Merck Ad5/HIV induces broad innate immune
activation that predicts CD8+T-cell responses but
is attenuated by preexisting Ad5 immunity
Daniel E. Zaka,1, Erica Andersen-Nissenb,1, Eric R. Petersonb, Alicia Satob, M. Kristina Hamiltona, Joleen Borgerdingb,
Akshay T. Krishnamurtyb, Joanne T. Changb, Devin J. Adamsb, Tiffany R. Hensleyb, Alexander I. Salterb,
Cecilia A. Morganb,c, Ann C. Duerrb,c, Stephen C. De Rosab,c,d, Alan Aderema,e,2,3, and M. Juliana McElrathb,c,f,2,3
aSeattle Biomedical Research Institute, Seattle, WA 98109;bVaccine and Infectious Disease Division andcHIV Vaccine Trials Network, Fred Hutchinson Cancer
Research Center, Seattle, WA 98109; anddDepartment of Laboratory Medicine,eDepartment of Immunology, andfDepartment of Medicine, University of
Washington, Seattle, WA 98195
Edited* by Rafi Ahmed, Emory University, Atlanta, GA, and approved October 16, 2012 (received for review June 5, 2012)
To better understand how innate immune responses to vaccination
can lead to lasting protective immunity, we used a systems ap-
proach to define immune signatures in humans over 1 wk following
MRKAd5/HIV vaccination that predicted subsequent HIV-specific T-
cell responses. Within 24 h, striking increases in peripheral blood
mononuclear cell gene expression associated with inflammation,
IFN response, and myeloid cell trafficking occurred, and lympho-
cyte-specific transcripts decreased. These alterations were corrobo-
rated by marked serum inflammatory cytokine elevations and
egress of circulating lymphocytes. Responses of vaccinees with pre-
existing adenovirus serotype 5 (Ad5) neutralizing antibodies were
strongly attenuated, suggesting that enhanced HIV acquisition in
Ad5-seropositivesubgroups in the Step Study may relate to the lack
of appropriate innate activation rather than to increased systemic
immune activation. Importantly, patterns of chemoattractant cyto-
nuclear cell transcripts at 72 h were predictive of subsequent in-
duction and magnitude of HIV-specific CD8+T-cell responses. This
systems approach provides a framework to compare innate re-
sponses induced by vectors, as shown here by contrasting the more
rapid, robust response to MRKAd5/HIV with that to yellow fever
vaccine. When applied iteratively, the findings may permit selection
of HIV vaccine candidates eliciting innate immune response profiles
more likely to drive HIV protective immunity.
immunology|innate immunity|systems biology|systems vaccinology|
in Thailand, where a canarypox vector prime and subunit protein
boost regimen showed 31% efficacy for reducing HIV-1 acquisition
(1), have given hope that development of a successful HIV vaccine
is possible, and suggest that the vector prime is important for
shaping a protective response. Innate immune responses direct the
adaptive immune response and thus influence the potential for in-
ducing long-lived protective immunity (2). A comprehensive un-
derstanding of the molecular programs underlying optimal innate
is known at present about the innate immune responses induced by
candidate HIV vaccines, how these responses drive adaptive im-
munity, and how these innate responses compare with those in-
duced by licensed efficacious vaccines against other pathogens.
Ib clinical trial (HVTN 071) to analyze, at the systems level, human
innate immune responses to the replication-incompetent Merck
adenovirus serotype 5 vaccine vector containing HIV-1 inserts gag/
being conducted using the same vaccine. Although this vaccine did
not offer protectionfromHIV acquisition or lower viral loads inthe
phase IIb Step or Phambili studies (HVTN 502 and 503), it elicited
high CD8+T-cell response rates to the HIV-1 inserts (3–5), and
highly efficacious HIV vaccine offers the greatest promise to
selective pressure on infecting HIV-1 strains (6). The MRKAd5/
HIV vaccine received particular attention when the Step Study
seropositivity exhibited increased HIV-1 acquisition rates, halting
its further use in all HIV-1 vaccine trials involving Ad5 seroposi-
tive subjects. Although hypotheses have been generated that may
explain vaccine-induced increased HIV-1 infection rates (3, 7, 8)
and enhanced acquisition was recently recapitulated in the simian
immunovirus (SIV) challenge model (9), no clear mechanisms
have been identified to date. These findings, coupled with the
importance of the Ad5 and other adenovirus serotype vectors to
vaccine development against many other pathogens (10, 11),
reinforced our motivation to use an unbiased systems biology
approach to better understand the innate immune response trig-
gered by MRKAd5/HIV.
