Regulatory T cells and dendritic cells in transplantation tolerance: molecular markers and...

Regulatory T cells and dendritic cells

in transplantation tolerance:

molecular markers and mechanisms

Stephen P. Cobbold

Kathleen F. Nolan

Luis Graca

Raquel Castejon

Alain Le Moine

Mark Frewin

Susan Humm

Elizabeth Adams

Sara Thompson

Diana Zelenika

Alison Paterson

Stephen Yates

Paul J. Fairchild

Herman Waldmann

Authors’ addresses

Stephen P. Cobbold1, Kathleen F. Nolan1, Luis Graca1,

Raquel Castejon2, Alain Le Moine3, Mark Frewin1, Susan

Humm1, Elizabeth Adams1, Sara Thompson4, Diana

Zelenika5, Alison Paterson1, Stephen Yates1, Paul

J. Fairchild1, Herman Waldmann1,1Sir William Dunn School of Pathology,

University of Oxford, Oxford, UK.2Laboratorio de Medicina Internal, Clınica

Puerta de Hierro, Madrid, Spain.3Faculte de Medecine, Hospital Erasme,

Bruxelles, Belgium.4University of Cambridge Neurology Unit,

Addenbrookes Hospital, Cambridge, UK.5Center National de Genotypage, Evry, France.

Correspondence to:

Stephen P. Cobbold

Therapeutic Immunology Group

Sir William Dunn School of Pathology

South Parks Road

Oxford OX1 3RE, UK

Tel.: þ44 (0) 1865 275504

Fax: þ44 (0) 1865 275501

E-mail: [email protected]

Acknowledgements

This work was supported by a program grant from the

Medical Research Council, UK, and by TolerRx Inc.,

Boston, MA, USA.

Summary: Transplantation tolerance can be induced in adult rodentsusing monoclonal antibodies against coreceptor or costimulationmolecules on the surface of T cells. There are currently two well-characterized populations of T cells, demonstrating regulatory capacity: the‘natural’ CD4þCD25þ T cells and the interleukin (IL)-10-producing Tr1cells. Although both types of regulatory T cells can induce transplantationtolerance under appropriate conditions, it is not clear whether either oneplays any role in drug-induced dominant tolerance, primarily due to alack of clear-cut molecular or functional markers. Similarly, althoughdendritic cells (DCs) can be pharmacologically manipulated to promotetolerance, the phenotype of such populations remains poorly defined. Wehave used serial analysis of gene expression (SAGE) with 29 differentT-cell and antigen-presenting cell libraries to identify gene-expressionsignatures associated with immune regulation. We found that independ-ently derived, regulatory Tr1-like clones were highly concordant in theirpatterns of gene expression but were quite distinct from CD4þCD25þ

regulatory T cells from the spleen. DCs that were treated with thetolerance-enhancing agents IL-10 or vitamin D3 expressed a genesignature reflecting a functional specification in common with the mostimmature DCs derived from embryonic stem cells.

Introduction

The availability of a diverse array of immunosuppressive drugs

has enabled the transplantation of cells and organs to become

a rapidly evolving therapeutic modality. These drugs usually

require lifelong administration and patient compliance, and

they risk side-effects such as susceptibility to infections, can-

cer, and a host of other adverse events. Clearly, the field of

transplantation would benefit if one could harness tolerance

mechanisms normally used by the immune system, reducing

the need for these drugs. The mechanisms exploited need not

reflect the same hierarchy as those used by the normal

immune system to ensure self-tolerance, but rather, those

best suited to the circumstances of the patient could be util-

ized. CD4þ regulatory T cells mediating a suppression of graft

rejection were discovered as a result of somewhat empirical

Immunological Reviews 2003

Vol. 196: 109–124

Printed in Denmark. All rights reserved

Copyright � Blackwell Munksgaard 2003

Immunological Reviews0105-2896

109

attempts in rodents to induce transplantation tolerance in a

mature immune system (1, 2). As dominant effectors of tolerance

processes, they would seem eminently suitable for exploitation.

This utilization requires that we understand exactly what they are

and how they work. However, their full characterization has been

hampered by a lack of appropriate surface markers and suitable

in vitro and in vivo assays. The parallel discovery of so-called natural

(CD4þCD25þ) regulatory T cells that prevent autoimmune

disease and chronic immunopathology (3, 4) has led many to

assume that these same T cells were responsible for all forms of

regulation by CD4þ T cells. That assumption, however, has not

yet been formally demonstrated. Indeed, evidence for hetero-

geneity of regulatory T cells already exists with the description

of Tr1 cells, which can also prevent immunopathology (5).

Transplantation tolerance maintained by CD4+

regulatory T cells

There are many ways of generating tolerance, and it is not yet

clear whether the same regulatory T cells are generated or

whether they function through identical mechanisms in dif-

ferent circumstances. In general, CD4þ regulatory T cells seem

to appear in any circumstance where tolerance has been

induced to transplanted tissues without the presence of

donor-derived hematopoietic macrochimerism. Regulatory

T cells have been demonstrated where tolerance is induced

with a diversity of antibodies (CD4, CD3, CD2, and CD154)

and a range of different drugs (6). More recently, a number of

reports have demonstrated that regulatory T cells can be

elicited where antigens are presented by dendritic cells (DCs)

that are pharmacologically treated to be decommissioned from

full maturation (7–12). Following any intervention that may

generate tolerance, it may be many weeks before regulatory

T cells become detectable in functional readouts (13, 14). They

are also highly dependent on a continuous supply of antigen;

tolerant cells, parked in T-cell deficient recipients, rapidly lose

the ability to suppress unless re-exposed to antigen (13, 15, 16).

The question of antigen specificity

Regulation, once induced, shows specificity for the eliciting

antigen, yet it can extend to other alloantigens located in

the same tissue (linked suppression). For example, an A-type

animal tolerant of B will often accept grafts carrying third-

party antigens from (B� C)F1 donors (16–20). Over time,

animals then become tolerant to C-type grafts. Tolerance is

‘infectious’ (21); the use of genetically marked cell popula-

tions has demonstrated that CD4þ regulatory cells not only

suppress naıve T cells but also guide them into a state of

tolerance. Such second-generation tolerance is also of the

dominant type, i.e. it is also dependent on regulatory CD4þ

T cells (2, 16, 19, 22). This process can be reiterated over

several generations of naıve recipients. The observation of

linked suppression further supports the notion that the

antigen-presenting cell (APC) is a key component in the

regulatory process. In the same way that CD4þ T cells recog-

nize foreign proteins presented on APCs, regulatory T cells

recognize their target graft-derived antigens after they have

been processed/reprocessed by host-type APCs, i.e. through

indirect presentation (23). Regulatory T cells can be found

colonizing tolerated tissues, such as accepted skin grafts (24),

and T cells from such tolerated tissue can repopulate the

peripheral immune system of immunodeficient recipients so

as to prevent graft rejection by naıve T cells. T cells from

tolerated kidneys, for example, proved exceptionally potent

compared to those from spleen, suggesting enrichment within

the tolerated tissue (25).

Identifying regulatory T-cell populations

A number of key properties of the dominant tolerant state have

been defined, but it is not yet clear whether the emerging

functions of either CD4þCD25þ or Tr1-like regulatory cells are

sufficient to explain all of these properties. Adoptive transfer

studies in lymphopenic mice have suggested that, in the case

of grafts mismatched at multiple minor histocompatibility

antigens, both CD4þCD25þ and CD4þCD25– T cells can

suppress rejection by naıve T cells. Similar adoptive transfer

studies, with T cells from animals tolerant across major histo-

compatibility complex (MHC) barriers, suggest that only

CD4þCD25þ T cells can be shown to block rejection (26,

27). Contrary to published reports (28), we have found no

evidence that CD4þCD25þ T cells carry the specificity for the

tolerizing antigen, as T cells from strain A mice tolerant of

B-type grafts are capable of preventing rejection of grafts

from strain C. Even naıve CD4þCD25þ T cells are able to do

the same (LG and ALM, unpublished data) (Fig. 1).

