The Journal of Experimental Medicine
JEM © The Rockefeller University Press
Vol. 202, No. 1, July 4, 2005 33–45
Promiscuous gene expression in thymic
epithelial cells is regulated at multiple levels
and Bruno Kyewski
Division of Developmental Immunology,
and Division of Genetic Alterations in Carcinogenesis, German Cancer Research Center, D-69120 Heidelberg, Germany
Department of Medical Genetics, University of Helsinki, 00029 HUS, Helsinki, Finland
Department of Genetics, Saarland University, D-66041 Saarbrücken, Germany
Division of Theoretical Bioinformatics,
Division of Molecular Biophysics,
The role of central tolerance induction has recently been revised after the discovery of
promiscuous expression of tissue-restricted self-antigens in the thymus. The extent of tissue
representation afforded by this mechanism and its cellular and molecular regulation are
barely defined. Here we show that medullary thymic epithelial cells (mTECs) are specialized
to express a highly diverse set of genes representing essentially all tissues of the body. Most,
but not all, of these genes are induced in functionally mature CD80
autoimmune regulator (Aire) is responsible for inducing a large portion of this gene pool,
numerous tissue-restricted genes are also up-regulated in mature mTECs in the absence of
Aire. Promiscuously expressed genes tend to colocalize in clusters in the genome. Analysis of
a particular gene locus revealed expression of clustered genes to be contiguous within such
a cluster and to encompass both Aire-dependent and –independent genes. A role for
epigenetic regulation is furthermore implied by the selective loss of imprinting of the
insulin-like growth factor 2 gene in mTECs. Our data document a remarkable cellular and
molecular specialization of the thymic stroma in order to mimic the transcriptome of
multiple peripheral tissues and, thus, maximize the scope of central self-tolerance.
mTECs. Although the
Self-tolerance is inextricably linked to immunity;
only when both features of the immune system
are balanced is the body’s integrity safeguarded.
Our perception of how self-tolerance of the
plethora of self-antigens is initially imposed and
maintained throughout life has recently changed.
Two areas of research, initially pursued indepen-
dently but which now converge, contributed to
this development. First, the observation was
made that a diverse array of tissue-restricted anti-
gens (TRAs) is expressed in the thymus and dis-
played there for repertoire selection (1). Second,
unambiguous experimental evidence emerged
that dominant tolerance mechanisms, foremost
CD4 regulatory T cells, are essential rather than
supplementary to recessive tolerance modes such
as deletion (2). These new insights, apart from
their conceptual implications, also open new
therapeutic possibilities, not least for the treat-
ment of autoimmune diseases.
The notion that aberrant expression of
TRAs (termed promiscuous gene expression) is
an inherent property of the thymic stroma has
been established by studies reporting the tran-
scription of genes coding for proteins that serve
cell type–specific functions; e.g.,
) in oligodendrocytes or interphotoreceptor
retinoid binding protein in retinal cells (3, 4).
Although promiscuous gene expression by thy-
mic epithelial cells (TECs) in various species is
by now undisputed, many aspects of this phe-
nomenon remain to be explored. Thus, the
complete scope of promiscuously expressed
genes is unknown. An initial expression analysis
focused on selected self-antigens including
many prominent autoantigens (5). Two recent
reports in mice and humans, however, indicate
that this gene pool is broadly inclusive rather
than selective (6, 7). A comprehensive analysis
of promiscuously expressed genes will delineate
the ultimate extent of tissue representation in the
thymus and possibly offer new clues as to the
underlying molecular regulation.
Particular features of promiscuous gene ex-
pression (i.e., being uncoupled from tissue or
developmental regulation) appear unique among
The online version of this article contains supplemental material.
Abbreviations used: Aire,
autoimmune regulator; ANOVA,
analysis of variance;
cyclin-dependent kinase inhibitor
; cTEC, cortical TEC;
H19 fetal liver
insulin-like growth factor 2
loss of imprinting; mTEC,
medullary TEC; SNuPE, single
nucleotide primer extension;
TEC, thymic epithelial cell;
testis lipid binding protein
TRA, tissue-restricted antigen.
REGULATION OF THYMIC PROMISCUOUS GENE EXPRESSION | Derbinski et al.
somatic cells, suggesting a novel mode of molecular regula-
tion. An important initial clue as to this regulation has been
the finding that the autoimmune regulator (Aire) controls
the expression of numerous genes in murine medullary
TECs (mTECs) with a predilection for TRAs (6). This im-
portant finding offers a cogent explanation for the patho-
physiology of the monogenic human autoimmune polyglan-
dular syndrome 1 (APS-1), which is caused by mutations in
gene (8). APS-1 patients suffer to various degrees
from failures of multiple endocrine organs and show height-
ened autoantibody titers to organ-specific self-antigens (9,
10), most of which are promiscuously expressed in human
mTECs (7). Based on these particular features of APS-1, the
functional properties of Aire as a transcriptional coregulator
and its conspicuous overexpression in mTECs, we had pre-
viously proposed a role for Aire in controlling promiscuous
gene expression (11). With Aire influencing intrathymic ex-
pression of numerous TRAs in a dose-dependent manner
distinct thymic stromal cells. (A) Quantitative analysis of differentially
expressed genes in mutual comparisons between mTECs and cTECs, DCs,
and macrophages based on Affymetrix microarray analysis. The black bars
indicate the fraction of TRAs contained in all genes overexpressed in the
respective subsets and the relative percentages of TRAs are indicated
above the bars. (B) ANOVA of all four stromal cell subsets showing the top
150 probe sets with adjusted p-values of ?0.01. Note that mTECs display
Global analysis of promiscuous gene expression in
the highest proportion (106 out of 150) of differentially expressed genes.
Yellow, up-regulated genes; blue, down-regulated genes; black, approxi-
mately the same gene expression as the mean for that gene across all
samples. Numbers beside the color key represent mean ? SD of the
respective gene. (C) Diverse tissue representation by genes overexpressed
in mTECs vs. cTECs. (D) The reverse gene set shows limited tissue represen-
tation. Genes were assigned to tissues according to their predominant
expression (Materials and methods). Mo, macrophages.
JEM VOL. 202, July 4, 2005
(12), it becomes apparent that the regulation of Aire itself
will be an important determinant in self-tolerance control.
receptor has been recently identified as
one upstream component of this molecular pathway (13).
Promiscuous gene expression, however, cannot solely be
accounted for by the action of this molecule. The contribu-
tion of additional mechanisms is clearly documented by the
fact that transcription levels of tissue-restricted antigens are
dependent on Aire to various degrees, with some genes not
being influenced by Aire at all (e.g.,
ence 6). The complexity of the regulation of promiscuous
gene expression is further exemplified by differences in cell
type–specific expression patterns. The expression of certain
) is restricted to mTECs, whereas others
(e.g., thyroglobulin) are found in both cortical TECs
(cTECs) and mTECs at similar levels (14). To decipher the
apparently complex cellular and molecular regulation of pro-
miscuous gene expression, we analyzed gene expression in
distinct thymic stromal cell lineages and subsets thereof at the
level of global gene expression and defined genomic regions.
MTECs specialize in promiscuous gene expression
To define the scope of promiscuous gene expression and the
antigenic representation of peripheral tissues in the thymus,
we performed a large-scale analysis of gene expression in
murine thymic stromal cells using Affymetrix chips. Differ-
ent stromal cell types were purified by a combination of se-
quential enzymatic digestion, density gradient centrifuga-
tion, and multicolor sorting yielding pure populations of
thymic DCs, macrophages, cTECs, and mTECs (5). The
mutual comparisons of mTECs with cTECs, DCs, and mac-
rophages revealed in each case a much higher number of
genes being overexpressed in mTECs compared with the
reference population (see Materials and methods; Fig. 1 A).
This observation has been corroborated by an analysis of
variance (ANOVA) of the gene expression pattern of these
four stromal cell subsets. Clearly, the highest number of
genes, which were differentially expressed among all four
groups, was found in mTECs (Fig. 1 B).
