investigacion Carlos III (ISCIII).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References 105

  1. 1.
    Diet, Gut Microbiome and Epigenetics: Emerging Links with Inflammatory Bowel Diseases and Prospects for Management and Prevention · 2017
    No notice
  2. 2.

    An J. Rao A. Ko M. (2017 ). TET family dioxygenases and DNA demethylation in stem cells and cancers. Exp. Mol. Med. 49, 1–12.

    Not in our library10.1038/emm.2017.5

  3. 3.

    Arumugam M. Raes J. Pelletier E. Le Paslier D. Yamada T. Mende D. R. (2011 ). Enterotypes of the human gut microbiome. Nature 473, 174–180.

    Not in our library10.1038/nature09944

  4. 4.

    Bäckhed F. Manchester J. K. Semenkovich C. F. Gordon J. I. (2007 ). Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl. Acad. Sci. 104, 979–984.

    Not in our library10.1073/pnas.0605374104

  5. 5.

    Barski A. Wei G. Chepelev I. Wang Z. Cui K. Zhao K. (2007 ). High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837.

    Not in our library10.1016/j.cell.2007.05.009

  6. 6.

    Bhat M. I. Kapila R. (2017 ). Dietary metabolites derived from gut microbiota: critical modulators of epigenetic changes in mammals. Nutr. Rev. 75, 374–389.

    Not in our library10.1093/nutrit/nux001

  7. 7.

    Bolduc J.-F. Hany L. Barat C. Ouellet M. Tremblaya M. J. (2017 ). Epigenetic metabolite acetate inhibits class I/II histone deacetylases, promotes histone acetylation, and increases HIV-1 Integration in CD4 T cells. J. Virol. 91, 1–15.

    Not in our library10.1128/jvi.01943-16

  8. 8.

    Booth M. J. Branco M. R. Ficz G. Oxley D. Krueger F. Reik W. (2012 ). Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution. Science (80-). 336, 934–937.

    Not in our library10.1126/science.1220671

  9. 9.

    Booth M. J. Marsico G. Bachman M. Beraldi D. Balasubramanian S. (2014 ). Quantitative sequencing of 5-formylcytosine in DNA at single-base resolution. Nat. Chem. 6, 435–440.

    Not in our library10.1038/nchem.1893

  10. 10.

    Bouchard L. Rabasa-Lhoret R. Faraj M. Lavoie M. Mill J. Pérusse L. (2010 ). Differential epigenomic and transcriptomic responses in subcutaneous adipose tissue between low and high responders to caloric restriction. Am. J. Clin. Nutr. 91, 309–320.

    Not in our library10.3945/ajcn.2009.28085

  11. 11.

    Boyle A. P. Davis S. Shulha H. P. Meltzer P. Margulies E. H. Weng Z. (2008 ). High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311–322.

    Not in our library10.1016/j.cell.2007.12.014

  12. 12.

    Brindle J. T. McKilligin E. Tranter G. Clarke S. Holmes E. Nicholson J. K. (2002 ). Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat. Med. 8, 1439–1445.

    Not in our library10.1038/nm802

  13. 13.

    Buenrostro J. D. Giresi P. G. Zaba L. C. Chang H. Y. Greenleaf W. J. (2013 ). Transposition of native chromatin for multimodal regulatory analysis and personal epigenomics. Nat. Methods 10, 1213–1218.

    Not in our library10.1038/nmeth.2688

  14. 14.

    Bungard D. Benjamin J. Fuerth Zeng P.-Y. Faubert B. Maas N. L. (2010 ). Signaling kinase AMPK activates stress-promoted transcription via histone H2B phosphorylation. Science (80-). 329, 1201–1205.

    Not in our library10.1126/science.1191241

  15. 15.

    Caesar R. Tremaroli V. Kovatcheva-datchary P. Patrice D. (2015 ). Crosstalk between gut microbiota and dietary lipids aggravates WAT inflammation through TLR signaling. Cell Metab., 22, 658–668.

    Not in our library10.1016/j.cmet.2015.07.026

  16. 16.

    Cai L. Sutter B. M. Li B. Tu B. P. (2011 ). Acetyl-CoA induces cell growth and proliferation by promoting the acetylation of histones at growth genes. Mol. Cell 42, 426–437.