Systems biology integrates global molecular measurements and
computational analysis with prior knowledge to generate holistic
biological insights. This approach therefore provides a framework
to address complex vaccine-induced immunological responses
(12, 13). Crosstalk and feedback can be elucidated between im-
mune signaling pathways and gene regulatory networks operating
on multiple spatial and temporal scales. We have previously ap-
plied systems analysis to identify gene and signaling networks that
coordinately amplify and attenuate Toll-like receptor (TLR)-
mediated responses underlying innate immune cell activation
(14–17). Recent systems analyses of responses to vaccination with
the highly efficacious YF-17D yellow fever vaccine (18, 19) and
seasonal influenza vaccine (20) have yielded novel insights about
their mechanisms of action. Building on this systems-level ap-
MRKAd5/HIV, how they are impacted by preexisting Ad5 neu-
tralizing antibodies (nAb), how they relate to induction of T-cell
responses, and how they differ from those induced by live-
Author contributions: D.E.Z., E.A.-N., A.A., and M.J.M. designed research; D.E.Z., E.A.-N.,
E.R.P., A.S., M.K.H., A.T.K., J.T.C., D.J.A., T.R.H., A.I.S., and M.J.M. performed research; D.E.Z.,
E.A.-N., E.R.P., A.S., M.K.H., J.B., A.T.K., J.T.C., D.J.A., T.R.H., A.I.S., S.C.D., and M.J.M. ana-
lyzed data; D.E.Z., E.A.-N., A.A., and M.J.M. wrote the paper; D.E.Z. performed microarray
data analysis; and C.A.M., A.C.D., and M.J.M. implemented the clinical protocol.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
Data deposition: The data reported in this paper have been deposited in the Gene Ex-
pression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE22822).
1D.E.Z. and E.A.-N. contributed equally to this work.
2A.A. and M.J.M. contributed equally to this work.
3To whom correspondence may be addressed. E-mail: email@example.com or
See Author Summary on page 20194 (volume 109, number 50).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
| Published online November 14, 2012
MRKAd5/HIV Dramatically Remodels Peripheral Blood Mononuclear
Cell Transcriptomes by Triggering Robust Innate Immune and Cell
Trafficking Responses. We assessed the innate immune response
to MRKAd5/HIV by profiling transcriptomes of peripheral blood
mononuclear cells (PBMC) isolated from seven Ad5 nAb sero-
negative individuals (Ad5 nAb titer ≤18; Ad5Neg) during the first
week after vaccination, by gene-level analysis of Affymetrix exon
microarrays. Responses to MRKAd5/HIV peaked at 24 h, with
1,026 genes exhibiting enhanced and 1,048 genes exhibiting re-
pressed expression levels compared with prevaccination (Fig. 1A
and Dataset S1, tab 1). At 72 h postvaccination, the differentially
expressed genes were a small subset of those detected at 24 h
(Dataset S1, tab 2). No significantly differentially expressed genes
were detected at 168 h.
We used a modular analysis framework (21) to interpret the
transcriptional response. This approach deconvolutes complex
transcriptional profiles into functionally interpretable patterns
through the evaluation of combined expression responses of pre-
defined disease, cell type, and stimulus-specific coexpressed gene
groups. We used versions of the functional modules defined by
of a much larger transcriptional dataset encompassing many more
disease states (23), to annotate the differentially expressed gene
lists and to examine the differential expression of the overall
modules themselves. We confirmed the functional annotations of
the gene modules themselves by performing canonical pathway
enrichment analysis (Dataset S1, tab 3). Mirroring the gene-level
results, the modular response peaked at 24 h (13 up-regulated and
11 down-regulated modules), waned by 72 h (two up-regulated
modules), and returned to baseline by 168 h (Fig. 1B). Modules
induced by MRKAd5/HIV were associated with cell intrinsic in-
nate immune responses (“Inflammation” and “Interferon re-
sponse” modules) and influx of inflammatory cells (“Myeloid
lineage” module). Concomitantly, the “Lymphoid lineage,” “T
cells,” and “Cytotoxicity” modules were suppressed, leading to the
hypothesis that the vaccine was stimulating an influx of myeloid
cells and an efflux of lymphoid cells from the circulation. This
hypothesis was further supported by comparing the lists of up- and
down-regulated genes with published cell-type enriched gene lists
generated from meta-analysis of a compendium of sorted cell
transcriptomes (20). Thirty-two percent of the genes we detected
as up-regulated at 24 h were identified as preferentially expressed
in monocytes in that study, whereas 28% of the down-regulated
genes we detected were preferentially expressed in lymphocytes
(Dataset S1, tab 1). Rapid lymphocyte trafficking in response to
MRKAd5/HIV is consistent with similar observations made in
previous studies with an adenoviral-vectored vaccine (24). Direct
canonical pathway enrichment analysis of the regulated gene sets
provided additional support for the module analysis results, in-
dicating that innate immune pathways and cell types were up-
regulated in response to vaccination, and lymphocyte cell types
and pathways were down-regulated (enrichment results and
pathway figures in Dataset S1, tabs 4 and 5).