Because promiscuous gene expression is obviously a par-
ticular property of mTECs, we regard the set of genes over-
expressed in mTECs versus cTECs as most informative to
delineate this gene pool. Given the close relationship be-
tween these two cell types (15), cell lineage–specific differ-
ences should be minimized, whereas aberrantly expressed
genes should be included. The pool of genes overex-
pressed in mTECs versus cTECs is probably
ble S1, available at http://www.jem.org/cgi/content/full/
jem.20050471/DC1); when we compared the gene chip re-
sults with RT-PCR data of promiscuously expressed genes,
we found that only
50% of genes analyzed by PCR could
be reliably detected as present on arrays. Therefore, the total
number of promiscuously expressed genes will be underesti-
mated by at least a factor of two (unpublished data). This is
545 genes (Ta-
probably because of the fact that the array analysis is less sen-
sitive than RT-PCR and that promiscuously expressed
genes are often expressed at low levels. To validate our crite-
ria for the identification of overexpressed genes, we analyzed
the expression of several mTEC-specific genes identified
with the microarray analysis by real-time PCR. Certain
“marker genes” for mTECs were reliably confirmed, and we
therefore regarded the chosen criteria as valid.
testis lipid binding protein
els in mTECs of both genders, including nonlactating female
mice. (Fig. S1, available at http://www.jem.org/cgi/con-
tent/full/jem.20050471/DC1, and not depicted). Thus,
promiscuous gene expression in mTECs overrides the nor-
mal tissue-, sex-, and development-dependent gene regula-
tion of these two genes.
during pregnancy in the mammary gland (16), and
expressed in male germ cells (17).
Promiscuous expression has been operationally defined as
the expression of genes that so far have not been known to
be part of the physiological gene expression program of thy-
mic stromal cells. To apply more stringent criteria, we deter-
mined the percentage of genes with restricted tissue expres-
sion, a definition relying on our present knowledge of cell
type–specific gene expression programs. On account of pub-
lished gene expression data, we categorized genes as tissue
restricted if expressed in
5 out of 45 tissues tested. Approx-
imately 28% of all genes overexpressed in mTECs (152 out
of 545 genes) could be categorized as tissue restricted ac-
cording to this approach (Fig. 1 C). One key finding is that
most, if not all, tissues are represented by at least one or mul-
tiple genes in mTECs. In contrast, genes overexpressed in
cTECs versus mTECs do not show such a bias. Although
the relative percentage of TRAs in cTECs appeared similar
at first sight (
28%), most of these transcripts are lympho-
cyte specific and likely derived from “contamination” of the
cTEC population with thymic nurse cells containing thy-
mocytes (Fig. 1 D). This interpretation is supported by the
finding that cTECs isolated from
sion of most of these lymphocyte-specific transcripts (un-
published data). In the same vein, genes overexpressed in
DCs versus mTECs and macrophages versus mTECs
(Fig. S2, available at http://www.jem.org/cgi/content/full/
jem.20050471/DC1) only showed limited tissue diversity
with the majority being restricted to hematopoietic cell lin-
eages. The comparative analysis of global gene expression
patterns among thymic stromal cells clearly singles out
mTECs as a cell type specialized in expressing TRAs.
) are detectable at equal lev-
is typically induced late
mice lack expres-
Promiscuous gene expression in mTECs is
It has been unclear so far whether promiscuous gene expres-
sion in mTECs is tightly correlated with lineage commit-
ment or whether it requires further differentiation steps
within this lineage. mTECs are heterogeneous with regard
to their phenotype, expressing varying levels of MHC class
REGULATION OF THYMIC PROMISCUOUS GENE EXPRESSION | Derbinski et al.
II, CD80, or binding sites for the lectin UEA (18). It is pre-
sumed that an increase in these surface molecules denotes
progressive maturation of mTECs either as a cell autono-
mous differentiation program or induced by mature thy-
mocytes. Here, we chose to separate mTECs according to
relative expression levels of the CD80 coreceptor and ad-
dressed the question of whether there is any correlation be-
tween promiscuous gene expression and induction of this
costimulatory molecule. mTECs were sorted into low, in-
termediate, and high CD80-expressing cells (Fig. 2 A), and
the expression of various promiscuously expressed genes was
examined by quantitative PCR. 21 out of 22 genes tested
showed a clear positive correlation with CD80 expression
levels. The same was true for the transcriptional regulator
Aire (Fig. 2 B and not depicted). This expression pattern of
Aire fits previous observations that describe Aire expression
in situ only in a subset of mTECs (13, 19). However, the
acute phase protein
was already strongly expressed in
subset and showed no maturation-dependent in-
duction. In keeping with this pattern,
to be independently regulated in Aire (6).
Given the concomitant induction of promiscuous gene
expression and Aire in CD80
Aire directs all or only part of these up-regulated genes. To
address this issue, we isolated CD80
from Aire-deficient mice and initially analyzed a limited set
of promiscuously expressed genes (Fig. 2 C). The expression
of genes that are reportedly Aire dependent, such as
, were barely detectable in CD80
mTECs and both subsets of Aire-deficient mTECs (Fig. 2 C
and see Fig. 5 B). In contrast, several genes, such as
, showed a strong up-regulation
concomitant with CD80 expression levels in
Thus, additional transcriptional regulators apart from Aire
become operative during the functional maturation of
mTECs and drive expression of different promiscuously ex-
pressed genes. To delineate the size and diversity of this
Aire-independent gene pool, we extended this analysis by
comparing the gene expression profile of CD80
CD80 mTECs of WT and Aire-deficient mice with gene
arrays (Fig. 3 A;
Tables S2 and S3, available at http://
analysis confirmed that the WT CD80
pressed the highest number, as well as the highest relative
percentage (33%), of TRAs among the subsets tested.
mTEC subset of Aire-deficient mice showed
a reduction in the total number of overexpressed genes by
25% (120 genes), and the relative percentage of TRAs de-
creased from 33 to 21% (74 out of 347 genes); nevertheless
this pool still comprised
300 genes that were representative
of many tissues (Fig. 3 B). In addition, the distributions of
fold changes among the genes overexpressed in the CD80
versus CD80 subsets in
similar; i.e., the quantitative representation of self-antigens in
both gene pools is similar (Fig. 3
has been shown
mTECs, we asked whether
mTEC subset ex-
mice were very
C). The fold changes de-
rived from the array analysis were validated for a selected set
of genes by quantitative PCR. Strikingly, some genes were
100-fold up-regulated (Fig. 3 D).
expression levels on mTECs. (A) Expression profile of CD80 expression on
mTECs with indicated gates that were chosen for sorting CD80lo, CD80int,
and CD80hi mTEC subpopulations. The gray region corresponds with the
isotype control staining. (B and C) The expression levels of different
promiscuous transcripts were analyzed in the various mTEC subsets by
real-time PCR normalized to the relative quantity of ?-actin. Note the
stepwise up-regulation of gene expression with increasing CD80 levels in
WT mTECs, with the exception of CRP (B). Expression analysis of a selected
panel of promiscuously expressed genes in the CD80lo and CD80hi mTEC
subsets of WT and Aire–deficient mice. (C) Aire-independent up-regulation
of expression of four of the analyzed genes in the CD80hi subset. Error bars
indicate the SD of triplicates of the same cDNA preparation. AFP, ?-feto-
protein; Aire, autoimmune regulator; CRP, C-reactive protein; Csnb, casein
?; Csnk, casein ?; GAD67, glutamic acid decarboxylase 67 kD; Ins1 or -2,
insulin 1 or 2; Tlbp, testis lipid binding protein.
