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. Author manuscript; available in PMC: 2010 Jun 15.
Published in final edited form as: J Infect Dis. 2006 Nov 22;195(1):55–69. doi: 10.1086/509895

Immune Gene Networks of Mycobacterial Vaccine–Elicited Cellular Responses and Immunity

Dan Huang 1,a, Liyou Qiu 1,a, Richard Wang 1, Xioamin Lai 1, George Du 1, Probhat Seghal 3, Yun Shen 1, Lingyun Shao 1, Lisa Halliday 2, Jeff Fortman 2, Ling Shen 4, Norman L Letvin 4, Zheng W Chen 1
PMCID: PMC2885892  NIHMSID: NIHMS197506  PMID: 17152009

Abstract

Gene networks of protective lymphocytes after immune activation with live attenuated vaccines remain poorly characterized. Because Mycobacterium bovis bacille Calmette-Guérin (BCG) vaccine can confer protection against fatal forms of tuberculosis in humans and monkeys, we made use of macaque models to optimally study immune gene networks after BCG vaccination/infection. We first established and validated a large-scale real-time quantitation system and then used it to measure expression levels of 138 immune genes after BCG vaccination/infection of rhesus macaques. Systemic BCG vaccination induced up to 600-fold increases in expression of 78 immune genes among the 138 genes tested at the time when BCG-elicited T cell responses and immunity were apparent. These up-regulated transcripts constituted multiple gene networks that were linked to various aspects of immune function. Surprisingly, the up-regulation of most of these immune genes in the gene networks occurred at 1 week and was sustained at ≥6 weeks after BCG vaccination/infection. Although early activation of immune gene networks was an immune correlate of anti-BCG immunity, prolonged up-regulation of these networks coincided with the development of vaccine-elicited T cell responses after BCG vaccination/infection. These findings provide molecular evidence suggesting that the BCG-induced gene networks may represent global transcriptomes and proteomes underlying the development of T cell responses and, ultimately, immunity to mycobacteria.

Live attenuated vaccines have proved to be protective against a number of infectious diseases [1–6]. Although the development and evaluation of live vaccines are mainly focused on safety and the efficacies of vaccine-elicited immune responses and vaccine-induced protection, little is known about the global transcriptional responses of protective lymphocytes elicited by a live vaccine. Studies of global transcriptional responses or gene networks during the development of protective immune responses after vaccination can facilitate our understanding of the molecular mechanisms of live vaccine–mediated immunity and may provide useful information for developing and evaluating new or better vaccines from safety and efficacy standpoints.

Among infectious diseases, tuberculosis remains one of the leading causes of death, with an estimated 8–9 million new cases occurring worldwide annually [7]. The high rate of an adult form of pulmonary tuberculosis may be attributed, in part, to the absence of a highly effective vaccine. Live attenuated Mycobacterium bovis bacille Calmette-Guérin (BCG) vaccine is protective against M. tuberculosis infection and fatal forms of tuberculosis in children [1, 3, 8], although it is unable to consistently confer protection against an adult form of pulmonary tuberculosis [1, 3, 8, 9]. Elucidating BCG-elicited immune responses that confer short-term protection against M. tuberculosis infection may facilitate the development of tuberculosis vaccines that are better than BCG [10, 11]. In this regard, exploring the gene networks of BCG-elicited lymphocytes may provide information for the rational design and evaluation of new tuberculosis vaccines [12, 13].

Studies by us and others suggest that BCG-vaccinated monkeys can provide a dual model system in which to explore both vaccine-elicited T cell responses and anti-mycobacterium immunity at cellular or molecular levels [14–16]. As a vaccine model, BCG immunization can serve as a prototype of live vaccine–elicited T cell immune responses. As an infection model, BCG-infected monkeys can be assessed for immune correlates for clearance of BCG bacteria after systemic BCG vaccination [15, 16]. In fact, BCG vaccination has been shown to confer protection against early fatal tuberculosis in rhesus macaques after aerosol and low-dose M. tuberculosis infection [15, 17–20]. These findings are consistent with BCG’s capability to protect against M. tuberculosis infection in children [1–3]. We therefore made use of BCG-vaccinated rhesus macaques to study global gene-expression profiles of protective lymphocytes. We first established and validated a large-scale real-time quantitation system for measuring 138 macaque immune genes and then used this system to identify global gene networks that underscore BCG-elicited T cell immune responses and associated anti-BCG immunity.

MATERIALS AND METHODS

Macaques and BCG vaccination/infection

Normal, healthy rhesus macaques, 2–6 years old, were included in the present study. Monkeys were vaccinated intravenously with 1 × 106 cfu of M. bovis BCG (Pasteur strain), as described elsewhere [15, 16]. The BCG vaccination/infection of monkeys was done on the basis of the animal protocol that was approved by the University of Illinois at Chicago Institutional Animal Care and Use Committee. Blood samples were collected weekly or biweekly after BCG vaccination/infection, to measure the expression kinetics of immune genes in peripheral-blood mononuclear cells (PBMCs).

Macaque immune gene templates

Because a number of macaque immune genes are not available in GenBank, we isolated cDNA of those genes through polymerase chain reaction (PCR)–based cloning and sequencing, using primer sets that were designed on the basis of the sequences of their human counterparts. The isolated PCR products of individual macaque genes were sequenced using a direct sequencing technique [21]. The sequences of individual genes were used for designing primer sets and probes for real-time quantitative PCR, on the basis of recommendations given in Primer Express software (PE Applied Biosystems); the cloned cDNA of individual genes serves as template standards for real-time quantitation. Table 1 shows the sequences of these isolated macaque genes and other genes available in GenBank.

RNA isolation and cDNA synthesis

PBMCs (6 × 106) were collected at each time point, mixed with 800 μL of TRIzol reagent (Invitrogen), and stored at −70°C until use. Total RNA was isolated from PBMCs by the TRIzol isolation method [15]. cDNA synthesis was done by use of the protocol provided in the cDNA Synthesis Kit from Clontech Laboratories, as described elsewhere [15].

Large-scale real-time quantitation system for measuring 138 macaque immune genes

The large-scale real-time quantitation system was established on the basis of our extensive experience with real-time quantitative PCR, as described elsewhere [14, 15, 22–26]. The names and abbreviations of individual genes as well as sequences of primers and FAM probes for the genes are listed in table 2. Real-time quantitative PCR was performed using PE Applied Biosystems 7700 single-reporter sequence-detection systems. The total reaction volume was 12.5 μL; it contained 6.25 μL of master mix (PE Applied Biosystems), 0.5 μL of 12.5 pmol/μL forward primer, 0.5 μL of 5 pmol/μL FAM probe (Applied Biosystems), 2.75 μL of H2O, and 2 μL of cDNA diluted 1:10 or 1:20. Reaction wells for each sample were done in triplicate. The real-time PCR conditions were 50°C for 2 min, 95°C for 10 min, 40 cycles of 95°C for 15 s, and 60°C for 1 min. Six defined copies of each gene template were included in each PCR microplate, to serve as standards for real-time quantitation. All amplifications were done in an optical 96-well reaction plate with an optical membrane cover (MicroAmp; PE Applied Biosystems). To minimize variation, cDNA samples prospectively collected from each rhesus macaque were run together in a plate for quantitation of each gene. All data were analyzed using GeneAmp 7700 SDS software. The expression values of individual genes were normalized on the basis of the values of the β-actin or GAPDH housekeeping gene (number of copies/1 × 105 β-actin or GAPDH transcripts). The changes in expression for each gene were calculated by dividing the postvaccination values by the prevaccination values. The data were then expressed as a fold change for the postvaccination value relative to the baseline value for each gene (i.e., fold change equals copies for the week 2 sample divided by the copies for the week 0 sample).

Table 2.

Names, abbreviations, and oligonucleotide sequences of 138 macaque immune genes investigated by use of a large-scale real-time quantitation system.

