news and views
The heritable immune system Judy Cho
npg
© 2015 Nature America, Inc. All rights reserved.
Big data sets are galvanizing our ability to decipher the heritability of immune responses. What is a healthy immune system? Parameters and assays to define immune system function are still rudimentary, but more sophisticated analyses are becoming possible with largescale measurements of immunological phenotypes and genotypes. A new study in Cell by Roederer et al.1 adds a valuable big data set to this effort. The authors examined immune phenotypes and genotypes in healthy twins, identifying ~78,000 immune ‘traits’ as well as genetic contributions to trait variability. This resource establishes a role for genetic variation in modulating immune cell traits and represents a rich database for further analysis. The immune system comprises a large number of cell subsets, some of which are tissue specific and many of which circulate in the blood, ready to access a tissue when recruited. The first step in any immune response is activation of innate immunity, with macrophages and other first responder cells producing cytokines and chemokines that in turn recruit and activate other immune cells. This triggers the adaptive arm of the immune response, composed of multiple subsets of T cells and B cells. Adaptive immunity is characterized by a high degree of antigen specificity determined by the T and B cell receptor repertoires and by the ability of these cells to generate memory. Whereas many variables in the immune response are encoded in the germline (e.g., HLA haplotype), the mature T and B cell repertoires are shaped by environmental exposures (e.g., pathogens and commensals, allergens, etc.). Thus, teasing apart the contributions of environment and genetics to the immune system in health and disease is a particularly complex task. A recent study2 carried out immune cell measurements in 105 twin pairs (78 monozygotic and 27 dizygotic) of a very broad age range (8 to 82 years) and concluded that variation in the human immune system is largely driven by nonheritable influences2. Roederer et al.1 reached similar conclusions, but because they focused on a larger and more homogeneous population—women who participated in the TwinsUK study between the ages of 40 and 77—their estimates of the heritability of traits are more accurate. The initial discovery Judy Cho is at the Icahn School of Medicine at Mount Sinai, New York, New York, USA. e-mail: [email protected]
608
cohort consisted of 75 monozygotic twin pairs, 170 dizygotic twin pairs and 7 singletons, with replication analysis performed on an additional 23 monozygotic twin pairs, 59 dizygotic twin pairs and 8 singletons, providing data on 669 women in total—the largest cohort studied to date. Roederer et al.1 analyzed peripheral blood leukocytes from the 669 women, measuring 98 cell-surface markers by flow cytometry (seven panels of 14 cell-surface markers each). Their data include both subset frequencies and expression levels of the cell-surface markers. In total they measured ~78,000 immune ‘traits’, defined as cell frequencies and fluorescence intensities. This data set captures much of the vast phenotypic heterogeneity that is measurable in peripheral blood. Next, they compared the ~78,000 traits in monozygotic and dizygotic twins to identify the traits that are most heritable (Fig. 1). Genetic variation in immune traits affects risk of specific autoimmune diseases and likely contributes to interindividual variability in responses to infection, vaccines and medication. This analysis yielded 151 traits, which allowed the authors to reduce the multiple testing burden and focus their subsequent genome-wide association study (GWAS). The GWAS identified 241 single-nucleotide polymorphisms in 11 genome-wide loci1. One of the most heritable traits identified was the frequency of CD39+ cells, which are functional T regulatory (Treg) cells. Polymorphisms in CD39 modulate protein levels. CD39 is an enzyme that converts ATP to adenosine, which induces an anti-inflammatory milieu. The level of catabolic activity in Treg subsets can therefore modulate responses to infections, injury and a host of chronic diseases. Although most GWAS have compared individuals with a disease to healthy controls, there is a long tradition in genetics of studying naturally occurring phenotypic variation in healthy people, often through anthropomorphic and biochemical measurements (e.g., BMI and liver function tests3, respectively). And even GWAS efforts are focusing more frequently on genetic variation in healthy cohorts4. In the context of immunology, testing healthy individuals, as in Roederer et al.1, is particularly advantageous because gene expression and cellular differentiation are
modulated by the inflammatory process itself, potentially obscuring genotype-phenotype mappings. Moreover, the authors’ detailed ana lysis provides clues about immune-mediated diseases. An understanding of immune trait heritability and variability in healthy individuals provides a frame of reference in which to interpret putative disease associations. As GWAS studies go, this sample size is quite modest and potentially subject to false positives. It will be important to determine whether the associations reported here could be replicated using independent twin data sets. Regardless, it could be argued that the genetic associations identified by Roederer et al.1— many overlapping with previously reported Monozygotic twins
Flow cytometry
Dizygotic twins
Genotyping
7 sets of 14-color panels 78,000 immune traits
Compare frequency of traits among MZ and DZ pairs Identify 151 most heritable traits GWAS reveals genetics of immune traits 241 SNP associations at 11 loci explain up to 36% of the variation in 19 traits
Understanding the heritability of immune traits at homeostasis Defining precise cellular subsets that contribute to immunemediated diseases Studying age-dependent differences in immune traits Figure 1 Comparisons of the variance of immune traits in monozygotic and dizygotic twins by Roederer et al.1 identified 151 heritable immune traits, which were tested by GWAS, resulting in the identification of 11 loci modulating 19 traits. MZ, monozygotic; DZ, dizygotic; SNP, singlenucleotide polymorphism.