    Not in our library10.1016/j.molcel.2011.05.004

  17. 17.

    Cani P. D. Osto M. Geurts L. Everard A. (2012 ). Involvement of gut microbiota in the development of low-grade inflammation and type 2 diabetes associated with obesity. Gut Microbes 3, 37–41.

    Not in our library10.4161/gmic.19625

  18. 18.

    Carrier T. J. Reitzel A. M. (2017 ). The hologenome across environments and the implications of a host-associated microbial repertoire. Front. Microbiol. 8, 1–13.

    Not in our library10.3389/fmicb.2017.00802

  19. 19.

    Chen Y. Sprung R. Tang Y. Ball H. Sangras B. Kim S. C. (2007 ). Lysine propionylation and butyrylation are novel post-translational modifications in histones. Mol. Cell. Proteomics 6, 812–819.

    Not in our library10.1074/mcp.m700021-mcp200

  20. 20.

    Christmann B. S. Abrahamsson T. R. Charles N. Duck L. W. Mannon P. J. Berg G. (2015 ). Human seroreactivity to gut microbiota antigens. J. Allergy Clin. Immunol. 136, 1378–1386.

    Not in our library10.1016/j.jaci.2015.03.036

  21. 21.

    Chu H. Mazmanian S. K. (2013 ). Innate immune recognition of the microbiota promotes host-microbial symbiosis. Nat. Immunol. 14, 668–675.

    Not in our library10.1038/ni.2635

  22. 22.

    Churchman L. S. Weissman J. S. (2011 ). Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469, 1–18.

    Not in our library10.1038/nature09652

  23. 23.

    Core L. J. Waterfall J. J. Lis J. T. (2008 ). Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science (80-). 322, 1845–1848.

    Not in our library10.1126/science.1162228

  24. 24.

    De Filippo C. Cavalieri D. Di Paola M. Ramazzotti M. Poullet J. B. Massart S. (2010 ). Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl. Acad. Sci. 107, 14691–14696.

    Not in our library10.1073/pnas.1005963107

  25. 25.

    Down T. A. Rakyan V. K. Turner D. J. Flicek P. Li H. Kulesha E. (2008 ). A bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat. Biotechnol. 26, 779–785.

    Not in our library10.1038/nbt1414

  26. 26.
    Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome · 2018
    No notice
  27. 27.
    Epigenetics and obesity: the devil is in the details · 2010
    Not checked
  28. 28.

    Franzosa E. A. Hsu T. Sirota-Madi A. Shafquat A. Abu-Ali G. Morgan X. C. (2015 ). Sequencing and beyond: integrating molecular “omics” for microbial community profiling. Nat. Rev. Microbiol. 13, 360–372.

    Not in our library10.1038/nrmicro3451

  29. 29.
    Detection of Large Numbers of Novel Sequences in the Metatranscriptomes of Complex Marine Microbial Communities · 2008
    No notice
  30. 30.
    Gut microbiota, probiotics and diabetes · 2014
    No notice
  31. 31.

    Greenblum S. Turnbaugh P. J. Borenstein E. (2012 ). Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and in flammatory bowel disease. Proc. Natl. Acad. Sci. 109, 594–599.

    Not in our library10.1073/pnas.1116053109

  32. 32.
    Unraveling the processes shaping mammalian gut microbiomes over evolutionary time · 2017
    No notice
  33. 33.

    Gupta S. Allen-Vercoe E. Petrof E. O. (2016 ). Fecal microbiota transplantation: In perspective. Therap. Adv. Gastroenterol. 9, 229–239.

    Not in our library10.1177/1756283x15607414

  34. 34.

    Hassig C. A. Tong J. K. Schreiber S. L. (1997 ). Fiber-derived butyrate and the prevention of colon cancer. Chem. Biol. 4, 783–789.

    Not in our library10.1016/s1074-5521(97)90111-3

  35. 35.

    Heintz-Buschart A. May P. Laczny C. C. Lebrun L. A. Bellora C. Krishna A. (2016 ). Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat. Microbiol. 2, 1–12.

    Not in our library10.1038/nmicrobiol.2016.180

  36. 36.