Gene 5′ primer 3′ primer Probe Accession no. or sourcea
Interleukin 1α (IL-1α) TTCGAGCCAATGATCAGCAC CCCATGTCAAATTTCACTGCTTC TCACGGCTGCTGCAATACATAATCTGGA MMU19844
Interleukin 1β (IL-1β) GGTTCCCTGCCCACAGATCT CGTCGTTATTGCGTGTGTC CCAGGACAATGACCTGAGCACCTTGA MMU19845
Interleukin 2 (IL-2) GGACTTAATCAGCAATATCAACGTAATAG TCTACAATGGTTGCTGTCTCATCA TCTGGAACTAAAGGGATCTGAAACAACACTGATG MMU19847
Interleukin 3 (IL-3) ATCAGCAATCGAGAGCATTCTTAA TGGGTGCGGCCGTG AATCTCCCACCATGCCTGCCCAT Chen Lab
Interleukin 4 (IL-4) ACTCTGTGCACCAAGTTGACCAT AGCCCTGCAGAAGGTTTCCT ACGGACATCCTTGCTGCCTCCAAG MACIN4A, NM001032904, AY376144
Interleukin 5 (IL-5) CACAGTTGGTGATGTTTATGTACAGG GCACTGCTTTCTACTCATCGAACTC CAGGAATCCTCAGAGTCTCATTGCCTATCAGC MMU19848
Interleukin 6 (IL-6) CCAGGCAAGTGTCCTCATTGA GAGGCACTGGCAGAAAACAAC CATCCATCTTTTTCAGCCATCTTTGGAAGGTT MACIN6A, AB000554, AY849928
Interleukin 7 (IL-7) TGCTACCAATTTCTTTCATGCTG ATTGAAGGTAAAGATGGCAAACAA T CCAATAATTGATCGATGCTGACCATTAGAACACT NM001032846, AF401221
Interleukin 8 (IL-8) AGCTCTCTTCCATCGGAAAGTTT CCTTTCCACCCCAAATTTATCAA TGTATTGGCACAGTGTGGTCCACTCTCAAT NM001032965, MMU19849
Interleukin 9 (IL-9) TGTGACCAGTTGTCTCTGTCTGC GACAGTCCCTCCCTGAAGCA CATTCCTTCTGACAACTGCCCTGGACC Chen Lab
Interleukin 10 (IL-10) AAGACCCTCAGGCTGAGGCT TCCACGGCCTTGCTCTTG CGCTGTCATCGATTTCTTCCCTGTGAA Chen Lab
Interleukin 12α (IL-12α) GCAGCTTCTTCATCAGGGACA TTCTTTAATGGCTTCAGCTGCA CATCAAACCCGACCCACCCAAGAAC MMU19841
Interleukin 12β (IL-12β) CAGCAACACGCTTCAGAAGG CAGGCCTCTACTGTGCTGGTT ACCCTTGCACTTCTGAAGAGATTGATCATGA MMU19842
Interleukin 13 (IL-13) AACCTGACAGCTGGCGTGTAC CTGGGTCTTCTCGATGGCAC TGCAGCCCTGGAATCCCTGATCAAC AY849927, NM001032929, Chen Lab
Interleukin 15 (IL-15) TCATTTTGGGCTGTTTCAGTG ACTTCATTGCTGTTACCTTGCAAC CTCCCTAAAACAGAAGCCAACTGGGTGAAT MMU19843, AB000555
Interleukin 16 (IL-16) GCACCTGGGACCACACATC AGGCAGTTGAGAGGAGCCAAA CCTCCATCAAGCAGAGAATCAGCTCCT TT NM001032808, AF017108
Interleukin 17 (IL-17) CACCTCACCTTGGAATCTCCA CATTTTGCCTCCCAGATCACA CGCAATGAGGACCCTGAGAGATATCCCT Chen Lab
Interleukin 18 (IL-18) TGAGAACAGAATTATTTCCTTTAAGGAA TCACAAGCTAGAAAGTATCCTTCGTATG TGACATCATATTCTTTCAGAGAAGTGTCCCAGGA NM001032834, AF303732
Interleukin 19 (IL-19) AGCTAAGGACACCTTCCCAAATG CTTGGTCACGCAGCACACA CACTATCCTGTCCACATTGGAGACTCTGCAG Chen Lab
Interleukin 20 (IL-20) GAGGACTGAGTCTTTGCAAGACAC TCCAGATAGAGTCGTAGCAAATGG AAGCCTGCGGATCAGTGCTGTCTTCTAC Chen Lab
Interleukin 21 (IL-21) TGTGAATGACTTGGACCCTGAA AAACAGGAAATAGCTGACCACTCA TCTGCCAGCTCCAGAAGATGTAGAGACAAACT Chen Lab
Interleukin 22 (IL-22) CCCTCAATCGGATAGGTTCCA AGACTGTTGCTGAGCCTGGC CTTATATGCAGGAGGTGGTGCCCTTCCT Chen Lab
Interleukin 23α (IL-23α) CTACTGGGCCTCAGCCAACT GGGCTTGGAATCTGCTGAGTC CTGCAGCCTGAGGGTCACCACTGG Chen Lab
Interleukin 27 (IL-27) GTTCAAGGCAGAGGTGCGA TTCCCTTTCAAATGCAGCTTTAG CGCCGAAATCGAGTACGCCATGG Chen Lab
Interferon-α (IFN-α) GACTAATACACCAGCTCACCTTTTTATG TAAATAGATAGTAGATCAGTCAGCATGGTCA TCTGCCATTTCAAAGATTCATGTTTCTGCTATG Chen Lab
Interferon-β (IFN-β) GCAATTGAATGGAAGGCTTGA AGTCTCATTCCAGCCAGTGCTAG TATTGCCTCAAGGACAGGA MFA011909, Chen Lab
Interferon-γ (IFN-γ) GCTGACCAATTATTCGGTAACTGA C AGTTCAGCCATCACTTGGATGA CAAATGTCCACCGCAAGGCAGTACATGAA MACIFNG, NM001032905, AY376145
Macrophage migration inhibitory factor (MIF) ATGTTCATCGTAAACACCAACGTG TGCTGGGTGAGCTCGGAG CCTCCGTGCCGGAC AY656809, AB169695, MN001032915, Chen Lab
Tumor necrosis factor–α (TNF-α) AACCCCAAGTGACAAGCCTG CCACTGGAGCTGCCCCT AGCCCATGTTGTAGCAAACCCTCAAGC MMU19850, AB000513, Chen Lab
Interleukin 1 receptor 1 (IL-1R1) TTCAGGACATTACTACTGTGTGGTAAGA TGGCTTCTGCATTATAACACAAGTT CATCTTACTGCCTCAGAATTAAAATAACTGCAAAATTTGT AY497008
Interleukin 1 receptor 2 (IL-1R2) GAGACCATTCCTGTGATCATTTCC CACGGGATTGTCAGTCTCGA CCCTCAAGACCATATCGGCTTCTCTGG AY172102
Interleukin 2 receptor α (IL-2Rα) GGCTTCATTTTCCCACGGT GCAGCTGGCGGACCAA TCCCTGCAGTGACCTGGAAGGCTC NM001032917, AY693777
Interleukin 2 receptor β (IL-2Rβ) TGGCCATCCAGGACTTCAA TGGACGACTTGGAGGGAGAT CCCTTTGAGAACCTTCGCCTGATGG DQ223724
Interleukin 2 receptor γ (IL-2Rγ) AGCTCCAGGACCCACGG GGGATCACCAGATTCTGCAGTT AACCCAGGAGACAGGCCACACAGATG NM001035529, DQ222417
Interleukin 3 receptor α (IL-3Rα) GTTCCCACATCCTGGTGAGG CTCCAGTCATGTTGGGTGGAG AGGAGCGCAGCCGTCAGTATCCC Chen Lab
Interleukin 4 receptor (IL-4R) AAAACGACCCGGCAGATTC GAAATTCCAGACTTCAGGGTGCT AGAATCCATAACGTGACCTACCTAAAACCCACC AY459192, Chen Lab
Interleukin 5 receptor α (IL-5Rα) CGCTCCAAAAGAAGATGATTATGA GGTCCGCACACTTGCTGAA ACCAGAATCACTGAAAGCAAATGCGTAACC Chen Lab
Interleukin 6 receptor (IL-6R) TTCGGCCGGACTGTTCTG GCACCCCATCTCCGACG AACTTCCTCACCAACAGCACAGCCTTG Chen Lab
Interleukin 7 receptor (IL-7R) TGAAAACAAATGGATGCATGTG TACATTGCTTCAGGTTGGAGGTT CCAGCACAAAGCTGACACTCCTGCA Chen Lab
Interleukin 8 receptor α (IL-8Rα) TTAGATCAAACCATTGCTGAAACT GTTGAGTGACTGAGTCTCTAGCCTACA CATGCCACCTACGGATGAAGATTATAGCCC Chen Lab
Interleukin 9 receptor (IL-9R) CACCCCGACAATGTGATCC TTCAAGAAGCAGGAAGAGGCC TGTGCTGGGCCCGCTCCC Chen Lab
Interleukin 10 receptor α (IL-10Rα) AAGGATGAAGCCATTGTGGATCT CTGGACCGTCACCAACACC ACACTGCCAACTGTCAGAGTCACTTCATCC Chen Lab
Interleukin 10 receptor β (IL-10Rβ) GGCTGAATTTGCAGATGAGCA ACGCATATGTAAAGAATCAGCAAGTAC TCAGACTGGGTAAACATCACCTTCTGTCCTGT Chen Lab
Interleukin 11 receptor α (IL-11Rα) CCAGCCAGATCAGCGGTT GCTATCAGCTCCTAGAACTGTCTTCTT ACCCACCCGCTACCTCACCTCCTACAG Chen Lab
Interleukin 12 receptor β1 (IL-12Rβ1) CGGTCCTGTCCACCTACCACT AGCTATGATTCTTCACCGAGACG CAATGCCTCAGCAGCTGGGACACC Chen Lab
Interleukin 12 receptor β2 (IL-12Rβ2) TCTGGCCTCTGTAACATTAAC CATAT GGGACTGGTACTGCTTAATCGACT TGCCTGCTGTTACTGGGCCGATATCT Chen Lab
Interleukin 13 receptor α1 (IL-13Rα1) ATTTGGCACAACCTGAGCTACA TCCAGGCTTCTGTGCCAATAG TGGAAGGAATACCAGTCCCGACACTAACTATACTCT Chen Lab
Interleukin 15 receptor α (IL-15Rα) GCCACAGCGACACCACTG CAGGAGAGACACAGCGCTCA CTATCTCCACATCCACCGTCCTGCTGTGT Chen Lab
Interleukin 17 receptor (IL-17R) CATGGAGAACCACAGCTGCTT TGGTGGAAGTCCTCTGGTCTG AGCACATGCACCACGTACCTGCG Chen Lab
Interleukin 18 receptor 1 (IL-18R1) TCAGCAAAGCAGAGCAGTTGA GTGCCAGTTCTTCTTGGACCA CCTTACATCTTTTCCTAATTCCACTGCAACATGGT Chen Lab
Interleukin 21 receptor (IL-21R) AGACACTTCTTGAGTCCACTGAGATC GAGCTGCAGTACAGAAACCGG CTTTCTCCTCGGACTCACAGCCCAGG Chen Lab
Interferon-γ receptor (IFN-γR) CTGGCACTGAATCTCGTCACAA AGGTGACAATGTGTATGTGAGAAT GA TCTGCGTCAGTATTTTATACT GGATCTCACTTCCG AF227551
Tumor necrosis factor receptor (TNFR) CTGCCTCAATGGGACCGT CCTGCATGGCAGGTGCA CACCTCTCCTGCCAGGAGAAACAGAACA AB220482, Chen Lab
Chemokine (C-C motif) ligand 27 (CCL27) TTCCTACTGCCACCCAGCAC ACCCGGATGACCTTCCTCAG CTGTACTCAGCTCTACCG NM001032876, AF449278
Chemokine (C-C motif) ligand 28 (CCL28) CATACTTCATGTCAAGCGCAGAA GCTGCTTGCACTTTCATCCA AATCTGTGTTAGCCCGCAC NM001032877, AF449279
Chemokine (C-X-C motif) ligand 11 (CXCL11) ATGAGTGTGAAGGGCATGGC CCTATGCAAAGACAGCGTCCT AGCCTTAGCTATGATATTGTGT NM001032950, AY044447
Chemokine (C-X-C motif) ligand 12 (CXCL12) CTCGTGCTGACCACGCTCT GCTTTCGAAGAATCGGCATG CCTCAGCGACGGGAA NM001032934, AF449283, Chen Lab
Eotaxin (EOTAXIN) GAGGCTGCAGAGCTACACAAGA AGACCTCCTTGCCCCATTTG ACCAACACCCAGTGTC NM001032851, AF449269
Early T lymphocyte activation 1 (Eta-1) CCGCAGACCTGACATCCAGT GCACCATTCAACTCCTCGCT CCCTGATGCTACAGACGAGGACATCACC Chen Lab
Fractalkine (FRA) AGGCAGCTGGACCCCTAAAG TAAGGACGCCCAGCCTCTG AGGAACCCATCCATG AY206690, M001032881, Chen Lab
γ-Tubulin complex component 2 (GCP2) CTGACAGAGCTGCGTTGCAC CTGGAGCACTGCGGACCTAC TGTTTACACTTTACGCCGAGAG Chen Lab
Interferon-γ–inducible protein-10 (IP-10) GAGGGAAACAAAGTGCTGCC TCCATGTAGGGAAGTGATGGG AGAATGGACCACACAGAGGCTGCCTC NM001032892, AY044446
Liver and activation-regulated chemokine (LARC) CTGGCCAATGAAACCTGTGA TTGGATTTGCGCACACAGAT TCAATGCTGTCGTCTTT NM001032854, AF449274, Chen Lab
Monocyte chemotactic protein 1 (MCP1) TGCAGAGGTTGGCG AATGGTCTTGAAGA AGAAGAATCACCAGCAGCAAGTGTCCCA AY206692, AF276081
Monocyte chemotactic protein 2 (MCP2) TCAGCCAGATTCAGTTTCCATTC CTCTGCAGCCTCTGGATAGGA AATCACCTGCTGCTTTA AY20669, NM001032851, AF449269
Monocyte chemotactic protein 3 (MCP3) AGCTACAGAAGGATCACCAGCAG GGGTCAGCACAGATCTCCTTG ACTGTCCCCGGGAAG AY206694
Monocyte chemotactic protein 4 (MCP4) GTGTCCCCAGAAGGCTGTCA TTCTGGACCCACTTCTCCTTTG CTTCAGAACCAAACTGG Chen Lab
