T lymphocyte activation gene identification by coregulated expression on DNA microarrays

www.elsevier.com/locate/ygeno

Genomics 83 (2004) 989–999

T lymphocyte activation gene identification by coregulated expression

on DNA microarrays$

Mao Mao, Matt C. Biery, Sumire V. Kobayashi, Terry Ward, Greg Schimmack, Julja Burchard,Janell M. Schelter, Hongyue Dai, Yudong D. He, and Peter S. Linsley*

Rosetta Inpharmatics LLC, Merck Research Laboratories, 401 Terry Avenue N, Seattle, WA 98109, USA

Received 8 July 2003; accepted 20 December 2003

Available online 20 March 2004

Abstract

High-capacity methods for assessing gene function have become increasingly important because of the increasing number of newly

identified genes emerging from large-scale genome sequencing and cDNA cloning efforts. We investigated the use of DNA microarrays to

identify uncharacterized genes specifically involved in human T cell activation. Activation of human peripheral blood T lymphocytes induced

significant changes in hundreds of transcripts, but most of these were not unique to T cell activation. Variation of experimental parameters

and analysis techniques allowed better enrichment for gene expression changes unique to T cell activation. Best results were achieved by

identification of genes that were most highly coregulated with the T-cell-specific transcript interleukin 2 (IL2) in a ‘‘compendium’’ of

experiments involving both T cells and other cell types. Among the genes most highly coregulated with IL2 were many genes known to

function during T cell activation, together with ESTs of unknown function. Four of these ESTs were extended to novel full-length clones

encoding T-cell-regulated proteins with predicted functions in GTP metabolism, cell organization, and signal transduction.

D 2004 Elsevier Inc. All rights reserved.

Keywords: Microarray; T cell; Activation; Coregulation

Gene expression pattern comparison is a widely used

means to identify genes involved in cellular processes of

interest, and many experimental approaches have been

developed for this purpose [1]. One technique for comparing

gene expression is DNA microarray hybridization, which

allows quantification of the expression of many thousands of

discrete sequences in a single assay [2,3]. Coordinated

expression of genes functioning in common processes, as

exemplified by bacterial operons, is also common in higher

organisms [4]. Coregulation in DNAmicroarray experiments

was suggested as a method to uncover and assign function to

genes for which information is not available [5]. Coregula-

0888-7543/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.ygeno.2003.12.019

1 Abbreviations: IL2, interleukin 2; ROAST, Rosetta Array Search

Tool.$ Supplementary data for this article may be found on Science Direct.

Sequence data from this article have been deposited with the Genbank

Data Library under Accession Nos. AF385429, AF385431, AF385435, and

AF385437.

* Corresponding author. Fax: (206) 802-6388.

E-mail address: [email protected] (P.S. Linsley).

tion has been used for functional characterization of un-

known genes from model organisms [6 –8], but this

approach has not been widely used for functional assignment

of unknown sequences in mammalian systems.

During an immune response, T lymphocytes interact with

antigen-presenting cells (APCs) in a complex process in-

volving intercellular interactions between many T cell

surface receptors and cognate ligands on APCs. During

these encounters, T cells undergo an elaborate transcription-

al response, leading to cellular differentiation and acquisi-

tion of immunologic function [9]. An understanding of the

molecular basis of T cell activation is essential to our

understanding of both immune responses and how to

manipulate them therapeutically. Therefore, gene expression

changes accompanying T cell activation and differentiation

have been intensively studied [10–17].