Promiscuous gene expression correlates with CD80
JEM VOL. 202, July 4, 2005
Notably, Aire had hardly any effect, both in number and
fraction of TRAs, on the set of genes that was down-regu-
lated during mTEC maturation (Fig. 3 A). In addition, the
fraction of TRAs is lower among genes that were down-reg-
ulated compared with those that were up-regulated in
Promiscuously expressed genes colocalize in
The structure, function, and physiological regulation of
promiscuously expressed genes did not insinuate any com-
monalities, which would explain their coexpression and co-
regulation in mTECs. Preferential chromosomal localiza-
tion has been reported for genes expressed in certain cell
lineages. Thus, genes expressed in spermatogonia cluster on
the X chromosome (20), and genes expressed in stem cells
(a composite of embryonic, hematopoietic, and neuronal
stem cells) were overrepresented on chromosome 17 (21).
This, however, was not the case for the different gene pools
expressed in thymic cell types. There was no marked un-
der- or overrepresentation for particular chromosomes
compared with the distribution of all mapped genes of the
array (not depicted). We analyzed whether the genes over-
expressed in mTECs and subsets thereof localize to clusters
on chromosomes, as has been recently reported, for the ex-
pression of tissue-specific or housekeeping genes in differ-
ent species (22). Indeed, we found that mTEC-specific
genes tend to colocalize in clusters comprising up to 16
genes. This clustering was highly significant when com-
pared with random distributions of genes mapped to the
same chromosomes (P
0.001; Fig. 4 A). The same holds
true for the array of genes up-regulated in CD80
though the mean and maximal number of clustered genes
was reduced (Fig. 4 B). Interestingly, genes up-regulated in
CD80 mTECs in the absence of Aire still clustered, yet
the number of clusters (from 40 to 24) and the number of
genes per cluster were further reduced. Although differ-
ences in pool size may explain differences in the number of
clusters of size 3 and 4, this does not apply to clusters larger
than four genes because these clusters have not been ob-
served at all by simulation of randomly sampled genes.
Genes overexpressed in cTECs were only enriched in clus-
mTECs shows distinct levels of control. (A) The number of differentially
expressed genes in the various CD80 subsets indicated is shown by gray
regions and the fraction of TRAs contained within these gene pools is
shown by red regions. The corresponding relative percentages are given
above the bars. (B) Diverse tissue representation by genes induced in the
CD80hi vs. CD80lo subsets of Aire-deficient mTECs. (C) The curves represent
Induction of promiscuous gene expression in CD80hi
the percentage of genes overexpressed in the different mTEC subsets
(mTEC vs. cTEC, CD80hi vs. CD80lo of WT and Aire-deficient mice) at the
indicated fold changes. Fold changes ?70 were combined. (D) Verification
of gene expression data derived from the microarray analysis by quantitative
PCR. Values in parentheses indicate fold changes in the microarray analysis;
values on the x axis denote fold changes of the quantitative PCR analysis
that were normalized to ?-actin expression.
REGULATION OF THYMIC PROMISCUOUS GENE EXPRESSION | Derbinski et al.
ters of three genes (not depicted), thus confirming that
colocalization in larger clusters is specific for the mTEC
gene sets. As a second quantitative measure of colocal-
ization, the frequency of neighboring genes within win-
dows of different size (50–5,000 kb) was determined
(Fig. S3, available at http://www.jem.org/cgi/content/full/
jem.20050471/DC1). This analysis confirmed the tendency
of promiscuously expressed genes to colocalize; the degree
of colocalization showed the same hierarchy as the cluster
analysis shown in Fig. 3. The progressive reduction in the
frequency of clustered genes was not necessarily caused by
the entire loss of a given cluster, but also because of reduc-
tion of genes within a cluster (Fig. 4, A–C). Reduction in
numbers of colocalized genes either affected contiguous
stretches or scattered genes, as defined by the array analysis
(Fig. 4 D). Notably, the gene pools with the highest degree
of gene clustering also displayed the highest percentage of
TRAs (compare Fig. 1 A, Fig. 3 A, and Fig. S3).
These data document clustering as a distinctive feature of
promiscuously expressed genes that is conserved across dif-
ferent species (7). Moreover, they suggest that genes within a
particular gene cluster are not subject to strict coregulation,
but seem differentially regulated. To further corroborate this
finding, we chose to analyze one cluster in detail.
subsets. (A–C) The number of clusters of 3–16 genes recorded within a
sliding window of 10 consecutive genes is shown for the different subsets
(see Materials and methods). The black bars refer to the experimental
values, and the white bars to the number of clusters observed in randomly
generated gene lists. The error bars indicate the SD among the randomly
generated gene lists. The identity and size of three clusters is indicated by
arrows: red, kallikrein cluster on chromosome 7; blue, S100 cluster on
Chromosomal clustering of genes overexpressed in mTEC
chromosome 3; green, casein cluster on chromosome 5. Note the progressive
reduction in number and size of clusters in the different gene pools.
P ? 0.001, except where indicated. Clusters of two genes were in no case
significantly different from randomly generated gene lists. (D) Composition
of the three different clusters as shown in A by arrows. The arrangement
from top to bottom reflects the alignment from centromere to telomere
on the respective chromosomes.
JEM VOL. 202, July 4, 2005
Contiguous expression and coregulation of clustered genes
The gene clustering deduced from bioinformatic processing
of the Affymetrix gene chip analysis is likely to be incom-
plete. These chips are estimated to cover about one third of
all murine transcripts, and many transcripts will escape detec-
tion because of low expression. Therefore, we decided to
address this directly by analyzing gene expression in a con-
tiguous chromosomal region by RT-PCR in mTECs and
their subsets. We chose the casein gene region on mouse
chromosome 5 that we identified during the cluster analysis
because expression of casein genes can be classified as bona
fide promiscuous (Fig. S1). In addition to the casein gene
family, this region of ?1 Mb encodes genes shown to be ex-
pressed in the salivary glands, testis, epididymis, liver, kid-
ney, and olfactory bulb and epithelium. Expression of the
various genes was analyzed in mTECs and the respective tis-
sues by semiquantitative PCR. Of the 14 genes analyzed, 11
were contiguously expressed in mTECs (Fig. 5 A). In con-
trast, expression of this locus was much more restricted in
the various peripheral tissues. The expression pattern largely
conformed to the prediction, but was often broader than that
deduced from published data. It is obvious from even this in-
complete analysis that expression of a given gene is rarely
confined to one tissue. None of the control tissues, however,
showed a similar transcriptional “read-through” of this ge-
nomic region as the mTECs, in which contiguous expres-
sion of genes purportedly specific for different tissues is ob-
served in a region covering ?800 kb. Interestingly, this
read-through ends in the upstream region of the casein locus
in mTECs. Although Ugt2a1 is still transcribed, three differ-
ent members of the UDP glycosyl-transferase family, located
more distal to the casein region, are barely transcribed in
mTECs, but clearly in liver and kidney. The nature of this
transcriptional boundary in mTECs is currently unclear and
a corresponding demarcation in the 3? region has not yet
been defined. Our finding of contiguous gene expression
within this genomic region is necessarily based on the cur-
rent status of gene mapping. We cannot preclude that, with
future refinement of gene maps, genes that are not expressed
in mTECs may be identified in this region.
To further characterize gene regulation in this local re-
gion we asked whether neighboring genes are coregulated
en bloc or whether individual genes are subject to differen-
tial regulation. When we examined the expression of the
core region of this cluster in CD80hi and CD80lo mTECs by
real-time PCR, we found that all six adjacent genes were
coinduced in CD80hi mTECs. Extension of this comparative
analysis to Aire?/? mice, however, revealed that individual
genes within this cluster differed in their dependency on
Aire (Fig. 5 B). Aire-dependent genes were not necessarily
grouped together, but dispersed among Aire-independent
genes. These data document two levels of regulation: one
targets clusters at large and requires mTEC maturation and
the other targets individual genes and requires Aire and still
unidentified transcriptional regulators.