Macrophage-derived chemokine (MDC) TGACAGACTTAGATATACAATATACTTACCTTGG TCTTCTGTATAGGTCCCCAATTGTT TTTGTCAGCACATGCAAGAAATGCTTCA Chen Lab
Macrophage inflammatory protein 1α (MIP-1α) GAGCCTGAGCCTTGGGAAC TGGGCAACAACCAGTCCAT TCCCTGTGACCTCCACAGCTACCTCTTC Chen Lab
Macrophage inflammatory protein 1β (MIP-1β) TTTGAAACGAGCAGCGAGTG ACTTGTCGCCCCTTCTTGG TCCAAGCCGGGTGTCATCTTCCTC Chen Lab
Macrophage inflammatory protein 3β (MIP-3β) TGCTGAAGACTGCTGCCTGT GCAGCCATCCTTGATGAGAAG TGACCCAGAAACCCA NM001032959, AF449273
Pulmonary and activation-regulated chemokine (PARC) GACAGATTCCACAAAAGTTTATAGTTGACT TGAGGAGGATGACACCTGGTT TTCTGAAACCAGCCCCCA NM001032853, AF449272, Chen Lab
Regulated on activation, normally T cell–expressed and–secreted (RANTES) ACCAGTGGCAAGTGCTCCA GGCACACACTTGGCGATTCT CCAGCAGTCGTCTTTGTCACCCGA NM001032850, AF449268
Thymus and activation-regulated chemokine (TARC) TTCCCCTTAGAAAGCTGAAGAC GTCCGAACAGATGGCCTTGT TACCAGACATCCGAGGAC NM001032852, AF449271, Chen Lab
Thymus-expressed chemokine (TECK) GTGTGTGGGAACCCCAAGAA AAAATCTTATTGCGAGCATCCAG AGGTGCAGAGAGCC NM001032875, AF449277, Chen Lab
Chemokine (C-C motif) receptor 1 (CCR1) GGAAGTGACGGAGGTGATCG CCGGCGAAGGCGTAAAT CAACATGCACTGCTGTGTCAACCCAGT NM001032858, AF017282, Chen Lab
Chemokine (C-C motif) receptor 2 (CCR2) ATGGGCATCTGCTACTCAGG CCCTGTGCCTCTTCTTCTCG CCTGAAAACCCTGCTTCGGTGTGAGA NM001032806, AF013958, Chen Lab
Chemokine (C-C motif) receptor 3 (CCR3) CGCTCTCCCTCTGCTCGTT GGCACCTCAGCAGCGT TGGCCATCTGCTACACAGGAATCATCA AF405536, AY065646, Chen Lab
Chemokine (C-C motif) receptor 4 (CCR4) CCATCCAGGCCACAGAAACT TCTCCCCCAGAAAAAAGTAGATGA TGGCTTTTGTTCACTGCTGCCTTAATCC Chen Lab
Chemokine (C-C motif) receptor 5 (CCR5) GCCATGCAGGTGACAGAGACT CCGACGAAGGCATAGATGATG TGGGATGACACACTGCTGCATCAACC AF161959, AF161958
Chemokine (C-C motif) receptor 6 (CCR6) CAGGCAGTTCTCCA TCCCCAGGAGGCCA TGTACCGATCGCCTACTCCTTGATCTGTG NM001032935, AF508730, Chen Lab
Chemokine (C-C motif) receptor 7 (CCR7) GCTTCTTCAGCGGCATGC CCTGGACGATGGCCACAT CCTACTTCTTTGCATCAGCATTGACCGC NM001032884, AF508731
Chemokine (C-C motif) receptor 8 (CCR8) TAAGGTCCCGCTGCCTTG TTCTCTGGATAAGTTCTCCATCACAG CAACAATGACCGACTACTACTACCCTGATAGCCTCT AF100205
Chemokine (C-C motif) receptor 9 (CCR9) CAAGCCCTGTTCCTAACATGG AGTCAGTGAAGTTGAAGTTAACGTAGTCTT TGATGACTATGGCTCTGAATCCACCTCTTCC Chen Lab
Chemokine (C-C motif) receptor 10 (CCR10) CTCCTCTAGATTCGCAGCCCTA GCCTTTCTTCCTGCTCAGCTC CCAGGAGAGACTGTGGGTCTCCGTG Chen Lab
Chemokine (C-C motif) receptor 11 (CCR11) CTGAATTTGGCTGTGGCAGA CCCACCCATGAACTGCATTAA TTACTCCTTCTATTCACTCTGCCTTTTTGGGC Chen Lab
Chemokine (C-X3-C motif) receptor 1 (CX3CR1) TGATTGGCAGATCCAGAGGTT GGGAACGGATCCATGGTG CCTTGGCAGTCCACGCCAGGC Chen Lab
Chemokine (C-X-C motif) receptor 3 (CXCR3) GGGCTACATGCACTGCTGC CCGCTCCCGGAACTTGA TCAACCCGCTGCTCTATGCCTTTGTAGG Chen Lab
Chemokine (C-X-C motif) receptor 4 (CXCR4) TTCCCTTCTGGGCA ATGGACTGCCTTGC CCGTGGCAAACTGGTACTTTGGGAACT MMU93311, AF291672
Chemokine (C-X-C motif) receptor 5 (CXCR5) TCCTGGGCCTGGCCA AACTTCACGCCGGCAAAA TGCTGCCTCAACCCCATGCTCTACA Chen Lab
Chemokine (C-X-C motif) receptor 6 (CXCR6) ACCTCTGCTGGTGTTCATCAGA TTGAAACTGTTGAGGAACCCATC CAAACACCATGGCAGAGTATGATCACTATGAAGA AF291671, AF124380
Chemokine receptor 1 (XCR1) AGGTACCACGTGAGTTCGGAAT TCCATCCTCGACGCCGT ATCACAGCCTGAAGAAAGCACCTTGTGG Chen Lab
Angiopoietin (ANGPT) GCCATTACCAGTCAGAGGCAGT TTCCTGCTGTCCCAGTGTGA CATGCTAAGAATTGAGTTAAT Chen Lab
Fibroblast growth factor 18 (FGF18) GCTCTACAGCCGGACCAGTG GAATTCCGTCTCCTTGCCCT AAACACATCCAGGTCCT Chen Lab
Fibroblast growth factor 2 (FGF2) GGCTGTACTGCAAAAACGGG CGGTTAGCACACACTCCTTTGA TTCTTCCTGCGCATTC Chen Lab
Insulin-like growth factor I (IGF1) AGATGCACACCATGTCCTCCT CTGCTGGAGCCATACCCTGT CATCTCTTCTACCTGGCACT Chen Lab
Transforming growth factor α (TGFα) GCAGCAGTGGTGTCCCATTT GGAGGTCCGCATGCTCAC ACTGCCCAGATTCCCACA Chen Lab
Transforming growth factor β1 (TGFβ1) TGTCATAGATTTCGTTGTGGGTTT GTACAACAGCACCCGCGAC ACCATTAGCACGCGGGTGACCTCC Chen Lab
Vascular endothelial growth factor (VEGF) ACTGCCATCCAATCGAGACC TTGGACTCCTCAGTGGGCAC TGGTGGACATCTTCCAGG S82167
C-MAF CAAGTCGACCACCT TTCTCCTTGTACGC TTCTCCTTGTACGC Chen Lab
Glutamyl-tRNA amidotransferase 3 (GATA-3) CCTCATTAAGCCCAAGCGAAG TGGTGGTCTGACAGTTCGCA TGCAGCCAGGAGAGCAGGGACG Chen Lab
Toll-like receptor 4 (PU.1) CCTGAGGCATTTAGGCAGCTA TTGTCTGGATTTCACACCTGGA AGCTTCCTCCGTTTTCCAGAACTGCAG AY864734
Single cysteine motif-1 (SCM1) AATTTTTATTACCAAACGTGGCCT TCCATGCTCTTGACCACGTC AAAGTCTGTGCTGATCC NM001032947, AF449285, Chen Lab
T-box transcription factor (T-bet) GCAGTGTGGAAAGGCCGA ATGAAACTTCCTGGCGCATC AGGAAACCGCCTGTACGTCCACCC Chen Lab
B7-1 GTGCTGGCTTGTCTTTCTCATTT CAGCGTTGCCACTTCTTTCA TGTTCAGGTGTTATCCACGTGACCAAG AF344849
B7-2 CTCTCTGGTGCTGCTCCCC AGCTCACTCAGGCTTCGGTTT CAGACCTGCCATGCCAGTTTGCAA AF344857, AF344851
CD28 TACTCCCAGCAGCTTCAGGTTT ATGTCACTGATTCATTGCCCAA CTCAAAGACGGGATTCAACTGTGATGG AF344855, AF344852
Cytotoxic T lymphocyte–associated protein 4 (CTLA4) GCCATGGCTTGCCTTGG GGTCCTGGTAGCCAGGTTGA TTTCAGCGGCACAAGGCTCGG AF344854, AF344846
Inducible T cell costimulator (ICOS) ACACAGCCAAAAAATCTAGACTCACA GGCCAACGTTGTTCATGC ATGTGACCGTATAATCTGGAACTCTGGCAT Chen Lab
Inducible T cell costimulator ligand (ICOSL) ACCATGCGCTGGGCAGT CTGAGTATCGGCTCGAAGGC CTGGACTGCTCCTCCTGCTCTTCAGC Chen Lab
Cathepsin G (CTSG) CCCCTCAGGTTACCTAGCAGC AGCCGGCACACTGCACA AGGTCCAGGTGAAGCCAGGGCAA Chen Lab
Granzyme A (GZMA) GAAAGAGTTTCCCTATCCATGCTATG AATTTTTGCTTTTTCTGTTAGCTGTAAA CCCAGCCACACACGAAGGTGATCTT Chen Lab
Granzyme B (GZMB) CGGTGGCTTCCTGATACGA AGGTGACATTTATGGAGCTTCCC AGGACTTCGTGCTGACAGCTGCTCACT Chen Lab
Granzyme K (GZMK) GCCCACTGCCAATATCCGT ATTCTTTGAGAGAGAGTGTGCTCCTA TCCAAAGGCCAGTCTCCCACTGTGGTT Chen Lab
Granzyme M (GZMM) TGTTGGTAGGCAGTTCCTTTGA TCTGCAGCGAGGCCATG CGGGAGGTCATCCCCCACTCG Chen Lab
Matrix metallopeptidase 9 (MMP-9) CTGACCGCCGGTTTGG CGTCAGCATTGCCGTCC TTCTGTCCCAGCGAGAGACTCTACACCC Chen Lab
Ubiquitin-specific protease (UBP43) CATGGCACAGTCGAGGCA GACCAGATCACTGATGTGCACTTG ATCAAGGAGTCCTTCACCCGGATCATATACA Chen Lab
T cell immunoglobulin mucin 1 (TIM1) TTCACCTCAGCCAGCAGAAA AGCTGGTGGGTTGTGTCCTT CCACCCTATGACACTGCTGGGAGCAA Chen Lab
T cell immunoglobulin mucin 3 (TIM3) AATGAATGATGAAAAACATAACCTGAA CTGCAGAGTTGGTGCAGGG TTGGTCGTCATCAAACCAGCCAAGGT Chen Lab
Endothelial monocyte–activating polypeptide 2 (EMAP2) TCCCGTCTGGATCTTCGAAT GCATCTGTTCAAGAGGAACATGAT TTGCATCATAACTGCCAGAA Chen Lab
Granulysin (GNLY) CCGGAGAAACTGCCCAGAA GGGCTCAGAGGGAACCCATA ATCTGTGTGGACCTCAGGTTGTGTGAACC Chen Lab
Intercellular adhesion molecule 1 (ICAM-1) CTCACCGTGTACTGGACTCCAG TAAGGTTCTTGCCCACCGG ACTGGCACCCCTTCCCCCTTGG AF340040
Programmed cell death protein 1 (PD1) TCCTTGGCCACTGGTGTTC CTTCTCCTGAGGGAAGGAGC AGACCCTCCACCATGAGCCCAG Chen Lab
Secretory leukocyte peptidase inhibitor (SLPI) TTAGATATGAGAAACCTGAGTGCCA TCAGGACAACATCTCTTCTTCCC AGTGACTGGCAGTGTC Chen Lab, DP000043
Eosinophil chemotactic cytokine (ECF-L) TGGGAGCCCTTCTCAAGACA GGCCTCCTGCTCAAAAGCTT CATCTCTTCACTGTCCTGG Chen Lab
Endothelin 1 (EDN1) ACTGGGAAGCCCTAGGTCCA TTGGCTAGCACATTGGCATC ACGAGCCTTGGAGAAT MFU20579, Chen Lab
Fas ligand (FasL) CCTGAGAAAAAGGAGCAGAGGA CCAGAGGCATGGACCTTGAG AGTGGCCCATTTAACAGGCAAGCCC AF344856, NM000639
Interferon-stimulated protein, 15 kDa (ISG15) GTGCCGCGTCCCACA TGACACCGACATGGAGTTGC CCACAGCCATGAGCTGGGACCTG Chen Lab
Myeloid progenitor inhibitory factor 1 (MPIF1) GGCCCAGGTCACAAATGATG TGGGATGTAGGAGGTGCAGC AGAGACAGGGTTCATGATG NM001032946, AF449276
Programmed cell death ligand 1 (PDL1) CCATACAGCTGAATTGGTCATCC CTCCCAGAATTACCAAGTGAGTCC AGAACTACCTCTGGCGCTTCCTCCAAA Chen Lab
Programmed cell death ligand 2 (PDL2) TGGCCAAACATCAGCGT TGGTGACCTGGTAGAGGCCT CACCAGCCACTCCAGGACCCCTG Chen Lab
Perforin 1 (PRF1) CCGCTTCTACAGTTACCATGTGG AGCCCGGATGAAGTGGGT ACACACTCCCCCGCTG Chen Lab
Signal transducer and activator of transcription 1 (STAT1) CAGAACGGAGGCGAACCTTA GTCAGGGAAAGTAACAGCAGAAAGT TCCATGCGGTTGAACCCTACACGA AF230106, Chen Lab
Signal transducer and activator of transcription 6 (STAT6) CCTGGTCACAGTTCAACAAGGA CATCAAACCACTGCCAAAAGG ATCCTGCTGGGCCGTGGCTTC Chen Lab
Toll-like receptor 2 (TLR2) GCTCCTGTGAATTCCTGTCCTT TGGCCAATCAACCAGGACTT CTCAGGAGCAGCAAGCACTGGCC AY045573, Chen Lab
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a