Previously, we have demonstrated the use of DNA

microarrays based on ink-jet technology [18] for the sys-

tematic identification of genes expressed under conditions

of interest [19]. We wished to identify genes specifically

regulated during T cell activation and not during other

Table 1

Enrichment of T cell activation genes by different analytic approaches

Gene list Total

genes

OMIM

annotated

T cell

genes

Percentage

of T cell

genes

Enrichment

of T cell

genes

p value

All genes on Hu25K array 23,968 5943 185 3.1% 1.0 1.0� 100

Signature genes from T cell kinetics 357 180 15 8.3% 2.7 3.1�10� 4

IL2 cluster branch/T cell kinetics 125 70 5 7.1% 2.3 4.5� 10� 2

IL2 cluster branch/compendium 80 42 5 11.9% 3.8 7.5� 10� 3

IL2 ROAST/T cell kinetics 80 36 6 16.7% 5.4 6.5� 10� 4

IL2 ROAST/compendium 80 47 11 23.4% 7.5 1.2� 10� 7

Shown are the total numbers of genes from various gene groups defined in the text, numbers of genes from those groups having OMIM annotations, and

numbers of OMIM-annotated genes associated with T cell activation, calculated as described under Materials and methods. The percentage of T cell genes was

calculated as (number of T cell genes/number OMIM annotated genes)� 100. The enrichment of T cell genes for each group was calculated as (% T cell genes/

% T cell genes on the chip as a whole (3.1%)). The p value for the overrepresentation of T cell genes from OMIM annotations in each group was calculated

from the hypergeometric distribution.

M. Mao et al. / Genomics 83 (2004) 989–999990

biological events in other cell types (T cell activation genes).

Here, we evaluate the use of these arrays for functional

characterization of T cell activation genes.

Results

Identification of T cell activation genes using the Online

Mendelian Inheritance in Man (OMIM) database

We wished to use objective techniques to compare

different methods of expression analysis for enrichment of

T cell activation genes, but were limited by the lack of

readily available functional annotation for many genes. We

therefore devised a method for identifying known T cell

activation genes by extracting information from OMIM

records (see Materials and methods). Of the 23,968 genes

on the Hu25K array, 5943 had OMIM records; of the latter

group of OMIM records, 185 (f3.1%) contained the terms

‘‘T-cell’’ and ‘‘activat’’ in at least one sentence. A literature

search for information about the genes in this group sug-

gested that the assignments were largely valid. We therefore

used these 185 genes to monitor the yield and enrichment of

different experimental and analytical techniques for T cell

activation genes (Table 1).

Analysis of expression kinetics for enrichment of genes

regulated during T cell activation

We first evaluated the kinetics of gene regulation

during T cell activation, as this method has been previ-

Fig. 1. Enrichment for T cell activation genes by kinetic analysis of gene regula

regulation. DNA microarrays were hybridized with a mixture of cRNA from restin

with immobilized anti-CD3 and soluble anti-CD28 mAbs. Shown are 357 genes re

the experiment. The order of genes along the x axis has been rearranged by cluster

125 genes; Group 2, 84 genes; Group 3, 11 genes; Group 4, 27 genes; and Group 5

kinetics gene group. The expression ratios of individual genes along the x axis are c

Red indicates genes that were up-regulated and green, genes that were down-r

Specificity of genes regulated during T cell activation. Shown is the regulation of g

kinetics gene group) in experiments involving different cell types. The order of g

ously used for functional gene grouping [20]. Phytohe-

magglutinin-activated T cell blasts (PHA blasts), a source

of peripheral blood T cells, were stimulated for various

periods of time with a combination of anti-CD3 and anti-

CD28 monoclonal antibodies (mAbs). Total RNA was

isolated from each culture, and cRNA was amplified and

used for competitive hybridizations on Hu25K micro-

arrays. This experiment revealed a total of 357 statisti-

cally significant gene regulations (>3-fold, p<0.01, see

[8] for details of the p-value calculations). Only 180 of

these regulated genes had OMIM records, of which only

15 (8.3%) were annotated as T cell activation genes.

Thus, the vast majority of genes annotated in OMIM as

being associated with T cell activation were not regulated

in our experiments with anti-CD3 and anti-CD28 activat-

ed PHA blasts. This result indicates that the OMIM gene

list does not optimally represent T cells activated in this

manner. Nonetheless, the OMIM record searching tech-

nique is useful for tracking some T cell activation genes

and we continued to use this technique as an objective

metric. However, we always corroborated results obtained

with the OMIM record searching technique by literature

searching or by statistical analysis of experimental data

(see below).