volume 33 number 6 JUNE 2015 nature biotechnology
npg
© 2015 Nature America, Inc. All rights reserved.
news and views GWAS loci linked to various autoimmune and infectious diseases1—define particularly valuable loci of large statistical and functional effects, as opposed to loci with more modest effects identifiable only through cohort sizes in the tens of thousands. Studies of human immune function are fraught with variability arising from genetic, environmental and stochastic factors. The successful identification of heritable traits and of genome-wide significant associations is a testament to the authors’ rigorous quality control process. The data set provided by Roederer et al.1 represents a rich resource to be mined in future analyses. Because many of the tested traits are correlated, it is possible that the Bonferroni correction was excessively conservative and that additional associations remain to be discovered. Among the 11 loci identified, up to 36% of the heritability is accounted for in 19 traits, indicating that the genetic basis for many heritable traits has yet to be elucidated. Although this missing heritability could merely reflect a lack of power, it is intriguing to speculate that variation in cell subset–defined phenotypes may be explained through multilocus analyses guided by epigenetic cell-specific enhancer data. The data set is also a treasure trove to study the age dependency of immune traits, as it includes participants ranging in age from 40 to 77. The identification of genetic associations for women in this age range is particularly notable given that epigenetic variation between monozygotic twins increases with age5. Further examination of the age effects of immune traits may shed light on factors that contribute to vaccination efficacy and to the increased risk for infection and malignancy observed with advancing age.
Finally, it would be interesting to integrate the data set of Roederer et al.1 with information on the epigenetics of immune function. For example, a recent report demonstrated marked enrichment of GWAS signals in cell-specific active enhancer marks6, helping to unravel the contributions of specific cell subsets to disease. Furthermore, integrating peripheral blood immune traits with epigenetic data in genetically high-risk individuals could provide key intermediate metrics on how tractable environmental factors (such as the intestinal microbiome7) affect immune function and, ultimately, disease risk. More generally, the study by Roederer et al.1 may provide a roadmap for performing
well-powered, detailed immune analyses that are a necessary stepping stone toward large disease-prevention studies. COMPETING FINANCIAL INTERESTS The author declares no competing financial interests. 1. Roederer, M. et al. Cell 161, 387–403 (2015). 2. Brodin, P. et al. Cell 160, 37–47 (2015). 3. Chambers, J.C. et al. Nat. Genet. 43, 1131–1138 (2011). 4. Hedl, M., Lahiri, A., Ning, K., Cho, J.H. & Abraham, C. Immunity 40, 734–746 (2014). 5. Fraga, M.F. et al. Proc. Natl. Acad. Sci. USA 102, 10604–10609 (2005). 6. Farh, K.K. et al. Nature 518, 337–343 (2014). 7. Kau, A.L., Ahern, P.P., Griffin, N.W., Goodman, A.L. & Gordon, J.I. Nature 474, 327–336 (2011).
Research Highlights Papers from the literature selected by the Nature Biotechnology editors. (Follow us on Twitter, @NatureBiotech #nbtHighlight) An implantable microdevice to perform high-throughput in vivo drug sensitivity testing in tumors Jonas, O. et al. Sci. Transl. Med. 7, 284ra57 (2015) A technology platform to assess multiple cancer agents simultaneously within a patient’s tumor Klinghoffer, R.A. et al. Sci. Transl. Med. 7, 284ra58 (2015) Spatially resolved, highly multiplexed RNA profiling in single cells Chen, K.H. et al. Science doi:10.1126/science.aaa6090 (9 April 2015) Heparanase promotes tumor infiltration and antitumor activity of CAR-redirected T lymphocytes Caruana, I. et al. Nat. Med. 21, 524–529 (2015) Understanding multicellular function and disease with human tissue-specific networks Greene, C.S. et al. Nat. Genet. doi:10.1038/ng.3259 (27 April 2015) High-resolution genetic mapping of maize pan-genome sequence anchors Lu, F. et al. Nat. Commun. doi:10.1038/ncomms7914 (16 April 2015)
nature biotechnology volume 33 number 6 JUNE 2015
609
The heritable immune system Judy Cho
npg
© 2015 Nature America, Inc. All rights reserved.