Sequences of 98 immune genes (those for which Chen Lab [University of Illinois at Chicago] is listed as the sequence source) were obtained by cloning and sequencing using the polymerase chain reaction–based cloning method; sequences of the other genes were available in GenBank (accession nos. are indicated). Primers were designed on the basis of the sequences obtained by Chen Lab (see Materials and Methods) or those sequences reported as accession nos. in PubMed. The Chen Lab macaque gene sequences have been submitted to GenBank.

Validation of the large-scale real-time quantitation system

The large-scale real-time quantitation system was validated for the extent of variation and reproducibility, as described elsewhere [26]. The coefficient variations for intraassays and interassays for 30 immune genes were both <28% (data not shown). More importantly, in vivo experiments were conducted to validate this large-scale real-time quantitation system. The assay system was used to test variation over time for each of the 138 genes in PBMCs from 4 normal, uninfected rhesus macaques. To this aim, 4 macaques were inoculated intravenously with 5 mL of saline (presumably causing no changes in gene expression in PBMCs); PBMCs were collected at week 0 (right before the inoculation) and at week 2 and were then assessed for changes in expression levels of the 138 immune genes. There was no significant change in expression values for individual genes over time after saline administration in these macaques, because the mean values for each gene at week 2 were close to those at week 0 (table 3, control data). These data therefore suggested that the large-scale real-time quantitation system could be used to measure potential changes in the expression of the 138 immune genes after BCG vaccination/infection.

Table 3.

Expression levels of 78 genes constituting multiple immune gene networks that were striking up-regulated by bacille Calmette-Guérin (BCG) vaccination/infection.