We used hierarchical clustering to identify groups of T

cell activation genes with similar kinetic patterns, yield-

ing five distinct groups containing >3 genes each (Fig.

1A). Group 1 (125 genes, see Supplemental Table 1 for

the complete gene group) contained 5 T cell activation

genes (IL2,1 TNFSF5, SLAM, NFIL3, and MIG). Group

4 also contained 5 genes associated with T cell activation

tion during T cell activation. (A) Hierarchical clustering analysis of gene

g peripheral blood PHA blasts and blasts activated for the indicated periods

gulated >3-fold, p< 0.01, at two or more time points following initiation of

ing of expression ratios (log10 R/G) to fall into five kinetic groups: Group 1,

, 91 genes. Group 1 is referred to in the text as the IL2 cluster branch/T cell

olor-coded according to a 10-fold response range specified by the color bar.

egulated. Gray shows missing data or log10 intensity less than � 1. (B)

enes from Group 1 (A; referred to in the text as the IL2 cluster branch/T cell

enes along the x axis was arranged as it as in A.

M. Mao et al. / Genomics 83 (2004) 989–999 991

(IFNG, TNSF6, SCYC1, MYC, and SCYA4), whereas

other groups contained one or no T cell activation genes.

Two of the kinetic groups (Groups 1 and 4) gave slight

enrichment for T cell activation genes; examination of the

literature for these genes suggested those from Group 1

were more T cell specific. Only one T cell activation


gene (FYB) was found among the down-regulated genes

(Group 5); we therefore focused on only up-regulated

genes in subsequent experiments. Group 1 genes (here-

after referred to as the IL2 cluster branch/T cell kinetics

gene group) were enriched for genes involved in T cell

Fig. 2. Enrichment for T cell activation genes by hierarchical clustering of data f

compendium. The same clustering parameters as in Fig. 1A was used. The brack

branch/compendium gene group). (B) Exploded view of the IL2 cluster branch/co

from the IL2 cluster branch/compendium gene group are described in Supplemen

activation by 2.3-fold from all the 23,968 genes repre-

sented on the Hu25K microarray (see Table 1). However,

the overall enrichment for T cell activation genes in

Group 1 was essentially the same as seen with the entire

group of 357 regulated genes in this experiment. Thus,

rom an experimental compendium. (A) Gene regulation in an experimental

et indicates the branch of 80 genes tightly clustered with IL2 (IL2 cluster

mpendium gene group. Arrow designates the position of IL2. The 80 genes

tal Table 2.


the yield of T cell activation genes in all of the kinetic

groups was low and the degree of enrichment was

modest.

Most of the genes in the IL2 cluster branch/T cell

kinetics gene group were not known T cell activation

genes. These genes could have previously undocumented

roles in T cell activation or they could be false positives. To

distinguish between these possibilities, we analyzed regu-

lation of the 125 genes from kinetic Group 1 (Fig. 1A) in a

panel of experiments involving different cell types stimu-

lated under a variety of conditions (hereafter referred to as

the compendium of experiments). This analysis (Fig. 1B)

revealed that the majority of genes sharing expression

kinetics with IL2 in PHA blast cells did not show gene

regulation in another source of activated T cells, activated

Jurkat cells. Furthermore, whereas some of these genes

were regulated relatively specifically in T cells, other genes

were also clearly regulated during stimulation of other cell

types (Fig. 1B). Thus, many if not most genes sharing

expression kinetics with IL2 do not display specificity for T

cell activation.