We validated the microarray results at the transcript, protein,
and cellular levels. First, we quantified and confirmed the dif-
ferential expression of mRNAs associated with several vaccine-
regulated modules, including “Interferon response” [C-X-C motif
chemokine 10 (CXCL10), ISG-15, and STAT1] (Fig. 1C). Next,
we corroborated the differential expression of many cytokines
and chemokines at the protein level using multiplex serum
analyte analysis (Fig. 1D and Dataset S1, tab 6), detecting robust
changes in serum levels of IP-10, I-TAC, monocyte chemo-
attractant protein-1 (MCP-1), and MCP-2, as well as immuno-
regulatory IL-10 and IL-1Ra. Finally, we validated the cellular
trafficking responses predicted from the modular analysis by
directly assessing circulating peripheral blood leukocyte con-
centrations (Fig. 1E and Dataset S1, tab 7), confirming vaccine-
induced influx of monocytes and pronounced efflux of lympho-
cyte populations (T, B, and NK cells). Monocyte increases are
likely a result of recruitment from the bone marrow in response
to MCP-1 and other chemokines (25). Taken together, these
results validate the robust systemic innate immune response to
MRKAd5/HIV revealed by the transcriptional profiling.
The In Vivo Innate Immune Response to MRKAd5/HIV Is Recapitulated
in Vitro and Engages a Coordinately Regulated Interacting Network
Involving Unique Gene Isoforms. To decouple the in vivo innate
responses intrinsic to the circulating cells from those associated
with cells trafficking into and out of the circulation, we extended
our transcriptional profiling to PBMC stimulated with the vac-
cine vector in vitro. We profiled RNA from unstimulated PBMC
and PBMC incubated for 24 h with MRKAd5 at a dose sufficient
to induce robust cytokine responses (Fig. S1). We found that 8 of
13 (62%) modules induced in vivo were also induced in vitro and
these consisted of the three “Interferon response modules” as
well as unannotated modules largely comprised of innate im-
mune response genes (Fig. 2A). Remarkably, 92% concordance
between the in vivo and in vitro induction of IFN response genes
was observed (Dataset S1, tab 8). Many of the modules discor-
dant between the in vitro and in vivo responses were associated
with particular cellular lineages (myeloid, lymphoid, T cell, B
cell) or cell-type specific attributes (cytotoxicity) (Fig. 2B), sug-
gesting that the much of the discrepancy between the in vivo and
in vitro responses arose from an absence of cell trafficking in
vitro. Comparison with cell-type specific genes lists (20) in-
dicated 35% of the genes up-regulated in vivo but not in vitro are
preferentially expressed in monocytes (Dataset S1, tab 8), sup-
porting this hypothesis.
Our exon-level transcriptional analyses from previous studies
demonstrated that defective alternative mRNA splicing results in
profound phenotypic differences in memory T cells (26), and that
alternative exon use occurs in the innate response. We therefore
the exon-level to further enrich our understanding of the action of
genes not already detected by the gene-level analysis, particularly
those behaving concordantly in vitro and in vivo. Exon-level
analysis led to the identification of 94 additional vaccine-induced
genes in vivo and in vitro (Dataset S1, tab 9), including critical
innate immune pathway genes (TLR3, RIPK1, and NLRC5) and
several genes with important roles in HIV infection (APOBEC3G,
APOBEC3F, CCR5, and CD74). Additionally, alternative tran-
scription analysis identified 16 genes with vaccine-induced re-
sponses that varied strongly from exon to exon, but were nev-
RERE, GBP6, and GBP7 (Fig. 2C and Dataset S1, tab 10). Al-
though the IFN-γ–induced antimicrobial GTPases GBP6 and
GBP7 have been associated with immune responses, most of the
other 16 genes have not been, suggesting additional leads that
could be investigated to further understand vaccine-induced im-
munological memory. Induction of the unique short isoform of
FANCA as part of the MRKAd5-induced innate immune re-
sponse, for example, provides a compelling link between DNA
damage pathways and the immunogenicity of adenoviral vectors.