Gene Gene ratio for BCG
 Gene ratio for control
Mean ± SD P Mean ± SD P
IL-1α 4.1 ± 6.5 .490 0.3 ± 0.1 .055
IL-1β 16.4 ± 26.5 .097 1.9 ± 1.4 .527
IL-2 UD UD UD UD
IL-3 113.9 ± 107.0 .028a 1.0 ± 0.1 .714
IL-4 4.3 ± 5.9 .178 1.5 ± 2.7 .827
IL-5 1.1 ± 1.6 .171 1.6 ± 2.1 .680
IL-6 3.0 ± 3.3 .413 1.4 ± 1.1 .734
IL-7 2.5 ± 4.7 .185 1.0 ± 0.1 .721
IL-8 65.1 ± 66.2 .040a 1.0 ± 0.1 .268
IL-9 UD UD UD UD
IL-10 0.4 ± 0.4 .134 1.6 ± 1.4 .565
IL-12α 1.2 ± 1.6 .178 1.0 ± 0.1 .631
IL-12β 1.5 ± 1.6 .197 2.7 ± 2.5 .282
IL-13 1.4 ± 1.7 .209 0.3 ± 0.2 .166
IL-15 10.0 ± 9.5 .029a 1.1 ± 0.1 .153
IL-16 25.9 ± 22.7 .004b 2.3 ± 1.0 .141
IL-17 1.5 ± 1.5 .216 1.0 ± 0.0 .177
IL-18 235.6 ± 335.7 .037a 1.0 ± 0.1 .293
IL-19 UD UD UD UD
IL-20 27.1 ± 30.2 .001b 1.1 ± 0.1 .239
IL-21 4.3 ± 5.4 .456 1.1 ± 0.1 .268
IL-22 115.1 ± 114.8 .005b 2.5 ± 3.6 .930
IL-23α 11.4 ± 7.0 .002b 0.9 ± 0.5 .648
IL-27 8.2 ± 10.8 .027a UD UD
IFN-α 96.3 ± 94.6 .001b 0.9 ± 0.2 .790
IFN-β 66.0 ± 67.5 .000b UD UD
IFN-γ 38.4 ± 37.7 .000b 1.8 ± 0.8 .280
MIF 20.5 ± 23.0 .006b 0.9 ± 0.1 .100
TNF-α 8.1 ± 5.1 .030a 1.8 ± 2.2 .568
IL-1R1 12.9 ± 18.7 .123 0.9 ± 0.4 .544
IL-1R2 1.8 ± 1.8 .323 UD UD
IL-2Rα 8.5 ± 6.9 .129 0.9 ± 0.1 .146
IL-2Rβ 34.1 ± 29.3 .001b 2.3 ± 2.0 .139
IL-2Rγ 39.7 ± 55.6 .070 1.0 ± 0.2 .631
IL-3Rα 44.7 ± 44.9 .001b UD UD
IL-4R 16.8 ± 21.4 .158 1.0 ± 0.1 .593
IL-5Rα 1.4 ± 1.5 .202 1.0 ± 0.2 .478
IL-6R 247.5 ± 168.5 .008b 2.1 ± 1.6 .342
IL-7R 25.6 ± 49.3 .481 1.1 ± 0.2 .805
IL-8Rα 73.0 ± 82.1 .000b UD UD
IL-9R 1.4 ± 1.4 .238 1.4 ± 1.2 .459
IL-10Rα 9.0 ± 12.5 .124 0.5 ± 0.3 .148
IL-10Rβ 5.0 ± 7.1 .291 1.1 ± 0.1 .147
IL-11Rα 40.5 ± 43.0 .001b 1.3 ± 1.0 .982
IL-12Rβ1 4.0 ± 2.8 .179 1.8 ± 1.1 .172
IL-12Rβ2 2.8 ± 1.9 .427 1.0 ± 0.1 .897
IL-13Rα1 119.6 ± 182.0 .003b 1.0 ± 0.1 .683
IL-15Rα 3.0 ± 3.0 .249 2.6 ± 2.6 .135
IL-17R 19.5 ± 19.6 .247 1.3 ± 0.4 .503
IL-18R1 134.0 ± 110.4 .002b 1.0 ± 0.0 .977
IL-21R 5.6 ± 8.4 .266 1.0 ± 0.1 .682
IFN-γR 76.7 ± 75.8 .001b 3.2 ± 3.5 .392
TNFR 18.5 ± 27.5 .005b UD UD
CCL27 9.7 ± 10.1 .004b 1.5 ± 1.7 .910
CCL28 87.5 ± 133.4 .003b 1.8 ± 2.5 .833
CXCL11 1.6 ± 2.1 .179 1.0 ± 0.0 .956
CXCL12 1.2 ± 1.5 .181 1.0 ± 0.1 .364
EOTAXIN UD UD 1.0 ± 0.1 .787
Eta-1 1.1 ± 1.2 .178 1.0 ± 0.1 .374
FRA 28.1 ± 41.8 .001b 1.0 ± 0.0 .110
GCP2 107.0 ± 155.1 .002b 1.2 ± 0.6 .936
IP-10 26.0 ± 22.3 .430 2.7 ± 3.5 .882
LARC 1.2 ± 1.5 .149 1.0 ± 0.1 .253
MCP1 18.0 ± 7.4 .003b UD UD
MCP2 21.9 ± 29.3 .007b 1.0 ± 0.1 .566
MCP3 2.1 ± 1.7 .279 1.0 ± 0.1 .475
MCP4 1.7 ± 1.8 .208 1.0 ± 0.1 .241
MDC 15.3 ± 20.8 .017b UD UD
MIP-1α 3.4 ± 0.8 .010b 0.9 ± 0.1 .183
MIP-1β 62.7 ± 49.0 .123 1.0 ± 0.0 .725
MIP-3β 1.2 ± 1.6 .183 1.0 ± 0.1 .307
PARC 10.8 ± 14.8 .092 1.7 ± 2.1 .807
RANTES 3.4 ± 0.8 .010a 1.0 ± 0.1 .871
TARC 1.4 ± 1.6 .216 1.0 ± 0.1 .422
TECK 17.7 ± 23.5 .001b 1.7 ± 1.7 .795
CCR1 12.0 ± 5.5 .000b 1.4 ± 0.5 .682
CCR2 21.9 ± 20.6 .003b 1.7 ± 1.2 .779
CCR3 9.8 ± 4.3 .001b 1.9 ± 3.0 .922
CCR4 51.8 ± 51.7 .141 1.1 ± 0.1 .434
CCR5 263.2 ± 459.1 .019a 2.8 ± 3.5 .668
CCR6 51.4 ± 89.6 .008b UD UD
CCR7 5.7 ± 6.7 .006b 2.6 ± 2.4 .082
CCR8 37.7 ± 37.4 .000b 1.2 ± 1.4 .939
CCR9 49.4 ± 49.0 .001b 1.5 ± 2.0 .926
CCR10 45.2 ± 38.2 .000b UD UD
CCR11 18.7 ± 29.6 .002b 3.8 ± 4.7 .671
CX3CR1 54.4 ± 47.3 .000b UD UD
CXCR3 5.3 ± 4.5 .022a 1.3 ± 1.3 .893
CXCR4 10.1 ± 5.9 .002b 1.0 ± 0.2 .276
CXCR5 4.4 ± 6.2 .336 1.7 ± 1.4 .432
CXCR6 17.5 ± 13.8 .010a 3.2 ± 2.3 .136
XCR1 26.5 ± 32.0 .000b 3.1 ± 4.6 .945
ANGPT UD UD 1.0 ± 0.0 .436
FGF18 3.0 ± 3.6 .311 0.9 ± 1.5 .554
FGF2 UD UD 1.4 ± 1.3 .873
IGF1 1.5 ± 2.2 .198 3.0 ± 4.8 .842
TGFα 1.4 ± 1.5 .211 2.7 ± 3.9 .428
TGFβ1 2.4 ± 2.4 .329 3.5 ± 2.6 .154
VEGF 90.8 ± 128.7 .001b 1.1 ± 0.1 .225
C-MAF 127.6 ± 214.5 .000b UD UD
GATA-3 16.8 ± 21.4 .158 UD UD
PU.1 73.9 ± 70.6 .010a 1.7 ± 1.7 .451
SCM1 14.2 ± 11.2 .001b 0.7 ± 0.9 .991
T-bet 14.9 ± 14.6 .007b 1.8 ± 1.4 .855
B7-1 11.5 ± 21.9 .144 1.0 ± 0.1 .494
B7-2 272.8 ± 522.8 .038a 2.8 ± 3.1 .735
CD28 603.9 ± 945.1 .003b 0.7 ± 0.4 .411
CTLA4 12.2 ± 8.3 .004b 0.8 ± 0.7 .602
ICOS 23.8 ± 35.3 .093 1.0 ± 0.0 .196
ICOSL 5.5 ± 6.5 .177 0.7 ± 0.4 .150
CTSG 9.4 ± 9.8 .001b 2.5 ± 2.9 .412
GZMA 260.7 ± 306.5 .021a 2.3 ± 2.1 .269
GZMB 30.0 ± 28.8 .000b UD UD
GZMK 104.0 ± 83.0 .000b 1.8 ± 1.0 .292
GZMM 41.3 ± 45.0 .001b 0.9 ± 0.7 .628
MMP-9 22.2 ± 24.0 .008b 0.9 ± 0.5 .366
UBP43 9.3 ± 10.7 .026a 1.0 ± 0.1 .497
TIM1 5.1 ± 4.0 .002b 1.4 ± 1.7 .815
TIM3 3.1 ± 1.7 .264 0.5 ± 0.2 .060
EMAP2 10.5 ± 16.4 .175 2.6 ± 3.1 .428
GNLY 15.3 ± 27.9 .165 0.9 ± 0.1 .191
ICAM-1 17.7 ± 24.3 .020a 1.0 ± 0.1 .842
PD1 39.4 ± 47.3 .025a 1.7 ± 1.8 .976
SLPI 45.2 ± 56.6 .000b 1.1 ± 0.0 .105
ECF-L 3.0 ± 3.6 .311 UD UD
EDN1 11.5 ± 11.4 .031a 1.0 ± 0.0 .753
FasL 4.6 ± 6.4 .483 0.5 ± 0.2 .043
ISG15 54.4 ± 31.8 .019a 1.0 ± 0.0 .793
MPIF1 1.3 ± 1.5 .198 1.4 ± 2.1 .411
PDL1 15.4 ± 16.9 .059 UD UD
PDL2 52.9 ± 39.0 .031a 2.0 ± 2.6 .858
PRF1 43.9 ± 62.8 .000b 2.7 ± 2.3 .413
STAT1 164.2 ± 133.7 .000b 1.0 ± 0.1 .388
STAT6 28.9 ± 48.0 .141 1.0 ± 0.1 .519
TLR2 33.9 ± 29.2 .008b 1.0 ± 0.1 .668
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NOTE. Peripheral-blood lymphocytes (PBLs) were collected from 4 rhesus monkeys before (week 0) and after (week 5) BCG vaccination/infection and assessed for global gene expression by a large-scale real-time quantitation system. Earlier studies had shown peak T cell responses and anti-BCG immunity occurring 5 weeks after intravenous BCG inoculation [14–16]. BCG data show the means of fold changes in expression levels for week 5 vs. week 0 in PBLs from the 4 BCG-vaccinated monkeys. P values for IL-1, IL-2, IL-4, IL-5, IL-6, and IL-9 were >.05 at this 5-week time point but were <.05 in the longitudinal analysis (table 4). As validation, the control data show the means of fold changes in expression levels for week 2 vs. week 0 in PBLs from 4 normal, uninfected rhesus macaques that received saline. UD, undetectable.

a

P < .05.

b

P < .01.

Measurement of mycobacterial burdens

Quantitation of mycobacterial infection was accomplished by measuring bacterial colony counts and levels of mycobacterial Ag85B mRNA expression [15]. Viable BCG mycobacterial colony counts in the blood were determined on the basis of the quantitation of mycobacterial colony-forming units in cell lysates from blood cells from BCG-vaccinated macaques. Five-fold dilutions of the lysate were plated in duplicate on Middlebrook 7H10 agar plates (Diffico) [15]. Colony-forming units were counted after a 3-week incubation at 37°C.

Enzyme-linked immunospot (ELISpot) assay for measuring antigen-specific T cells

To measure the numbers and interferon (IFN)–γ–production capacity of mycobacteria-specific T cells, peripheral-blood lymphocytes (PBLs) were assessed for their specific recognition of purified protein derivative (PPD) antigens by ELISpot assay, as described elsewhere [16, 24]. Data were expressed as numbers of PPD-specific IFN-γ–producing T cells per 1 × 106 PBMCs.

Statistical analysis

As described elsewhere [23], Student’s t test and nonparametric test were used to examine whether any differences in the numbers of PPD-specific T cells or in the levels of individual gene transcripts identified after BCG vaccination/infection were statistically significant.

RESULTS

Selective up-regulation of many immune genes or gene networks 5 weeks after BCG vaccination/infection

Our previous studies showed that vaccine-elicited T cell responses and coincident clearance of BCG bacteria were most striking 4–8 weeks after intravenous BCG inoculation in macaques [14–16, 24]. We presumed that investigation of gene expression at these time points would allow us to optimally identify gene networks connected to the peak immune response and anti-BCG immunity. Thus, PBL samples were collected from 4 macaques at week 5 after intravenous BCG inoculation and assessed for the expression of 138 immune genes. Systemic BCG infection was generated on the basis of the consideration that blood lymphocytes, but not lung cells, could be prospectively collected from individual macaques and readily measured for transcriptional immune responses. Many immune genes in blood lymphocytes were selectively up-regulated 5 weeks after BCG vaccination/infection of macaques (table 3). Of 138 immune genes, 93 were apparently up-regulated. Of the up-regulated genes, the mean expression of 14 was increased >100-fold in circulating lymphocytes. The mean expression of 14 genes was increased 51–100-fold, that of 41 was increased 16–50-fold, and that of 24 was increased 5–15-fold. For 78 of these up-regulated genes, statistically significant differences existed relative to baseline levels (see the P values in table 3; see also table 4).

