Int. J. Med. Sci. 2009, 6
http://www.medsci.org
51
I
I
n
n
t
t
e
e
r
r
n
n
a
a
t
t
i
i
o
o
n
n
a
a
l
l
J
J
o
o
u
u
r
r
n
n
a
a
l
l
o
o
f
f
M
M
e
e
d
d
i
i
c
c
a
a
l
l
S
S
c
c
i
i
e
e
n
n
c
c
e
e
s
s
2009; 6(1):51-64
© Ivyspring International Publisher. All rights reserved
Research Paper
Transcriptome analysis of murine thymocytes reveals age-associated
changes in thymic gene expression
Ana Lustig
1
, Arnell Carter
1
, Dorothy Bertak
1
, Divya Enika
1
, Bolormaa Vandanmagsar
1
, William Wood
2
,
Kevin G. Becker
2
, Ashani T. Weeraratna
1
and Dennis D. Taub
1
1. Laboratory of Immunology, National Institute on Aging-Intramural Research Program, National Institutes of Health,
Baltimore, MD. 21224, USA
2. The Research Resources Branch, National Institute on Aging-Intramural Research Program, National Institutes of
Health, Baltimore, MD. 21224, USA
Correspondence to: Dennis D. Taub, Ph.D., Laboratory of Immunology, National Institute on Aging-Intramural Research
Program, National Institutes of Health, 5600 Nathan Shock Drive, Baltimore, MD. 21224, Phone: (410) 558-8159, Fax: (410)
558-8284; Email: taubd@grc.nia.nih.gov
Received: 2009.01.28; Accepted: 2009.02.08; Published: 2009.02.09
Abstract
The decline in adaptive immunity, naïve T-cell output and a contraction in the peripheral T
cell receptor (TCR) repertoire with age are largely attributable to thymic involution and the
loss of critical cytokines and hormones within the thymic microenvironment. To assess the
molecular changes associated with this loss of thymic function, we used cDNA microarray
analyses to examine the transcriptomes
of thymocytes from mice of various ages ranging
from very young (1 month) to very old (24 months). Genes associated with various bio-
logical and molecular processes including oxidative phosphorylation, T- and B- cell receptor
signaling and antigen presentation were observed to significantly change with thymocyte age.
These include several immunoglobulin chains, chemokine and ribosomal proteins, annexin
A2, vav 1 and several S100 signaling proteins. The increased expression of immunoglobulin
genes in aged thymocytes could be attributed to the thymic B cells which were found to be
actively producing IgG and IgM antibodies. Upon further examination, we found that purified
thymic T cells derived from aged but not young thymi also exhibited IgM on their cell surface
suggesting the possible presence of auto-antibodies on the surface thymocytes with ad-
vancing age. These studies provide valuable insight into the cellular and molecular mecha-
nisms associated with thymic aging.
Key words: thymus, involution, aging, microarray, AGEMAP, thymocytes, caloric restriction
Introduction
The aging immune system is often characterized
by a general decline in the ability to resist infection
and an increase in autoimmune complications such as
type 2 diabetes, inflammation, and cancer [1-9]. One
of the underlying causes of the reduced effectiveness
of the immune system with age is the involution of the
thymus. As the thymus involutes, there is a resulting
decrease in naïve T cell output, and consequently
memory T cells occupy a larger portion of the pe-
ripheral T cell pool [10-16]. However, this loss in
thymic output with age does not result in any sig-
nificant change in the total number of peripheral T
cells. The maintenance of peripheral T-cell numbers
appears to be regulated via a thymus-independent
homeostatic process involving expansion of mature
peripheral T cells which results in a much more lim-
ited T-cell receptor (TCR) repertoire with age. While
the precise mechanism(s) facilitating thymic involu-
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
52
tion has yet to be determined, it appears that this
thymic loss is an active process involving a variety of
factors including the loss and apoptosis of the thy-
mocytes and supportive cell populations within the
microenvironment, alterations in the appropriate
signals from supporting stromal and epithelial cells,
diminished progenitor cell recruitment and expansion
and a decrease in steroid signaling essential for thy-
mocyte development [17-25]. Other factors which are
yet to be identified may also be involved. Given that
the loss in thymic function is one of the earliest and
most consistent steps in the progression to immune
dysfunction, thymic involution seems to be a most
promising target for therapeutic intervention to re-
verse thymic atrophy and restore thymic function.
Given that the aged thymus consists of large ar-
eas of fat and connective tissue [26, 27], we have fo-
cused our efforts on performed DNA array analysis
specifically on isolated thymocyte populations in or-
der to obtain a clearer picture of which genes or gene
families may demonstrate altered expression levels
with age. Our results demonstrate that genes associ-
ated with various biological and molecular processes
including oxidative phosphorylation, T- and B- cell
receptor signaling and antigen presentation were ob-
served to significantly change with thymocyte age.
Interesting, the expression of several immunoglobulin
chains were also found to be significantly increased in
aged thymocytes. Understanding the changes in gene
expression in thymocytes with age may hold the key
in determining thymocyte fate and decreased thymic
output with age.
Materials and Methods
Mice. Specific pathogen-free C57BL/6 mice of
various ages were purchased through the Office of
Biological Resources and Resource Development of
the National Institute on Aging (Bethesda, MD). All
mice were maintained in an AAALAC-certified bar-
rier facility and were acclimated for 2 weeks prior to
use. All mice were fed autoclaved food and water ad
libitum. All mice with evidence of disease (e.g.,
enlarged spleen, gross tumors) were not utilized in
these studies.
Thymocyte isolation. Freshly-extracted thymi
from mice of various ages were dissociated in RPMI
using a syringe and forceps. Cell clumps were broken
up with repeated pipetting and then poured through
70μm nylon mesh cell strainers (BD Falcon, Bedford,
MA) to remove connective tissue and any remaining
clumps. The cells were washed once to remove fat
cells, which will float to the top rather than pelleting
at the bottom with the thymocytes. The red blood cells
then were lysed with ammonium chloride buffer. The
remaining thymocyte population, which reflects the
actual interactive environment of the thymus, was
counted, washed twice in RPMI followed by PBS. The
cell pellets were either used directly in the Qiagen
RNEasy mini kits for RNA preparation or lysed in
RIPA buffer containing protease and phosphatase
inhibitors (Sigma, St Louis, MO) to use in Western
blot analysis, or resuspended in the appropriate
buffer for whatever assay was used following cell
preparation.