Loss of imprinting (LOI) of insulin-like growth factor 2
(Igf2) in mTECs
Colocalization of promiscuously expressed genes in regional
clusters and their coregulation within such clusters is strongly
suggestive of epigenetic gene regulation. Genes for which
epigenetic regulation has been extensively documented are
imprinted genes (23). Among the genes overexpressed in
mTECs versus cTECs, we observed several imprinted genes,
including the pleiomorphic adenoma gene-like 1 (Plagl1), cyclin-
dependent kinase inhibitor 1C (Cdkn1c), and the colocalized
genes Igf2 and H19 fetal liver mRNA (H19). Up-regulation
of expression of these genes in mTECs could either be
caused by enhanced expression of the imprinted active allele
(either paternal or maternal) or by biallelic transcription after
derepression of the silent allele. Mouse strains exhibiting sin-
gle nucleotide polymorphisms on the distal arm of chromo-
some 7, where H19, Igf2, and Cdkn1c are located, allowed us
to experimentally distinguish between these two mecha-
nisms. Expression of Igf2, which was 17-fold overexpressed
in mTECs, was monitored in crosses between C57BL/6 and
SD7 mice in different organs and in mTECs by RT-PCR,
followed by single nucleotide primer extension (SNuPE)/
HPLC analysis (Fig. 6). Igf2 expression showed normal im-
printing in the kidney and liver (i.e., only the paternal allele
was expressed), but biallelic expression in the brain, as previ-
ously reported (24–26). Intriguingly, we also observed bial-
lelic expression in mTECs; this finding was confirmed in the
reciprocal crossing between SD7 and C57BL/6 mice. Thus,
mTECs represent an additional somatic cell lineage in adult
mice apart from certain cell types in the central nervous sys-
tem in which imprinting of the Igf2 gene is abolished. To
determine whether this LOI extends to other imprinting loci
as a reflection of more widespread epigenetic deregulation in
mTECs, we analyzed the expression of the cell cycle inhibi-
tor Cdkn1c, also known as p57Kip2, which also was 34-fold
up-regulated in mTECs. Cdkn1c is encoded ?800 kb telo-
meric on the same chromosome but, in contrast to Igf2, is
paternally imprinted and controlled by a different imprinting
center (23). Remarkably, imprinting of this gene was not
abolished when both reciprocal crossings were analyzed. We
only observed expression from the maternal allele both in
control tissues and in mTECs.
The comparative analysis of gene expression in distinct thymic
stromal cells at the global level, the level of chromosomal re-
gions, and individual genes documents the complexity of pro-
miscuous gene expression. Although promiscuous gene ex-
pression is a basal feature of TECs, mTECs clearly display the
highest degree of promiscuous expression both with regard to
number and diversity of expressed genes. Extrapolating from
our gene array analysis, we estimate that 2,000–3,000 genes,
or 5–10% of all known mouse genes, are turned on in
mTECs, in addition to their cell lineage–specific expression
program. The complexity of promiscuous gene expression in-
REGULATION OF THYMIC PROMISCUOUS GENE EXPRESSION | Derbinski et al.
creases in ascending order from cTECs, to immature mTECs,
to mature CD80hi mTECs. These different gene pools are not
complementary but additive, with the more complex one en-
compassing the less complex pool. Promiscuous gene expres-
sion is thus not only specified by commitment into the TEC
lineages, but also by differentiation after lineage commitment.
Our observation that the bulk of promiscuously expressed
genes are only turned on in CD80hi mTECs strongly supports
casein cluster. (A) Schematic representation of 1.2 Mb of chromo-
some 5, depicting the casein gene region flanked upstream by mem-
bers of the sulfo-transferase and UDP glycosyl-transferase families
and flanked downstream by salivary gland genes (top). The bottom
panel shows the expression profile of the various genes of this region
Contiguous promiscuous gene transcription in the
in mTECs, as analyzed by semiquantitative RT-PCR (fourfold serial
dilutions). The same analysis was performed for the various tissues
in which the various genes are specifically transcribed. Note that
contiguous gene expression was only observed in mTECs. These expres-
sion patterns are representative of two independent experiments;
discordant expression results were only observed for one gene (*).
Figure 5 continues on next page
JEM VOL. 202, July 4, 2005
the “terminal differentiation model.” This model holds that
promiscuous gene expression is enacted during mTEC dif-
ferentiation/maturation. Terminally differentiated mTEC
clones would express an assortment of TRAs of mixed tissue
and germ layer derivation. Alternatively, it had been pro-
posed that mTECs emulate the gene expression program of
tissue-specific cell lineages. The medulla would thus repre-
sent a patch quilt of different tissues (the “mosaic model”;
reference 27). According to this model, promiscuous expres-
sion of a given gene would follow rules of tissue-specific reg-
ulation. This prediction is, however, not supported by recent
findings. Thus, insulin expression in mTECs and ? cells of
the pancreas is differently regulated in response to decreasing
copy numbers of the insulin genes (28).
At least four pools of promiscuously expressed genes can
be discerned: (a) genes that are expressed at similar levels in
cTECs and mTECs and at much lower levels, if at all, in he-
matopoietic cells (e.g., PLP or thyroglobulin); (b) genes that
are only expressed in mTECs, irrespective of the maturation
stage (e.g., CRP); (c) genes that are strongly induced in
CD80hi mTECs contingent on Aire (e.g., insulin or casein
?); and (d) genes that are induced in CD80hi in the absence
of Aire (e.g., GAD67 or casein ?). These different gene pools
obviously differ in size, composition, and mode of regula-
tion. To date, only one molecular component of this regula-
tion has been identified, the transcriptional regulator Aire,
which directs the maturation-dependent induction of a few
hundred genes. Considering the role of promiscuous gene
expression in tissue-specific tolerance, not only the number
of expressed genes but also the balanced antigenic represen-
tation of diverse tissues matters. In this regard, the fraction of
bona fide TRAs (i.e., genes expressed in less than five tissues,
based on currently available data) is of particular interest.
The percentage of TRAs among genes up-regulated in
CD80hi mTECs of WT mice was 33% (i.e., 152 genes; Fig.
3). Interestingly, this enrichment of TRAs was even higher
when the CD80hi subset was examined between WT and
Aire?/? mice; i.e., 45% of Aire-dependent genes were cate-
gorized as TRAs (unpublished data). This remarkable feature
of Aire to preferentially target genes of restricted tissue ex-
pression (6) awaits a molecular explanation.
Despite the important quantitative and qualitative contri-
bution of Aire in directing promiscuous gene expression in
mTECs and, thus, protecting from autoimmunity, a sizable
number of genes are still expressed in the absence of Aire.
These genes still represent diverse tissues and are composed
of up to 21% TRAs (Fig. 3). This “residual” promiscuous
gene expression could explain the relative mild autoimmune
(B) Expression analysis of selected casein genes in the core region of
this cluster by real-time PCR. Expression levels in CD80lo and CD80hi
mTEC subsets of WT and Aire–deficient mice were examined. Although
all six genes were coinduced in mature mTECs, they still differed in
their dependency on Aire. Error bars indicate SD of triplicates of the
same cDNA preparation.
of Igf2 and Cdkn1c was analyzed by RT-PCR amplification and SNuPE/
HPLC in mTECs and control tissues from the F1 generation of C57BL/6 ?
SD7 and SD7 ? C57BL/6 crosses. Elution profiles of the SNuPE products
are shown. The first peak corresponds to unextended primers and the
second and third peak to products transcribed from the maternal or paternal
allele, respectively, as indicated. Igf2 is paternally expressed with the
exception of the choroid plexus and leptomeninges. Note that biallelic
expression also occurs in mTECs. In contrast, imprinting of Cdkn1c is
maintained in all tissues tested including mTECs; i.e., the gene is maternally
expressed. The analysis of genomic DNA (top right) indicates the position
of both allele-specific PCR products. pat, paternal; mat, maternal.