Table 4.

Longitudinal analysis of bacille Calmette-Guérin (BCG)–induced immune gene networks.

Network, gene W1:W0 ratio W2:W0 ratio W3:W0 ratio W4:W0 ratio W6:W0 ratio W19:W0 ratio
Mean ± SD P Mean ± SD P Mean ± SD P Mean ± SD P Mean ± SD P Mean ± SD P
Network (i)
 IL-1α 53.7 ± 25.5 .049a 9.1 ± 11.3 .085 8.8 ± 15.1 .154 12.4 ± 12.6 .052 17.3 ± 28.6 .140 0.5 ± 0.5 .418
 IL-1β 21.9 ± 1.1 .001b 4.4 ± 5.2 .197 0.9 ± 0.6 .137 9.9 ± 15.3 .167 62.5 ± 62.1 .049a 1.0 ± 1.0 .086
 IL-2 33.2 ± 13.9 .041a 8.7 ± 7.8 .062 26.9 ± 24.4 .042a 26.2 ± 24.1 .044a 2.2 ± 1.4 .285 1.1 ± 0.8 .229
 IL-3 55.6 ± 18.1 .025a 34.1 ± 25.5 .020a 24.0 ± 17.4 .019a 26.9 ± 11.1 .002b 19.3 ± 16.0 .031a 2.0 ± 2.8 .320
 IL-4 31.9 ± 1.3 .005b 3.5 ± 2.8 .171 15.0 ± 26.2 .172 3.5 ± 2.9 .174 1.5 ± 1.7 .482 0.2 ± 0.2 .459
 IL-5 47.6 ± 10.0 .011a 1.9 ± 0.6 .020a 1.1 ± 1.1 .497 3.9 ± 6.1 .192 14.6 ± 25.5 .165 2.9 ± 2.6 .212
 IL-6 16.7 ± 3.5 .011a 17.1 ± 13.3 .030a 13.4 ± 20.5 .148 29.7 ± 27.6 .044a 4.1 ± 4.3 .172 4.9 ± 6.0 .336
 IL-7 9.0 ± 8.9 .189 2.9 ± 2.5 .164 1.1 ± 0.8 .333 11.0 ± 18.5 .171 5.1 ± 8.7 .212 3.2 ± 4.3 .270
 IL-8 24.5 ± 7.0 .021a 44.6 ± 39.5 .035a 14.7 ± 17.9 .089 52.9 ± 44.3 .029a 6.8 ± 9.0 .125 1.7 ± 1.5 .306
 IL-9 10.6 ± 13.2 .190 4.3 ± 1.9 .034a 3.3 ± 2.2 .087 5.6 ± 0.6 .006b 2.3 ± 1.3 .158 UD UD
 IL-15 15.1 ± 3.2 .012a 6.3 ± 5.7 .060 7.1 ± 10.3 .144 11.2 ± 13.8 .097 2.1 ± 0.7 .021a 3.1 ± 4.4 .302
 IL-16 8.2 ± 9.5 .229 8.9 ± 4.3 .013a 6.2 ± 6.4 .137 3.4 ± 2.0 .171 14.5 ± 16.1 .090 1.6 ± 1.3 .180
 IL-18 65.9 ± 3.1 .001b 39.9 ± 34.4 .032a 81.4 ± 44.5 .006b 108.2 ± 52.5 .003b 46.5 ± 23.7 .004b 3.0 ± 4.0 .278
 IL-19 UD UD UD UD UD UD UD UD UD UD UD UD
 IL-22 36.7 ± 0.8 .006b 58.3 ± 47.0 .028a 22.7 ± 15.0 .020a 23.3 ± 4.9 .000b 10.0 ± 11.3 .127 4.6 ± 6.6 .257
 IL-23α 9.7 ± 9.3 .159 5.5 ± 2.7 .008b 3.0 ± 2.2 .050 4.8 ± 2.0 .004b 1.7 ± 0.8 .076 1.0 ± 1.4 .409
 IFN-α 51.6 ± 12.6 .015a 49.1 ± 42.7 .032a 21.0 ± 18.3 .035a 33.1 ± 25.5 .023a 8.8 ± 10.7 .096 1.1 ± 1.6 .425
 IFN-β 50.5 ± 23.1 .047a 36.7 ± 21.9 .010a 12.3 ± 16.1 .091 12.5 ± 5.0 .003b 3.1 ± 4.1 .169 UD UD
 IFN-γ 20.4 ± 24.5 .197 18.8 ± 15.7 .036a 9.1 ± 7.4 .049a 54.6 ± 51.6 .043a 9.8 ± 8.4 .051 5.9 ± 6.6 .240
 TNF-α 5.7 ± 4.2 .123 3.3 ± 2.5 .211 3.5 ± 2.6 .188 18.7 ± 21.4 .084 1.4 ± 1.2 .363 4.9 ± 6.9 .369
 IL-2Rα 13.4 ± 14.8 .176 9.7 ± 12.8 .110 16.6 ± 15.7 .046a 45.6 ± 40.5 .035a 17.4 ± 18.8 .065 1.8 ± 1.0 .177
 IL-2Rβ 23.9 ± 10.6 .047a 12.6 ± 10.1 .047a 29.3 ± 19.6 .017a 24.3 ± 19.1 .031a 28.3 ± 24.9 .042a 5.6 ± 7.3 .364
 IL-2Rγ 20.1 ± 6.3 .025a 32.7 ± 29.8 .039a 28.3 ± 18.9 .014a 31.3 ± 16.2 .005b 17.1 ± 9.1 .006b 1.6 ± 1.0 .259
 IL-3Rα 25.8 ± 8.6 .037a 12.5 ± 8.1 .028a 13.6 ± 8.5 .024a 17.0 ± 7.2 .004b 9.6 ± 12.5 .160 0.4 ± 0.6 .140
 IL-4R 18.5 ± 14.1 .111 21.1 ± 20.3 .048a 23.9 ± 12.5 .005b 35.9 ± 25.1 .016a 15.0 ± 18.5 .092 1.8 ± 2.5 .358
 IL-6R 58.4 ± 10.1 .008b 33.1 ± 30.0 .042a 44.8 ± 16.5 .001b 89.0 ± 61.0 .015a 48.4 ± 24.5 .005b 1.3 ± 1.8 .215
 IL-8Rα 78.8 ± 89.0 .171 41.2 ± 28.6 .016a 50.7 ± 50.1 .047a 22.0 ± 8.4 .001b 23.5 ± 22.0 .044a 0.7 ± 1.0 .386
 IL-11Rα 33.3 ± 25.8 .112 20.7 ± 8.1 .002b 20.9 ± 15.5 .023a 10.9 ± 1.5 .000b 19.7 ± 15.9 .030a 9.1 ± 12.8 .235
 IL-13Rα1 102.0 ± 13.7 .005b 34.8 ± 27.5 .025a 23.3 ± 21.2 .040a 65.2 ± 58.7 .036a 25.3 ± 10.7 .002b 1.1 ± 1.5 .494
 IL-17R 15.7 ± 14.2 .141 12.9 ± 9.2 .023a 8.3 ± 4.3 .009b 21.6 ± 17.4 .029a 9.2 ± 3.6 .002b 4.7 ± 6.6 .284
 IL-18R1 47.5 ± 16.0 .027a 23.4 ± 19.9 .033a 32.7 ± 12.9 .001b 54.4 ± 38.8 .017a 25.6 ± 12.7 .004b 1.0 ± 0.9 .480
 IFN-γR 32.9 ± 8.6 .017a 17.7 ± 13.6 .043a 15.4 ± 3.5 .001b 34.3 ± 25.5 .026a 9.0 ± 4.5 .044a 0.3 ± 0.4 .095
 TNFR 16.2 ± 3.6 .013a 10.7 ± 14.4 .112 10.9 ± 9.6 .043a 18.0 ± 10.7 .009b 8.3 ± 10.5 .106 0.3 ± 0.5 .242
Network (ii)
 CCL27 19.7 ± 5.2 .024a 5.6 ± 3.0 .025a 2.9 ± 1.4 .126 22.5 ± 30.6 .110 2.0 ± 0.9 .315 4.3 ± 6.0 .262
 CCL28 58.3 ± 18.1 .026a 26.2 ± 29.0 .072 11.4 ± 9.0 .044a 14.9 ± 4.9 .002b 4.1 ± 4.0 .193 6.8 ± 9.7 .236
 GCP2 32.9 ± 9.2 .019a 59.8 ± 54.8 .038a 23.5 ± 22.2 .045a 27.9 ± 11.8 .002b 8.0 ± 8.2 .074 4.9 ± 6.9 .288
 IP-10 33.0 ± 4.4 .013a 22.0 ± 26.5 .099 7.1 ± 5.9 .119 22.5 ± 19.6 .047a 7.9 ± 3.1 .033a 7.0 ± 9.9 .254
 MCP1 20.6 ± 5.4 .018a 22.6 ± 14.5 .013a 9.0 ± 10.7 .094 16.5 ± 20.6 .091 6.9 ± 7.9 .094 0.9 ± 0.8 .454
 MCP2 15.6 ± 15.2 .154 35.4 ± 33.0 .041a 4.9 ± 1.4 .001b 20.9 ± 19.2 .042a 6.1 ± 6.9 .096 2.2 ± 2.6 .291
 MDC 25.6 ± 8.5 .027a 23.1 ± 20.5 .037a 4.5 ± 3.6 .050 16.1 ± 11.9 .022a 3.3 ± 4.8 .176 6.0 ± 8.5 .246
 MIP-1β 19.1 ± 18.6 .151 15.3 ± 17.7 .079 17.1 ± 19.0 .070 22.1 ± 17.1 .024a 7.0 ± 5.9 .044a 0.6 ± 0.8 .270
 CCR4 24.4 ± 18.9 .111 33.5 ± 21.6 .012a 14.2 ± 7.4 .006b 16.4 ± 9.3 .008b 9.7 ± 7.5 .031a 3.6 ± 5.1 .279
 CCR5 37.8 ± 11.6 .032a 42.5 ± 38.3 .042a 27.4 ± 23.7 .043a 36.4 ± 32.6 .043a 15.1 ± 7.3 .011a 1.4 ± 2.0 .494
 CCR8 23.6 ± 0.8 .002b 23.6 ± 10.9 .003b 15.9 ± 13.9 .040a 16.9 ± 15.6 .046a 6.2 ± 9.6 .171 4.3 ± 6.1 .248
 CCR9 43.1 ± 16.0 .036a 19.5 ± 17.1 .041a 6.6 ± 5.1 .058 22.3 ± 21.1 .049a 9.2 ± 7.6 .049a 1.1 ± 1.5 .387
 CCR10 34.0 ± 13.4 .039a 19.0 ± 12.3 .014a 16.4 ± 10.1 .013a 14.6 ± 4.6 .001b 5.7 ± 6.4 .112 4.5 ± 6.3 .244
 CCR11 99.4 ± 3.4 .002b 24.1 ± 31.4 .124 5.8 ± 5.5 .295 10.8 ± 12.4 .165 1.7 ± 1.9 .223 2.9 ± 4.1 .411
 CXCR4 12.2 ± 10.1 .126 9.2 ± 2.3 .000b 6.3 ± 3.5 .011a 6.5 ± 2.8 .003b 2.7 ± 1.3 .015a 2.5 ± 1.3 .141
 CXCR6 13.0 ± 7.8 .138 17.8 ± 13.3 .037a 15.4 ± 6.8 .007b 16.1 ± 12.8 .047a 8.1 ± 8.5 .155 3.9 ± 5.5 .360
 XCR1 54.0 ± 6.9 .009b 12.5 ± 13.2 .113 3.4 ± 2.8 .461 8.8 ± 9.9 .167 1.0 ± 0.5 .194 3.7 ± 5.3 .286
 MIF 14.4 ± 13.2 .144 12.3 ± 8.7 .019a 8.3 ± 4.0 .005b 9.2 ± 7.2 .031a 4.0 ± 1.6 .004b 1.9 ± 2.7 .315
 ISG15 0.7 ± 0.5 .263 4.0 ± 5.5 .157 5.3 ± 2.0 .003b 5.1 ± 2.4 .008b 2.0 ± 2.4 .209 1.0 ± 1.3 .496
Network (iii)
 B7-2 48.8 ± 18.3 .039a 51.1 ± 44.1 .036a 38.1 ± 17.7 .004b 25.5 ± 18.7 .027a 17.6 ± 20.5 .102 8.9 ± 12.6 .249
 CD28 79.2 ± 26.0 .026a 75.3 ± 30.5 .001b 91.7 ± 48.5 .005b 90.9 ± 69.6 .021a 39.3 ± 8.9 .000b 7.3 ± 10.2 .229
 CTLA4 18.1 ± 21.6 .191 7.8 ± 5.4 .020a 5.2 ± 3.1 .015a 5.3 ± 3.1 .015a 2.2 ± 0.6 .014a 2.2 ± 3.2 .260
 ICOS 10.7 ± 14.2 .218 13.5 ± 8.5 .013a 33.9 ± 31.8 .042a 19.1 ± 15.2 .027a 8.3 ± 9.2 .080 2.7 ± 3.8 .295
 PDL1 5.4 ± 2.0 .186 6.7 ± 4.1 .031a 51.5 ± 73.4 .111 14.4 ± 8.3 .012a 3.8 ± 4.1 .175 0.3 ± 0.3 .377
 PDL2 42.0 ± 18.1 .048a 33.0 ± 31.6 .049a 13.9 ± 11.1 .041a 37.7 ± 33.9 .040a 7.1 ± 8.4 .147 7.3 ± 10.2 .239
 PD1 35.0 ± 39.8 .175 15.7 ± 23.1 .126 11.8 ± 8.2 .019a 12.4 ± 10.9 .040a 13.1 ± 18.6 .121 3.0 ± 4.2 .293
Network (iv)
 C-MAF 47.3 ± 21.1 .045a 28.8 ± 34.0 .077 15.5 ± 17.6 .075 20.4 ± 21.4 .060 8.4 ± 7.6 .049a 1.2 ± 1.7 .441
 PU.1 49.0 ± 20.2 .038a 31.8 ± 23.6 .022a 34.4 ± 19.4 .008b 47.9 ± 29.3 .010a 27.6 ± 24.4 .039a 2.3 ± 2.2 .329
 SCM1 52.4 ± 17.4 .027a 13.8 ± 15.0 .066 3.5 ± 2.7 .048a 5.9 ± 4.7 .036a 1.2 ± 1.0 .250 1.8 ± 2.6 .243
 T-bet 13.1 ± 11.9 .139 6.7 ± 5.5 .068 2.8 ± 1.6 .205 3.5 ± 2.3 .125 1.8 ± 0.6 .458 1.4 ± 0.5 .066
 GATA-3 3.7 ± 2.6 .233 3.4 ± 3.9 .148 7.6 ± 10.3 .110 4.7 ± 5.6 .119 4.3 ± 4.9 .118 UD UD
 STAT1 46.1 ± 17.1 .032a 85.7 ± 46.1 .005b 75.7 ± 50.7 .013a 74.2 ± 37.8 .004b 21.4 ± 7.9 .001b 7.2 ± 10.1 .239
 STAT6 12.6 ± 3.1 .017a 20.1 ± 16.9 .032a 29.5 ± 19.6 .014a 65.1 ± 59.7 .038a 22.0 ± 17.7 .028a 0.5 ± 0.6 .169
 FoxP3 4.4 ± 4.0 .391 2.5 ± 1.0 .465 2.0 ± 0.4 .362 1.7 ± 0.5 .302 1.1 ± 0.1 .209 UD UD
Network (v)
 IFN-γ 20.4 ± 24.5 .197 18.8 ± 15.7 .036a 9.1 ± 7.4 .049a 54.6 ± 51.6 .043a 9.8 ± 8.4 .051 5.9 ± 6.6 .240
 T-bet 13.1 ± 11.9 .139 6.7 ± 5.5 .068 2.8 ± 1.6 .205 3.5 ± 2.3 .125 1.8 ± 0.6 .458 1.4 ± 0.5 .066
 TIM1 37.0 ± 14.4 .039a 17.6 ± 13.6 .027a 10.1 ± 10.7 .073 20.3 ± 12.6 .014a 0.5 ± 0.2 .267 UD UD
 TIM3 26.3 ± 11.5 .043a 5.4 ± 2.2 .003b 16.6 ± 22.0 .083 10.5 ± 5.7 .008b 2.5 ± 0.6 .002b UD UD
 IP-10 33.0 ± 4.4 .013a 22.0 ± 26.5 .099 7.1 ± 5.9 .119 22.5 ± 19.6 .047a 7.9 ± 3.1 .033a 7.0 ± 9.9 .254
Network (vi)
 GZMA 70.1 ± 25.1 .032a 96.5 ± 92.7 .044a 29.0 ± 20.5 .021a 117.6 ± 114.5 .045a 8.0 ± 7.2 .091 3.6 ± 3.6 .271
 GZMK 15.7 ± 15.7 .174 40.0 ± 28.9 .019a 79.1 ± 26.5 .001b 149.9 ± 45.7 .000b 50.1 ± 42.5 .032a 5.0 ± 7.1 .280
Network (vii)
 TLR2 44.9 ± 7.3 .007b 16.1 ± 18.6 .077 8.9 ± 4.6 .007b 7.8 ± 5.1 .019a 12.7 ± 9.1 .021a 3.1 ± 4.4 .284
 VEGF UD UD 19.8 ± 0.2 .000b 19.2 ± 2.6 .000b 15.3 ± 0.7 .000b 8.2 ± 3.2 .003b 4.9 ± 7.0 .255
 MMP-9 12.3 ± 11.3 .140 12.3 ± 10.7 .039a 9.1 ± 7.8 .040a 10.9 ± 9.6 .042a 1.6 ± 0.4 .038a 6.3 ± 8.9 .256
 UBP43 5.6 ± 1.3 .019a 7.8 ± 8.3 .076 4.4 ± 6.5 .170 15.0 ± 20.1 .107 3.2 ± 3.0 .099 2.0 ± 2.0 .285
 ICAM-1 16.0 ± 11.1 .098 9.5 ± 5.9 .014a 7.7 ± 8.2 .076 10.1 ± 7.5 .027a 6.8 ± 5.1 .033a 4.3 ± 5.9 .261
 CTSG 6.2 ± 5.0 .327 58.1 ± 55.0 .045a 62.8 ± 61.0 .048a 37.0 ± 30.8 .033a 30.1 ± 27.8 .048a 8.9 ± 9.8 .192
 EDN1 47.4 ± 20.3 .042a 14.7 ± 8.4 .008b 5.1 ± 3.2 .021a 8.8 ± 3.6 .002b 4.3 ± 4.9 .116 3.5 ± 4.9 .273
 SLPI 40.4 ± 7.5 .009a 17.7 ± 19.1 .066 5.4 ± 6.3 .111 12.9 ± 10.2 .030a 2.4 ± 2.0 .110 2.2 ± 3.1 .329
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NOTE. BCG-induced gene networks were detectable at 1 week after BCG vaccination/infection and were stable over time. Seventy-four genes identified in the initial experiment were longitudinally measured for their expression in peripheral-blood lymphocytes collected from an additional 4 rhesus macaques after intravenous vaccination/infection. Data were calculated as for those in table 3, except that postvaccination expression levels are for multiple time points and that no quantitation was done for genes that were not apparently up-regulated 5 weeks after BCG vaccination (table 3). UD, undetectable; W, week.