We performed interaction network analysis to determine
whether the genes commonly regulated by MRKAd5 in vitro
and in vivo constituted established pathways or represented
isolated nodes. This analysis revealed a densely intercon-
nected network involving multiple modules and included
genes detected by gene-level analysis, exon-level analysis, and
alternative transcription analysis [visualized using Cytoscape
(27) in Fig. 2D]. These findings indicate coordinate regulation
of large functional subnetworks, including viral nucleic acid
sensors, innate immune adaptors, inflammasome components,
and antiviral effectors.
Preexisting Neutralizing Antibodies to Ad5 Attenuate the Innate
Immune Response to MRKAd5/HIV. An important observation in
the Step Study was that the presence of Ad5 nAb before vacci-
nation resulted in increased postvaccination risk of HIV acqui-
sition (4), and thus far, no clear mechanism for this has been
| www.pnas.org/cgi/doi/10.1073/pnas.1208972109Zak et al.
elucidated (3, 7, 8). We therefore analyzed the in vivo innate
immune responses of vaccinated Ad5 seropositive subjects to
determine if we could identify alternate programs of innate ac-
tivation in these individuals. The early termination of the Step
and Phambili studies resulted in the cessation of HVTN 071,
limiting the number of subjects we could analyze. Given the
possibility of threshold effects of Ad5 nAb (3, 4), we compared
the responses between subjects with Ad5 nAb titers ≤ 200 and
>200, and thereby identified 306 seropositivity effect genes (302
at 24 h, six at 72 h) for which the vaccine-induced responses were
markedly attenuated (Dataset S1, tab 11). Canonical pathway
enrichment analysis of these genes revealed that induction of
In vivo PBMC transcriptional responses to vaccination with MRKAd5/HIV [n = 7 Ad5 seronegative individuals, false-discovery rate (FDR) ≤ 10%, absolute average
log2fold-change ≥ 0.5]. Genes significantly differentially expressed in response to MRKAd5/HIV vaccination at any time point are annotated and grouped
according to membership in functional gene modules (21, 64). Each column represents subject-specific log2(fold-changes) compared with prevaccination. To
emphasize regulation patterns, expression fold-changes for each gene are scaled by the maximum observed expression response. Pink intensity indicates up-
regulation compared with prevaccination, cyan indicates down-regulation. (B) Functional gene modules significantly differentially expressed in PBMC in response
to vaccination. Each black line represents the average log2(fold change) of all genes in the module for a single subject. Up-regulated modules have values >0,
down-regulated modules have values <0. FDR < 10%, |average log2(fold-change)|>0.5. (C) Quantitative RT-PCR validation of the differential expression of
module-associated genes identified by microarray analysis. Each line represents the response of one individual (n = 7). (D) Protein-level validation of vaccine-
induced cytokine and chemokine differential expression by multiplex analyte analysis. (E) Validation of vaccine-induced cellular fluxes predicted by module-level
analysis of the PBMC transcriptional responses. *P < 0.05 with Hochberg adjustment from the statistical model assessing change in concentration over time.
Systems analysis identifies widespread innate immune activation and cellular trafficking responses response to MRKAd5/HIV vaccination in humans. (A)
Zak et al. PNAS
| Published online November 14, 2012
complement pathways, innate immune sensors, and G-protein
coupled receptor signaling was significantly attenuated (Dataset
S1, tab 12). This attenuation extended to all modules regulated by
the MRKAd5 vaccine (Fig. 3A), including impaired down-regula-
tion of lymphocyte modules and genes preferentially expressed in
lymphocytes (Dataset S1, tab 11), suggesting suppression of the
acute lymphopenia observed in the Ad5 seronegative subjects (Fig.