In certain experiments, thymocytes were mag-
netically labeled using the Pan T cell isolation kit, then
passed through LD magnetic cell separation columns
(Miltenyi Biotec, Auburn, CA), to separate them into T
cell and non-T cell subsets. RNA was then isolated
from these cell subsets using the Qiagen RNEasy Mini
Kit as described below, and used for real-time
RT-PCR as described above.
RNA extraction and array analysis. For each array
sample, the RNA was prepared from the purified
thymocytes. The thymocytes were processed by using
the RNEasy Mini Kit (Qiagen, Valencia, CA). Quality
and quantity of total RNA samples was assessed us-
ing an Agilent 2100 Bioanalyzer (Agilent Technolo-
gies, Palo Alto, CA). This total RNA was used to gen-
erate fluorescent cRNA for use with Agilent’s oli-
gonucleotide microarrays. The RNA was amplified
and labeled using the Agilent Low RNA Input Fluo-
rescent Linear Amplification Kit following manufac-
tures protocols. In Short: Between 0.5μg to 2μg of total
RNA was used to generate first and second strands of
cDNA containing a T7 RNA polymerase promoter.
Then cRNA was synthesized using T7 RNA poly-
merase which simultaneously incorporates cyanine 3-
or cyanine 5- labeled CTP (Perkin Elmer, Wellesley,
MA). Qiagen RNeasy columns (Qiagen Valencia, CA)
were used to purify the labeled cRNA and the final
concentration was assessed using a Nanodrop
ND-1000 spectrophotometer (Nanodrop Technolo-
gies, Wilmington, DE). 750 ng of Cy3-labeled cRNA
and 750 ng of Cy5-labeled control sample were com-
bined with spiked in control probes specific for targets
on the arrays and hybridized over night at 60
0
C to
Agilent Mouse Whole Genome 44K Oligo Microarrays
(Agilent Technologies, Palo Alto, CA). The arrays
were washed at room temperature 6X SSC with
0.005% Triton X-102 for 10 minutes and 0.01x SSC
with 0.005% Triton X-102 at 4
0
C for 5 minutes. The
slides were then dried in a nitrogen stream and
scanned at 10 micron resolution using an Agilent Mi-
croarray scanner G2565BA. Data was extracted using
Agilent Feature Extractor Software (v7.1).
Statistical data analysis. All data was processed a
Z score statistical analysis method developed at NIA
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
53
[28]. In order to be selected for the final gene list, the
expression value of a particular gene had to be at least
1.5 times different from the Z score of the control.
Differences were considered statistically significant
only if they had a p value less than 0.01.
Gene expression profiles. The selected gene lists
were uploaded along with their Z scores to the Mi-
croarray Data Analysis website of the National Hu-
man Genome Research Institute
(http://arrayanalysis.nih.gov/). Using dis-
tance-based gene selection, gene expression profiles
were created in order to visualize differences between
age, gender and diet.
Real-time PCR. Array results were verified by
semi-quantitative RT-PCR. One-half to one micro-
gram of RNA from thymocyte samples were used to
make cDNA with the iScript cDNA synthesis kit (Bio-
Rad, Hercules, CA). One microliter of each cDNA
sample was then used to measure quantity using the
SYBR Green PCR master mix (Applied Biosystems)
and reactions were run on the 7500 fast or 7300 PCR
system (Applied Biosystems). The results were nor-
malized to 18S using the QuantumRNA universal 18S
(Ambion, Austin, TX) and were also used to deter-
mine relative quantities. The primers are shown in
Table 4.
Western blot analysis. Equal amounts of protein
from thymocytes were run on 10% tris-glycine gels
and transferred to PVDF membranes (Invitrogen,
Carlsbad, CA) on the Novex gel-blot system (Invitro-
gen, Carlsbad, CA). The nitrocellulose filters were
then probed using HRP-conjugated specific antibod-
ies to the immunoglobulin heavy chain M (IgM) and
IgG obtained from Abcam (Cambridge, MA). The
antibody to beta-actin was from Sigma-Aldrich (St.
Louis, MO). The HRP-conjugated secondary antibody
for the beta-actin was from Amersham (Piscataway,
NJ). Bands were visualized using the ECL Plus west-
ern blotting detection reagents (Amersham) and CL-X
Posure film from Pierce (Rockford, IL).
Flow cytometry. Cell suspensions were washed in
HBSS with 0.1% BSA and 0.1% sodium azide
(Sigma-Aldrich, St. Louis, MO). Antibody against Fc
receptors was used to block non-specific binding (BD
Biosciences, San Jose, CA). Cells were stained ex-
tracellularly for B220 (BD) ten minutes on ice and ei-
ther simultaneously stained for extracellular IgM
(Caltag, Carlsbad, CA), or subsequently stained for
intracellular IgM. For intracellular staining, the cells
were fixed in PBS containing 2% paraformaldehyde
(Sigma-Aldrich) and washed twice in PBS with 0.03%
saponin to permeabilize the cell membranes.
Anti-IgM was added for 30 minutes at 4 degrees, and
then the cells were washed twice in PBS with sodium
azide. The stained cells were then run on a FACScan
flow cytometer (BD) and the data analyzed using Cell
Quest software (BD).
Results
Gene expression changes were identified by
comparing gene expression profiles across the indi-
cated age groups to the gene expression profiles of the
1 month age group (Figure 1). While the numbers of
genes with decreased expression were fairly consis-
tent throughout the three older ages, the number of
genes with increased expression peaked in the
16-month age group. Table 1 lists the canonical cellu-
lar pathways affected by changes in gene expression
levels with thymocytes age. This list was generated by
uploading the list of genes with the most significant
changes to the Ingenuity Analysis website
(http://www.ingenuity.com/). Table 1A lists all
pathways affected at any age group. As would be
expected, these included pathways involved in lym-
phocyte receptor signaling and antigen presentation.