This finding suggests that our suppression readouts in

lymphopenic hosts may only represent one aspect of regulatory

T-cell activity. In such circumstances, these cells may be acting

to regulate homeostatic expansion irrespective of antigen, and

they may also be more effective competitors in repopulation

(29). In light of these data, the surface phenotype of regulatory

T cells in transplantation tolerance remains unresolved.

CD4þ T-cell populations from tolerant mice are able to

suppress rejection by both naıve and primed CD4þ or CD8þ

Cobbold et al �Regulatory T cells and APCs in tolerance

110 Immunological Reviews 196/2003

T cells (30) on adoptive transfer to lymphopenic recipients.

Tr1-like clones can also prevent graft rejection by fully differ-

entiated effector T-helper (Th)-1 and Th2 clones (Fig. 2) in

such lymphopenic models (31). Consequently, it would be

important to establish the relationship, if any, between

CD4þCD25þ T cells and Tr1-like cells and to determine

whether both may play a part in dominant tolerance. Unfortun-

ately, there are too few discriminating surface markers

described for this study to be done at present.

Naıve T cells introduced into a mouse exhibiting dominant

tolerance can still proliferate and accumulate in response to

graft antigens, but the cells may fail to become competent

effectors (32, 33). There is no a priori theoretical reason or

evidence to suggest that suppression blocks necessarily the

initial T-cell proliferative response to antigen. Indeed, there

has never been a strong correlation between tolerance and any

loss of donor antigen-driven T-cell proliferation, whether

measured in vitro or in vivo, in the dominant transplantation

tolerance models (6, 15, 32). Therefore, we have to remain

very cautious in any functional interpretations based on the

suppression of proliferation in vitro or on the homeostatic

expansion of T cells in vivo. It may be that many of the

molecules so far implicated in suppressive mechanisms, such

as cytotoxic T-lymphocyte antigen-4 (CTLA-4), interleukin

(IL)-10 (26, 34), and transforming growth factor (TGF)-b(35), are only major contributors under experimental condi-

tions where an inhibition of proliferation is a required com-

ponent.

We have interpreted the fundamental observations above as

indicating that the generation of regulatory T cells is a normal

evoked response of the immune system where immunity has

been compromised by antibodies or drugs. The data also

CD25+

CD25+

H-2dTolerant

H-2dTolerant

B10BRTolerant

CBA (H-2k) RAG-KO recipients

CD25–CD4+ T cells

CD4+ T cells

CD4+ T cells

CD4+ T cells

CD4+ T cells

CD25–

CD25–

Naïve

Skin graft

Rejection

Rejection

Non-specificsuppression

Non-specificsuppression

Specificsuppression

Skin grafts

12 weeks

12 weeks

12 weeks

12 weeks

12 weeks

Naïve

H-2d H-2d H-2b

H-2d H-2b

H-2d H-2b

H-2d H-2b

H-2b

H-2d

H-2d

H-2d

B10BR B10BR

Fig. 1. A comparison of CD25þ and CD25–

regulatory T cells in transplantation models. Arecognized assay for regulatory T-cell activity is tocotransfer the population of interest intoimmunodeficient recipient mice together withnormal naıve spleen cells capable of rejecting a graft,unless they are suppressed by the test population.Spleen cells from either tolerant mice (given a skingraft and non-depleting anti-CD4, anti-CD8, andanti-CD40L mAbs at least 4 weeks previously) ornormal mice can be sorted (magnetically or in aflow cytometer) into CD4þ populations that areeither CD25þ or CD25–, and cotransferred withnaıve CD4þ T cells into immunodeficient[recombination-activating gene (RAG)-1 knockout]recipient mice. Such mice receiving CD25– cells areunable to suppress the rejection of majorhistocompatibility complex (MHC)-mismatchedskin grafts, regardless of whether the cells camefrom tolerant or naıve donors, and in fact, CD25–

cells from tolerant mice can cause rejection on theirown (data not shown). Mice given CD25þ cells,however, accept skin grafts from MHC incompatibledonors, and this acceptance does not require the Tcells to have previously experienced the donorantigens, as naıve or tolerant CD25þ cells are equallyeffective. Furthermore, if recipient mice arerechallenged with fresh donor plus third partygrafts, the suppression observed is not specific to theoriginal donor-type MHC antigens. In contrast,when skin grafts differ only in non-MHC antigens(i.e. minor antigens), the CD25– cells from tolerantmice (but not naıve mice) are not only suppressivebut also are able to transfer antigen-specificregulation and infectious tolerance. CD25þ cellsfrom either tolerant or naıve mice are able tosuppress the rejection of non-MHC skin grafts, butas above, the effect is found to be non-antigenspecific.


Immunological Reviews 196/2003 111

suggest that the influence of regulatory T cells is local rather

than systemic. This effect could result from their action on

inductive events within the microenvironment of the APC, at

the effector stage in the transplant itself, or both. Infectious

tolerance, the process by which one set of regulatory T cells

encourages development of further cohorts of regulatory

T cells, could be explained as a consequence of graft accept-

ance (6). An accepted healed graft would provide a steady

source of antigens to immature DCs, so enabling these DCs to

elicit further regulatory T-cell function from the naıve T-cell

population that can ‘chronically’ stimulate new T cells, which

are prevented from rejecting the graft. In other words, the

process of generating and maintaining further cohorts of

regulatory T cells appears to be self-sustaining once the graft

is accepted. Removal of the graft would, as a corollary, remove

the drive for this process.

Drugs or blocking therapeutic antibodies that enable regulatory

T cells to be generated

We have, in a previous review, proposed the following

explanation for how drugs and antibodies may bring about

dominant tolerance (36). During the induction phase of

tolerance, effective agents block rejection by preventing

T cells from generating effector function. Consequently,

the inflammation in the graft settles down and the graft

heals. Graft antigens constitutively released by the

transplant organ will be processed and presented to

T cells by host APCs. Such APCs would remain decom-

missioned, as they would have no ‘danger’ signals (37)

to activate them. Instead, we have proposed, they would

generate chronic, albeit incomplete, signals to T cells

that recognize them. Sustained but incomplete signaling,

we propose, is what drives T cells to regulatory activity.

All T cells could be vulnerable to such sustained incom-

plete signaling irrespective of their prior history, i.e. Th1,

Th2, or Th0. The result would be that some T cells

become regulatory, resetting their threshold for activation

so as to appear functionally anergic. Aware of the

presence of anergic T cells in recipients showing dom-

inant tolerance (38, 39), we speculated that such T cells

could include regulatory T cells. The ‘Civil Service’

analogy (6, 40, 41) was coined to reflect the ability of

compromised ineffectual T cells to obstruct the function

Th1

4 weeks

4 weeks

4 weeks

4 weeks

Skin grafts Skin graft

Rejection

Rejection

Suppression

Suppression

Male MaleFemale

Female RAG-KO recipients

Th2

Anti-HY

Anti-HY

Anti-HY

Anti-HY

Tr1

Tr1

Th1

Th2

Th1

Th2

Fig. 2. Tr1-like clones can suppress rejection byboth Th1 and Th2 clones with identical

specificity. T-cell clones with identical specificity forthe male antigen H-Y as presented by H-2Ek weregenerated from female A1�RAG-1–/– T-cellreceptor-transgenic mice. It was found that both Th1and Th2 clones were able to rapidly reject male, butnot control female, skin grafts given to recipientRAG-1 knockout mice. Such mice could be givenfresh male skin grafts and further injections of theTh1 or Th2 clones and were still able to causerejection as expected. In contrast, Tr1-like cloneswere unable to cause skin graft rejection. If theserecipients were later given fresh male skin grafts andsufficient Th1 or Th2 clones to cause rejection incontrol mice that had not previously received Tr1cells, rejection was abolished, demonstrating theability of Tr1-like cells to act as regulatory cells in atransplantation setting.



of otherwise competent T cells. This hypothesis predicted

that any agent enabling chronic, but incomplete signaling,

of T cells would achieve the same outcome. Drugs that

decommission DCs or those that interfere with effector

functions of T cells might all enable graft acceptance and

consequently guarantee a continuous source of antigen to

sustain the incomplete signals to T cells. Regulatory T cells

would, by virtue of their appropriate T-cell receptor (TCR),

localize to sites of maximal antigen exposure and act there to

subvert the immune (rejection) response.