Big data sets are galvanizing our ability to decipher the heritability of immune responses. What is a healthy immune system? Parameters and assays to define immune system function are still rudimentary, but more sophisticated analyses are becoming possible with largescale measurements of immunological phenotypes and genotypes. A new study in Cell by Roederer et al.1 adds a valuable big data set to this effort. The authors examined immune phenotypes and genotypes in healthy twins, identifying ~78,000 immune ‘traits’ as well as genetic contributions to trait variability. This resource establishes a role for genetic variation in modulating immune cell traits and represents a rich database for further analysis. The immune system comprises a large number of cell subsets, some of which are tissue specific and many of which circulate in the blood, ready to access a tissue when recruited. The first step in any immune response is activation of innate immunity, with macrophages and other first responder cells producing cytokines and chemokines that in turn recruit and activate other immune cells. This triggers the adaptive arm of the immune response, composed of multiple subsets of T cells and B cells. Adaptive immunity is characterized by a high degree of antigen specificity determined by the T and B cell receptor repertoires and by the ability of these cells to generate memory. Whereas many variables in the immune response are encoded in the germline (e.g., HLA haplotype), the mature T and B cell repertoires are shaped by environmental exposures (e.g., pathogens and commensals, allergens, etc.). Thus, teasing apart the contributions of environment and genetics to the immune system in health and disease is a particularly complex task. A recent study2 carried out immune cell measurements in 105 twin pairs (78 monozygotic and 27 dizygotic) of a very broad age range (8 to 82 years) and concluded that variation in the human immune system is largely driven by nonheritable influences2. Roederer et al.1 reached similar conclusions, but because they focused on a larger and more homogeneous population—women who participated in the TwinsUK study between the ages of 40 and 77—their estimates of the heritability of traits are more accurate. The initial discovery Judy Cho is at the Icahn School of Medicine at Mount Sinai, New York, New York, USA. e-mail: [email protected]
608
cohort consisted of 75 monozygotic twin pairs, 170 dizygotic twin pairs and 7 singletons, with replication analysis performed on an additional 23 monozygotic twin pairs, 59 dizygotic twin pairs and 8 singletons, providing data on 669 women in total—the largest cohort studied to date. Roederer et al.1 analyzed peripheral blood leukocytes from the 669 women, measuring 98 cell-surface markers by flow cytometry (seven panels of 14 cell-surface markers each). Their data include both subset frequencies and expression levels of the cell-surface markers. In total they measured ~78,000 immune ‘traits’, defined as cell frequencies and fluorescence intensities. This data set captures much of the vast phenotypic heterogeneity that is measurable in peripheral blood. Next, they compared the ~78,000 traits in monozygotic and dizygotic twins to identify the traits that are most heritable (Fig. 1). Genetic variation in immune traits affects risk of specific autoimmune diseases and likely contributes to interindividual variability in responses to infection, vaccines and medication. This analysis yielded 151 traits, which allowed the authors to reduce the multiple testing burden and focus their subsequent genome-wide association study (GWAS). The GWAS identified 241 single-nucleotide polymorphisms in 11 genome-wide loci1. One of the most heritable traits identified was the frequency of CD39+ cells, which are functional T regulatory (Treg) cells. Polymorphisms in CD39 modulate protein levels. CD39 is an enzyme that converts ATP to adenosine, which induces an anti-inflammatory milieu. The level of catabolic activity in Treg subsets can therefore modulate responses to infections, injury and a host of chronic diseases. Although most GWAS have compared individuals with a disease to healthy controls, there is a long tradition in genetics of studying naturally occurring phenotypic variation in healthy people, often through anthropomorphic and biochemical measurements (e.g., BMI and liver function tests3, respectively). And even GWAS efforts are focusing more frequently on genetic variation in healthy cohorts4. In the context of immunology, testing healthy individuals, as in Roederer et al.1, is particularly advantageous because gene expression and cellular differentiation are
modulated by the inflammatory process itself, potentially obscuring genotype-phenotype mappings. Moreover, the authors’ detailed ana lysis provides clues about immune-mediated diseases. An understanding of immune trait heritability and variability in healthy individuals provides a frame of reference in which to interpret putative disease associations. As GWAS studies go, this sample size is quite modest and potentially subject to false positives. It will be important to determine whether the associations reported here could be replicated using independent twin data sets. Regardless, it could be argued that the genetic associations identified by Roederer et al.1— many overlapping with previously reported Monozygotic twins
Flow cytometry
Dizygotic twins
Genotyping
7 sets of 14-color panels 78,000 immune traits
Compare frequency of traits among MZ and DZ pairs Identify 151 most heritable traits GWAS reveals genetics of immune traits 241 SNP associations at 11 loci explain up to 36% of the variation in 19 traits
Understanding the heritability of immune traits at homeostasis Defining precise cellular subsets that contribute to immunemediated diseases Studying age-dependent differences in immune traits Figure 1 Comparisons of the variance of immune traits in monozygotic and dizygotic twins by Roederer et al.1 identified 151 heritable immune traits, which were tested by GWAS, resulting in the identification of 11 loci modulating 19 traits. MZ, monozygotic; DZ, dizygotic; SNP, singlenucleotide polymorphism.