Genes involved in oxidative phosphorylation were
the most numerous regardless of age group. Pathways
involving purine metabolism, PI3/Akt signaling, and
ubiquinone synthesis are the ones most affected dur-
ing aging (Table 1B). All of these pathways are in-
volved in cell survival [29-33], and deficiencies in the
ubiquinone pathway have already been linked to in-
creased longevity in mice [34].
Figure 1. Number of genes at each age group with expression
levels higher or lower than levels exhibited by the 1 month age
group. The bars labeled total show the total number of all
genes changed at all ages. Red bars depict the number of
genes which increased expression levels and green bars
depict the number of genes which decreased expression
levels.
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
54
Table 1. Canonical pathways containing thymocyte genes which
changed expression levels with age. These pathways were
identified by the ingenuity data analysis program
(www.ingenuity.com) using our uploaded data.
ALL CHANGES AT ALL AGES
CANONICAL PATHWAYS NUMBER of GENES p VALUE
Oxidative Phosphorylation 18 0.001
T Cell Receptor Signaling 15 1.2 x 10
-5
Antigen Presentation 14 4.7 x 10
-8
B Cell Receptor Signaling 14 0.007
Purine Metabolism 14 NS
G Protein Coupled Receptor 13 0.001
Integrin Signaling 13 0.17
Erk/MAPK Signaling 11 0.10
PI3/Akt Signaling 10 0.03
Apoptosis Signaling 9 0.017
CHANGES at 24 MONTHS
CANONICAL PATHWAYS NUMBER of GENES p VALUE
Oxidative Phosphorylation 11 6.8 x 10
-4
Purine Metabolism 9 2.0 x 10
-1
Antigen Presentation 8 7.6 x 10
-6
TCR Signaling 6 9.7 x 10
-3
PI3/Akt Signaling 6 2.4 x 10
-2
Ubiquinone Synthesis 4 3.1 x 10
-2
Pathway analysis (www.ingenuity.com) identi-
fied signaling pathways and gene families that were
affected in aging thymocytes. Table 2A lists all af-
fected functions regardless of age and Table 2B lists
the functions most affected at the oldest age group
and highlighted groups of genes involved in immune
response, development and disease. In the oldest age
group, the functional category including the most
genes was cell-to-cell signaling and interaction, which
could play a key role in thymocyte survival or death
associated with age. Many genes associated with
cancer are also affected in aging thymocytes. Given
the increased incidence of cancer with age [7, 35, 36]
(http://www.nia.nih.gov/ResearchInforma-
tion/ConferencesAndMeetings/WorkshopReport/Fi
gure1.htm), this may be a valuable group of genes
warranting further scrutiny.
In order to obtain an expression profile of the
genes which changed the most with age, we uploaded
all of our array data into the array analysis program of
the NHGRI (http://arrayanalysis.nih.gov). Table 3
lists the top genes that were up- or down-regulated
with age. A. A complete list of all genes that changed
with age is available on request. Most of the genes
with the greatest increase were immunoglobu-
lin-associated genes. This was a very interesting
finding, given that the number of immunoglobu-
lin-producing cells within the thymus is actually quite
limited. In order to identify genes that can discrimi-
nate among aging versus young thymocytes, we sub-
jected our array data to distance-based analysis. Fig-
ure 2 shows the top 100 genes with significant differ-
ences in expression levels at all ages. The most notable
aspect of the profile is the fact that all 100 genes ex-
hibiting the greatest differences with age actually in-
creased in expression. In light of these results, we fo-
cused our attention on genes demonstrating increased
transcription, more specifically several of the immu-
noglobulin-associated genes found to be up-regulated
in their expression in thymocytes with age. As shown
in Table 4, real time RT-PCR confirmed the increased
levels of many of these genes identified in the array
analysis. Moreover, Western blot analysis of thymo-
cyte lysates also confirmed that IgM increases with
age (Figure 3). Flow cytometry analysis shows that
there is also a modest age-associated increase in the
percentage of IgM
+
/B220
+
B cells within the thymus
and that this increase in IgM is both extra- and in-
tra-cellular (Figure 4).
Table 2. Functional categories containing thymocyte genes
which changed expression levels with age. These groups were
identified by the ingenuity data analysis program
(www.ingenuity.com) using our uploaded data.
ALL CHANGES AT ALL AGES
GENE FUNCTION NUMBER of
GENES
p VALUE
Hematological System Development
and Function
48 3.82 x 10
-4
Immune and Lymphatic System
Development and Function
45 3.82 x 10
-4
Cell-to-Cell Signaling and Interaction 42 8.00 x 10
-3
Cancer 32 3.44 x 10
-4
Immune Response 31 2.40 x 10
-2
Small Molecule Biochemistry 22 9.52 x 10
-3
DNA Replication, Recombination
and Repair
20 2.48 x 10
-2
CHANGES at 24 MONTHS
GENE FUNCTION NUMBER of
GENES
p VALUE
Cell-to-Cell Signaling and Interaction 22 0.016
Hematological System Development
and Function
18 0.012
Immune and Lymphatic System
Development and Function
17 0.010
Small Molecule Biochemistry 16 0.005
Cellular Assembly and Organization 16 0.023
Cancer 15 0.010
Cellular Function and Maintenance 14 0.001
Cell Cycle 13 9.52 x 10
-3
Immunological Disease 12 0.002
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
55
Figure 2. Expression
profile of the most sig-
nificantly changed genes
with age. This profile
was generated in the
NHGRI website
(https://arrayanalysis.ni
h.gov), using dis-
tance-based analysis of
total data and showing
the top 100 changed
genes. Red represents
up-regulation of gene
expression, and green
represents down regu-
lation of gene expres-
sion.
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
56
Table 3. Top genes that increased or decreased with age. This table lists the Z ratios at each age for the genes with the greatest
differences in expression levels when comparing the oldest age group, 24 months, to the youngest, 1 month. As can be seen,
most of the genes which increased expression levels are immunoglobulin-associated.