Characterizing the regulatory T cells and the dendritic cells that

drive them

From the discussion above, it is clear that further charac-

terization of regulatory T cells, the DCs that drive them,

and any heterogeneity of such cells requires us to establish

what patterns of gene products they might be expressing,

both as diagnostic markers and as plausible explanations for

their regulatory function. The following sections represent

our efforts in this direction, and we not only provide the

reader with a list of gene products of interest but also a

resource that we hope others will utilize to compare the

phenotypes of regulatory T cells and tolerogenic DCs in

their own laboratories.

Gene expression analysis of regulatory T cells and

dendritic cells

It is clear from the discussion above that we are in desperate

need of molecular markers that can distinguish between dif-

ferent regulatory T cell and dendritic cell populations. It even

remains a fundamental question whether there are specific

T cells with a ‘professional’ regulatory function or more global

mechanisms of immune regulation, shared across a range of

cells. One way to approach these problems is to correlate gene

expression with functional subsets in a broad and unbiased

way, without any preconceptions about which gene products

are likely to be discriminatory.

Proteomics is still insufficiently advanced for this purpose.

Alternatively, one can analyze mRNA transcripts as an indirect

measure of potential protein expression. The two main

methods are based either on hybridization to a microarray of

known cDNAs or sampling and sequencing of short tags

reflecting the entire population of mRNA from a given cell

type. While microarrays have some advantages in conveni-

ence, they are limited to known gene sequences and are

difficult to standardize, due to variability of sequence

dependent hybridization. Serial analysis of gene expression

(SAGE) (42) works by extracting tags and concatenating

them for efficient sequencing, and the relative abundance of

each tag is a measure of the frequency of the associated

transcript. Such data then, in principle, can be directly

compared between libraries, even if they derive from different

laboratories.

We focused our analysis on the question of how the

immune system is regulated and whether there are gene

signatures that correlate with pro-tolerogenic compared to

immunogenic phenotypes. We used SAGE libraries of well-

characterized resting Th1, Th2, and Tr1-like clones (43, 44) as

a starting point, and then we included a range of purified

ex vivo T cells (27) and APCs (dendritic and B cells) at different

stages of maturation and after treatment with agents thought

to bias antigen presentation for tolerance (10, 45). We have

been able to identify gene signatures of regulatory T cells,

which suggest that these cells reflect an altered state of

known T-cell types, and of pro-tolerogenic DCs, which reflect

expression of a defined gene set. In addition, this study

establishes the limits of utility of current SAGE methodology

for this purpose.

Reliability and limitations of SAGE analysis

The SAGE analysis presented here is based on 29 different

libraries (Table 1) and is made up of 811 804 tags, containing

24259 unique tags represented three or more times in total. Of

these, 682 tags were mapped to ribosomal proteins (excluded

from further analysis), and 625 tags were hand annotated for

known genes. Four thousand seven hundred and sixty two were

computationally assigned to known mRNAs, 4117 to Unigene

clusters with a putative poly A signal sequence, 1853 to

expressed sequence tags (ESTs) from Unigene clusters without

a poly A signal, and 13 420 were unassigned.

The high number of unassigned tags was of considerable

interest. This occurrence has been noted in previous SAGE

analyses (46–48), where the unassigned tags were considered

novel transcripts (48). Closer inspection of these tags in our

analysis suggested that approximately 60% of them could be

explained by non-random accumulation of sequence errors in

highly abundant gene tags or linker sequences. Using the

highly abundant ferritin heavy chain tag as an example, 21

tags differing by a single base were found to occur >6 times

within the data. The most abundant putative error tag repre-

sented approximately 3%, and similar tags made up approxi-

mately 12% of the total. In addition, tags from incompletely



Table 1. Details of SAGE Libraries

Library name Details of cell type Number of tags Preamplified method NCBI GEO accession References

Th1 Th1 anti-HY cloneR2.2: derived fromprimed A1�RAG-1–/–

female (resting)

10498 SMART GSM3677 43

Th2 Th2 anti-HY cloneR2.4: derived fromprimed A1�RAG-1–/–

female (resting)

11159 SMART GSM3678 43

Tr1D1(Mast) Treg anti-HY cloneTr1D1 derived fromnaıve A1�RAG-1–/–

female (resting): contains�2% mast cells

30246 SMART GSM3679 43

A1RAG_nodes Draining lymph nodesfrom male skingrafted A1xRAG-1–/–

mice (day 7)

11525 None GSM3680 43

CBA_nodes Draining lymph nodesfrom B10.BR skingrafted CBA/Camice (day 7)

5597 None GSM3687 43

CD4þCD25-Act Splenic CD4þCD25–

T cells (MACS sortedfrom CBA/Ca):CD3 activation (16 h)

21446 SMART GSM3685 27

CD4þCD25– Splenic CD4þ

CD25– T cells(MACS sorted fromCBA/Ca): resting

23802 SMART GSM3683 27

CD4þCD25þ Splenic CD4þCD25þ

T cells (MACS sortedfrom CBA/Ca):resting

21243 SMART GSM3686 27

CD4þCD25þAct Splenic CD4þCD25þ

T cells (MACS sortedfrom CBA/Ca):CD3 activation (16 h)

20926 SMART GSM3684 27

Tr1D1_CD4 Treg clone Tr1D1 (resting):APC depleted,CD4þ (MACS sorted)

12127 SMART GSM3681 43

Skin_LineA Treg line grown fromtolerated male skingraft on A1(M) female mouse

22135 SMART GSM3682 43

Skin_LineB Treg line grown in anti-CTLA4mAb, from toleratedmale skin on A1(M) female

20219 SMART GSM3824

Tr1D1_Act Treg clone Tr1D1 (resting):APC depleted,CD4þ (MACS sorted):CD3 activation (16 h)

21336 SMART GSM3825

Th2_Act Th2 clone R2.4:CD3 activation (16 h)

11316 SMART GSM3826

Th1_Act Th1 clone R2.2:CD3 activation (16 h)

20848 SMART GSM3827

BW5147 Thymoma cell linefrom AKR mice

19750 None GSM3828

ESF_116 Embryonic stem cell line(CBA/Ca derived): multiplepassages in LIF without feeders

15711 None GSM3829

ESDC Embryonic stem cell(ESF116) derived dendritic cells

31723 None GSM3830



NlaIII-digested mRNA were present at up to 5%, consistent

with previous observations (49). The occurrence of these

artefactual tags tended to mirror that of the most abundant

correct tag. Non-random sequencing errors were also found in

many other Unigene ESTs. It has been suggested that these

errors might represent true polymorphisms in human SAGE

analyses (48). In the mouse, most ESTs derive from a few

inbred strains, so they are probably sequence errors.