Identification of T cell activation genes in a compendium of

experiments

Since the OMIM record technique was not optimal for

identifying T cell activation genes, we sought to develop

additional methods for this purpose. We reasoned that

analysis of T-cell-regulated genes in experiments involving

other cell types might help identify genes that are broadly

regulated and improve the enrichment for T cell activa-

tion. We therefore tested whether a compendium of

experiments would be superior to kinetic experiments for

identifying T cell activation genes. We grouped the

regulation of a total of 1652 genes from 42 experiments

(Supplemental Material Table 6) by unsupervised one-

dimensional hierarchical clustering (Fig. 2A). Numerous

groups of coregulated genes were identified, but few

showed specificity for T cell activation. To localize gene

groups containing T cell activation genes, we examined

genes most closely coregulated with IL2. The branch of

the cluster tree containing IL2 included a group of 80

genes up-regulated in activated PHA blasts and Jurkat

cells (Fig. 2B, see Supplemental Table 2 for the complete

gene group, referred to hereafter as the IL2 cluster branch/

compendium gene group). Only a fraction of these 80

genes (those most tightly clustered with IL2, f1/5 of the

total genes in the cluster) were up-regulated in both PHA

blasts and Jurkat cells and less regulated in other cell

types. This indicated that there were still many false

positives in this cluster. The yield of this procedure was

low since only 5 of these genes were identified as T cell

activation genes by OMIM record parsing (Table 1). Thus,

there was a 3.8-fold enrichment for T cell activation genes

in this cluster, slightly higher than was achieved in the

kinetic experiment (Table 1).

ROAST analysis gave better enrichment for T cell activation

genes

We next evaluated ROAST analysis as an alternative

to hierarchical clustering for enrichment of T cell activa-

tion genes. This technique measures similarities in gene

regulation using a correlation coefficient to identify genes

most closely coregulated with a marker gene. We per-

formed ROAST analysis using the same two experiment

sets used previously (T cell kinetics and compendium of

different cell types). For a more direct comparison with

the IL2 cluster branch/compendium approach, we chose a

group size to correspond to the size of the IL2 cluster

(80 genes, Fig. 2A). We identified the top 80 genes most

closely regulated with IL2 in each experiment set (see

Supplemental Table 3 for the IL2 ROAST/T cell kinetics

gene group and Supplemental Table 4 for the IL2

ROAST/compendium gene group). There was overlap in

the four gene groups identified by clustering and by

ROAST analysis of the two experiment sets (Supplemen-

tal Tables 1–4), but each analytic approach produced

unique results.

In both experiment sets, ROAST analysis yielded more

T cell activation genes than hierarchical clustering, and

the degree of enrichment was higher and more significant

(Table 1). While this result is not conclusive because of

previously discussed limitations in the OMIM record

parsing technique, it suggests that ROAST analysis is

superior to clustering. IL2 ROAST/compendium analysis

yielded the most T cell activation genes as judged by

OMIM record parsing: IL2, EMT, TNFRSF8, TNFSF4,

HRB, IL2RA, SCYC1, TNFSF6, THFSF5, CD69, and

SH2D2A. To confirm the results with the OMIM record

parsing, we examined published literature describing

these and other genes from the IL2 ROAST/compendium

gene group. This analysis also suggested that this group

was enriched for genes with important functions during T

cell activation (data not shown). Therefore, both compu-

tational (OMIM) and manual literature searches showed

that ROAST analysis provided the best enrichment for T

cell activation genes and had less potential for false

positives.

Further support for the T cell specificity of the genes

selected by ROAST analysis is provided by visual displays

of the gene regulation in groups generated from the kinetics

and compendium experiment sets (Fig. 3A and B, respec-

tively). Most genes selected by ROAST/compendium anal-

ysis showed up-regulation in both PHA blasts and Jurkat

cells and little or no regulation in other cell types. Thus,

genes selected by ROAST analysis were more specific for T

cells. Experimental data therefore supported the conclusion

reached by literature searching that ROAST analysis gave

higher yield and specificity of T cell activation genes than

other techniques.

Finally, we used a statistical approach (see Materials

and methods) to estimate the significance of specificity of

Fig. 3.


Fig. 4. The specificity of different gene groups during T cell activation. Shown are the percentages of genes passing p-value threshold for four gene groups

identified by different analytic approaches (see text). The p value was estimated based on the hypergeometric distribution by counting number of up-regulations

in T-cell-related experiments versus number of regulations in non-T-cell experiments (see Materials and methods).


gene up-regulation during T cell activation as identified by

different methods. A p value for association with T cell

activation was assigned to each gene in groups generated

in the four different method/experiment combinations. The

percentages of genes surviving different p-value thresholds

for four gene groups are shown in Fig. 4. The IL2

ROAST/compendium gene group showed the highest per-

centage of genes passing the threshold activation for all p

values <10�2; therefore, this group contained the most

genes with experimentally determined specificity for T cell

activation. The p values for the 11 genes in this group

annotated in OMIM as T cell activation genes (Table 1)

ranged from 1.8�10�11 to 4.0�10�3 (see Supplemental

Table 4).