    Hullar M. A. J. Fu B. C. (2014 ). Diet, the gut microbiome, and epigenetics. Cancer J. (United States) 20, 170–175.

    Not in our library10.1097/ppo.0000000000000053

  37. 37.
    Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study · 2017
    No notice
  38. 38.

    Katada S. Imhof A. Sassone-Corsi P. (2012 ). Connecting threads: epigenetics and metabolism. Cell 148, 24–28.

    Not in our library10.1016/j.cell.2012.01.001

  39. 39.

    Kau A. L. Ahern P. P. Griffin N. W. Goodman A. L. Gordon J. I. (2011 ). Human nutrition, the gut microbiome and the immune system. Nature 474, 327–336.

    Not in our library10.1038/nature10213

  40. 40.

    Kim Y.-I. (2005 ). Nutritional epigenetics: impact of folate deficiency on dna methylation and colon cancer susceptibility. J. Nutr. 135, 2694–2697.

    Not in our library10.1093/jn/135.11.2703

  41. 41.

    Kivioja T. Vähärautio A. Karlsson K. Bonke M. Enge M. Linnarsson S. (2012 ). Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74.

    Not in our library10.1038/nmeth.1778

  42. 42.

    Knight R. Navas J. Quinn R. A. Sanders J. G. Zhu Q. (2018 ). Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422.

    Not in our library10.1038/s41579-018-0029-9

  43. 43.

    Kola B. Grossman A. B. Korbonits M. (2008 ). The role of AMP-activated protein kinase in obesity. Front. Horm. Res. 36, 198–211.

    Not in our library10.1159/000115366

  44. 44.

    Kootte R. S. Levin E. Salojärvi J. Smits L. P. Hartstra A. V. Udayappan S. D. (2017 ). Improvement of insulin sensitivity after lean donor feces in metabolic syndrome is driven by baseline intestinal microbiota composition. Cell Metab. 26, 611–619.e6.

    Not in our library10.1016/j.cmet.2017.09.008

  45. 45.
    Chemical transformation of xenobiotics by the human gut microbiota · 2017
    No notice
  46. 46.

    Krautkramer K. A. Kreznar J. H. Romano K. A. Vivas E. I. Barrett-Wilt G. A. Rabaglia M. E. (2016 ). Diet-microbiota interactions mediate global epigenetic programming in multiple host tissues. Mol. Cell 64, 982–992.

    Not in our library10.1016/j.molcel.2016.10.025

  47. 47.

    Krautkramer K. A. Rey F. E. Denu J. M. (2017 ). Chemical signaling between gut microbiota and host chromatin: What is your gut really saying? J. Biol. Chem. 292, 8582–8593.

    Not in our library10.1074/jbc.r116.761577

  48. 48.

    Lahiri S. Sun N. Solis-Mezarino V. Fedisch A. Ninkovic J. Feuchtinger A. (2016 ). In situ detection of histone variants and modifications in mouse brain using imaging mass spectrometry. Proteomics 16, 437–447.

    Not in our library10.1002/pmic.201500345

  49. 49.

    Laker R. C. Garde C. Camera D. M. Smiles W. J. Zierath J. R. Hawley J. A. (2017 ). Transcriptomic and epigenetic responses to short-term nutrient-exercise stress in humans. Sci. Rep. 7, 1–12.

    Not in our library10.1038/s41598-017-15420-7

  50. 50.

    Landry B. P. Tabor J. J. (2017 ). Engineering diagnostic and therapeutic gut bacteria. Microbiol. Spectr. 5, 1–22.

    Not in our library10.1128/microbiolspec.bad-0020-2017

  51. 51.
    The effects of antibiotics on the microbiome throughout development and alternative approaches for therapeutic modulation · 2016
    No notice
  52. 52.

    Lieberman-Aiden E. Berkum5, van N. L. Williams L. Imakaev2 M. Ragoczy T. (2009 ). Comprehensive mapping of long range interactions reveals folding principles of the human genome. Science (80-). 326, 289–293.

    Not in our library10.1126/science.1181369

  53. 53.

    Ling C. Groop L. (2009 ). Epigenetics: a molecular link between environmental factors and type 2 diabetes. Diabetes 58, 2718–2725.

    Not in our library10.2337/db09-1003

  54. 54.

