Nearly 24 million Americans have been diagnosed with an autoimmune disease.1 Recently, a number of significant advances in our understanding of the genetics of autoimmunity have been made through genome-wide association studies and the use of omics technologies. These research findings are helping scientists move toward a more personalized treatment approach for patients with autoimmune disease.
Key Features of Autoimmune Disease
Autoimmune diseases are marked by their complexity, and the heterogeneity of many of these diseases has made them difficult to define and treat.2 A number of confirmed gene coding regions predispose an individual to autoimmunity; for instance, the human leukocyte antigen (HLA) region is associated with all autoimmune disease.3 Other loci associated with multiple autoimmune diseases are IL23R, TNFAIP3, and IL2RA.3 Yet despite these commonalities, the genetic heritability of autoimmune diseases is exceedingly variable, ranging from very high in Crohn’s disease and ankylosing spondylitis to almost negligible in systemic sclerosis.3 One estimate is that less than 15% of genes related to autoimmune risk have been identified.4
Many autoimmune diseases, including lupus, disproportionately affect women.5 Scientists recently looked at blood cells from healthy women and men with Klinefelter syndrome (who carry one or more supernumerary X chromosomes and are also at an increased risk of lupus) and found that the gene that codes for toll-like receptor 7 (TLR7) escapes X-inactivation, a process that occurs during embryonic development and switches off one of the two X chromosomes to prevent the overexpression of genes.5 TLR7 binds single-stranded RNA and activates type I interferon signaling, a pathway that is also activated in systemic lupus erythematosus (SLE) patients. The authors concluded that biallelic expression of TLR7 contributes to greater risk of lupus in individuals with two X chromosomes.5
Autoimmune diseases tend to run in families, but inheritance is not Mendelian.6 Generally, familial autoimmunity does not cluster by condition, but rather, diverse autoimmune conditions can run in families.7,8 The relatives of individuals with a specific autoimmune disease have a heightened incidence of systemic autoimmune diseases, such as SLE and rheumatoid arthritis, or organ-specific autoimmune diseases, such as chronic thyroiditis, Graves’ disease, insulin-dependent diabetes mellitus, and related autoimmune endocrinopathies. Only a few families have been shown to exhibit both systemic and organ-specific autoimmune diseases.9
Genome-wide association studies have identified hundreds of susceptibility genes among autoimmune diseases, largely affecting adults. One study of the pediatric population found that many of the autoimmune-related gene signals were in biological pathways functionally linked to cell activation, cell proliferation, and signaling systems important in immune processes.10
One recent evaluation of shared genetic autoimmune loci suggests that autoimmunity may result from multiple different pleiotropic effects.3 This suggests that different population genetic factors (for example, natural selection and coevolution with pathogens, random mutation, isolations, etc.) led to the current loci that predispose an individual to autoimmunity, pointing to a potential interplay between population genetic factors and environmental factors.3 Alessio Fasano’s triad takes the connection even further, suggesting that genetic susceptibility, coupled with increased intestinal permeability, sets the stage for a specific environmental trigger to precipitate autoimmune disease. The trigger causes a break in immunological tolerance leading to the onset of an autoimmune cascade.11
In addition to specific genes that indicate risk for autoimmunity, gene expression affects disease risk and progression.2 Transcriptomics, or RNA sequencing, allows scientists to study gene expression in a sample from a patient at a given time and monitor changes in gene expression over time from multiple samples.2 This suggests that in the future, individual risk for autoimmunity may be measured over time, allowing tailored interventions.
Proteomics is also being widely used in autoimmune research to analyze molecules secreted by a variety of immune cells, including autoantibodies, cellular metabolites, and cytokines.2 A team of scientists at Stanford University School of Medicine are using bead-based multiplex arrays to better diagnose, subtype, and treat patients with autoimmune diseases. They are also studying how the epigenome influences the immune system and have discovered a significant epigenetic difference between the immune system of elderly individuals versus the young.
Epigenetic mechanisms, which include DNA methylation, histone modification, and microRNA (miRNA), can produce heritable phenotypic changes without a change in DNA sequence.12 Recent research suggests that disruption of gene expression patterns that are governed by epigenetics may result in autoimmune diseases, including multiple sclerosis,13 primary Sjögren’s syndrome,14 type 1 diabetes,15 psoriasis,16 and lupus erythematosus.17
Omics (e.g., genomics, proteomics, and metabolomics) data-processing tools have also been successful in revealing mechanisms underlying autoimmune pathogenesis and in identifying novel therapeutic targets.18 This research is helping scientists move toward a more personalized treatment approach for patients with autoimmune disease.2,19
Although genetics play a key role in autoimmune risk, environmental factors are known to have a much larger effect on the emergence of autoimmune conditions.20 Functional Medicine looks at all of the factors that shape the immune system—hereditary, environmental, and intestinal—that together cause the cascade that leads to the development of autoimmune disease.