Imprinting in mTECs (Igf2 vs. Cdkn1c genes). Expression
REGULATION OF THYMIC PROMISCUOUS GENE EXPRESSION | Derbinski et al.
phenotype of Aire?/? mice. Promiscuous gene expression
independent of Aire falls into three categories as represented
by pools (a), (b), and (d). Possible genetic and/or epigenetic
mechanisms directing expression of these pools remain to be
identified. An important inference from the terminal differ-
entiation model of promiscuous gene expression (1) is that
arrest of differentiation at the CD80lo stage will result in the
absence of both gene pools (c) and (d), and this should result
in a more severe autoimmune phenotype than that of Aire
Clustering is one distinctive feature of promiscuously ex-
pressed genes that otherwise do not show obvious common-
alities. Chromosomal gene clustering has been described in
different species and has been interpreted to be the result of
juxtaposition in order to facilitate their coregulation (22). In
contrast, we suggest that the observed clustering of promis-
cuously expressed genes in mTECs is a result of accessibility
of chromosomal regions to transcription irrespective of tis-
sue-specific differentiation patterns. The selection of genes
within clusters would thus be determined by the genetic his-
tory of the species rather than immunological selection crite-
ria. Such clusters encompass up to 1 Mb, as exemplified with
the casein region, but may be larger because the boundaries
have not been defined. The coordinated induction of the
core region of the casein cluster in CD80hi mTECs speaks
for long-range regulatory effects (opening of domains),
whereas the differential dependency of adjacent casein genes
on Aire shows gene-specific regulation. Caseins ? and ? are
Aire independent, and the remaining genes are Aire depen-
dent. Interestingly, this pattern does not concur with the
gene ontology of the casein family members; although casein
? belongs to the group of calcium-sensitive genes (caseins ?,
?, ?, and ?), which have originated from a common ances-
tral gene, the casein ? gene is not related to this group (16).
One interpretation of these findings is as follows. During
mTEC maturation, alterations in the accessibility of scat-
tered, local regions in the genome would allow DNA-bind-
ing complexes to differentially control transcription of genes
within such open domains. Aire may be part of such com-
plexes. The position-independent control of several trans-
genes directed by tissue-restricted promoters by Aire sug-
gests that promoter-specific sequences directly or indirectly
specify the activity of Aire (29). The control of promiscuous
gene expression by Aire would thus be contingent on spe-
cific conditions in mTECs. This interpretation concurs with
a recent report that shows that Aire, when overexpressed in
different human monocytic cells, targets a different set of
genes than in mTECs (30).
It is currently unclear whether mTECs are unique among
all somatic cells in expressing such a diversity of TRAs. Yet, a
limited comparison showed that none of the seven different
tissues of mixed cellular composition, including epithelial
cells, showed a similar read-through of the casein locus as pu-
rified mTECs. In addition, when comparing global gene ex-
pression between four thymic stromal cells and unseparated
liver tissue (unpublished data), mTECs clearly expressed the
largest set of genes not shared by the other cell types. Promis-
cuous gene expression has also been reported for multi- and
oligopotent stem cells (31, 32). Although the biological role
in this context is most certainly different, the molecular regu-
lation may share common features.
A striking observation of our study is that the four im-
printed genes Igf2, Cdkn1c, Plagl1, and H19 are overex-
pressed in mTECs. Because imprinted gene expression is
controlled by DNA methylation and chromatin alterations
(23), this suggests that changes in such epigenetic marks may
play an important role in promiscuous gene expression. This
view is supported by the additional observation that several
promiscuously expressed genes in mTECs are also found to
be overexpressed in epigenetically modified, hypomethyl-
ated mouse fibroblasts (33). However, the simple explana-
tion that global DNA methylation changes might be the
trigger for LOI and overexpression in mTECs seems to be
rather unlikely. First, several other imprinted genes known
to be controlled by DNA-methylation are not overexpressed
in mTECs. Second, Cdkn1c and H19 overexpression is not
accompanied by LOI (unpublished data). Third, the level of
overexpression by far exceeds the expected twofold increase
as a consequence of LOI.
We can envisage two scenarios as possible explanations
for the overexpression of imprinted genes and other genes
whose expression was shown to be methylation sensitive.
Epigenetic silencing may simply be overridden (or masked)
by other expression mechanisms, or locally induced epige-
netic changes affect only selected genes but do not abrogate
general epigenetic marks like imprints. A striking observa-
tion in this context is the LOI and overexpression of the Igf2
gene in mTECs and the simultaneous overexpression of
H19. Biallelic expression of Igf2 has been also reported in
other somatic cells; i.e., the choroid plexus and the lepto-
meninges (24, 25). Here the H19 gene is expressed monoallel-
ically, with the paternal allele being silent. This uncoupling
of Igf2 expression from imprinting at the H19 locus has been
shown to involve the centrally conserved domain enhancer
between Igf2 and H19 (26). So far, the mechanisms driving
biallelic Igf2 overexpression in mTECs remain unclear.
LOI alone would not explain the 17-fold up-regulation
of Igf2 transcription. Intriguingly, Igf2 is not only biallelically
expressed in mTECs, but is also Aire dependent. Aire was
shown to direct promiscuous expression of Ins2 (6). Because
Ins2 is located in direct proximity to Igf2, it is conceivable
that Aire may also affect the neighboring Igf2 gene com-
pletely independent of the imprinting control by the H19–
Induction of promiscuous gene expression in CD80hi
mTECs correlates with concomitant up-regulation of differ-
ent sets of genes involved in antigen processing and presen-
tation, including MHC class II, H-2M, CD80, and several
cathepsins (E, H, K, S, and Z). The induction of these two
gene expression programs probably occurs independently
because up-regulation of MHC class II, CD80, H-2M, and
cathepsin S was also observed in Aire?/? mice (Table S4,
JEM VOL. 202, July 4, 2005
available at http://www.jem.org/cgi/content/full/jem.
20050471/DC1). The acquisition of professional antigen
presentation competence parallel to the induction of the
complete complement of promiscuously expressed genes en-
ables mature mTECs to present a host of self-peptides at suf-
ficient epitope density. In conjunction with the display of
appropriate coreceptors, mTECs are thus able to autono-
mously tolerize tissue-reactive T cells, possibly both via de-
letion and induction of T regulatory cells (1).
The maturation sequences of mTECs and DCs into
competent APCs share certain common features. Both cell
types, despite their different origins, express CD80/86 and
the immuno-proteasome (34) and are able to activate naive
T cells in vitro (Koble, C., personal communication). In
contrast, cTECs, which share a common precursor with
mTECs (15), do not express CD80 or the immunoprotea-
some and lack complete competence to activate naive T cells
(35). The common function of mTECs and DCs in toler-
ance induction in the thymic medulla thus overrides their
different lineage derivation. A further aspect of DC matura-
tion is the up-regulation of chemokines, which attract naive
T cells and, thus, facilitate the encounter between rare anti-
gen-specific T cells and antigen-laden DCs (36). MTECs
also up-regulate an array of chemokines (Table S5, available
DC1), and this may serve the same purpose, namely to at-
tract highly mobile thymocytes to those mTECs that display
the complete repertoire of self-determinants.
CD80hi mTECs also up-regulate genes that characterize
terminally differentiated keratinocytes; e.g., claudin-4 and -7,
keratin 10, and the epidermal differentiation complex (37,
38). This gene complex serves to provide the barrier activity
of stratified squamous epithelia. Interestingly, human mTECs
also build up a barrier activity when forming Hassall’s cor-
puscles, which presumably are formed by terminally differ-
entiated mTECs (37). Whether this is of any physiological
significance or is a byproduct of the differentiation program
is not clear.
In conclusion, our data document a remarkable cellular
and molecular specialization of the thymic stroma, which is
highly conserved between mice and humans (reference 7;
unpublished data). This serves to comprehensively mimic
the transcriptome of peripheral tissues and, thus, maximize
the scope of central self-tolerance. Understanding these pro-
cesses in more detail will be of considerable biological inter-
est and may also help to unravel the complex genetic regula-
tion of organ-specific autoimmune diseases.