ROAST analysis was also more robust than clustering,

in which small and biologically insignificant changes in

experiments analyzed sometimes resulted in dispropor-

tionate effects on the results obtained. In contrast,

ROAST analysis yielded results more consistent with

changes made to the biological content of the experiment

Fig. 3. Enrichment for T cell activation genes by ROAST analysis. (A) Regulation

cell kinetic experiment was subjected to ROAST analysis using IL2 as a query g

kinetics gene group in the experimental compendium (see Supplemental Table 3 f

correlation to IL2 in the ROAST analysis, with descending correlation from left to

compendium. The experimental compendium was subjected to ROAST analysis

compendium gene group) are described in Supplemental Table 4. Shown is a colo

ordered as described in A.

set. An additional advantage of ROAST over hierarchical

clustering is that it is computationally less intensive so

that larger data sets could be examined on a desktop PC.

Identity of ESTs coregulated with IL2

Among the 80 genes in the IL2 ROAST/compendium

gene group, there were 30 ESTs of unknown function

(Supplemental Table 4). The coregulation of these ESTs

with so many T cell activation-associated genes suggests

that some of the protein products of these EST transcripts

may also function during T cell activation. These ESTs may

represent unknown genes or extensions of known genes. To

distinguish these possibilities and to identify unknown

genes, we assigned transcripts for 20 of these ESTs (Sup-

plemental Table 5); 10 of these ESTs remain unidentified.

Transcripts corresponding to these ESTs were identified

by a combination of informatics and experimental

approaches. The EST sequences were first mapped to

known cDNA or genomic sequences by BLAST. Putative

of the IL2/T cell kinetics gene group in an experimental compendium. The T

ene. Shown is a color display of the regulation of the IL2 ROAST/T cell

or the gene group). The order of genes on the x axis represents the rank of

right. (B) Regulation of the IL2/compendium gene group in an experimental

using IL2 as a query gene. The top 80 genes coregulated with IL2 (IL2/

r display of the regulation of these genes in the experimental compendium,

Table 2

The properties of four new genes

Gene

name

EST

Accession

No.

mRNA

Accession

No.

Homolog Protein domain

TA-GAP AI253155 AF385429 NP_004299

human Rho

GTPase-

activating

protein 1

Rho GAP

TA-PP2C AA521311 AF385435 P49593 human

putative protein

phosphatase 2C

Protein

phosphatase

2C

TA-WDRP R24201 AF385437 T41051 fission

yeast htransducin

WD domain,

G-h repeat

TA-GPCR AA040696 AF385431 NP_006009

human putative

chemokine

receptor

7 transmembrane

receptor

(rhodopsin

family)

Shown are the properties of T cell activation genes identified in this study.

Gene name, name of new gene identified in this study; ESTAccession No.,

EST represented on the Hu25k chip; mRNA Accession No., accession

number of sequence deposited with GenBank to represent the new gene;

Homolog, closest protein homolog identified by BLAST analysis of the

predicted open reading frame from the new mRNA; Protein domain,

characteristic protein domain of the new predicted protein sequence.