a

P < .05.

b

P < .01.

These 78 up-regulated immune genes could be grouped into at least the following gene networks, which are linked to many aspects of immune function: (i) a gene network of lymphokines and lymphokine receptors for immune activation/adaptive T cell responses—IL-3, IL-8, IL-15, IL-16, IL-18, IL-22, IL-23α, IL-27, IFN-α, IFN-β, IFN-γ, TNF-α, IL-2Rα, IL-2Rβ, IL-2Rγ, IL-3α, IL-4R, IL-6R, IL-8Rα, IL-11Rα, IL-13Rα, IL-17R, IL-18R1, IFN-γR, and TNFR (see table 4 for changes in IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, and IL-9); (ii) a gene network of chemokines and chemokine receptors for mucosal/tissue migration and T cell activation [27, 28]—CCL27, CCL28, GCP2 (CCL6), IP-10, MCP1, MCP2, MDC, MIP-1β, CCR4, CCR5, CCR8, CCR9, CCR10, CCR11, CXCR4, CXCR6, and XCR1; (iii) a gene network of signal costimulators for vaccine-elicited T cell responses [29, 30]—B7-2, CD28, CTLA4, ICOS, PD1, PDL1, and PDL2; (iv) a gene network of transcription and trans-activation—C-MAF, PU.1, SCM1, T-bet, STAT1, and STAT6; (v) a gene network of T helper commitments—IFN-γ, T-bet, IP-10, TIM1, and TIM3; (vi) a gene network of cytotoxic effectors—GZMA, GZMB, and GZMK; and (vii) a gene network of innate and other immune factors—TLR2, VEGF, MMP-9, UBP43, ICAM-1, CTSG, EDN1, SLPI, etc. It should be pointed out that up-regulated genes in each presumed gene network would have other potential or speculated functions. Thus, these results demonstrate that many immune genes that constitute complex gene networks were selectively up-regulated at 5 weeks after BCG vaccination/infection.