MATERIALS AND METHODS
Animals. C57BL/6 mice were obtained from Charles River Laboratories.
Aire?/? mice were genotyped as previously described (39). These mice
were of a mixed genetic background. For analyses of allele-specific gene ex-
pression, Mus musculus domesticus (C57BL/6) and domesticus mice harboring a
Mus spretus allele on distal chromosome 7 (SD7) were mated and various tis-
sues were dissected from the F1 generation. All mice were kept under spe-
cific pathogen-free conditions at the animal facilities of the German Cancer
Isolation of murine thymic stromal cells. Thymic stromal cells were
purified as described previously (5) with the following staining modifica-
tions. Thymic rosettes were stained with anti-CD11c–PE (HL3; BD Bio-
sciences) and anti-F4/80–FITC (CI:A3-1; Serotec). The TEC-enriched
fraction was stained with either anti-CDR1–Alexa488 or anti-Ly51–FITC
(6C3; BD Biosciences), anti–Ep-CAM–Cy5 (G8.8), and anti-CD45–PE
(30-F11; BD Biosciences). After staining, cells were resuspended in FACS
buffer containing 1 ?g/ml propidium iodide to exclude dead cells. For sub-
division of mTECs, the following combinations were used: anti-CD80–PE
(16-10A1) or anti-KLH–PE (Ha 4/8; isotype control), anti-CD45–PerCP
(30-F11), anti-Ly51–FITC (6C3; all were obtained from BD Biosciences),
and anti–Ep-CAM–Cy5 (G8.8). FcR blocking with the anti-FcR mAb
2.4G2 preceded all stainings. Cell sorting was performed with a cell sorter
(FACSVantage Plus; Becton Dickinson).
RNA preparation and cDNA synthesis. Whole tissue RNA was iso-
lated using DNaseI digestions on-column with an Ultra-Turrax T25 (IKA)
and the RNeasy Mini Kit (QIAGEN) and from single-cell suspensions with
the High Pure RNA Isolation Kit (Roche). Total RNA (4 ?g of tissue-
extracted RNA or an equivalent of 4 ? 104 – 1 ? 106 single cells) was re-
verse transcribed into cDNA with Oligo(dT)20 Primer and Superscript II Re-
verse Transcriptase (Invitrogen), followed by RNase H digestion (Promega).
RT-PCR analysis. PCRs were performed as previously described (5). All
primer pairs were synthesized by the oligonucleotide synthesis facility of the
German Cancer Research Center and, when possible, were designed to span
at least one intron. PCR products were revealed with the Lumi-Imager F1
Workstation (Roche) and bands were quantified with LumiAnalyst 3.0 soft-
ware (Roche). For semiquantitative PCR, the different cDNA preparations
were normalized to ?-actin expression before testing expression of the gene
Quantitative PCR. Real-time PCR reactions were performed in a final
volume of 25 ?l with optimal concentrations of the forward and reverse
primers (50–900 nM) using the qPCR Core Kit for SybrGreen I (Eurogen-
tec) containing Hot GoldStar polymerase and uracil-DNA glycosylase.
Probes were used with a concentration of 200 nM in combination with the
qPCR Core Kit (Eurogentec). Reactions were run on a sequence detection
system (GeneAmp 5700; Applied Biosystems) in triplicates, and expression
values were normalized to ?-actin expression using the comparative CT
method. Primers were purchased from MWG and, when possible, were de-
signed to span at least one intron. Probes were purchased from Eurogentec.
SNuPE analysis. Igf2 and p57kip2 were RT-PCR–amplified from RNA
isolated from organs and thymic stromal cells of the F1 generation derived
from crossings between SD7 and C57BL/6 mice using AmpliTaq DNA
polymerase (Applied Biosystems) and the following primer pairs (sense and
antisense, respectively): Igf2, 5?-GGCCCCGGAGAGACTCTGTGC-3?
and 5?-TGGGGGTGGGTAAGGAGAAACCT-3?; and p57kip2, 5?-
TTCAGATCTGACCTCAGACCC-3? and 5?-AGTTCTCTTGCGCT-
TGGC-3?. PCR products were separated in agarose gels; bands of an ap-
propriate size were excised and purified using the QIAquick Gel Extraction
For the SNuPE reaction, 20–130 ng/?l of gel-purified RT-PCR prod-
ucts were used as templates. SNuPE primers were placed immediately adja-
cent to the polymorphic sites and had the following sequences: Igf2,
5?-TCAGTGAATCAAATTA-3?; and p57kip2, 5?-CTGTTCCTCGC-
CGTCC-3?. Before performing the SNuPE reaction on p57kip2, PCR
products had to be digested with 0.2 U FOKI to avoid secondary structures.
The primers were extended in 20-?l reactions using the following condi-
tions: 3.6 ?M SNuPE primer, 0.05 mM ddNTPs, and 0.15 U Thermo-
Sequenase (GE Healthcare) in reaction buffer supplied by the manufacturer.
After denaturation for 2 min at 96?C, 50 cycles (15 s at 96?C, 30 s at 37?C, and
2 min at 52.5?C) were performed. Extension products were separated on an
IP-HPLC system (WAVE DNA Fragment Analysis System; Transgenomics).
REGULATION OF THYMIC PROMISCUOUS GENE EXPRESSION | Derbinski et al.
Microarray analysis. Microarray analysis using MGU74Av2 chips (Af-
fymetrix, Inc.) was performed as previously described (7, 40). Gene expres-
sion data have been deposited in the GEO database (available under
accession no. GSE2585) at http://www.ncbi.nlm.nih.gov/projects/geo/.
Reagents were provided by T. Wintermantel and D. Engblom (German
Cancer Research Center, Heidelberg, Germany).
Identification of tissue-restricted genes. Gene expression data from
the public database at http://symatlas.gnf.org (41) were taken as a starting
point for the identification of tissue-restricted genes among the total num-
ber of genes overexpressed in the different thymic stromal cell populations.
This database contains expression assignments for many different tissues and
cell types derived by gene array analysis using Affymetrix U74A or custom-
made GNF1M arrays. In combination with data from the mouse genome
informatics database (http://www.informatics.jax.org), Swissprot, and the
literature, genes were assigned to tissues of their predominant expression
when applicable. Genes with expression restricted to less than five tissues
were designated as tissue restricted. Among these genes, expression in a sin-
gle tissue was rare (e.g., Csnk), whereas expression in two to four tissues
represented the most cases (e.g., Mep1a, Tff2, and Calb1).
Gene mapping and bioinformatic analysis. The overexpressed genes
were mapped to the genome using the MapIt program (unpublished data).
MapIt is an automated database driven tool designed to identify and localize
the absolute position of a given list of genes to the organism’s specific ge-
nome. We performed queries across databases such as Locuslink, UniGene,
and Ensembl, implemented under the Sequence Retrieval System using the
gene symbol as input. The quality of the data was ascertained by applying
checks to minimize the annotation inconsistencies across different databases
and by reducing the redundancy from the given list of entries. We queried
Locuslink and Unigene databases to obtain the gene-related information.
Taking into account the gene information and the input identifier, we re-
trieved the relative start and end location of each gene from the Ensembl
database. The absolute start and end locations were calculated and the genes
(geneID) were then mapped to the genome.
To determine the number of clusters for a given set of differentially ex-
pressed genes, all genes represented on the array were aligned according to
their physical position on the chromosomes. We then counted the number
of genes from the set of differentially expressed genes in a moving window
of 10 genes and recorded the largest clusters. As it turned out that in some
experiments clusters of differentially expressed genes were immediately
neighbored, we appended an assembly step, where clusters were joined,
when they were ?10 genes apart. The significance of the clustering was de-
termined by repeating the same procedure 1,000 times in each case with a
list of random genes of the same length as the experimental dataset, and we
compared the results with the number of clusters found. This simulation
yielded the empirical null distribution which allows p-values to be derived.