    Lister R. Pelizzola M. Dowen R. H. Hawkins R. D. Hon G. Nery J. R. (2009 ). Human DNA methylomes at base resolution show widespreadepigenomic differences. Nature 462, 315–322.

    Not in our library10.1038/nature08514

  55. 55.

    Liu S. Da Cunha A. P. Rezende R. M. Cialic R. Wei Z. Bry L. (2016 ). The host shapes the gut microbiota via fecal MicroRNA. Cell Host Microbe 19, 32–43.

    Not in our library10.1016/j.chom.2015.12.005

  56. 56.

    Lu W. Bennett B. D. Rabinowitz J. D. (2008 ). Analytical strategies for LC-MS-based targeted metabolomics. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 871, 236–242.

    Not in our library10.1016/j.jchromb.2008.04.031

  57. 57.

    Maier L. Pruteanu M. Kuhn M. Zeller G. Telzerow A. Anderson E. E. (2018 ). Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628.

    Not in our library10.1038/nature25979

  58. 58.
    The Human Gut Phage Community and Its Implications for Health and Disease · 2017
    No notice
  59. 59.

    Manzo V. E. Bhatt A. S. (2015 ). The human microbiome in hematopoiesis and hematologic disorders. Blood 126, 311–318.

    Not in our library10.1182/blood-2015-04-574392

  60. 60.

    Maslowski K. M. MacKay C. R. (2011 ). Diet, gut microbiota and immune responses. Nat. Immunol. 12, 5–9.

    Not in our library10.1038/ni0111-5

  61. 61.
    Gut microbiome diversity and high-fibre intake are related to lower long-term weight gain · 2017
    No notice
  62. 62.

    Meyer K. D. Saletore Y. Zumbo P. Elemento O. Mason C. E. Jaffrey S. R. (2012 ). Comprehensive Analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646.

    Not in our library10.1016/j.cell.2012.05.003

  63. 63.

    Mortazavi A. Williams B. A. McCue K. Schaeffer L. Wold B. (2008 ). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628.

    Not in our library10.1038/nmeth.1226

  64. 64.

    Muegge B. D. Kuczynski J. Knights D. Clemente J. C. Fontana L. Henrissat B. (2011 ). Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science (80-). 332, 970–974.

    Not in our library10.1126/science.1198719

  65. 65.
    Human-origin probiotic cocktail increases short-chain fatty acid production via modulation of mice and human gut microbiome · 2018
    No notice
  66. 66.

    Pamer E. G. (2014 ). Fecal microbiota transplantation: effectiveness, complexities, and lingering concerns. Mucosal Immunol. 7, 210–214.

    Not in our library10.1038/mi.2013.117

  67. 67.

    Patti G. J. Yanes O. Siuzdak G. (2012 ). Metabolomics: the apogee of the omics trilogy. Nat. Cell Biol. 13, 263–269.

    Not in our library10.1038/nrm3314

  68. 68.
    Influences of diet and the gut microbiome on epigenetic modulation in cancer and other diseases · 2015
    No notice
  69. 69.

    Peleg S. Feller C. Ladurner A. G. Imhof A. (2016 ). The metabolic impact on histone acetylation and transcription in ageing. Trends Biochem. Sci. 41, 700–711.

    Not in our library10.1016/j.tibs.2016.05.008

  70. 70.

    Petrof E. O. Khoruts A. (2012 ). From stool transplants to next-generation microbiota therapeutics. Gastroenterology 1, 233–245.

    Not in our library10.1016/j.dcn.2011.01.002

  71. 71.

    Poirier L. A. Wise C. K. Delongchamp R. R. Sinha R. (2001 ). Blood determinations of S -adenosylmethionine, S -adenosylhomocysteine, and homocysteine: correlations with diet. Cancer Epidemiol. Biomarkers Prev. 10, 649–655.

    Not in our library

  72. 72.

    Qin J. Li R. Raes J. Arumugam M. Burgdorf S. Manichanh C. (2010 ). A human gut microbial gene catalog established by metagenomic sequencing. Nature 464, 59–65.

    Not in our library10.1038/nature08821

  73. 73.

    Reid G. Burton J. (2002 ). Use of Lactobacillus to prevent infection by pathogenic bacteria. Microbes Infect. 4, 319–324.