Many published SAGE libraries claim to approach the entire

transcriptome (50) of a given cell type, often exceeding

100 000 tags (51). The limitations shown above suggest that

artefacts accumulate faster than novel gene tags once the

library reaches a depth equivalent to 0.1–1% of the most

abundant transcripts (in practice between 10 000 and 30 000

total tags), and so most of the libraries described here are

limited to this size. Tags were excluded if they remained

unidentified and differed in sequence by only one base from

a more abundant (�10) tag, linker, or primer sequence. In the

future, the new long SAGE method (52) should overcome this

limitation by providing sequence redundancy.

ESDC_LPS Embryonic stem cell (ESF116)derived dendritic cellsþ LPS

31861 SMART GSM3831

BMDC_LPS Bone marrow-deriveddendritic cells (day7)þ LPS

13518 None GSM3832

BMDC Bone marrow-deriveddendritic cells (day7)

22126 None GSM3833

BMDC_IL10 Bone marrow-deriveddendritic cellsþ IL-10 (days 6–9)

31561 None GSM3834

BMDC_IL10LPS Bone marrow-deriveddendritic cellsþ IL-10(days 6–9)þ LPS

31531 None GSM3835

B-cells Splenic B cells(MACS sorted): resting

10148 None GSM3837

3T3_Fibro Untransformed3T3 fibroblasts

28531 None SAGENet* Veculescu, V.(unpublished data)

R1_ES Embryonic stemcell line (B6 derived): R1

137906 None GSM580 53

Brain Normal mouse braingranular precursor cells

61256 None GSM767 Wechsler-Reya, R.,et al.(unpublished data)

abIEL a/b intraepitheliallymphocytes (CD8þ)

91000 None See URLy 54

Grand Total All of the above libraries 811804

Legend to SAGE libraries (43): Total RNA was isolated using thiocyanate buffer and pelleted through a 5.7 M CsCl cushion. The poly(A)þ fraction waspurified using oligo(dT)25 dynabeads (Dynal, Oslo, Norway), and cDNAs generated using a kit from Roche (Lewes, UK). In the case of SMART libraries,total RNA was isolated using the SV total RNA isolation system (Promega). First strand cDNAs were prepared from approximately 1 mg of total RNAusing the anchoring primer: 50-GACTCGAGTTGACATCGAGG(T)20V-30 with the SMART PCR cDNA synthesis kit (Clontech, Palo Alto, CA, USA),and preamplified using the forward SMART PCR and reverse 50-GACTCGAGTTGACATCGAG-30 primers and the advantage-GC cDNA PCRpolymerase mix (Clontech), with 1 M of GC-Melt, for 16–25 cycles of 94 �C for 30 s, 68 �C for 7 min (43). All SAGE used NlaIII as anchoring enzyme,BsmF1 as tagging enzyme, and SphI as cloning enzyme, as described (42, 80). DNA sequencing used either the 377 (ABI) or MegaBACE (MolecularDynamics, Piscataway, NJ, USA) automated sequencers.Generation of Th1, Th2, Treg, and Tskin CD4þ T-cell clones: The production of Th1, Th2, and Tr1D1 clones, and lines from tolerated skin grafts, havebeen described elsewhere (31, 43). Short-term polarized lines were generated from A1(M)�RAG-1–/– (43) mice after Barret et al. (81).MACS enrichment of CD4þ T-cell clones: T-cell cultures were stimulated for 7–14 days with mitomycin-treated male spleen cells from CBK (Kbþ)transgenic mice, and the Kbþ cells depleted by AutoMACSTM (Miltenyi Biotec, Auburn, CA, USA) as described (43). Purity of all fractions was >97%CD4þKb– cells. Activation was overnight with 1mg/ml of plate bound anti-CD3 (KT3).MACS enrichment of spleen CD4þCD25þ cells (27): CBA/Ca mice were depleted of CD8þ T cells and MHC-IIþ and Igþ cells depleted with Dynabeads(Dynal). CD25þ cells were positively selected using biotin antimouse CD25 (Pharmingen, San Diego, CA, USA) and streptavidin MACSTM microbeads.This CD4þCD25þ fraction was >90% pure by fluorescence-associated cell sorter (FACS) analysis. CD4þ cells were positively selected from the CD25–

fraction and were >98% CD4þCD25– cells. Activation was overnight with 1 mg/ml of plate bound anti-CD3 (KT3).Splenic B cells: The B-cell untouched kit (Miltenyi Biotec) and AutoMACSTM was used to obtain a population >93% CD3–B220þ cells from T-cell-depleted CBA/Ca mice.Bone marrow derived DCs: Day 7 bmDCs were generated from CBA/Ca mice (82). 1a,25-dihydroxycholecalciferol(10�7 M day 3–7; Sigma),recombinant mouse IL-10 (20 ng/ml day 6–9, R&D Systems), or lipopolysaccharide (LPS) (1mg/ml for the final 18–20 h; Sigma) were added asappropriate. Each population was >90% CD11cþ, MHC-IIþ and was tested in an allogeneic mixed lymphocyte reaction.ES-derived DCs: ESF116 was maintained and differentiated to dendritic cells (esDC) as described (83), and the esDC matured using LPS (84). ESF116cells for SAGE analysis were passaged six times on gelatinized flasks in 1000 U/mL of recombinant leukemia-inhibitory factor.Database accession numbers: All SAGE libraries from our laboratory have been deposited at GenBank under the NCBI Gene Expression Omnibus SAGEaccession numbers GSM 3677–3687 and GSM 3824–3837 inclusive.*http://www.sagenet.org/SAGEData/3T3.htmyhttp://www.kcl.ac.uk/ip/jeremycridland/research/sage/SAGE_IEL_Master_Database.xls



An important aspect of the SAGE approach is the reliability

of interlibrary comparisons. A comparison of two libraries

from independent regulatory T-cell clones (Fig. 3A) demon-

strated very few differences. Further, two independent

embryonic stem (ES) cell lines [one library from the literature

(53) and one generated in house] were also similar (Fig. 3B),

with differences being compatible with residual fibroblasts

(used to maintain ES cell lines) in the R1 library, as indicated

by overexpression of genes such as procollagen type Ia2 and

fibronectin 1. This finding confirms that useful comparisons

can be made between SAGE data from our own and other

laboratories, and therefore four published datasets [one a/bintraepithelial lymphocyte (IEL) (54) and three non-immune

cell types, see Table 1] have been included.

Relationships between cell types defined by gene expression

patterns

Cluster analysis was performed using different tag abundance

thresholds (from 100 to 4000 of the most abundant, but

excluding all ribosomal derived tags) and using a variety of

methods. Most analyses generated similar clusters, and a repre-

sentative dendrogram is shown in Fig. 4A. The relationships

between cell types was broadly compatible with expectations:

CD4þ T-cell clones clustered together, with the three inde-

pendent Tr1-like regulatory T cell (Treg) clones most closely

related [Fig. 4A(iv)], but separate from the ex vivo purified

naıve T cells [Fig. 4A(vii)]. In vitro generated DCs clustered

together [Fig. 4A(ii)], whether derived from bone marrow

(bmDC) or ES cells (esDC), while the two ES cell lines were

most similar to each other [Fig. 4A(i)]. Ex vivo derived resting

B cells clustered with the lymph node populations from nor-

mal or TCR-transgenic mice [Fig. 4A(iii)]. Surprisingly, CD3-

activated Th1, Th2, and Tr1-like clones were generally more

similar to each other [Fig. 4A(v)] than to their resting partners,

but the cells did not particularly resemble CD3-stimulated

naıve CD4þ T cells, whether CD25þ or CD25–.

