Interindividual variation in human T regulatory cells
Supplementary Material
Author contributions: A.F., A.M.D., A.B., L.L., B.E.S., P.L.D.J., D.M., and C.B. designed research; A.F., A.M.D., T.R., N.A., R.P., A.E., J.M.R., and A.B. performed research; A.F., A.M.D., T.R., and A.E. analyzed data; and A.F., L.L., B.E.S., P.L.D.J., D.M., and C.B. wrote the paper.
Significance
Control of immunologic tolerance and homeostasis rely on regulatory T lymphocytes that express the transcription factor FOXP3. To characterize the interindividual variation of human Treg cells, we performed a genome-wide expression and genotypic analysis of 168 human donors, healthy or affected by type-1 or type-2 diabetes (T1D, T2D). We identify cis-acting genetic variants that condition Treg effector but not specification genes, and gene clusters that suggest Treg-specific regulatory pathways for some key signature genes (CTLA4, DUSP4). We also identify factors that may control FOXP3 mRNA or protein expression, the specification of the Treg signature, and Treg suppressive efficacy. Although no single transcript correlates with diabetes, overall expression of the Treg signature is perturbed in T1D, but not T2D, patients.
Abstract
FOXP3 regulatory T (Treg) cells enforce immune self-tolerance and homeostasis, and variation in some aspects of Treg function may contribute to human autoimmune diseases. Here, we analyzed population-level Treg variability by performing genome-wide expression profiling of CD4 Treg and conventional CD4 T (Tconv) cells from 168 donors, healthy or with established type-1 diabetes (T1D) or type-2 diabetes (T2D), in relation to genetic and immunologic screening. There was a range of variability in Treg signature transcripts, some almost invariant, others more variable, with more extensive variability for genes that control effector function (ENTPD1, FCRL1) than for lineage-specification factors like FOXP3 or IKZF2. Network analysis of Treg signature genes identified coregulated clusters that respond similarly to genetic and environmental variation in Treg and Tconv cells, denoting qualitative differences in otherwise shared regulatory circuits whereas other clusters are coregulated in Treg, but not Tconv, cells, suggesting Treg-specific regulation of genes like CTLA4 or DUSP4. Dense genotyping identified 110 local genetic variants (cis-expression quantitative trait loci), some of which are specifically active in Treg, but not Tconv, cells. The Treg signature became sharper with age and with increasing body-mass index, suggesting a tuning of Treg function with repertoire selection and/or chronic inflammation. Some Treg signature transcripts correlated with FOXP3 mRNA and/or protein, suggesting transcriptional or posttranslational regulatory relationships. Although no single transcript showed significant association to diabetes, overall expression of the Treg signature was subtly perturbed in T1D, but not T2D, patients.
CD4FOXP3 regulatory T (Treg) cells are important mediators of immune tolerance, prevent overwhelming immune responses, and regulate extraimmunological functions (1–3). Their absence leads to lethal lymphoproliferation and multiorgan autoimmunity in scurfy mice and in patients with immunodysregulation polyendocrinopathy enteropathy X-linked syndrome.
Treg cells differ substantially from conventional CD4 T cells (Tconv) with respect to their transcriptomes. In mice, a canonical “Treg signature” of transcripts that are over- or underexpressed in Tregs relative to Tconv has been well defined (4, 5). This signature encodes proteins ranging from cell-surface molecules (e.g., IL2RA, CTLA4) to transcription factors [e.g., FOXP3 or Helios (Ikzf2)] and includes several molecular mediators of Treg action (6). The Forkhead family transcription factor (TF) FOXP3 is essential for the specification and maintenance of Tregs and plays an important part in determining the Treg signature (4, 7–9). However, FOXP3 is not completely necessary for the differentiation of Treg cells, and some aspects of the Treg signature are independent of FOXP3 (4, 10–12). A number of other transcription factors have been reported to interact with FOXP3 and to promote Treg function (refs. 13 and 14 and refs therein). In addition, different Treg subphenotypes are dependent on differential expression of TFs, such as T-bet, Irf4, or PPARγ (15–17). We have recently shown that several of these TFs make up, together with FOXP3, a genetic switch that locks in the Treg phenotype (13). The transcriptome of Treg cells has been less extensively studied in humans although early studies indicate that several of the more prominent members of the mouse Treg signature are also differentially expressed in human Treg cells (18–20).
Dysregulation of Treg cells has been invoked in the determinism of organ-specific autoimmune diseases such as type-1 diabetes (T1D) (21). Experimentally, genetic deficiencies that reduce Treg numbers or some facets of their function result in accelerated diabetes in mouse models (22–25), and Treg transfer can be protective (22, 26, 27). Whether Treg defects are directly implicated in the determinism of autoimmune disease remains an open question. Some of the T1D susceptibility loci uncovered by large genome-wide association studies (GWASs) may plausibly influence Treg activity (e.g., IL2RA, IL2, CTLA4) (28). One can hypothesize that programmed Treg defects render an individual globally more susceptible to unrestrained activity of autoreactive cells or that Treg deficiencies occurring locally in the target organ, perhaps in response to environmental or infectious triggers, destabilize the local Treg/effector T equilibrium and allow terminal organ damage (29, 30).
It is now recognized that the frequency of FOXP3 Treg cells in the blood of human T1D patients is comparable with that of healthy subjects (30–32), as in NOD mice (29, 33). Whether Tregs from T1D patients are dysfunctional is controversial (30, 32, 34–37), and it is possible that only one facet of their activity is altered (38). Recent results also suggest that, in T1D patients, effector T cells may be refractory to inhibition by Treg cells (39, 40) although this point has also been debated (30, 38). However, there is an intrinsic limitation to addressing this question experimentally in the human system: Only blood cells are readily accessible, and the in vitro suppression assay, the only practicable tool for functional evaluation, may not be relevant to the control of diabetes in vivo.
Human genetic diversity and its adaptation to novel environments during out-of-Africa migrations have strong impacts on genes of the immune system (41). Indeed, under the strong selective pressure elicited by pathogens, immune system genes are those that show the strongest marks of adaptation and positive selection for variant alleles in different populations (42). Inflammatory disease-associated variants such as those underlying T1D are enriched in signatures of positive selection (43). Some of these variants are eQTL (expression quantitative trait loci) that affect transcriptional rate or mRNA stability (44) and may mediate the effect of inflammatory-disease susceptibility loci (45).
Little is known about the range of genetic and epigenetic variation in Treg cells within the human species. Various studies have found a wide (up to fourfold) range of variation in the proportion of FOXP3 Treg cells in healthy individuals (30, 32, 34–37), as is the case in inbred mice (33). Here, we have assessed the interindividual transcriptomic variability in Treg cells as this interindividual variance encompasses and integrates all of the genetic, epigenetic, environmental, and stochastic influences that govern the Treg transcriptome of an individual. This study was performed on cohorts of healthy subjects and patients with established T1D, aiming to tease out aspects of this variability that correlate with autoimmune diabetes.
Click here to view.Acknowledgments
We thank Dr. D. Koller for helpful discussion; Katie Rothamel, Joyce LaVecchio, and Girijesh Buruzula for help with flow cytometry and RNA preparations; and Jeff Ericson, Scott Davis, and Henry Paik for help with bioinformatics analyses. This work was supported by Juvenile Diabetes Research Foundation Grant 4-2007-1057 (to D.M., C.B., and L.L.) and National Institutes of Health Grant RC2 GM09308 (to D.M. and C.B.).
Footnotes
The authors declare no conflict of interest.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1401343111/-/DCSupplemental.
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