1E). Direct comparison between regulation of innate immune
networks in seronegative and Ad5 nAb >200 subjects revealed
coordinate dysregulation that included the RIG-I, NLR/inflam-
masome, and TLR pathways (visualized using Cytoscape in Fig.
3B). Finally, we validated these transcriptional results at the pro-
tein level by analyzing serum analytes from a larger set of vacci-
nated subjects (Fig. S2). Consistent with the transcriptional results,
cytokine responses were markedly attenuated in Ad5 nAb >200
subjects compared with Ad5 nAb ≤ 200 subjects (Fig. 3C and
Dataset S1, tab 13). Taken together, these results suggest that the
predominant effect of preexisting Ad5 nAb on the innate immune
response is global attenuation. These data do not support the
hypothesis that preexisting immunity leads to enhanced systemic
innate immune activation.
MRKAd5/HIV Innate Immune Responses Predict Immunogenicity. We
next identified MRKAd5/HIV-induced innate immune sig-
natures that predict subsequent HIV-specific adaptive immune
responses. Based on the frequency of Gag-specific CD8+T-cell
responses detected at day 28 after one immunization (Fig. S3),
we categorized vaccine recipients (n = 31) into high, moderate,
or low responders. We determined whether fold-changes in serum
cytokine concentrations, measured 24 h postvaccination (Fig. 1D
and Dataset S1, tab 6), could predict the Gag-specific CD8+T-
cell response magnitudes. We performed two analyses: (i) dis-
crimination between subjects with detectible (CD8pos= CD8mod
and CD8high) and undetectable (CD8neg) responses; and (ii) dis-
crimination between subjects with high (CD8high) and moderate
or undetectable (CD8mod, CD8neg) responses. The predictive
potential of individual cytokines and all cytokine pairs was eval-
uated by 60 iterations of eightfold cross-validation of linear dis-
criminant analysis (LDA) classifiers.
Two chemokines, MCP-1 and MCP-2 (Fig. S4 A and B), dis-
criminated between CD8mod/CD8highsubjects and CD8negsubjects
with high accuracy (81% and 88%, respectively), and thus were
qualitatively predictive of the vaccine CD8+T-cell immunoge-
nicity. In both cases, higher chemokine induction predicted in-
creased likelihood of positive CD8+T-cell responses. Combining
MCP-1 and MCP-2 into a single classifier did not increase pre-
dictive accuracy. However, the accuracy was increased by combi-
nation with other cytokines that were not individually predictive
(Fig. 4A and Fig. S4A). For example, the growth factor PDGF-AA
was 71% predictive individually but 85% predictive in combina-
tion with MCP-1. The network of predictive pairwise signatures
for CD8+T-cell responses is shown in Fig. 4A, and the receiver
operating characteristic (ROC) for predicting positive CD8+T-
cell responses based on GRO and MCP-2 is shown in Fig. 4B.
Classifiers performing as well as MCP-2 individually (or top pairs
involving MCP-2) were generated only 13% of the time when the
analysis was repeated on randomized datasets, indicating that the
result is moderately robust. Nevertheless, repeat analyses using
and B) In vitro responses of modules regulated by MRKAd5/HIV in vivo. Each black line represents the average log2fold-change of all genes in the module for PBMC
derived from separate donors and stimulated in vitro (n = 4); red line indicates average module-level response in vivo. *P ≤ 0.01 comparing module fold-changes
(C) Single gene heatmaps showing average exon-level expression responses to MRKAd5/HIV in vivo (n = 7)and in vitro (n = 4). Pink intensity indicates up-regulation,
by representative subcellular localization. Node colors indicate functional module associations; node shapes indicate the mode of differential expression. Red lines
indicate protein-protein interactions, gray lines indicate mutual membership in common complexes, and blue edges indicate protein–DNA interactions.
Responses to MRKAd5/HIV are recapitulated in vitro and involve coordinate regulation of an innate immune network involving unique gene isoforms. (A
| www.pnas.org/cgi/doi/10.1073/pnas.1208972109 Zak et al.