Distinct clusters of gene tags were associated with each cell

cluster (Fig. 4B). The three genes most strongly correlated

with ES cells were pH 34 (ES cell specific gene 1), Unigene

cluster Mm.30502 and Oct3/4, the latter being a transcrip-

tion factor associated with pluripotency (55). Potential

markers for DCs included CCL9, C-type lectin (calcium

dependent) member 10, and cathepsin H, although these

genes are more widely expressed on myeloid cells. The

B-cell and lymph node cluster included components of the

B-cell receptor [immunoglobulin (Ig) M heavy and J chains,

CD79a,b], although most other tags in this cluster were more

widely distributed (e.g. Igk). The resting Th1 and Th2 clones

clustered together with the Tr1D1 culture dominated by mast

cell products (44). This cluster expressed an unusually high

number of unknown tags as well as CD4 and the g chain of

the common cytokine receptor. The genes most closely

0

0.7

2.1

4.6

9

16.

30.

Ski

n tr

eg li

ne B

Skin treg line B ES cell line ESF116

ES

cel

l lin

e R

155.

99

176

>320 A B

0.7 2.1 4.6 9 16. 30. 55. 99 176 >3200

0.7

2.1

4.6

9

16.

30.

55.Over expressed

Key:99

176

>320

0.7 2.1 4.6 9 16. 30. 55. 99 176 >320

(Nor

mal

ized

tag

freq

uenc

y ×1

04)

(Normalized tag frequency ×104)

Uniquelyexpressed

Non-differential

Common(house keep)Excluded tags

Fig. 3. Reproducibility of serial analysis of gene expression (SAGE)

library data. Scatter plots of SAGE libraries from two independent, skin-derived, Tr1-like lines (A) and two unrelated ES cell lines generated bydifferent laboratories (B). The normalized frequency of each tag is plottedwith green representing non-differential tags and gray representing‘housekeeping’ transcripts, defined as tags with a standard error less thanthe mean across all the libraries. The marked cone encloses an area of>95% confidence of at least 1.5-fold difference, and overexpressed tagsare color-coded as indicated. Tags uniquely overexpressed in only the SAGElibrary shown are marked in red. SAGE data analysis: SAGE 3.04 beta (KWKinzler, Johns Hopkins Oncology Center, Baltimore, MD, USA) was usedto extract ditags and unique tags before export into Access (Microsoft,Seattle, WA, USA). Automated unique tag to gene assignments used the

Unigene full mapping files and a search algorithm giving priority to hand-annotated genes, followed by cDNAs with a polyA signal in the correctorientation, then EST clusters with a probable polyA signal, cDNAs with nopolyA signal, and finally other matching ESTs. The tabulated data wasimported into custom written software (!SAGEClus) for cluster analysis andto generate scatter plots and charts. Tags were excluded if they matched 9/10 bases of any other tag that occurred at a >10-fold frequency within theentire dataset or any linkers or primers used. A conservative estimate of thedifferential upregulation of each was calculated using a Bayesian statisticsmodel (43, 79). An upregulation of 1.5-fold with 95% confidence wasgenerally considered significant. The !SAGEClus software and collated SAGEdata tables can be obtained from the website at http://www.molbiol.ox.ac.uk/pathology/tig/software/softlist.html.



correlating with the cluster of three Tr1-like cells included

some already published as potential Treg markers, such as

glucocorticoid-induced tumor necrosis factor receptor family-

related gene (GITR) (56) and CD137 (57).Most of the transcripts,

however, were highly expressed only on resting Tr1-like cells and

were downregulated after activation with anti-CD3 monoclonal

antibody (mAb). After activation, Tr1D1 clustered with CD3-

activated Th1 and Th2 clones, partly due to coexpression of the

chemokine CCL4 and other unassigned tags.

The analysis of splenic CD4þ T cells was based on a fractionation

into CD25þ and CD25– cells, as the former are thought to include

naturally occurring ‘professional’ Treg cells (58).Most of the SAGE

tags overexpressed in these ex vivo T cells remain uncharacterized

(Fig. 4B). Surprisingly, no splenic subpopulation was closely

related in its pattern of transcripts to any of the T-cell lines or

clones maintained in vitro, even after CD3 activation. a/b IELs and

the BW5147 thymoma were not associated with any of the other

T-cell libraries, although there were no other representatives of

non-CD4þ subsets.

The expression pattern of the few restricted genes (13–30

per cluster and <150 in total) was sufficient to generate

a similar dendrogram, but their removal from the database

did not abolish the clusters. This finding suggests that subtle

patterns of gene expression define genetic and therefore, by

implication, functional relationships between different

immune cell types. The evidence for truly lineage- or subset-

specific markers is relatively weak.

Global patterns of gene expression are primarily associated

with context

Very few genes were found to associate with conventional

lineage relationships. The only genes expressed on all T cells

A BDistance between clusters of cell types (%)

10 20 300

α/β IEL (CD8+)

Brain (PGCP)

R1 (ES cell line)

3T3 (Fibroblast)

ESF-116

BMDC + IL-10 + LPS

ESDC

BMDC + VitD3

BMDC

BMDC + IL10

BMDC + LPS

ESDC + LPS

Th1 (CD3 activated)

Th2 (CD3 activated)

Tr1D1 (CD3 activated)

Skin Treg Line B

Skin Treg Line A

Tr1D1 clone (CD4+)

Splenic B cells

CBA nodes

A1 × RAG nodes

Tr1D1(inc. mast cells)

Th2 clone (R2.4)

Th1 clone (R2.2)

CD4+CD25+ (CD3 act.)

CD4+CD25+ spleen

CD4+CD25– spleen

CD4+CD25– (CD3 act.)

BW5147 (thymoma)

i

ii

iii

iv

v

vi

viiT

h1 (

clon

e R

2.2)

Th2

(cl

one

R2.

4)T

r1D

1 (in

c. m

ast)

A1

× R

AG

–/– n

odes

C

BA

nod

esC

D4+

CD

25+ (

CD

3)C

D4+

CD

25+

CD

4+C

D25

–

CD

4+C

D25

– (C

D3)

Tr1

D1

clon

e (C

D4)

Ski

n tr

eg li

ne A

S

kin

treg

line

B

Tr1

D1

clon

e (C

D3)

Th2

clo

ne (

CD

3)T

h1 c

lone

(C

D3)

BW

5147

thym

oma

ES

F-1

16 (

ES

cel

ls)

ES

DC

ES

DC

+ L

PS

BM

DC

+ L

PS

BM

DC

BM

DC

+ IL

-10

BM

DC

+ IL

-10

+ L

PS

BM

DC

+ V

itD3

B c

ells

3T3

fibro

blas

tR

1 (E

S c

ells

)B

rain

(P

GC

P)

IEL

(α/β

CD

8+)

Unknown, GTAACAACGCUnknown, TCCTACAGTGESTs, TGGTAACAAC, 35653Unknown, TCCTAAAGTGUnknown, GAAAATGATAIL-17/poly Ig R, ATTTTCAGAT, 5419Unknown, GAATATGGCAUnknown, CCAGGTATGAUnknown, TCTAAGTACGRIKEN 1110013G13, ATCGGTTCCA, 212Unknown, GCCGTTCTTAUnknown, CCGACGGGCGUnknown, GGATGTCTCTUnknown, GTCAAGCGAGtrans. reg. SIN3A, CACACACACA, 15755

GITR (TNFRsf18); CTCTGCACCC, 3180CD137 (4-1BB) alt sp1, GGAAACAACT, 3720RGS-1 truncated, CCAAGGTTGA, 103701GA3PDH seq error?, GCTCCAAGGA, 5289kallikrein 8, GACAACAGGG, 5193Unknown, AGGAAGCGGCUnknown, ACCGCCCACARIKEN 2200003J09, ACACCTTCCT, 21160Unknown, ACCAATGAACUnknown, AGTCGGGTGGprotein tyr. phosphatase, GCTGACACTT, 361RIKEN 1110001H19, GGAGCACTGG, 732histone gc 2, GCGGCGGTGC,154564RIKEN 3110001N18, GCGCCGCAGA, 21724Unknown, TTGCTGCCCAUnknown, GCCTTCAGTGtumor stroma, TAGACCAAGC, 116687Unknown, CAAGTGACAGtransgelin 2, CTCTGGGGCT, 22632serine (or C) proteinase, AAGCTACAGT, 2044Ly6A, TATGCCTGTC, 1583TAR (HIV) RNA binding, GTGAGCTTGT, 176Unknown, GCGCCCAGCCUnknown, CCACTTTGGCpituitary tumor-transf. 1,GGAATCTGAT,6856Unknown, CACTACACGGexpr. seq. AU022351, AGGGTGAGGA, 13774RIKEN 2410003F05, CATCCGATCG, 60230CD137 (4-1BB) alt sp2, ACTCCTGGAC,3720Unknown, GACGGAGTGG

