PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Expert Rev Gastroenterol Hepatol. Author manuscript; available in PMC 2017 April 1.
Published in final edited form as:
PMCID: PMC5278927
NIHMSID: NIHMS820946

The Microbiome As a Possible Target To Prevent Celiac Disease

Currently there are no effective strategies to cure or prevent autoimmune diseases. The recent advent of genomics, proteomics, and now microbiome analysis has raised the expectation of a therapeutic solution that has yet to materialize. It is now becoming clear that these diseases are final destinations, but the path to disease development can differ from patient to patient. Unfortunately, the complexity, the multi-factorial nature and, most importantly, the lack of information on environmental factors that might trigger the autoimmune process have hampered progress in defining host-environmental interactions that cause autoimmunity in genetically susceptible individuals. Thus, prospective longitudinal studies from birth integrating metagenomic, metatranscriptomic, and metabolomic data with genomic and environmental data are necessary to build a systems-level model of interactions between the host and the development of disease.

There is now clear evidence that the human intestinal microbiota is essential to the development of a healthy immune system. It develops chaotically within the first three years after birth, eventually convening into an adult-like and relatively stable pattern1. It is hypothesized that this assembly period and any insults that may alter the developing microbiota during this crucial time may have important lasting effects on an individual’s future health and susceptibility to disease. In order to understand the intestinal microbiome’s contribution to disease, it is necessary to evaluate its early assembly period and assess the impact of environmental factors on its development. This must be done through a rigorous, prospective, and longitudinal incorporation of multiomic information with environmental information to create theoretical models capable of integrating data, beginning with the gestational period through to the development of disease.

Celiac disease (CD) is an ideal model to study autoimmune disease, as it is the only autoimmune disease for which the trigger (gluten) is known. Thus the autoimmune process, once developed, can be turned on and off with the addition and removal of gluten-containing grains. The time of introduction of gluten can be precisely traced, and the frequency and dose can be calculated. There is a highly specific humoral autoimmune response (autoantibodies to tissue transglutaminase) that can be measured, which indicates the loss of tolerance to gluten2. Finally, there is a close genetic association with human Leukocyte antigen (HLA) genes (DQ2 or DQ8). While nearly 40% of the population may carry one of these genes, only 3% go on to develop CD3. However, essentially all individuals who go on to develop CD carry one or both of these genotypes. This allows for uniquely informative control groups. Patients who go on to develop CD can be compared to those who carry the genetic risk but do not go on to develop the disease (or who have not yet developed the disease). These patients also can be compared to individuals with a family history of CD but who do not carry the compatible genes and thus cannot develop CD.

Despite progress made in understanding the adaptive immunological aspects of CD pathogenesis, the early steps following intestinal mucosal exposure to gluten that lead to the loss of tolerance and development of the autoimmune process are still unknown. While gluten is the trigger in CD, the loss of gluten tolerance does not necessarily occur at the time of its introduction into the diet in individuals genetically at-risk but can occur at any time in life as a consequence of other unknown stimuli4. The gut microbiome composition and consequent changes in specific metabolomic pathways have been implicated in the switch from tolerance to immune response to gluten5. Preliminary data suggest that particular changes in both the microbiome and metabolomic profiles precede the onset of CD in genetically susceptible individuals5. These changes have been linked to modifications in gut barrier function, regulatory T cells functionality, and characteristic changes in gene expression in intestinal stem cells. For example, it has been demonstrated that microbial-specific indole 3-propionic acid regulates the intestinal barrier function through the xenobiotic sensor, pregnane X receptor, with subsequent downregulation of enterocyte TNF-α and upregulation of junctional protein-coding mRNAs6. Bacteria producing these metabolites have been detected in the small intestine of CD patients7,8. Whether this metabolic profile was purely coincidental or mechanistically associated to loss of gluten tolerance and subsequent onset of CD remains to be established.

The Human Genome Project, while poised to provide novel diagnostic and therapeutic approaches to human disease, has not uncovered the clinically relevant transformational discoveries expected. The Human Microbiome Project may find itself on a similar path. These large-scale, one-dimensional models are impeded by the likelihood that, as humans, we are afflicted by similar diseases, but as unique individuals, our path to the development of disease is different. The creation of network models, especially those utilizing infants’ longitudinal analysis of individual samples both before and after the development of disease, is essential in providing a mechanistic approach to exploring the development of the human microbiota and its contribution to the development of disease. This can only be done through prospective longitudinal birth cohort studies. CD is an ideal model to study interactions between the host microbiota and the development of disease and create novel integrative models that can be applied to other complex autoimmune diseases because, in addition to its unique properties as an autoimmune disease, evidence suggests that the majority of at-risk infants with a first-degree family member with CD will develop CD in the first three years of age9. Furthermore, a recent study showed that 25.8% of infants with a first-degree family member with CD and homozygosity for DQ2 developed CD during a ten year follow-up period and that 80% did so by age three9. Thus, with this rationale, we have launched the Celiac Disease Genomic, Environmental, Microbiome and Metabolomic Study (CDGEMM). CDGEMM is an international, prospective, longitudinal cohort of infants with a first-degree family member with CD. Through this study we aim to identify and validate specific microbiota and metabolic profiles mechanistically linked to gut functions (including permeability, immune function, and stem cell niche biology) that can predict loss of gluten tolerance in subjects genetically at risk of autoimmunity10.