GENE NAMES GENE
NAME
ENTREZ
GENE
UNIGENE Z Ratio
6M-1M
Z Ratio
16M-1M
Z Ratio
24M-1M
Similar to Ig heavy-chain region precursor LOC380800 380800
3.74 13.81 12.67
Mus musculus kappa light chain of Mab7 mRNA,
partial cds
AF152371
Mm.333124 .55 9.02 7.98
S25058 Ig kappa chain- mouse, partial (86%)
[TC1067460]
S25058
2.04 6.89 6.85
Immunoglobulin kappa chain LOC545854 545854 Mm.426205 0.60 6.34 6.38
Immunoglobulin joining chain IgJ 16069 Mm.1192 1.64 7.26 6.32
Immunoglobulin heavy chain 6 (heavy chain of IgM) Igh-6
Mm.342177 0.15 4.50 5.16
Immunoglobulin kappa chain variable 28 (V28) IgK-V28 16114 Mm.333124 1.76 7.14 5.07
RIKEN cDNA 3110049J23 9130427K04 67307 Mm.368563 4.61 3.67 5.06
RIKEN cDNA 5730437N04 5730437N04Rik 70544 Mm.46654 5.20 5.88 4.79
Similar to Ig lambda-1 chain C region J00582 433053 J00582 1.66 6.52 4.63
RIKEN cDNA B230118H07 Rag1/Nwc
fusion
68170 Mm.31263 -2.49 -2.26 -2.62
Mus musculus mRNA for Zfp-1 zinc finger protein
[X16493]
Zfp1 22640 Mm.4184 -3.09 -2.34 -2.72
Mus musculus CDC28 protein kinase regulatory sub-
unit 2
Cks2 33197 Mm.222228 -3.27 -3.26 -2.76
Mus musculus cytochrome C oxidase subunit VIIb
mRNA [NM_025379]
Cox7b 66142 Mm.197728 -1.92 -3.39 -2.83
Integrin beta 3 binding protein (beta3-endonexin) Itgb3bp
Mm.257094 -2.15 -2.93 -2.95
Mus musculus BCL-2 modifying factor homolog ,
A430110F10
Bmf 171543 Mm.210125 -3.30 -3.42 -2.96
Membrane metallo-endopeptidase-like 1 Mmel1 27390
-3.11 -3.76 -3.88
Mus musculus chemokine (C-C motif) ligand 25
(Ccl25) mRNA
Ccl25 20300 Mm.7275 -7.31 -6.14 -4.31
Mus musculus S100 calcium binding protein A9 (cal-
granulin B)
S100a9 20202 Mm.2128 -8.16 -2.66 -9.39
Mus musculus S100 calcium binding protein A (cal-
granulin B)
S100a8 20201 Mm.21567 -8.99 -3.76 -10.12
Table 4. Real-time RT-PCR confirmation of genes up-regulated with age in the DNA array. This table lists a series of confirmations
of array results using real-time RT-PCR. Many of the down-regulated genes were difficult to confirm, probably due to already
low levels of expression. Up-regulated immunoglobulin-associated genes were easily confirmed and validated the array
results for that group of genes.
ARRAY RESULTS
PCR
Results
GENE NAME
Gene ID
Gene
Name
(zratio)
6m-1m
(zratio)
16m-1m
(zratio)
24m-1m
Fold
Change
24M-1M
Forward Primer
Reverse Primer
kappa light
chain of Mab7
mRNA, partial
cds
AF152371 IgKV1,
IgKVK
0.55 9.02 7.98 57* ACCAAGCTGGAAATC
AATCG
TGTCGTTCACTGCCATCAAT
immunoglobu-
lin joining chain
(Igj)
NM_152839 Igj 1.53 6.83 5.83 38* AAGCGACCATTCTTG
CTGAC
GGGAGGTGGGATCAGAGATA
TT
histocompatibil-
ity 2, class II
antigen A, beta 1
(H2-Ab1)
NM_010379 H2-Ab
1
2.51 4.72 4.53 3.3 TTCATCCGTCACAGG
AGTCA
AGGAATTCGGAGCAGAGAC
A
chemokine
(C-X-C motif)
receptor 6
(Cxcr6)
NM_030712 Cxcr6 2.49 7.91 3.98 4.7* AACAGCCAGGAGAA
CAAACG
GGGCAAGTTCAGCAGAAACA
decorin (Dcn) NM_007833 Dcn 1.84 4.40 3.88 13 ACCCTGACAATCCCC GCCCCTTCTTTGATCTCTGT
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
57
ARRAY RESULTS
PCR
Results
GENE NAME
Gene ID
Gene
Name
(zratio)
6m-1m
(zratio)
16m-1m
(zratio)
24m-1m
Fold
Change
24M-1M
Forward Primer
Reverse Primer
TGATA
Mus musculus
immunoglobu-
lin heavy chain
6 (heavy chain
of IgM)
BC018315 IgH6 0.75 4.41 3.89 14 ATGGAATGGACCTGG
GTCTT
ATGTCCAGGCCTCTGCTTTA
ribosomal pro-
tein, large, P1
(Rplp1)
NM_018853 Rplp1 3.90 3.49 3.78 4.7 CACGGAGGATAAGAT
CAATGC
ATGAGGCTCCCAATGTTGAC
acidic ribosomal
phosphoprotein
P0 (Arbp)
NM_007475 Arbp 3.38 3.79 3.78 77* TCGTTGGAGTGACAT
CGTCT
GCTCCCACAATGAAGCATTT
Actin, cyto-
plasmic 1
(Beta-actin)
NAP018710-0
01
B-actin 2.78 2.14 3.51 NC CTAAGGCCAACCGTG
AAAAG
CCATCACAATGCCTGTGGTA
Mouse MHC
class I
H2-K-alpha-2
gene (haplotype
bm9), partial
exon 3
M13200 H2K1 1.57 4.52 3.42 NC AAGAGCGATGAGCAG
TGGTT
CCACGTTTTCAGGTCTTCGT
cytochrome c
oxidase subunit
II (Cox2)
AF378830 Cox2 3.07 3.66 3.25 NC TAATTGCTCTCCCCTC
TCTACG
CACCAGGTTTTAGGTCGTTTG
annexin A2
(Anxa2)
NM_007585 Anxa2 3.01 3.15 3.25 2.5* ACGAAATCCTGTGCA
AGCTC
ATCCCTCTCAGCATCGAAGT
CD8 antigen,
beta chain
(Cd8b)
NM_009858 Cd8b 2.27 2.23 2.32 3.8 ACGAAGCTGACTGTG
GTTGA
AAGCAGGATGCAGACTACCA
vav 1 oncogene
(Vav1)
NM_011691 Vav1 1.99 2.12 1.90 3.2* CGAACCTTCCTGTCTA
CTTGCT
TTCCTTTGTTCTGGGCAATG
chemokine
(C-X-C motif)
receptor 4
(Cxcr4)
NM_009911 Cxcr4 1.82 1.75 1.72 3.5* TTGCCATGGAACCGA
TCA
TCCGTCATGCTCCTTAGCTT
phosphoinosi-
tide 3-kinase
regulatory sub-
unit p85alpha
U50413 U5041
3
1.73 1.80 1.63 NC CCCAAGCTGGATGTG
AAGTT
TTTCCTGGGAAGTACGGGTGT
A
Figure 3. Protein levels of immunoglobulin increase within the thymocytes with age. Western blot analysis of IgM heavy chain
protein levels with successive age. This image is representative of 3 experiments with at least 7 total young and old samples.