Key to Gene expression chart:Abundantly expressed gene (>1%)Abundantly expressed gene (>0.3%)Moderately expressed gene (>0.1%)Significant expression (≥7 tags)

Positive expression (≥3 tags)Positive (not significant <3 tags)Negative (undetected or no tags)Negative (but library <10 000 tags)

Fig. 4. Cluster analysis of serial analysis of gene expression (SAGE)

libraries. A representative dendrogram is shown (A) illustrating therelationships between different SAGE libraries. Pairwise similaritycomparisons were here calculated using the 4000 most abundant tags(excluding ribosomal proteins and artefactual tags) and a root meansquared distance method for logarithmically transformed data with a five-

fold weighting to points with >95% confidence of >1.5-fold change.Clusters of cell types are highlighted and charts that depict expressionpatterns of gene tags most restricted to the Tr1-like Treg (iv) and theCD4þCD25þ (vii) clusters are shown (B), with colors relating to tagfrequency as indicated. Each tag is identified by a description (may betruncated), the 10 bp tag sequence and Unigene cluster number.



were components of the TCR: CD3g, CD3d, TCRa, TCRb, andCD4 (all but one of the T-cell libraries derived from CD4þ

cells). Similarly, the only transcripts unique to B cells were

those related to the Ig receptor (as above), and some Ig genes

were even expressed in Th2 cells, probably germline

transcripts driven from the enhancer in response to Th2 cyto-

kines (59). A pairwise comparison of different T cells before

and after activation (Fig. 5) shows a change in gene expression

after activation similar to that between totally different

lineages (26–59 gene tags were upregulated in the four

T cells analyzed, while the Th1 clone, for example, differs

from fibroblasts, brain granular precursor cells, and ESF116

cells by 42, 36, and 29 increased tags, respectively). This result

cannot be simply explained by suggesting that T-cell activation

induces common genes of cell growth and division because

the genes induced in T cells are generally not shared with, for

example, the fibroblast or BW5147 cell lines that are already

dividing. This finding suggests that active gene expression is

largely ‘contextual’, responding to growth and stimulation

factors, perhaps explaining why there was relatively low

concordance between in vitro and ex vivo T cells. Similarly, the

vitamin D3 and IL-10 plus lipopolysaccharide (LPS) treated

bmDCs clustered most closely with the immature esDCs

[Fig. 4A(ii)], suggesting a common functional context for

these cell populations.

While activated Th1 cells and Tr1/Treg clones expressed

some products exclusively [e.g. interferon (IFN)-g and IL-9,

respectively], all the genes expressed by activated Th2 cells

were shared with either Th1 or Tr1-like cells (Fig. 5B has no

unique tags shown red). The splenic CD4þCD25þ T cells were

quite unlike any of the cloned memory CD4þ subsets (Fig. 5):

they failed to downregulate many gene tags after CD3 activation,

the tags significantly overexpressed were mostly unidentified,

and they did not include cytokines or chemokines that dom-

inated the activated memory cells (see below). It is currently

unclear if there is any relationship between regulatory Tr1-like

clones and ‘natural’ CD4þCD25þ splenic Treg cells, although

both populations have been shown capable of suppressing

autoimmune disease (60, 61) and graft rejection in adoptive

transfer systems (27, 43, 44). Some of the genes overexpressed

on the Tr1-like regulatory cells are known to be expressed

by CD4þCD25þ populations, such as GITR (62), OX40, and

CTLA-4 (63), although these examples are not significantly

expressed in the splenic T-cell SAGE data. This low expression

may be because CD4þCD25þ-sorted cells are heterogeneous

(43), perhaps masking the differential expression of some

Activated Th1 clone

0

0.7

2.1

4.6

9

16.

30.

55.

99

176

>320

0.7 2.1 4.6 9 16. 30. 55. 99176 >320

+

+

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Res

ting

Th1

clo

ne

0

0.7

2.1

4.6

9

16.

30.

55.

99

176

>320

0.7 2.1 4.6 9 16. 30. 55. 99176 >320

+

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Activated Th2 clone

Res

ting

Th2

clo

ne

0

0.7

2.1

4.6

9

16.

30.

55.

99

176

>320

0.7 2.1 4.6 9 16. 30. 55. 99176>320

+

+

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Activated Tr1D1 clone

Res

ting

Tr1

D1

clon

e

0

0.7

2.1

4.6

9

16.

30.

55.

99

176

>320

0.7 2.1 4.6 9 16. 30. 55. 99176>320

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Activated CD4+CD25+

Res

ting

CD

4+C

D25

+

(Nor

mal

ized

tag

freq

uenc

y ×1

04)


A B

DC

Fig. 5. Gene expression after activation ofdifferent CD4+ T-cell subsets. Scatter plotsare shown for four different T cells: (A) Th1clone R2.2 (B) Th2 clone R2.4 (C) Treg cloneTr1D1 (D) splenic CD4þCD25þ cells, in theresting state compared to overnight activationwith plate-bound anti-CD3. Normalized tagfrequencies are plotted on a logarithmic scale,and overexpressed tags are color-coded as inFig. 3.



Treg-associated markers. For example, the tag for FoxP3

(CCTAAGCGTG), a recently described transcription factor

associated with CD25þ Treg cells (64) appeared only once in

this library. We are now in the process of generating SAGE

libraries from more highly purified CD4þCD25þ subsets,

although the phenotype of the most active Treg cell within

this population remains unclear (63, 65).

Gene transcripts that distinguish Th2 and Tr1-like Treg clones

Resting Tr1-like Treg clones have a similar pattern of gene

expression to Th2 cells, but they overexpress transcripts such as

ppENK and Granzyme A with a reduction in the transcription

factors GATA-3 and early growth response gene-1 (Egr-1) (43).

GATA-3 promotes transcription of a number of genes involved in

Th2 effector function (66, 67), such as IL-4, IL-13 and ST2L, and is

itself regulated by the repressor of GATA (ROG) (68). The tags for

these two genes in the SAGE data suggested that GATA-3 was

highly expressed in resting Th2 cells, while ROG was expressed

in the Tr1-like cells. Quantitative reverse transcriptase-polymerase

chain reaction (RT-PCR) confirmed these observations (Fig. 6A),

particularly after CD3 activation, suggesting that ROG may be a

useful marker for Tr1-like regulatory cells. ROG may act in these

cells to repress effector and cytokine function in a manner

analogous to FoxP3 in CD25þ Treg cells (64), as the latter is not

expressed in Tr1-like cells by quantitative RT-PCR (data not

shown).

A gene previously found in both Tr1-like and CD4þCD25þ

cells is the regulator of G protein signaling, RGS-1 (69). Two

different tags mapping to this gene were found in the SAGE

data, one derived from the 30 UTR and the other from the

beginning of the first exon (data not shown). Alternative

polyadenylation signals were found within the third and

fourth introns, which could generate truncated transcripts,

and the presence of these, particularly in the Tr1-like cell

mRNA, was confirmed by specific RT-PCR (Fig. 6B). The trun-

cated forms of RGS-1 no longer encode the regulatory domain

but retain the N-terminal region interacting with target G

proteins and may compete with the full length protein (70).