volume 33 number 6 JUNE 2015 nature biotechnology
npg
© 2015 Nature America, Inc. All rights reserved.
news and views GWAS loci linked to various autoimmune and infectious diseases1—define particularly valuable loci of large statistical and functional effects, as opposed to loci with more modest effects identifiable only through cohort sizes in the tens of thousands. Studies of human immune function are fraught with variability arising from genetic, environmental and stochastic factors. The successful identification of heritable traits and of genome-wide significant associations is a testament to the authors’ rigorous quality control process. The data set provided by Roederer et al.1 represents a rich resource to be mined in future analyses. Because many of the tested traits are correlated, it is possible that the Bonferroni correction was excessively conservative and that additional associations remain to be discovered. Among the 11 loci identified, up to 36% of the heritability is accounted for in 19 traits, indicating that the genetic basis for many heritable traits has yet to be elucidated. Although this missing heritability could merely reflect a lack of power, it is intriguing to speculate that variation in cell subset–defined phenotypes may be explained through multilocus analyses guided by epigenetic cell-specific enhancer data. The data set is also a treasure trove to study the age dependency of immune traits, as it includes participants ranging in age from 40 to 77. The identification of genetic associations for women in this age range is particularly notable given that epigenetic variation between monozygotic twins increases with age5. Further examination of the age effects of immune traits may shed light on factors that contribute to vaccination efficacy and to the increased risk for infection and malignancy observed with advancing age.
Finally, it would be interesting to integrate the data set of Roederer et al.1 with information on the epigenetics of immune function. For example, a recent report demonstrated marked enrichment of GWAS signals in cell-specific active enhancer marks6, helping to unravel the contributions of specific cell subsets to disease. Furthermore, integrating peripheral blood immune traits with epigenetic data in genetically high-risk individuals could provide key intermediate metrics on how tractable environmental factors (such as the intestinal microbiome7) affect immune function and, ultimately, disease risk. More generally, the study by Roederer et al.1 may provide a roadmap for performing
well-powered, detailed immune analyses that are a necessary stepping stone toward large disease-prevention studies. COMPETING FINANCIAL INTERESTS The author declares no competing financial interests. 1. Roederer, M. et al. Cell 161, 387–403 (2015). 2. Brodin, P. et al. Cell 160, 37–47 (2015). 3. Chambers, J.C. et al. Nat. Genet. 43, 1131–1138 (2011). 4. Hedl, M., Lahiri, A., Ning, K., Cho, J.H. & Abraham, C. Immunity 40, 734–746 (2014). 5. Fraga, M.F. et al. Proc. Natl. Acad. Sci. USA 102, 10604–10609 (2005). 6. Farh, K.K. et al. Nature 518, 337–343 (2014). 7. Kau, A.L., Ahern, P.P., Griffin, N.W., Goodman, A.L. & Gordon, J.I. Nature 474, 327–336 (2011).
Research Highlights Papers from the literature selected by the Nature Biotechnology editors. (Follow us on Twitter, @NatureBiotech #nbtHighlight) An implantable microdevice to perform high-throughput in vivo drug sensitivity testing in tumors Jonas, O. et al. Sci. Transl. Med. 7, 284ra57 (2015) A technology platform to assess multiple cancer agents simultaneously within a patient’s tumor Klinghoffer, R.A. et al. Sci. Transl. Med. 7, 284ra58 (2015) Spatially resolved, highly multiplexed RNA profiling in single cells Chen, K.H. et al. Science doi:10.1126/science.aaa6090 (9 April 2015) Heparanase promotes tumor infiltration and antitumor activity of CAR-redirected T lymphocytes Caruana, I. et al. Nat. Med. 21, 524–529 (2015) Understanding multicellular function and disease with human tissue-specific networks Greene, C.S. et al. Nat. Genet. doi:10.1038/ng.3259 (27 April 2015) High-resolution genetic mapping of maize pan-genome sequence anchors Lu, F. et al. Nat. Commun. doi:10.1038/ncomms7914 (16 April 2015)
nature biotechnology volume 33 number 6 JUNE 2015
609