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
58
Figure 4. Flow cytometry confirms an increase in IgM+ cells with age within the thymus. There are greater relative percentages
of both surface and intracellular IgM in the old thymocytes when compared to the young. Flow cytometry images are
representative of at least 2 experiments using 5 young and 6 old mice.
In humans, thymic B cells increase with age [37] as well as in some autoimmune diseases such as myas-
thenia gravis [38]. Other groups have characterized small populations of B cells within the thymus (Akashi et al.,
2000; Mori et al., 1997 [39, 40]. These B cells are B220 low and IgM negative [40]. They appear to mature within
the thymus, and produce IgM largely from the IgH6 family, which was the IgM group that showed the greatest
increase in expression with age in our array system. Ultimately, as these cells mature, they begin to express CD5
on their surfaces [40]. It is possible that the increase in B cells we observe with age is a result of an increase in the
maturation of these thymic B cells. If this is the case, perhaps we would see correlating increases in genes which
facilitate B cell maturation. With this in mind, we examined the mRNA expression levels of BAFF and APRIL
within the thymocytes. These two genes are expressed by cells of lymphoid lineage, and are linked to increased
B cell proliferation, maturation and survival [41-43]. They are specifically involved in the progression from the
T1 to T2 stages of immature B cells. We observed that both of these genes are elevated in expression in the old
thymocytes when compared to the young by real-time RT-PCR (Figure 5). These genes thus may be a contrib-
uting factor to the increase in B cells, both in total number and in maturity, within the thymus.
The increase in B cell numbers in the thymus with age seems too small to account for the greatly increased
quantities of immunoglobulin expression as determined by real time PCR and Western analyses. Therefore, we
also looked at other cell types which could be producing immunoglobulin within the thymus, such as plasma
cells. However, we found that plasma cells were not contributing to the increased production of immunoglobu-
lin with age, as flow cytometric analysis for B220, CD38 and CD138 on the thymocytes as well as RT-PCR
analysis for the plasma cell markers, blimp1 and XBP1, failed to demonstrate any significant differences in ex-
pression levels with age (Figure 6). There is also no relative increase in the size of the B1 B cell population with
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
59
age as assessed by flow cytometric analysis of the B220/CD5 populations, although we do see a minute, but
statistically significant difference in the CD5 negative, IgM positive population (Figure 7).
Figure 5. Real-time RT-PCR analysis reveals increased levels of mRNA expression of BAFF and APRIL in aged thymocytes. BAFF and
APRIL are both involved in maturation and survival of B cells, and their expression levels are elevated in aged thymocytes
when compared to the levels in young thymocytes. This correlates with the increase in B cells detected with age. These
Figures represent one experiment with six mice in each age group.
Figure 6. A) CD138 analysis by
flow cytometry demonstrates
that there is no difference in the
percentages of plasma cells in
thymocytes from young and old
mice. Averages of CD138
+
B220
-
cells are 1.12% and 1.07%, re-
spectively. These Figures are
representative of three experi-
ments with a total of 10 young
and 10 old mice. B) If plasma cell
numbers increase in old thymi, an
increase in Blimp1 and XBP1
expression and a decrease in
BCL6 should be observed.
Real-time RT-PCR quantitation
of plasma cell markers XBP1 and
BCL6 show no differences be-
tween young and old thymocytes.
Blimp1 RNA was too low to be
detected in any of our thymocyte
samples, although positive con-
trols were detectable.
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
60
Figure 7. CD5 analysis by flow cytometry demonstrates that there is no difference in the percentages of B1 B cells in
thymocytes from young and old mice. Averages of CD5
+
IgM
+
cells are 0.12% and 0.29%, respectively. These Figures are
representative of two experiments with a total of 5 young and 6 old mice.
Surprisingly, we did observe a shift in the fluo-
rescence intensity of IgM on the surfaces of aged
CD4+ and CD8+ thymocytes (Figure 8). Given that
immunoglobulins are not associated with T cells, it
was of interest to determine if the increase in IgM
expression by older thymocytes was due to actual
production by thymic T cells or non-T cell popula-
tions. To this end, we separated the cells into T and
non-T subsets using the pan T-cell isolation kit and
magnetic columns from Miltenyi. We then isolated the
RNA from each group of cells, and this RNA was
used for real-time RT-PCR to determine the relative
amounts of IgM being produced by young and old
thymocytes. As shown in Figure 9, the majority of the
IgM production in the thymus originates from the
non-T cell populations. Since the only increase in
immunoglobulin production detected in the aged
samples was associated with B220+ B cells within the
thymus (as previously shown by flow cytometry,
Figure 4), it appears that the increased production of
immunoglobulin detected in DNA microarrays and
PCR within the aged thymocytes is a reflection of the
generalized increased output of antibodies by B cells
in the aged thymus. Given that the actual number of
circulating B cells within the thymus is very small,
even slight alterations in immunoglobulin expression
may be detected as significant. The large concentra-
tion of immunoglobulin protein detected within the
thymus by Western blot may also be amplified by the
presence of circulating auto-antibodies bound to
thymic T cell surfaces as suggested by Figure 8.