transcripts from genomic regions were then identified by

mapping adjacent known or predicted exons or by an

experimental hybridization approach for exon identification

[19]. Linkage of putative exons to EST sequences was

accomplished by RT-PCR. Overlapping RT-PCR clones

Fig. 5. Selective expression of novel genes in multigene superfamilies

during T cell activation. (A) TA-GPCR expression. Hu25k DNA micro-

arrays were hybridized with a mixture of cRNA from resting peripheral

blood PHA blasts versus blasts activated for the indicated periods with

immobilized anti-CD3 and soluble anti-CD28 mAbs. Gray lines represent

expression kinetics (log10 R/G ratios vs time) for most highly regulated

genes during T cell activation (i.e., genes showing >2-fold regulation in 2/

8 time points; p<1�10�4, 2/8 time points; and log10 intensity >�1, 7/8 time

points). Under these conditions, a total of 326 genes were up-regulated and

221 genes down-regulated (547 total regulated genes). Red lines indicate

expression kinetics of 14 known GPCRs represented on Hu25K DNA

microarray (GPR9, EB12, AI208357, GPRK6, LANCL1, GPR51, GPR4,

GPR39, GPR48, GPRK5, AI161367, GPR68, GPR19, and AI659657), and

the blue line indicates expression kinetics of TA-GPCR (EST AA040696).

(B) TA-GAP expression. Expression data from the same Hu25K DNA

microarrays from A are shown. The gray lines represent expression kinetics

(log10 R/G ratios vs time) for the most highly regulated genes during T cell

activation defined in Fig. 4A, the red lines represent expression kinetics of

16 known GAP-domain-encoding genes represented on the Hu25k DNA

microarray (RAB3GAP, IQGAP2, AI479025, ARHGAP4, NGAP,

RAR1GA1, IQGAP1, KIAA1501, GIT2, ARHGAP1, GAPL, GIT1,

KIAA0660, ABR, GAPCENA, and RASA1), and the blue line represents

expression kinetics of TA-GAP (EST AI253155). (C) Real-time PCR

validation. Real-time PCR analysis of TA-GPCR and TA-GAP transcripts

in total RNA samples isolated at time points ranging from 0.25 to 18 h post-

activation. The endogenous control gene for amplifications is GAPDH. D

Ct standard deviation between duplicates for each time point varied by less

than 10%.

were used to establish linkage to known transcripts or to

identify novel open reading frames.

The majority of these ESTs (16/20) correspond to tran-

scripts whose complete coding sequences have recently

been determined or at least partially characterized (‘‘known

transcripts’’). Fourteen ESTs were linked to newly published

cDNAs by continuous updating of UniGene clusters during

this study. Two other ESTs (IL2RA and IL21R) were linked

to known transcripts by RT-PCR cloning (the cloning of

IL21R was reported while this work was under way [21]).

Of the ESTs linked to known transcripts, 4 have well-


characterized functions during T cell activation (IL2RA,

IL21R, SH2D2A, and TBX21) and one is functional in B

cells (BACH2 [22]).

For four ESTs, full-length cDNA clones that had not

been previously described (‘‘unknown transcripts’’) were

obtained.2 The properties of the predicted protein products

of the unknown transcripts are summarized in Table 2 and

Fig. 1, Supplemental Material. All of the predicted protein

products contained distinctive protein sequence motifs.

Although these motifs characterize proteins with many

diverse functions, three of them are found in proteins

involved in GTP metabolism (Refs. [24, 25 and 26],

respectively, for TA-GAP, TA-GPCR, and TA-WDRP).

GTP hydrolysis is known to play a key role in T cell

receptor signaling [27]. Two of the unknown protein prod-

ucts (TA-GPCR, TA-GAP) belong to large protein super-

families. Many other members of these protein

superfamilies were represented on the Hu25K DNA micro-

arrays used in these experiments. To test the specificities of

these unknown proteins further for T cell activation, we

compared the regulation of TA-GPCR and TA-GAP tran-

scripts with transcripts for other members of their super-

families during PHA blast activation (Fig. 5). TA-GPCR

transcript levels reached a maximum after approximately 6

h of activation. TA-GPCR was more highly regulated than

14 other G-protein-coupled receptor genes represented on

the Hu25k chip (Fig. 5A). TA-GAP transcript levels rose

transiently and reached maximal levels after approximately

4 h of activation. TA-GAP was more highly regulated than

16 other GTPase-activating protein domain genes repre-

sented on the Hu25K chip (Fig. 5B). The regulation of TA-

GPCR and TA-GAP during T cell activation was confirmed

by quantitative PCR analysis (Fig. 5C). These findings

suggest that TA-GPCR and TA-GAP may play important

roles during T cell activation.