If, for example, the number of clusters of size 3 was five for a particular gene
set, and this value was reached or exceeded only twice in 1,000 simulations,
then the empirical p-value would be 2/1,000 ? 0.002.
In this analysis we did not exclude homologous genes or gene families,
as expression of individual members of such gene families may also reflect
promiscuous gene expression (7). We independently assessed gene cluster-
ing by calculating the number of pairs of genes that were located on the
same chromosome within a distance of 35, 50, 80, 120, 200, 300, 500,
1,000, 2,000, 3,000, or 5,000 kb. The numbers obtained from the list of
genes overexpressed in a given cell type were compared with those ob-
tained from 1,000 random lists. p-values were calculated from the empirical
distribution according to Roy et al. (42).
To check for diversity of gene expression in different tissues, we inves-
tigated a panel of gene expression measurements from mouse liver, DCs,
and macrophages, as well as cortical and medullary thymic cells, by
ANOVA. A one-way design with tissue type as the only factor was applied.
p-values from the F test were corrected for multiple testing by applying the
procedure of Benjamini and Hochberg (43). Genes with adjusted p-values
?0.01 were considered to be significantly differentially expressed. These
genes were ordered by the tissue type in which they displayed characteristi-
cally high expression. Gene expression of these genes was visualized by heat
maps of the z-transformed values. The z-transform brings values for each
gene to zero mean and unit variance. Rows and columns of the gene ex-
pression matrix visualized by the heat map have been reordered by hierar-
chical clustering of euclidean distances using the complete linkage algorithm
(44). All calculations were performed in R version 2.0.1 (http://www.R-
project.org) with the extension package multitest, version 1.5.2 (45).
Online supplemental material. Fig. S1 shows validation of the criteria
for the identification of genes identified as overexpressed in mTECs as com-
pared with cTECs by real-time PCR (normalized to ?-actin) of RNA iso-
lated from purified thymic APCs of young adult male and female C57BL/6
mice. Fig. S2 shows the tissue representation in mutual comparisons of glo-
bal gene expression between mTECs, DCs, and macrophages. Fig. S3
shows the frequencies of neighbored genes in the gene pools defined by the
comparison of various thymic stromal cell subsets.
Table S1 lists all genes overexpressed in mTECs versus cTECs accord-
ing to chosen criteria described in Material and methods. Table S2 lists all
genes overexpressed in the CD80hi versus CD80lo mTEC subset. Table S3
lists all genes overexpressed in the Aire?/? CD80hi versus Aire?/? CD80lo
mTEC subset. Table S4 lists cathepsins overexpressed in mTECs versus
cTECs, CD80hi versus CD80lo mTECs, or Aire?/? CD80hi versus Aire?/?
CD80lo mTEC subsets. Table S5 lists chemokines overexpressed in mTECs
versus cTECs, CD80hi versus CD80lo mTECs, or Aire?/? CD80hi versus
Aire?/? CD80lo mTEC subsets. Online supplemental material is available at
We thank Klaus Hexel, Gordon Barkowsky, and Manuel Scheuermann for cell sorting
and Steffi Rösch and Esmail Rezavandy for excellent technical assistance. We are
indebted to Marc Kenzelmann and Ralf Klären for generous advice on RNA
amplification and microarray analysis, Tim Wintermantel and David Engblom for
providing reagents, and Wolfgang Schmid and Jörn Gotter for critical comments.
This work was supported by the German Cancer Research Center and the
Deutsche Forschungsgemeinschaft (grant SFB 405). B. Kyewski is supported by the
European Union–funded consortium “Thymaide.”
The authors have no conflicting financial interests.
Submitted: 4 March 2005
Accepted: 29 April 2005
Note added in proof. A recent study reports clustering of the subset of promiscuously
expressed genes controlled by Aire (Johnnidis, J.B., E.S. Venanzi, D.J. Taxman, J.P. Ting,
C.O. Benoist, and D.J. Mathis. 2005. Proc. Natl. Sci. USA. 102:7233–7238).
1. Kyewski, B., and J. Derbinski. 2004. Self-representation in the thymus:
an extended view. Nat. Rev. Immunol. 4:688–698.
2. Sakaguchi, S. 2004. Naturally arising CD4? regulatory t cells for im-
munologic self-tolerance and negative control of immune responses.
Annu. Rev. Immunol. 22:531–562.
3. Klein, L., M. Klugmann, K.A. Nave, V.K. Tuohy, and B. Kyewski.
2000. Shaping of the autoreactive T-cell repertoire by a splice variant
of self protein expressed in thymic epithelial cells. Nat. Med. 6:56–61.
4. Avichezer, D., R.S. Grajewski, C.C. Chan, M.J. Mattapallil, P.B. Sil-
ver, J.A. Raber, G.I. Liou, B. Wiggert, G.M. Lewis, L.A. Donoso, and
R.R. Caspi. 2003. An immunologically privileged retinal antigen elic-
its tolerance: major role for central selection mechanisms. J. Exp. Med.
5. Derbinski, J., A. Schulte, B. Kyewski, and L. Klein. 2001. Promiscuous
gene expression in medullary thymic epithelial cells mirrors the periph-
eral self. Nat. Immunol. 2:1032–1039.
6. Anderson, M.S., E.S. Venanzi, L. Klein, Z. Chen, S.P. Berzins, S.J.
Turley, H. von Boehmer, R. Bronson, A. Dierich, C. Benoist, and D.
Mathis. 2002. Projection of an immunological self shadow within the
thymus by the aire protein. Science. 298:1395–1401.
JEM VOL. 202, July 4, 2005
7. Gotter, J., B. Brors, M. Hergenhahn, and B. Kyewski. 2004. Medullary
epithelial cells of the human thymus express a highly diverse selection
of tissue-specific genes colocalized in chromosomal clusters. J. Exp.
8. Pitkanen, J., and P. Peterson. 2003. Autoimmune regulator: from loss
of function to autoimmunity. Genes Immun. 4:12–21.
9. Vogel, A., C.P. Strassburg, P. Obermayer-Straub, G. Brabant, and
M.P. Manns. 2002. The genetic background of autoimmune polyen-
docrinopathy-candidiasis-ectodermal dystrophy and its autoimmune
disease components. J. Mol. Med. 80:201–211.
10. Soderbergh, A., A.G. Myhre, O. Ekwall, G. Gebre-Medhin, H. Hed-
strand, E. Landgren, A. Miettinen, P. Eskelin, M. Halonen, T. Tuomi,
et al. 2004. Prevalence and clinical associations of 10 defined autoanti-
bodies in autoimmune polyendocrine syndrome type I. J. Clin. Endo-
crinol. Metab. 89:557–562.
11. Klein, L., and B. Kyewski. 2000. “Promiscuous” expression of tissue
antigens in the thymus: a key to T-cell tolerance and autoimmunity? J.
Mol. Med. 78:483–494.
12. Liston, A., D.H. Gray, S. Lesage, A.L. Fletcher, J. Wilson, K.E. Web-
ster, H.S. Scott, R.L. Boyd, L. Peltonen, and C.C. Goodnow. 2004.
Gene dosage–limiting role of Aire in thymic expression, clonal dele-
tion, and organ-specific autoimmunity. J. Exp. Med. 200:1015–1026.
13. Chin, R.K., J.C. Lo, O. Kim, S.E. Blink, P.A. Christiansen, P. Peter-
son, Y. Wang, C. Ware, and Y.X. Fu. 2003. Lymphotoxin pathway
directs thymic Aire expression. Nat. Immunol. 4:1121–1127.
14. Kyewski, B., J. Derbinski, J. Gotter, and L. Klein. 2002. Promiscuous
gene expression and central T-cell tolerance: more than meets the eye.
Trends Immunol. 23:364–371.