Detectability at 1 week and stability at ≥6 weeks of BCG-induced gene networks after BCG vaccination/infection

Because BCG-induced gene networks composed of 78 genes were identified 5 weeks after BCG inoculation, we sought to determine whether these gene networks emerged early and stabilized over time after BCG vaccination/infection. We analyzed the expression kinetics of these up-regulated genes in an additional 4 monkeys weekly or biweekly after BCG vaccination/infection. Interestingly, systemic BCG vaccination/infection induced early and relatively stable up-regulation patterns of these selected immune genes after BCG vaccination/infection (table 4). All of these genes except ISG15 and GATA-3 exhibited up to 160-fold up-regulation 1 week after BCG vaccination/infection (table 4). These genes were remarkably up-regulated at multiple-week time points after BCG vaccination/infection (table 4). Some of these immune genes underwent short-term increases in expression in circulating lymphocytes (table 4). For example, the genes for major T cell–proliferating lymphokines, IL-2 and IL-15, were up-regulated 2–4 weeks and were back to baseline level 6 weeks after BCG vaccination/infection. The BCG-induced immune genes and gene networks returned to baseline at week 19 after BCG vaccination/infection. Importantly, a number of selected lymphokine receptors and chemokine receptors were apparently up-regulated at week 1 and were sustained through ≥6 weeks after the systemic BCG vaccination (table 4), suggesting that these immune receptors serve as transcriptional platforms for vaccine-elicited immune responses. These results, therefore, provide evidence that BCG vaccination/infection induces early and prolonged up-regulation of selected immune genes or gene networks in circulating lymphocytes.

Establishment of early activation of immune gene networks as an immune correlate of anti-BCG immunity and correlation between prolonged gene network up-regulation and the development of T cell responses

The sequential events for up-regulated gene networks, cellular immune responses, and anti-mycobacterial immunity after vaccination or infection have not been well described, although global immune activation after vaccination/infection is generally believed to drive cellular responses and immunity. To explore whether and when the early and sustained up-regulation of gene networks after BCG vaccination could be linked to the development of T cell immune responses and anti-BCG immunity, we sought to examine the correlation between up-regulated gene networks and antigen-specific cellular responses as well as between the gene networks and immune clearance of BCG bacteria in the blood after BCG vaccination/infection. We first calculated the means of fold changes in the expression of selected genes in each of the gene networks and then plotted these values over the levels of PPD-specific IFN-γ–producing T cells or BCG colony-forming units (BCG bacteremia) at each time point after BCG vaccination/infection (figure 1). The up-regulation of these gene networks occurred earlier than did PPD-specific T cell responses after BCG vaccination/infection (figure 1). One week after BCG vaccination/infection, most immune genes in the networks were up-regulated, whereas PPD-specific IFN-γ–producing T cells were detectable only 2–3 weeks after the BCG inoculation (figure 1). However, prolonged up-regulation of these genes or gene networks clearly coincided with the sustained development of PPD-specific T cell responses after BCG vaccination/infection (figure 1). Such prolonged up-regulation of immune gene networks was also associated with major expansions of CD4 T cell, CD8 T cell, and Vγ2Vδ2 T cell populations (data not shown and [16]). Interestingly, although BCG bacteremia was the driving force of the up-regulation of genes, early up-regulation of gene networks occurred coincidently with the clearance of BCG bacteremia (figure 1). Previous studies have demonstrated that the clearance of BCG bacteremia is related to the immune response, because suppression of antigen-specific T cells during simian immunodeficiency virus infection results in prolonged BCG bacteremia or persistent BCG coinfection [14]. The present findings, therefore, elucidate the in vivo event sequences for immune gene networks, an immune correlate for anti-BCG immunity, and the development of vaccine-elicited T cell responses after BCG vaccination.

Figure 1.

Figure 1

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Gene networks, immune response, and bacteremia. Early and prolonged up-regulation of immune gene networks correlated with the development of vaccine-elicited T cell responses and anti–bacille Calmette-Guérin (BCG) immunity after BCG vaccination. The mean levels of expression of individual gene networks were plotted over the levels of purified protein derivative (PPD)–specific interferon (IFN)–γ–producing T cells and BCG colony-forming units at each time point after BCG vaccination/infection. The mean expression levels of individual gene networks were calculated as the means of fold changes in the expression of selected genes in each of the gene networks (i.e., the mean for a gene network is the total of the means of fold changes for the up-regulated genes divided by the no. of the up-regulated genes). The levels of BCG colony-forming units in simian immunodeficiency virus–infected monkeys were persistent and were higher than those in healthy naive monkeys [14]. Network (i) is the gene network of lymphokines and lymphokine receptors for immune activation/adaptive T cell responses; network (ii) is the gene network of chemokines and chemokine receptors for mucosal/tissue migration and T cell activation; network (iii) is the gene network of signal costimulators for vaccine-elicited T cell responses; network (iv) is the gene network of transcription and trans-activation; network (v) is the gene network of T helper commitments; network (vi) is the gene network of cytotoxic effectors; and network (vii) is the gene network of innate and other immune factors (see the first section of Results). PBMCs, peripheral-blood mononuclear cells.

DISCUSSION

In the present study, we conducted the first large-scale real-time quantitation of immune gene expression in the settings of vaccine-elicited T cell responses and immune clearance of live vaccine in the blood. Our real-time quantitative PCR system appears to be more sensitive than gene-microarray assays and can detect up to 100-fold increases in gene expression, whereas microarray assays usually allow a detection of <5-fold changes in host gene expression in most cases of infections [31–35]. The real-time quantitative PCR system may also be more specific than microarrays, which may be more likely to introduce potential artifacts or nonspecificity while analyzing wider gene profiles. Our extensive validation studies suggest that this system was reproducible and reliable. Importantly, although our real-time quantitation system revealed no significant changes in 138 analyzed immune gene transcripts in control macaques inoculated with saline (table 2), the system allowed us to identify up-regulated immune gene networks after BCG vaccination/infection in macaques. Such a large-scale real-time quantitation system may be useful for preclinical or clinical studies of immune gene networks for both vaccine-elicited immune responses and disease pathogenesis.

One of the interesting findings of the present study is the early and prolonged up-regulation of various immune genes or gene networks after BCG vaccination/infection. Some groups have reported that certain targeted cytokines are either undetectable or detectable for only a short time, coincident with antigenemia [36–38]. Our ability to identify prolonged up-regulation of immune gene networks after BCG vaccination/infection can certainly be attributed to the great sensitivity of real-time PCR–based quantitation, compared with that of conventional ELISA. BCG vaccination/infection induced transcriptional networks composed of a minimum of 78 up-regulated immune genes in circulating lymphocytes. Most of these up-regulated genes in the transcriptional networks can emerge early and sustain a high level of expression for ≥6 weeks after BCG vaccination/infection.

Another interesting observation is that BCG-elicited protective responses involve many genes or gene networks, rather than a few genes. The up-regulated genes or gene networks may act in concert to initiate and maintain translational events after BCG vaccination and likely represent the fundamental proteomes that mount the innate response and BCG-elicited T cell immune responses. Up-regulated TLR and the genes for certain lymphokines and chemokines or even some cell-surface receptors can act as innate immune components and contribute to the early containment of mycobacteria. Meanwhile, up-regulated immune genes can help to develop and sustain T cell responses. Although IL-2, IFN-γ, T-bet, IP-10, and TIM1 and TIM3 may facilitate the development of a Th1 response [39, 40], up-regulated IL-2 and IL-15 transcripts can encode the lymphokines that drive the clonal expansion of vaccine-elicited T cells in BCG-vaccinated monkeys. It is also likely that the networking effect of other up-regulated lymphokine genes—such as IL-3, IL-4, IL-8, IL-16, IL-18, IL-20, IL-22, IL-23, IFN-α, IFN-β, IFN-γ, and TNF-α—contributes to the clonal expansion of vaccine-elicited T cells. Correspondingly, the up-regulation of lymphokine receptors after BCG vaccination may provide the platforms supporting the development of adaptive immune responses of antigen-specific T cells after BCG vaccination/infection. Expression of these surface receptors on lymphocytes can certainly confer to these cells the ability to readily proliferate or expand in response to autocrine or exocrine lymphokines after BCG vaccination/infection. Likewise, the gene network of chemokine and chemokine receptors may play a role in facilitating adaptive immune responses as well. Some chemokines drive leukocyte migration along chemokine gradients; others may regulate dendritic cell (DC) maturation and direct encounters with and interactions between DCs, T cells, and B cells [27, 28]. Up-regulated chemokine receptors allow circulating lymphocytes to readily migrate to the tissue and mucosae for immune clearance of mycobacteria.

The up-regulated immune gene networks analyzed here may contribute to immunity against mycobacterial infection. Our data show a correlation between early up-regulated gene networks and immune clearance of BCG mycobacteria. The data also indicate that correlates of protection or clearance may not be attributed simply to 1–5 selected genes but, rather, to a set of coordinately regulated genes. Presumably, these up-regulated immune genes may represent gene networks of both innate and adaptive anti-mycobacterial immunity, because these networks are connected in time to early immune clearance of BCG bacteria and BCG-specific T cell responses. Given the capability of BCG to confer protection against fatal forms of M. tuberculosis infection and tuberculosis [1, 3, 8, 15], BCG-induced gene networks of immunity may provide standards for comparisons with other TB vaccines that are currently under development. On the other hand, these gene networks of adaptive immunity may be different from those profiles of inflammation or immune failures that occur as a result of tuberculosis or AIDS-related tuberculosis. Information from such comparative studies should enhance our understanding of vaccine development and the pathogenesis of tuberculosis.

Supplementary Material

Table1

Acknowledgments

We thank other members of the Chen Lab, for technical assistance.

Financial support: National Institutes of Health (R01 grants HL64560 and RR13601, both to Z.W.C.).

Footnotes

Potential conflicts of interest: none reported.

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Supplementary Materials

Table1
NIHMS197506-supplement-Table1.doc (173KB, doc)

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