Discussion

Historically, many techniques have been used to iden-

tify and clone differentially expressed genes [28]. These

techniques are generally not well suited to discerning the

specificity of gene expression differences, since they

generally rely on comparisons between only a few exper-

imental conditions. The specificity of gene expression

changes detected by these techniques becomes apparent

only after secondary characterization using labor-intensive

techniques [1].

2 These unknown transcripts were named by the acronym TA (for T

cell activation), followed by an acronym designating the most distinctive

type of protein domain of their protein product: TA-GAP, GTPase-

activating protein; TA-PP2C, protein phosphatase 2C; TA-WDRP, WD40

repeat protein; TA-GPCR, G-protein-coupled receptor. During preparation

of this article, a cDNA encoding a protein identical to TA-GPCR was

deposited with GenBank on August 17, 2001, and later described in Ref.

[23]. These authors referred to this sequence as GPR81.

The completion of the human genome sequence has

highlighted the need for functional understanding of many

poorly characterized genes. One approach to this problem is

development of high-throughput methods for inferring gene

function or specificity for cellular processes. In this study,

we have examined one such high-throughput methodology,

DNA microarray hybridization, for identifying genes regu-

lated specifically during T cell activation. Although we

identified hundreds of transcripts that were differentially

regulated during T cell activation, most of these showed

poor specificity for this process because they were also

regulated in other cell types. Thus, as with other techniques,

examination of gene regulation with DNA microarrays

under a limited set of experimental conditions can be

misleading.

By examining genes coregulated with IL2 in a combina-

tion of experiments involving T cell activation and other

conditions, we observed enrichment for genes with specific-

ity for T cell activation. While this approach is similar in

principle to the approach of Hughes et al. [8], the present

study utilized cellular activation and differentiation experi-

ments instead of genetic disruptions to provide experimental

diversity. This is advantageous when using mammalian cells,

in which genetic disruptions are less readily obtained. It will

be important to determine whether recently developed gene

modification technologies [29] might also be used for com-

pendium building. Gene disruption techniques may also be

useful for the functional associations predicted with our

techniques.

The results reported here suggest several possible ave-

nues for improvement in our approach of using coregulation

to assign unknown genes to biological processes. At pres-

ent, the optimal balance between experiments involving a

process of interest and other conditions is poorly under-

stood, as is the optimal total number of experiments.

Furthermore, it is unclear whether the algorithms used for

examining enrichment have been optimized. Our experience

suggested that ROAST is more robust than hierarchical

clustering and yields greater enrichment, but it may be

possible to develop even better algorithms.

In theory, it should be possible to improve our ability to

use coregulation to assign unknown genes to biological

processes by systematically examining gene regulation in

several systems under multiple conditions and quantitatively

determining optimal experimental and computational

approaches. Such approaches have shown promise for the

systematic annotation of unknown ORFs from Saccharo-

myces cerevisiae [30]. In contrast to S. cerevisiae, however,

a much lower percentage of human genes has been anno-

tated sufficiently to allow successful application of these

techniques at present. Advances in human gene annotation

[31] will undoubtedly aid in the improvement of methods

described in this study.

In conclusion, this study shows that genome-scale anal-

yses of gene expression during T cell activation can assign

poorly characterized genes to this process. We expect that


broadly diverse and comprehensive experimental conditions

will bring many of the f30,000 human genes into high-

resolution synexpression groups. Analysis of these groups

for unknown genes coregulated with other well-studied

markers should facilitate elucidation of gene function and

provide information on genetic networks.

Materials and methods

Microarray experiments

Sequences for microarrays were selected from UniGene

(a nonredundant set of sequence clusters or genes in

GenBank, http://www.ncbi.nlm.nih.gov/UniGene/). Each

UniGene cluster was represented on a microarray by a

single 60-mer oligonucleotide chosen from the longest

mRNA sequence belonging to the cluster [18]. Hu50k

microarrays represented a total of 49,218 UniGene clusters

present in UniGene Release 111, April 14, 1999. Hu25K

microarrays represented the 23,965 oligonucleotides from

the Hu50K microarrays that hybridized most strongly with

cRNA from PHA blasts (see Supplemental Material).