The future of medicine lies in recognizing the unique paths that individuals take to arise at a common disease state, the so-called Precision Medicine. Transformational clinically applicable discoveries will come from appreciating a complex model of disease in which multi-omic analysis is utilized along with longitudinal prospective studies to combine multi-omic data with clinical outcomes. Through CDGEMM, changes in microbiome and metatranscriptomic profiles over time can be analyzed in relation to factors such as mode of delivery, exposure to antibiotics and feeding regimens, including breast feeding and timing of gluten introduction. It will allow for an in-depth characterization of the infants’ metabotypes (microbe-derived metabolomes), which will be linked to the microbiome composition, genomic information, and environmental factors to build top-down system models of integrated multi-omic profiles. These profiles can then be interrogated with respect to outcome (tolerance vs. immune response) to obtain predictive models and mechanistic insight into predisposing factors leading to CD autoimmunity. Ultimately, creating theoretical models of interaction between host genetic makeup, the microbiome, the metabolome, and environmental “pressure” in infants at risk of CD will help to identify and validate specific microbiome and metabolomic profiles that can predict loss of tolerance in children genetically at risk of autoimmunity. The identification of these profiles, which will serve as biomarkers, will also provide possible targets for therapeutic and early preventive interventions through the manipulation of the microbiome composition in order to re-establish gluten tolerance and ultimately prevent CD.

Acknowledgments

This work was conducted with support from the Harvard Catalyst | The Harvard Clinical and Translational Science Center (NCRR and NCATS, NIH Award UL1 TR001102) and financial contributions from Harvard University and its affiliated academic healthcare centers.

Abbreviations

CD
Celiac Disease
HLA
human leukocyte antigen
CDGEMM
Celiac Disease Genomic Environmental Microbiome and Metabolomic

Footnotes

Clinical Trial Registration: Celiac Disease Genomic Environmental Microbiome and Metabolomic Study (CDGEMM) ClinicalTrials.gov Identifier: NCT02061306

Conflict of Interest Statement: The authors have no conflicts of interest relevant to this article to disclose.

Financial Disclosure Statement: Maureen M. Leonard MD: No disclosures

Alessio Fasano, MD: Alba Therapeutics: Stock holder; General Mills, Inc: Consultant, Crestovo, LLC: Consultant, Mead Johnson Nutrition: Speaking Agreement; Pfizer: Consultant

References

Papers of special interest have been highlighted as;

  • *
    Of interest
  • **
    Of considerable interest
1. Palmer C, Bik EM, DiGiulio, et al. Development of the human infant intestinal microbiota. PLoS Biol. 2007;5(7):e177. [PubMed]
2. Fasano A, Catassi C. Current approaches to diagnosis and treatment of celiac disease: an evolving spectrum. Gastroenterology. 2001;120(3):636–651. [PubMed]
3. Mazzilli MC, Ferrante P, Mariani P, et al. A study of Italian pediatric celiac disease patients confirms that the primary HLA association is to the DQ(alpha 1*0501, beta 1*0201 heterodimer. Hum Immunol. 1992;33(2):133–9. [PubMed]
4. Catassi C, Kryszak D, Bhatti B, et al. Natural history of celiac disease autoimmunity in a USA cohort followed since 1974. Ann Med. 2010;42:530–8. [PubMed]
5** Sellitto M, Bai G, Serena G, et al. Proof of concept of microbiome-metabolome analysis and delayed gluten exposure on celiac disease autoimmunity in genetically at-risk infants. PLoS One. 2012;7(3):33387. This proof of concept paper identified alterations in the microbiome that corresponded with alterations in the metabolome prior to the onset of celiac disease in a small sample of at-risk infants. [PMC free article] [PubMed]
6. Venkatesh M, Mukherjee S, Wang H, et al. Symbiotic bacterial metabolites regulate gastrointestinal barrier function via the xenobiotic sensor PXR and Toll-like receptor 4. Immunity. 2014;41:296–310. [PMC free article] [PubMed]
7. Nistal E, Caminero A, Herrán AR, et al. Differences of small intestinal bacteria populations in adults and children with/without celiac disease: effect of age, gluten diet, and disease. Inflamm Bowel Dis. 2012;18:649–56. [PubMed]
8. Wacklin P, Kaukinen K, Tuovinen E, et al. The duodenal microbiota composition of adult celiac disease patients is associated with the clinical manifestation of the disease. Inflamm Bowel Dis. 2013;19:934–41. [PubMed]
9** Lionetti E, Castellaneta S, Francavilla R, et al. Introduction of gluten, HLA status, and the risk of celiac disease in children. N Engl J Med. 2014;371:1295–303. Established that HLA is the strongest predictor of celiac disease in at-risk infants known thus far and that the majority of children who develop celiac disease in the first decade of life do so by age three. [PubMed]
10. Leonard MM, Camhi S, Huedo-Medina TB, Fasano A. Celiac Disease Genomic, Environmental, Microbiome, and Metabolomic (CDGEMM) Study Design: Approach to the Future of Personalized Prevention of Celiac Disease. Nutrients. 2015;7(11):9325–9336. [PMC free article] [PubMed]