Regulatory T cells expressing excess truncated isoforms might

be hyper-responsive to certain chemokines.

Abundant transcripts with differential expression across the

immune system

Overall, cytokines, chemokines, and their receptors were the

most abundant and differentially expressed transcripts. A

summary of their expression is shown Fig. 7. Th2 cells have

almost no unique transcripts; they are nearly all shared either

by Th1 or Tr1-like Treg cells. While Th1 cells overexpress a

number of chemokine ligands and receptors, especially after

activation, Tr1-like Treg cells mostly expressed transcripts in

common with Th2 cells, as previously published (43), with

the exception of IL-9 (confirmed by enzyme-linked immuno-

sorbent assay, data not shown). IL-9 is known to be regulated

independently of the other Th2 cytokines (71) and may play a

role in the previously observed association of Tr1-like clones

and mast cells (44). CCR1 and CCR5 (expressed on unstimu-

lated and IL-10-treated bmDCs; Fig. 7) mediate recruitment of

immature DCs to inflamed sites, while a switch to CCR7

expression directs homing to lymphoid organs, where

CCL17 and CCL21 serve to promote DC–T-cell interactions

(as seen in LPS-treated DCs; Fig. 7). Also of note is the receptor

for programmed cell death (PD-1) on both Th2 and Treg

clones, while the PD-1 ligand (PD-1L) is expressed on both

mature and IL-10-treated DCs, where it has been implicated in

the regulation of T-cell responses (72).

IL-10-treated dendritic cells are modulated rather than blocked

from maturing

While mature DCs drive naıve T cells to immunity, immature

DCs have been implicated in mediating tolerance (7). In

addition, IL-10 (10) and vitamin D3 (45) are reported to

modulate DCs in favor of promoting tolerance, either by

inhibiting normal maturation (73) or by generating an alter-

native activation state (74). LPS-induced maturation of bmDCs

induced significant changes in 73 gene tags (Fig. 8A). In

response to IL-10 treatment (Fig. 8B), 31 of these same changes

were seen, suggesting IL-10 itself induces a partial maturation,

while 102 other tags were modulated. After LPS treatment of

these cells, 41 of the 73 changes associated with maturation

were still present, while 71 additional genes were different

from untreated bmDCs (data not shown). In general, the SAGE

data support the hypothesis that IL-10 limits inflammation

whilst enhancing phagocytosis (75) and antigen proces-

sing (cathepsins B, C, D, S, and L, lysozyme, CD16, CD64,

complement components C1q all increase, while MHC class

II a/b remain low). IL-10 treatment alone mediated

expression of CXCL7 (Fig. 7), the precursor forms of which

can be anti-inflammatory (76).

Cluster analysis revealed a signature of genes coexpressed in

IL-10-treated (�LPS) and vitamin D3-treated bmDCs and the

esDCs (Fig. 8C). The signature included anti-inflammatory

genes, such as IL-1R antagonist and hemoxygenase-1 (HO-1),

which are also implicated in the suppression of graft rejection



(77), and genes enhancing phagocytic clearance, such as the

putative C-type (Mm.30109) and galactose-binding lectins

CD16 and Tlr2. CCL6 represented a potential marker for

tolerogenic DCs and was indeed most highly expressed in

immature ES cell-derived and modulated (IL-10 or vitamin D3

treated) DCs, as assessed by quantitative RT-PCR (data not

shown), while CCL9 was more constantly expressed in myeloid

cells, including various macrophage populations.

A model for regulatory T-cell interactions with antigen-

presenting cells based on some of the signature genes

Regulatory T cells, whether they are CD4þCD25þ or Tr1-like

cells, are able to suppress both Th1- and Th2-mediated effec-

tor functions. It is proposed that this ability may be primarily

due to competition for APCs, both physically by competing for

antigen and costimulatory ligands, thereby reducing the

signals that would otherwise prompt maturation of the DCs,

RGS-1 full length transcriptRGS- truncated transcript (intron 4)RGS- truncated transcript (intron 3)

1 10 100 1000 10000

Treg clone Tr1D1 rest

Treg clone Tr1D1 CD3

Skin line A rest

Skin line A CD3

Skin line B rest

Skin line B CD3

Th1 line

Th2 line

Tr1 line (Vit/Dex)

Th1 clone R2.2 rest

Th1 clone R2.2 CD3

Th2 clone R2.4 rest

Th2 clone R2.4 CD3

Normalized mRNA levels (arbitrary units)

Normalized mRNA levels (arbitrary units)

1 10 100 1000

GATA-3-binding protein

Repressor of GATA (ROG)

A B

Fig. 6. Reverse transcriptase-polymerase

chain reaction (RT-PCR) confirmation ofgenes expressed on Tr1-like Treg cells.

TaqManTM RT-PCR measurements of GATA-3and ROG [normalized to hypoxanthineguaninephosphoribosyl transferase (HPRT)] on a rangeof mRNA samples (A), as indicated (rest,resting, CD3, CD3 activated). RT-PCR for full-length RGS-1 transcripts and those truncatedeither in intron 3 or intron 4 on a range ofmRNA as listed (B). Error bars indicate standarddeviation of triplicates. Real-time quantitativeRT-PCR (43). RNA was prepared using the SVtotal RNA isolation system (Promega, Madison,WI, USA) that includes DNase I treatment.Reverse transcription used the proStar kit withrandom hexamers (Stratagene, La Jolla, CA,USA). Real-time quantification was performedwith gene-specific, fluorogenic probes and theUniversal MasterMix kit (PE AppliedBiosystems, Foster City, CA, USA) withprimers at 300 nM and the probe at 200 nM.A hot start, two-step PCR (15 s at 95 �C and60 s at 60 �C) was applied for 40 cycles. PCRand TaqManTM analysis were performed usingthe ABI/PRISM 7700 sequence detector system(PE Applied Biosystems). Multiplex PCRreactions were performed in triplicate usingVIC-labeled HPRT probes and FAM-labeledtest probes as shown Table 2. Standard curvesof appropriate cDNAs were used to calibrateCT to amounts of test cDNAs on each 96-wellplate.

Table 2. Real-time reverse transcriptase-polymerase chain reaction primers and TaqMan probes

Gene transcript Forward primer Reverse primer TaqMan probe

HPRT 50-gaccggtcccgtcatgc-30 50-tcataacctggttcatcatcgc-30 VIC-50-acccgcagtcccagc-gtcgtg-30-TAMRA

GATA-3 50-tcgaggtggtgtctgcattc-30 50-ttacagctatccaggtacaataaagtcttc-30 FAM-50-atccggatcccatttgtgaat-aagcca-30-TAMRA

ROG 50-cttctcccatagtacccccatca-30 50-agagggtaaaggagggtgttggt-30 FAM-50-ctggcaggtccggcctcaa-gatca-30-TAMRA

RGS-1 Full length 50-acgtgaaaacatacctgagatcga-30 50-agtctggcaggcagtctcattg-30 FAM-50-atgaaatcggccaagtccaaagaca-tactttctg-30-TAMRA

RGS-1 truncated(intron 3)

50-acgtgaaaacatacctgagatcga-30 50-gataggaatacttactctggttggcaag-30 FAM-50-atgaaatcggccaagtccaaagac-atactttctg-30-TAMRA

RGS-1 truncated(intron 4)