15. Gordon, J., V.A. Wilson, N.F. Blair, J. Sheridan, A. Farley, L. Wilson,
N.R. Manley, and C.C. Blackburn. 2004. Functional evidence for a single
endodermal origin for the thymic epithelium. Nat. Immunol. 5:546–553.
16. Rijnkels, M., D.A. Wheeler, H.A. de Boer, and F.R. Pieper. 1997.
Structure and expression of the mouse casein gene locus. Mamm. Ge-
17. Korley, R., F. Pouresmaeili, and R. Oko. 1997. Analysis of the protein
composition of the mouse sperm perinuclear theca and characterization
of its major protein constituent. Biol. Reprod. 57:1426–1432.
18. Nelson, A.J., S. Hosier, W. Brady, P.S. Linsley, and A.G. Farr. 1993.
Medullary thymic epithelium expresses a ligand for CTLA4 in situ and
in vitro. J. Immunol. 151:2453–2461.
19. Heino, M., P. Peterson, N. Sillanpaa, S. Guerin, L. Wu, G. Anderson,
H.S. Scott, S.E. Antonarakis, J. Kudoh, N. Shimizu, et al. 2000. RNA
and protein expression of the murine autoimmune regulator gene
(Aire) in normal, RelB-deficient and in NOD mouse. Eur. J. Immunol.
20. Wang, P.J., J.R. McCarrey, F. Yang, and D.C. Page. 2001. An abun-
dance of X-linked genes expressed in spermatogonia. Nat. Genet. 27:
21. Ramalho-Santos, M., S. Yoon, Y. Matsuzaki, R.C. Mulligan, and
D.A. Melton. 2002. “Stemness”: transcriptional profiling of embryonic
and adult stem cells. Science. 298:597–600.
22. Hurst, L.D., C. Pal, and M.J. Lercher. 2004. The evolutionary dynam-
ics of eukaryotic gene order. Nat. Rev. Genet. 5:299–310.
23. Reik, W., and J. Walter. 2001. Genomic imprinting: parental influence
on the genome. Nat. Rev. Genet. 2:21–32.
24. DeChiara, T.M., E.J. Robertson, and A. Efstratiadis. 1991. Parental
imprinting of the mouse insulin-like growth factor II gene. Cell. 64:
25. Hemberger, M., C. Redies, R. Krause, J. Oswald, J. Walter, and R.H.
Fundele. 1998. H19 and Igf2 are expressed and differentially imprinted
in neuroectoderm-derived cells in the mouse brain. Dev. Genes Evol.
26. Charalambous, M., T.R. Menheniott, W.R. Bennett, S.M. Kelly, G.
Dell, L. Dandolo, and A. Ward. 2004. An enhancer element at the
Igf2/H19 locus drives gene expression in both imprinted and non-
imprinted tissues. Dev. Biol. 271:488–497.
27. Farr, A.G., J.L. Dooley, and M. Erickson. 2002. Organization of thy-
mic medullary epithelial heterogeneity: implications for mechanisms of
epithelial differentiation. Immunol. Rev. 189:20–27.
28. Chentoufi, A.A., and C. Polychronakos. 2002. Insulin expression levels
in the thymus modulate insulin-specific autoreactive T-cell tolerance:
the mechanism by which the IDDM2 locus may predispose to diabe-
tes. Diabetes. 51:1383–1390.
29. Liston, A., S. Lesage, J. Wilson, L. Peltonen, and C.C. Goodnow.
2003. Aire regulates negative selection of organ-specific T cells. Nat.
30. Sillanpaa, N., C.G. Magureanu, A. Murumagi, A. Reinikainen, A. West,
A. Manninen, M. Lahti, A. Ranki, K. Saksela, K. Krohn, et al. 2004. Au-
toimmune regulator induced changes in the gene expression profile of hu-
man monocyte-dendritic cell-lineage. Mol. Immunol. 41:1185–1198.
31. Miyamoto, T., H. Iwasaki, B. Reizis, M. Ye, T. Graf, I.L. Weissman,
and K. Akashi. 2002. Myeloid or lymphoid promiscuity as a critical
step in hematopoietic lineage commitment. Dev. Cell. 3:137–147.
32. Zipori, D. 2004. The nature of stem cells: state rather than entity. Nat.
Rev. Genet. 5:873–878.
33. Jackson-Grusby, L., C. Beard, R. Possemato, M. Tudor, D. Fam-
brough, G. Csankovszki, J. Dausman, P. Lee, C. Wilson, E. Lander,
and R. Jaenisch. 2001. Loss of genomic methylation causes p53-depen-
dent apoptosis and epigenetic deregulation. Nat. Genet. 27:31–39.
34. Nil, A., E. Firat, V. Sobek, K. Eichmann, and G. Niedermann. 2004.
Expression of housekeeping and immunoproteasome subunit genes is
differentially regulated in positively and negatively selecting thymic
stroma subsets. Eur. J. Immunol. 34:2681–2689.
35. Lorenz, R.G., and P.M. Allen. 1989. Thymic cortical epithelial cells
lack full capacity for antigen presentation. Nature. 340:557–559.
36. Yoneyama, H., S. Narumi, Y. Zhang, M. Murai, M. Baggiolini, A. Lan-
zavecchia, T. Ichida, H. Asakura, and K. Matsushima. 2002. Pivotal role
of dendritic cell–derived CXCL10 in the retention of T helper cell 1
lymphocytes in secondary lymph nodes. J. Exp. Med. 195:1257–1266.
37. Langbein, L., U.F. Pape, C. Grund, C. Kuhn, S. Praetzel, I. Moll, R.
Moll, and W.W. Franke. 2003. Tight junction-related structures in the
absence of a lumen: occludin, claudins and tight junction plaque pro-
teins in densely packed cell formations of stratified epithelia and squa-
mous cell carcinomas. Eur. J. Cell Biol. 82:385–400.
38. Cabral, A., P. Voskamp, A.M. Cleton-Jansen, A. South, D. Nizetic,
and C. Backendorf. 2001. Structural organization and regulation of the
small proline-rich family of cornified envelope precursors suggest a role
in adaptive barrier function. J. Biol. Chem. 276:19231–19237.
39. Ramsey, C., O. Winqvist, L. Puhakka, M. Halonen, A. Moro, O.
Kampe, P. Eskelin, M. Pelto-Huikko, and L. Peltonen. 2002. Aire de-
ficient mice develop multiple features of APECED phenotype and
show altered immune response. Hum. Mol. Genet. 11:397–409.
40. Kenzelmann, M., R. Klaren, M. Hergenhahn, M. Bonrouhi, H.J. Grone,
W. Schmid, and G. Schutz. 2004. High-accuracy amplification of nano-
gram total RNA amounts for gene profiling. Genomics. 83:550–558.
41. Su, A.I., T. Wiltshire, S. Batalov, H. Lapp, K.A. Ching, D. Block, J.
Zhang, R. Soden, M. Hayakawa, G. Kreiman, et al. 2004. A gene atlas
of the mouse and human protein-encoding transcriptomes. Proc. Natl.
Acad. Sci. USA. 101:6062–6067.
42. Roy, P.J., J.M. Stuart, J. Lund, and S.K. Kim. 2002. Chromosomal
clustering of muscle-expressed genes in Caenorhabditis elegans. Nature.
43. Benjamini, Y., and Y. Hochberg. 1995. Controlling the false discovery
rate: a practical and powerful approach to multiple testing. J. Roy. Stat.
Soc. Ser. B. 57:289–300.
44. Eisen, M.B., P.T. Spellman, P.O. Brown, and D. Botstein. 1998. Clus-
ter analysis and display of genome-wide expression patterns. Proc. Natl.
Acad. Sci. USA. 95:14863–14868.
45. Pollard, K.S., S. Dudoit, and M.J. van der Laan. 2004. Multiple testing
procedures: R multtest package and applications to genomics. U.C.
Berkeley Division of Biostatistics Working Paper Series. Working Paper