Further evidence supporting the presence of
auto-antibodies bound to the surface of thymic T cells
can be observed in Figure 10. CD3
+
positive thymo-
cytes have a slightly higher fluorescence intensity for
surface IgM in the old samples when compared to the
young (Figure 10A). This shift in intensity is not evi-
dent in the same cells stained for intracellular IgM
(Figure 10B), meaning these are not the cells produc-
ing the IgM; it is merely binding on their cell surfaces.
Attempts to elute and isolate these antibodies have
proven quite difficult and more intensive studies are
underway to examine these thymic T cell-bound an-
tibodies.
Figure 8. CD4
+
and CD8
+
T cells in the thymus exhibit an
increase in cell surface IgM with age. Red lines designate the
young samples and blue lines the old samples. Flow cy-
tometry demonstrates that there is a clear shift in the mean
fluorescence intensity of IgM in the old thymocytes when
compared to the young thymocytes. This indicates a greater
number of IgM molecules on the cell surfaces. These data
are representative of 3 separate experiments with 3 to 7
mice in each group.
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
61
Figure 9. Real-time RT-PCR results of IgH6 RNA relative
quantities from total non-fractionated thymocytes and T cell and
non-T cell fractions from column-separated thymocytes. Thy-
mocytes were separated by magnetic columns into T cell
and non-T cell fractions. RNA isolated from these samples
was then used in real-time RT-PCR analysis of IgG heavy
chain transcription. This clearly demonstrates that all of the
immunoglobulin production appears to be occurring in the
non-T cells within the thymus. Similar results were obtained
for IgH6, H2-Ab1 and IgJ. These data are representative of
four experiments using pooled thymocytes from at least 3
mice of each age group.
Figure 10. CD3
+
cells of the thymus exhibit a shift in mean
fluorescence intensity with age, indicating an increased presence
of IgM on their surfaces. (A). However, this phenomenon is
not evident intracellularly (B), indicating the IgM comes
from a source outside of those cells. Data shown is for
CD4
+
cells. Similar results were obtained for CD8
+
cells.
Red lines represent young samples and blue lines represent
old samples. These data are representative of two ex-
periments using pooled thymocytes form 3 young and 3 to 5
old mice in each experiment.
Discussion
There are a myriad of complications arising from
the weakening of the effectiveness of the immune
system with age [1-9]. One of the most prominent
landmarks of the aging immune systems is the invo-
lution of the thymus, which results in decreased
thymic output of nascent T cells [10-16]. Many theo-
ries have been examined as to why this involution
occurs with advancing age [18, 44, 45]; however, little
is known about the molecular changes that occur
within the thymus and in thymocytes with age. Here,
we have examined the actual interactive thymocyte
populations derived from C57BL/6 mice after re-
moval of fat and connective tissue, at various stages of
aging using microarray gene analysis. These thymo-
cytes represent the actual relative composition of the
thymus at the various ages and thus are better suited
to understanding how the relative dynamics of sub-
populations may affect thymic aging. At least 600
genes were found to vary in expression levels when
compared to the 1-month age group. From these
analyses, there were also twice as many genes with
increased gene expression as there were demonstrat-
ing decreased expression. Moreover, genes demon-
strating increased expression levels were consistently
of higher statistical significance than those with de-
creased levels. This is more likely a reflection of the
difference in the sensitivity of the assay than a repre-
sentation of an actual bias toward increased gene ex-
pression levels within biological systems with age. As
would be expected, genes involved in immune sig-
naling pathways such as B and T cell receptor signal-
ing and antigen presentation were among the path-
ways containing the largest number of genes chang-
ing with age. The canonical pathway with the largest
number of detected genes was the oxidative phos-
phorylation pathway. This is not surprising, given
that oxidative phosphorylation controls the energy
levels within the cells and is directly linked to lym-
phocyte survival as well as changes in the redox
status of aging organisms [46-49]. Thus, this group of
pathways may be vital to thymocyte survival or death
during thymic involution. Both the PI3/Akt and
ubiquinone synthesis pathways contained a relatively
large number of genes which changed expression
levels with age and both of these pathways have also
been shown to affect cell survival [50-54]. There were
large numbers of genes involved in the hematological,
immune and lymphatic systems, which vary from
control levels at 1 month. Another category of
up-regulated genes were associated with cell-to-cell
signaling and interaction, important processes in de-
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
62
termining cell fate, and likely to be involved in thymic
selection and output.
The individual genes demonstrating the most
significant and highest levels of age-associated dif-
ferences in expression were the immunoglobu-
lin-associated genes. These genes included several
heavy and light chain sequences. Although not de-
finitively identified, it is interesting to speculate that
these genes are not only associated with the increased
numbers of B cells within the aging thymus but also
may be associated with the production of
auto-antibodies to thymic subsets (as shown in Fig-
ures 8 and 10). Such auto-antibodies have been dem-
onstrated to increase with age [18, 55-57]. Of the top
genes that decreased with age, two were S100 signal-
ing proteins, which are very important in T cell sig-
naling and survival [58-60]. Such decreased expres-
sion may result in a diminished capacity for thymo-
cytes activate, differentiate and/or survive.
The large number of immunoglobulin genes that
changed with age was quite surprising as there are
few antibody-producing cells in the thymus, even
with advancing age. Western blot analysis of IgM
quantities demonstrated levels of IgM heavy chain in
the thymocytes of 24-month-old mice as high as in the
spleen of 1-month-old mice. The source of these large
quantities of immunoglobulin is still unclear. Al-
though flow cytometry analysis displayed increased
numbers of IgM
+
B220
+
B cells (average 1% in young
vs. 5% in old), the total numbers of cells did not ap-
pear to reflect the amount of change detected by ar-
ray, PCR and western blot. Therefore, we looked at
other cell types within the thymus that may be pro-
ducing immunoglobulin. There were no detectable
increases in plasma cells or CD5+ B1 B cells in the
thymi of young and aged mice as measured by flow
cytometric analysis [61, 62]. The increase in IgM posi-
tive cells we observed does not appear to be within
the CD5+ population (Figure 7) that has been identi-
fied as maturing within the thymus [40]. They could
represent a separate B cell population that matures
and increases in number within the thymus with in-
creased age. Thymic B cells may play a role in T cell
negative selection [63]. These B cells express IgM on
their surface and are thus of a more mature pheno-
type. Since these are the ones we observe increasing
with age within the thymus, it would be interesting to
speculate that this may be another factor contributing
to thymic involution with age.