For more information about Functional Medicine and autoimmune disease, consider getting the Functional Medicine Guide to Autoimmunity. Produced by IFM in collaboration with Cleveland Clinic Center for Functional Medicine, this first-of-its-kind eBook details a comprehensive Functional Medicine approach to evaluating and treating patients with autoimmune conditions. The guide is suitable for use with patients in any stage of autoimmune disease and provides specific Functional Medicine evaluation and treatment protocols in the safest, most personalized manner possible.
- Fairweather D, Frisancho-Kiss S, Rose NR. Sex differences in autoimmune disease from a pathological perspective. Am J Pathol. 2008;173(3):600-609. doi:10.2353/ajpath.2008.071008
- Albert H. A new approach to autoimmunity: using multi-omic technology to reclassify and improve treatment for autoimmune disease. Genetic Engineering & Biotechnology News. Published January 22, 2018. Accessed May 12, 2020. https://www.genengnews.com/gen-exclusives/a-new-approach-to-autoimmunity/77901039
- Ramos PS, Shedlock AM, Langefeld CD. Genetics of autoimmune diseases: insights from population genetics. J Hum Genet. 2015;60(11):657-664. doi:10.1038/jhg.2015.94
- Ceccarelli F, Agmon-Levin N, Perricone C. Genetic factors of autoimmune diseases. J Immunol Res. 2016;2016:3476023. doi:10.1155/2016/3476023
- Souyris M, Cenac C, Azar P, et al. TLR7 escapes X chromosome inactivation in immune cells. Sci Immunol. 2018;3(19):eaap8855. doi:10.1126/sciimmunol.aap8855
- Castiblanco J, Sarmiento-Monroy JC, Mantilla RD, Rojas-Villarraga A, Anaya J-M. Familial aggregation and segregation analysis in families presenting autoimmunity, polyautoimmunity, and multiple autoimmune syndrome. J Immunol Res. 2015;2015:572353. doi:10.1155/2015/572353
- Cárdenas-Roldán J, Rojas-Villarraga A, Anaya J-M. How do autoimmune diseases cluster in families? A systematic review and meta-analysis. BMC Med. 2013;11:73. doi:10.1186/1741-7015-11-73
- Mariani SM. Genes and autoimmune diseases—a complex inheritance: highlights of the 54th Annual Meeting of the American Society of Human Genetics; October 26-30, 2004; Toronto, Ontario, Canada. MedGenMed. 2004;6(4):18.
- Rose NR, Burek CL. Genetic predisposition to thyroid autoimmune disease: introduction. Mt Sinai J Med. 1986;53(1):3-5.
- Li YR, Li J, Zhao SD, et al. Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases. Nat Med. 2015;21(9):1018-1027. doi:10.1038/nm.3933
- Fasano A. Leaky gut and autoimmune diseases. Clin Rev Allergy Immunol. 2012;42(1):71-78. doi:10.1007/s12016-011-8291-x
- Zhang L, Lu Q, Chang C. Epigenetics in health and disease. Adv Exp Med Biol. 2020;1253:3-55. doi:10.1007/978-981-15-3449-2_1
- Chan VS. Epigenetics in multiple sclerosis. Adv Exp Med Biol. 2020;1253:309-374. doi:10.1007/978-981-15-3449-2_12
- Bordron A, Devauchelle-Pensec V, Le Dantec C, Capdeville A, Brooks WH, Renaudineau Y. Epigenetics in primary Sjögren’s syndrome. Adv Exp Med Biol. 2020;1253:285-308. doi:10.1007/978-981-15-3449-2_11
- Xie Z, Chang C, Huang G, Zhou Z. The role of epigenetics in type 1 diabetes. Adv Exp Med Biol. 2020;1253:223-257. doi:10.1007/978-981-15-3449-2_9
- Shao S, Gudjonsson JE. Epigenetics of psoriasis. Adv Exp Med Biol. 2020;1253:209-221. doi:10.1007/978-981-15-3449-2_8
- Wu H, Chang C, Lu Q. The epigenetics of lupus erythematosus. Adv Exp Med Biol. 2020;1253:185-207. doi:10.1007/978-981-15-3449-2_7
- Langan D, Rose NR, Moudgil KD. Common innate pathways to autoimmune disease. Clin Immunol. 2020;212:108361. doi:10.1016/j.clim.2020.108361
- Bae RH, Leung PSC, Hodge DL, et al. Multi-omics: differential expression of IFN-? results in distinctive mechanistic features linking chronic inflammation, gut dysbiosis, and autoimmune disease. J Autoimmun. 2020;111:102436. doi:10.1016/j.jaut.2020.102436
- Anaya J-M, Ramirez-Santana C, Alzate MA, Molano-Gonzalez N, Rojas-Villarraga A. The autoimmune ecology. Front Immunol. 2016;7:139. doi:10.3389/fimmu.2016.00139