Microarrays were synthesized and hybridized using meth-

ods described elsewhere [18]. Probe sequences were chosen

near the 3Vends of transcript sequences to minimize detec-

tion biases resulting from the use of reverse transcriptase in

microarray sample preparation (50–350 bases from the 3Vend of the longest mRNA sequence representing each

Unigene cluster). The PCR-IVT technique described in

Ref. [18] was used to prepare samples for hybridization.

All hybridizations were performed in duplicate with fluor

reversal; data presented are the averages of duplicate deter-

minations. The primary microarray data are available at

Gene Expression Omnibus (GEO), http://www.ncbi.nlm.

nih.gov/geo/, Accession No. GLP771.

Quantitative PCR

RNA quantitation was performed by real-time PCR,

using AP Biosystems TaqMan Assays-on-Demand gene

expression products (50249551_C and 50289949_D) for

TA-GPCR and TA-GAP, respectively. mRNA values were

normalized to mRNA for GAPDH (No. 4333764F).

Data analysis

Hierarchical clustering was performed as described [5].

Rosetta Array Search Tool analysis was performed using a

modification of the algorithm used in the Rosetta Resolver

expression data analysis system. This technique takes a

pattern of interest, performs a correlation coefficient-based

similarity search against a library of patterns, and outputs a

ranked list of patterns in the library according to the degree

of similarity with the pattern of interest. The implementation

of ROAST used in these studies calculates the similarity by

the correlation coefficient between the gene expression

profiles. Given a gene response across multiple experiments,

ROAST analysis returns the genes showing the most similar

regulation.

Identification of genes associated with T cell activation via

OMIM annotation

Known gene associations with T cell activation were

assessed from the published literature by automated analysis

of the Online Mendelian Inheritance in Man database (http://

www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM).

OMIM records were linked to genes represented on the

arrays according to LocusLink (http://www.ncbi.nlm.nih.-

gov/LocusLink/). Genes were scored positive for relevance

to T cell activation if their OMIM records contained the

terms ‘‘T cell’’ and ‘‘activat’’ in the same sentence. More

specifically, positive sentences were required to have these

features, in any order: <word boundary>t<any character>

cell<word boundary>activat. The search covered the text and

reference fields of OMIM records and was case insensitive.

Statistical significance of gene regulation unique to T cell

activation

The significance of gene associationswith Tcell activation

was assessed by statistical analysis of expression data from

experiments involving activated T cells and non-T cells. The

regulationofdifferentgene sets (>2-foldchanges,p<0.01)was

compared in the following twoexperimentgroups: (1)12Tcell

activation experiments involving PHA blasts and Jurkat cells;

(2) 30 myelocyte activation or differentiation experiments

including THP-1, HL60, K562, and NB4 cells. We hypothe-

sized that genes involved in T cell activation would be

preferentially up-regulated in group (1), but not regulated in

group (2). Hence, the number of experiments showing regula-

tionofaparticulargeneof interest ingroup(1)vs (2)wasusedas

a measure of how specific that regulation was for T cell

activation compared with other biological events. The hyper-

geometricdistributionwasusedtocalculate theprobabilitypof

observing n1 up-regulations in group (1) and n2 up- or down-

regulations in group (2). The validity of this approach for

assessing the specificity of gene regulation forTcell activation

was verified by many randomization tests.

Cell culture, PCR, and cloning methods

These are described in the Supplemental Material.

Acknowledgments

We thank Michael Carleton, Jason Johnson, Dan

Shoemaker, and Michele Cleary for helpful comments on

the manuscript and Sergey Stepaniants for array data

submission to GEO.

http:\\www.ncbi.nlm.nih.gov\UniGene\

http:\\www.ncbi.nlm.nih.gov\geo\

http:\\www.ncbi.nlm.nih.gov\LocusLink\


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T lymphocyte activation gene identification by coregulated expression on DNA microarrays

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