50-acgtgaaaacatacctgagatcga-30 50-ttaatactcacttgtttcacagcatctg-30 FAM-50-atgaaatcggccaagtccaaag-acatactttctg-30-TAMRA



but also by modulating the APCs to express protective and

anti-inflammatory genes such as HO-1, IL-1 receptor antag-

onist (IL1RA), the chemokine CCL6, and the PD-1L. The SAGE

data suggest a close relationship between Th2 effector cells and

Tr1-like regulatory cells, although the latter seem to lack

effector functions driven by GATA-3, possibly as a conse-

quence of expressing the ROG and a truncated form of RGS-

1 that may modify chemokine responsiveness. CD4þCD25þ

regulatory cells may have an analogous relationship to Th1

effector cells, with FoxP3 acting to repress cytokine produc-

tion and responsiveness with the loss of the signal transducer

and activator of transcription (STAT) molecules, STAT4 and

STAT6. On the assumption that APCs are required to drive

immune responses through to Th1 and Th2 effector cells to

Th1

Th2

Upregulated after CD3 stimulation

Downregulated after CD3 stimulation

CCR2

IFN-γ

CCL5CXCR3

IL-25IL-2RγTRAF2IL-15RaCCL4

Granzyme B IL-9

CD25–

CD4+

CD25+

CD4+IL-17

IL-6Rα

CCL1

CCL3,4

CD134

GITR

Treg

Tr1-like

Shared with T-cell subset(s)

DC (unstimulated)

DC + IL-10

CCL17

CCL5

CCL21b

CCR7

IL-12p40

IL-1β

CCL9 CCR1,5HO-1 CCL6

Lymphotoxin B

CCL21a TNFRSF6 IL-1RII

CXCL7

B cells

IL-10R α

IL-3Rα

CD137

PD-1LCCL22

Lymphotoxin B

DC + LPS

CXCR6 IL-4RαPD-1 IL-1RII

CD137IL-4,5

Granzyme A

Fig. 7. The expression of abundant gene familieson T cells and antigen-presenting cells. Venndiagrams of gene expression according to serialanalysis of gene expression tag abundance forchemokines, cytokines, and their receptors, andother transcripts of note. All transcripts shown arestatistically differential (i.e. three or more tags intotal or a difference of seven or more tags betweenlibraries).

A

B

Isocitrate dehydrogenase 1,GGCAATAATG,9925Expressed seq. C88302,TTTATGGAAT,23968TGF beta-induced 68 kDa,GTGCATTTGT,14455Sterol O-acyltransferase 1,CCTGGCCAAG,28099Ninjurin 1,CTGGTACTTC,18503Aminolevulinic acid synth 1,AGCAAGATGG,19143Heme oxygenase 1,GCCACTTTGA,17980Liver glycogen phosph.,GAGCCTTCGG,30047α-N-acetylglucosaminidase,GCTGAGCTGG,6142C-type lectin???Mm.30109,AAAGCTGCAA,30109Hexosaminidase A,GTACGCCTGA,2284CCL 6 long form,TAGCCACAAA,137RIKEN 2700038C09,ATAGAAGAGA,182914Neutr. cytosolic factor 2,TGAATGAAGA,10729ESTs,CTGGACATCC,140215NIMA (never in mitosis),GACTATGCGA,143818CCL 6,TAGCCAAAAA,137CSF-1 receptor,AGGGGTCTGG,22574CCAAT binding protein,GCGGCCGGTT,4863ESTs,GGAAGGTGGG,29046Interleukin-1 RA,CTATGTATTG,882Lectin; galactose binding,CTGAGAGAAA,2970CD 16 (low affinity FcR),TATATTTTTA,22119CD107b LAMP-2,ACCATTATAA,486Expressed seq. AI840826,GCTCTGGCCT,38488RIKEN 1110012E06,CTGGACACTG,41331Syntaxin binding 2,TCGGAAATGA,7247Expressed seq. AI132321,GGAAGACAGC,203915Legumain,GACAAAGTGT,17185Toll-like receptor2,GTCCAGCAGC,87596Prog. cell death 1 ligand,GTGTTGGATT,168681

Th1

(cl

one

R2.

2)T

h2 (

clon

e R

2.4)

Tr1

D1

(inc.

mas

t)A

1 ×

RA

G–/

– nod

esC

BA

nod

esC

D4+

CD

25+ (

CD

3)C

D4+

CD

25+

CD

4+C

D25

–

CD

4+C

D25

– (C

D3)

Tr1

D1

clon

e (C

D4)

Ski

n tr

eg li

ne A

S

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LPS treated bmDC

Unt

reat

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C

Fig. 8. Gene expression signatures for modulated dendritic cells.

Scatter plots of untreated bmDCs compared to those treated withlipopolysaccharide (LPS) (A) or with interleukin-10 (IL-10) (B).Normalized tag frequencies are plotted on a logarithmic scale and

overexpressed tags are color-coded as in Fig. 3. The expression pattern of acluster of genes overexpressed in immature (esDC) and modulated (IL-10,IL-10þ LPS, or vitamin D3) bmDCs are shown in (C), color-coded as inFig. 4.



mediate graft rejection, infectious tolerance may then be

explained if the result of such competition at the APC leads

to a reduced or incomplete signal to the emerging effector cells

such that they are converted to regulatory cells. Such a

conversion can be achieved artificially by transducing FoxP3

into naıve T cells (64) that would otherwise become Th1 cells,

but it remains to be determined whether similar conversions

take place in physiological conditions (Fig. 9).

Conclusions

The two T-cell populations with known regulatory ability, the

CD4þCD25þ and Tr1-like Tregs, are apparently no more

related in their overall pattern of gene expression to each

other than they are to Th1 or Th2 cells. Both types of Treg

cells have lost some transcripts and, by implication, effector

functions associated with other memory cell phenotypes such

as Th1 or Th2 (43), but many of these functions are reduced

in all the T cells after activation. This finding may reflect

a chronic activation state of both Treg populations, which

would fit with their common expression of CD25 and

CTLA4 (43, 63). The recent discovery of a functional role

for FoxP3 that includes the repression of cytokines in CD25þ

Treg cells (64) and the finding here that ROG is expressed in

Tr1-like Treg cells suggest a mechanism whereby disabled

effector cells (32) are able to have regulatory activity. One

can speculate that CD25þ cells may compete with potential

effectors during homeostatic expansion (29) and control innate

immune responses (78), while Tr1-like cells may regulate in the

local vicinity of their specific antigen (i.e. the ‘Civil Service’

hypothesis) (6), such as in a tolerated skin graft (24).

On the APC side, two different treatments that claimed to

promote tolerance did not simply inhibit DC maturation, but

they resulted in expression of a cluster of genes associated with

the modulated phenotype. The gene expression data presented

here provides a valuable baseline to look for the expression of

novel genes associated with the physiological activation of

mixed populations of T cells and DCs in vitro or directly ex vivo

in tissues from tolerant mice. Such approaches should allow us

to identify and characterize the regulatory T cells and APCs that

maintain transplantation tolerance in the mouse models, with

a view to developing surrogate markers for tolerance in clinical

settings and new molecular targets for immunomodulatory

therapies.

Th2 cell

STAT4,6

Modulateddendritic

cell

PD1-L

HO-1

IL1RA?

CD40L

CCL6?

GATA3

ROGRGS1 ∆

Tr1-likeRegulatory

T cell

FoxP3

IFN-γ

IFN-γ

CD4+CD25+

RegulatoryT cell

IL-4

IL-4

CD28

Th1 cell

Fig. 9. A model of CD4þCD25þ and Tr1-likeimmune regulation. CD4þCD25þ and Tr1-likeregulatory T cells are shown competing with Th1and Th2 cells for the antigen-presentingdendritic cells (DCs), with the consequence thatthe DC is blocked from maturing and isadditionally modulated to produce anti-inflammatory and protective molecules. Thisgeneralized model may apply at both theinduction phase of the immune response,continuing through to the effector and memoryphases, and may take place in either the draininglymph nodes or directly within the grafted tissue.Example gene products expressed by each celltype, as determined by the SAGE analysis, areindicated.

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Regulatory T cells and dendritic cells in transplantation tolerance: molecular markers and...

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