Interestingly, flow cytometric analysis did indi-
cate the presence of IgM on the surface of both CD4
+
and CD8
+
T cells within the thymus. Obviously, it is
highly unlikely that this T-cell associated immu-
noglobulin reflects actual production of IgM by aging
T cells but is more likely membrane bound IgM due to
an increase in the levels of circulating auto-antibodies
adhering to the T cell surface [64]. Additional flow
cytometric analysis revealed that although there was a
shift in the surface IgM mean fluorescence intensity
on thymic T cells with age, this increase was not in-
tracellular and thus was bound to the cell surface
suggesting the presence of autoantibody. Further tests
are necessary to determine the exact nature of this
binding. The phenomenon of age-associated increase
in auto-antibodies has been well documented [18,
55-57] and these auto-antibodies could influence
thymic involution. This shift in IgM mean fluores-
cence intensity in aged murine T cells has also been
previously described [64]. Together, these data sug-
gest that increased numbers of thymic B cells and
auto-antibodies may be associated with aging in
thymocytes and that autoimmune interactions may
play important roles in thymocyte death and thymic
involution.
Acknowledgements
We would like to thank Dr. Dan Longo for his
thoughtful review of this manuscript. This research
was entirely supported by the Intramural Research
Program of the National Institute on Aging, National
Institutes of Health.
Contribution statement: AL, ATW and DDT de-
signed and supervised the research at NIA; AL, AC,
DB, DE, BV, WW and KB conducted the microarray
and follow-up experiments; AL, ATW and DDT wrote
and edited the paper.
Conflict of Interest
The authors have declared that no conflict of in-
terest exists.
References
1. Targonski P.V, Jacobson R.M, and Poland G.A. Immunosenes-
cence: role and measurement in influenza vaccine response
among the elderly. Vaccine 2007;25: 3066-3069
2. Noreddin A.M and Haynes V. Use of pharmacodynamic prin-
ciples to optimise dosage regimens for antibacterial agents in
the elderly. Drugs Aging 2007;24: 275-292
3. Liang S.Y and Mackowiak P.A. Infections in the elderly. Clin
Geriatr Med 2007;23: 441-456
4. Vasto S, Candore G, Balistreri C.R, Caruso M, Colonna-Romano
G, et al. Inflammatory networks in ageing, age-related diseases
and longevity. Mech Ageing Dev 2007;128: 83-91
5. High K.P, Prasad R, Marion C.R, Schurig G.G, Boyle S.M, et al.
Outcome and immune responses after Brucella abortus infec-
tion in young adult and aged mice. Biogerontology 2007;8:
583-593
6. Htwe T.H, Mushtaq A, Robinson S.B, Rosher R.B, and Khardori
N. Infection in the elderly. Infect Dis Clin North Am 2007;21:
711-743
7. Hakim F.T and Gress R.E. Immunosenescence: deficits in adap-
tive immunity in the elderly. Tissue Antigens 2007;70: 179-189
Int. J. Med. Sci. 2009, 6
http://www.medsci.org
63
8. Mellanby R.J, Thomas D, Phillips J.M, and Cooke A. Diabetes in
non-obese diabetic mice is not associated with quantitative
changes in CD4+ CD25+ Foxp3+ regulatory T cells. Immunol-
ogy 2007;121: 15-28
9. Taub D.D and Longo D.L. Insights into thymic aging and re-
generation. Immunol Rev 2005;205: 72-93
10. Ribeiro R.M and Perelson A.S. Determining thymic output
quantitatively: using models to interpret experimental T-cell
receptor excision circle (TREC) data. Immunol Rev 2007;216:
21-34
11. Dixit V.D, Yang H, Sun Y, Weeraratna A.T, Youm Y.H, et al.
Ghrelin promotes thymopoiesis during aging. J Clin Invest
2007;117: 2778-2790
12. Mitchell W.A, Meng I, Nicholson S.A, and Aspinall R. Thymic
output, ageing and zinc. Biogerontology 2006;7: 461-470
13. Hale J.S, Boursalian T.E, Turk G.L, and Fink P.J. Thymic output
in aged mice. Proc Natl Acad Sci U S A 2006;103: 8447-8452
14. Effros R.B. Role of T lymphocyte replicative senescence in vac-
cine efficacy. Vaccine 2007;25: 599-604
15. Ely K.H, Roberts A.D, Kohlmeier J.E, Blackman M.A, and
Woodland D.L. Aging and CD8+ T cell immunity to respiratory
virus infections. Exp Gerontol 2007;42: 427-431
16. Clambey E.T, Kappler J.W, and Marrack P. CD8 T cell clonal
expansions & aging: a heterogeneous phenomenon with a
common outcome. Exp Gerontol 2007;42: 407-411
17. Petrovic-Dergovic D.M, Rakin A.K, Dimitrijevic L.A, Ristovski
J.S, Kustrimovic N.Z, et al. Changes in thymus size, cellularity
and relation between thymocyte subpopulations in young adult
rats induced by Somatostatin-14. Neuropeptides 2007; 41:
485-493
18. Prelog M. Aging of the immune system: a risk factor for auto-
immunity? Autoimmun Rev 2006;5: 136-139
19. Jondal M, Pazirandeh A, and Okret S. Different roles for glu-
cocorticoids in thymocyte homeostasis? Trends Immunol
2004;25: 595-600
20. Yajima N, Sakamaki K, and Yonehara S. Age-related thymic
involution is mediated by Fas on thymic epithelial cells. Int
Immunol 2004;16: 1027-1035
21. Pazirandeh A, Jondal M, and Okret S. Glucocorticoids delay
age-associated thymic involution through directly affecting the
thymocytes. Endocrinology 2004;145: 2392-2401
22. Dominguez-Gerpe L and Rey-Mendez M. Evolution of the
thymus size in response to physiological and random events
throughout life. Microsc Res Tech 2003;62: 464-476
23. Li L, Hsu H.C, Grizzle W.E, Stockard C.R, Ho K.J, et al. Cellular
mechanism of thymic involution. Scand J Immunol 2003;57:
410-422
24. Mocchegiani E, Santarelli L, Costarelli L, Cipriano C, Muti E, et
al. Plasticity of neuroendocrine-thymus interactions during
ontogeny and ageing: role of zinc and arginine. Ageing Res Rev
2006;5: 281-309
25. Hannestad J, Monjil D.F, Diaz-Esnal B, Cobo J, and Vega J.A.
Age-dependent changes in the nervous and endocrine control
of the thymus. Microsc Res Tech 2004;63: 94-101
26. Elcuman E.A and Akay M.T. Age-dependent immunolocaliza-
tion of fibronectin and histological changes in the thymus of
rats. Vet Res Commun 1998;22: 525-532
27. Oksanen A. Multilocular fat in thymuses of rats and mice asso-
ciated with thymus involution: a light- and electron-microscope
and histochemical study. J Pathol 1971;105: 223-226
28. Cheadle C, Vawter M.P, Freed W.J, and Becker K.G. Analysis of
microarray data using Z score transformation. J Mol Diagn
2003;5: 73-81
29. Apasov S.G, Blackburn M.R, Kellems R.E, Smith P.T, and
Sitkovsky M.V. Adenosine deaminase deficiency increases
thymic apoptosis and causes defective T cell receptor signaling.
J Clin Invest 2001;108: 131-141
30. El-Darahali A, Fawcett H, Mader J.S, Conrad D.M, and Hoskin
D.W. Adenosine-induced apoptosis in EL-4 thymoma cells is
caspase-independent and mediated through a non-classical
adenosine receptor. Exp Mol Pathol 2005;79: 249-258
31. Juntilla M.M, Wofford J.A, Birnbaum M.J, Rathmell J.C, and
Koretzky G.A. Akt1 and Akt2 are required for alphabeta thy-
mocyte survival and differentiation. Proc Natl Acad Sci U S A
2007;104: 12105-12110
32. Swat W, Montgrain V, Doggett T.A, Douangpanya J, Puri K, et
al. Essential role of PI3Kdelta and PI3Kgamma in thymocyte
survival. Blood 2006;107: 2415-2422
33. Kelso G.F, Porteous C.M, Coulter C.V, Hughes G, Porteous
W.K, et al. Selective targeting of a redox-active ubiquinone to
mitochondria within cells: antioxidant and antiapoptotic prop-
erties. J Biol Chem 2001;276: 4588-4596
34. Aguilaniu H, Durieux J, and Dillin A. Metabolism, ubiquinone
synthesis, and longevity. Genes Dev 2005;19: 2399-2406
35. Arkenau H.T, Chua Y.J, and Cunningham D. Current treatment
strategies in elderly patients with metastatic colorectal cancer.
Clin Colorectal Cancer 2007;6: 508-515
36. Fraga M.F, Agrelo R, and Esteller M. Cross-talk between aging
and cancer: the epigenetic language. Ann N Y Acad Sci
2007;1100: 60-74
37. Flores K.G, Li J, and Hale L.P. B cells in epithelial and perivas-
cular compartments of human adult thymus. Hum Pathol
2001;32: 926-934
38. Leprince C, Cohen-Kaminsky S, et al. Thymic B cells from my-
asthenia gravis patients are act
ivated B cells. Phenotypic and
functional analysis. J Immunol 1990;145: 2115-2122
39. Mori S, Inaba M, Sugihara A, Taketani S, Doi H, et al. Presence
of B cell progenitors in the thymus. J Immunol 1997;158:
4193-4199
40. Akashi K, Richie L.I, Miyamoto T, Carr W.H, and Weissman I.L.
B lymphopoiesis in the thymus. J Immunol 2000;164: 5221-5226
41. MacLennan I and Vinuesa C. Dendritic cells, BAFF, and APRIL:
innate players in adaptive antibody responses. Immunity
2002;17: 235-238
42. Hanada T, Yoshida H, Kato S, Tanaka K, Masutani K, et al.
Suppressor of cytokine signaling-1 is essential for suppressing
dendritic cell activation and systemic autoimmunity. Immunity
2003;19: 437-450
43. Mackay F and Browning J.L. BAFF: a fundamental survival
factor for B cells. Nat Rev Immunol 2002;2: 465-475
44. Gruver A.L, Hudson L.L, and Sempowski G.D. Immunosenes-
cence of ageing. J Pathol 2007;211: 144-156
45. Capri M, Monti D, Salvioli S, Lescai F, Pierini M, et al. Com-
plexity of anti-immunosenescence strategies in humans. Artif
Organs 2006;30: 730-742
46. Vier J, Gerhard M, Wagner H, and Hacker G. Enhancement of
death-receptor induced caspase-8-activation in the
death-inducing signalling complex by uncoupling of oxidative
phosphorylation. Mol Immunol 2004;40: 661-670
47. Perl A, Nagy G, Gergely P, Puskas F, Qian Y, et al. Apoptosis
and mitochondrial dysfunction in lymphocytes of patients with
systemic lupus erythematosus. Methods Mol Med 2004;102:
87-114
48. Gramaglia D, Gentile A, Battaglia M, Ranzato L, Petronilli V, et
al. Apoptosis to necrosis switching downstream of apoptosome
formation requires inhibition of both glycolysis and oxidative
phosphorylation in a BCL-X(L)- and PKB/AKT-independent
fashion. Cell Death Differ 2004;11: 342-353
49. Bustamante J, Bersier G, Romero M, Badin R.A, and Boveris A.
Nitric oxide production and mitochondrial dysfunction during
rat thymocyte apoptosis. Arch Biochem Biophys 2000;376:
239-247
50. Wofford J.A, Wieman H.L, Jacobs S.R, Zhao Y, and Rathmell
J.C. IL-7 promotes Glut1 trafficking and glucose uptake via
Không có nhận xét nào:
Đăng nhận xét