    Not in our library10.1016/s1286-4579(02)01544-7

  74. 74.

    Ringel A. E. Tucker S. A. Haigis M. C. (2018 ). Chemical and physiological features of mitochondrial acylation. Mol. Cell 72, 610–624.

    Not in our library10.1016/j.molcel.2018.10.023

  75. 75.

    Romano K. A. Martinez-del Campo A. Kasahara K. Chittim C. L. Vivas E. I. Amador-Noguez D. (2017 ). Metabolic, epigenetic, and transgenerational effects of gut bacterial choline consumption. Cell Host Microbe 22, 279–290.

    Not in our library10.1016/j.chom.2017.07.021

  76. 76.

    Sampson T. Mazmanian S. (2015 ). Control of brain development, function, and behavior by the microbiome. Cell Host Microbe 17, 565–576.

    Not in our library10.1016/j.chom.2015.04.011

  77. 77.

    Savage D. D. C. (1977 ). Microbial ecology of the gastrointestinal tract. Annu. Rev. Microbiol. 31, 107–133.

    Not in our library10.1146/annurev.mi.31.100177.000543

  78. 78.

    Seeley J. J. Baker R. Mohamed G. Bruns T. Hayden M. S. Deshmukh S. D. (2018 ). Induction of innate immune memory via microRNA targeting of chromatin remodeling factors. Nature 559, 114–119.

    Not in our library10.1038/s41586-018-0253-5

  79. 79.

    Sender R. Fuchs S. Milo R. (2016 a). Are we really vastly outnumbered? Revisiting the ratio of bacterial to host cells in humans. Cell 164, 337–340.

    Not in our library10.1016/j.cell.2016.01.013

  80. 80.
    Revised Estimates for the Number of Human and Bacteria Cells in the Body · 2016
    No notice
  81. 81.

    Sharon G. Garg N. Debelius J. Knight R. Dorrestein P. C. Mazmanian S. K. (2014 ). Specialized metabolites from the microbiome in health and disease. Cell Metab. 20, 719–730.

    Not in our library10.1016/j.cmet.2014.10.016

  82. 82.

    Sidoli S. Cheng L. Jensen O. N. (2012 ). Proteomics in chromatin biology and epigenetics: elucidation of post-translational modifications of histone proteins by mass spectrometry. J. Proteomics 75, 3419–3433.

    Not in our library10.1016/j.jprot.2011.12.029

  83. 83.

    Strandwitz P. Kim K. H. Terekhova D. Liu J. K. Sharma A. Levering J. (2018 ). GABA-modulating bacteria of the human gut microbiota. Nat. Microbiol. 4, 396–403.

    Not in our library10.1038/s41564-018-0307-3

  84. 84.

    Thaiss C. A. Levy M. Korem T. Dohnalová L. Shapiro H. Jaitin D. A. (2016 ). Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510.e12.

    Not in our library10.1016/j.cell.2016.11.003

  85. 85.

    Tillisch K. Labus J. Kilpatrick L. Jiang Z. Stains J. Ebrat B. (2013 ). Consumption of Fermented Milk Product with Probiotics Modulates Brain Activity. Gastroenterology 144, 1394–1401.

    Not in our library10.1053/j.gastro.2013.02.043

  86. 86.

    Ting J. P. Y. Trowsdale J. (2002 ). Genetic control of MHC class II expression. Cell 109, 21–33.

    Not in our library10.1016/s0092-8674(02)00696-7

  87. 87.

    Trial R. D. P. Reijnders D. Goossens G. H. Hermes G. D. A. Zoetendal E. G. Dejong C. H. C. (2016 ). Effects of gut microbiota manipulation by antibiotics on host metabolism in obese humans. Cell Metab. 24, 63–74.

    Not in our library10.1016/j.cmet.2016.06.016

  88. 88.

    Turnbaugh P. J. Bäckhed F. Fulton L. Gordon J. I. (2008 ). Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223.

    Not in our library10.1016/j.chom.2008.02.015

  89. 89.

    Turnbaugh P. J. Hamady M. Yatsunenko T. Cantarel B. L. Duncan A. Ley R. E. (2009 ). A core gut microbiome in obese and lean twins. Nature 457, 480–484.

    Not in our library10.1038/nature07540

  90. 90.

    Tyson G. W. Chapman J. Hugenholtz P. Allen E. E. Ram R. J. Richardson P. M. (2004 ). Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43.

    Not in our library10.1038/nature02340

  91. 91.

    Umesaki Y. Okada Y. Matsumoto S. Imaoka A. Setoyama H. (1995 ). Segmented filamentous bacteria are indigenous intestinal bacteria that activate intraepithelial lymphocytes and induce MHC Class II molecules and fucosyl asialo GM1 glycolipids on the small intestinal epithelial cells in the ex-germ-free mouse. Microbiol. Immunol. 39, 555–562.

    Not in our library10.1111/j.1348-0421.1995.tb02242.x

  92. 92.

    Ursell L. K. Metcalf J. L. Parfrey L. W. Knight R. (2013 ). Definig the human microbiome. Nutr. Rev. 70, S38–S44.

    Not in our library10.1111/j.1753-4887.2012.00493.x

  93. 93.

    Ussar S. Fujisaka S. Kahn C. R. (2016 ). Interactions between host genetics and gut microbiome in diabetes and metabolic syndrome. Mol. Metab. 5, 795–803.

    Not in our library10.1016/j.molmet.2016.07.004

  94. 94.
    Humans as holobionts: implications for prevention and therapy · 2018
    No notice
  95. 95.

    van Nood E. Vrieze A. Nieuwdorp M. Fuentes S. Zoetendal E. G. de Vos W. M. (2013 ). Duodenal infusion of donor feces for recurrent clostridium difficile. N. Engl. J. Med. 368, 407–415.

    Not in our library10.1056/nejmoa1205037

  96. 96.

    Wahlström A. Sayin S. I. Marschall H. U. Bäckhed F. (2016 ). Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 24, 41–50.

    Not in our library10.1016/j.cmet.2016.05.005

  97. 97.

    Watanabe K. Gilchrist C. A. Uddin M. J. Burgess S. L. Abhyankar M. M. Moonah S. N. (2017 ). Microbiome-mediated neutrophil recruitment via CXCR2 and protection from amebic colitis. PLoS Pathog. 13, 1–20.

    Not in our library10.1371/journal.ppat.1006513

  98. 98.

    Williamson I. A. Arnold J. W. Samsa L. A. Gaynor L. DiSalvo M. Cocchiaro J. L. (2018 ). A high-throughput organoid microinjection platform to study gastrointestinal microbiota and luminal physiology. Cmgh 6, 301–319.

    Not in our library10.1016/j.jcmgh.2018.05.004

  99. 99.

    Woese C. R. Fox G. E. (1977 ). Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc. Natl. Acad. Sci. 74, 5088–5090.

    Not in our library10.1073/pnas.74.11.5088

  100. 100.

    Wu G. D. Chen J. Hoffmann C. Bittinger K. Chen Y. Sue A. (2011 ). Linking long-term dietary patterns with gut microbial enterotypes. Science (80-). 334, 105–108.

    Not in our library10.1126/science.1208344

  101. 101.

    Xi Y. Bock C. Müller F. Sun D. Meissner A. Li W. (2012 ). RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing. Bioinformatics 28, 430–432.

    Not in our library10.1093/bioinformatics/btr668

  102. 102.

    Yano J. M. Yu K. Donaldson G. P. Shastri G. G. Ann P. Ma L. (2015 ). Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276.

    Not in our library10.1016/j.cell.2015.02.047

  103. 103.

    Yatsunenko T. Rey F. E. Manary M. J. Trehan I. Dominguez-Bello M. G. Contreras M. (2012 ). Human gut microbiome viewed across age and geography. Nature 486, 222–227.

    Not in our library10.1038/nature11053

  104. 104.

    Youle M. Knowlton N. Rohwer F. Gordon J. Relman D. A. (2013 ). Superorganisms and holobionts. Microbe Mag. 8, 152–153.

    Not in our library10.1128/microbe.8.152.1

  105. 105.

    Zhao L. Zhang F. Ding X. Wu G. Lam Y. Y. Shi Y. (2018 ). Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Sci. 1156, 1151–1156.

    Not in our library10.1126/science.aao5774

Cited by papers we hold 4

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.