similar vaccines are required to confirm the association between
these chemokines and CD8+responses. In the second analysis, the
combination of RANTES (regulated upon activation, normal T
cell expressed and secreted) and IL-28A was predictive of CD8+
response magnitudes with high accuracy (87%), even though
neither cytokine was strongly predictive individually (Fig. S4 C and
D). Strong down-regulation of RANTES or up-regulation of IL-
28A was associated with induction of high magnitude CD8+T-cell
responses (Fig. S4D). The ROC for predicting high magnitude
CD8+T-cell responses based on IL-28A and RANTES is shown
in Fig. 4C. Repeating the analysis on randomized datasets gen-
erated classifiers performing as well as IL-28A and RANTES 25%
of the time, indicating that this particular result should be regar-
ded as a hypothesis until additional studies have validated the role
of these cytokines in the fine tuning of adenoviral vector CD8+T-
Using Systems Biology to Generate Additional Hypotheses Regarding
the Immunogenicity of MRKAd5/HIV.Toidentifyadditionalpotential
mechanisms controlling MRKAd5/HIV-induced T-cell responses,
we extended our signatures analysis to the transcriptional level.
Given our small data sample sizes, it was not possible to imple-
ment approaches described above or in previous studies (19, 20),
and the results must be regarded as hypothesis-generating until
possible. We defined groups of subjects with high-, moderate-, or
specific responses. Genes with statistically significant differences
in vaccine-induced transcriptional responses between the high and
low CD8+groups were then identified through direct comparison.
Surprisingly, significant differences between these groups of sub-
jects were only identified from the transcriptional responses
measured 72 h postvaccination (88 genes were positively associ-
Dataset S1, tab 14), and none of the MRKAd5-responsive mod-
ules differed significantly between the groups. Several of the im-
plicated genes have clear functional relationships to cytotoxic
responses, including the inhibitory killer cell Ig-like receptor
KIR2DL1, the NK-cell activating receptor CLEC2D (28), and the
NK-cell signaling adaptor EWS-FLI1–activated transcript 2 (EAT-
2) (29) (Fig. 5 A and B). Consistent with this association between
EAT-2 expression and CD8+T-cell responses, it was recently
reported that adenoviral expression of EAT-2 as part of a vaccine
strategy enhanced vaccine-induced T-cell responses (30). Because
interaction network analysis of the overall CD8+T-cell response
gene set itself was found to be uninformative, we investigated
whetherany thegene setmembers are protein–protein interaction
neighbors of genes belonging to MRKAd5 regulated functional
modules (Fig. 1B). We found that many of the CD8+T-cell re-
sponse associated genes are nearest neighbors of members of the
“Cytotoxicity,” “T cells,” and “Lymphoid lineage” modules (Fig.
5C), providing additional support for the association between
these genes and CD8+T-cell immunogenicity.
Replication-Incompetent MRKAd5 Induces a Greater Number of
Innate Immune Genes than Does Live-Attenuated YF-17D, but the
Response Is More Transient. Recent studies have suggested that
may result from robust innate immune activation (18, 19, 32). We
therefore contrasted the transcriptional responses induced
MRKAd5 and published in vivo profiles for YF-17D (19). Al-
and repressed a similar number, the YF-17D vaccine only induced
181 genes and repressed 10 genes (Fig. 6A). However, the re-
sponse to MRKAd5/HIV was rapid and transient, but the re-
sponse to YF-17D lagged and was persistent (Fig. 6A). Modular
analysis further illuminated differences between the two vaccines
(Fig. 6B). Whereas MRKAd5/HIV induced the “Inflammation,”
“Interferon response,” and “Myeloid lineage” modules and
inhibited the “Lymphoid lineage,” “T cells,” and “Cytotoxicity”
modules (Figs. 1B and 4B), YF-17D vaccination induced only
a subset of the “Interferon response” modules (M1.2 and M3.4)
Given that dosage and replication kinetics could likely account
for the gross differences in innate immune activation between
replication defective MRKAd5 and live-attenuated YF-17D, we
performed a new set of comparative in vitro experiments to di-
rectly contrast responses to the two vaccines, focusing first on
differences identified in vivo that recapitulated in vitro. We
identified 43 genes preferentially induced by MRKAd5/HIV in
vivo that confirmed in vitro (Fig. 6C), including several associ-
ated with innate immune responses (IRF1), the complement
pathway (C1QB), pathogen recognition (TLR8), the inflamma-
some (CASP10, P2RX7), and NK-cell activation [SLAMF7 (33)].
This group also included several immunosuppressive factors,
including the T-cell inhibiting IDO1 (34), M2-macrophage
tenuate the MRKAd5/HIV-induced innate immune
response in vivo. (A) Effect of Ad5 nAbs on gene
modules regulated by MRKAd5/HIV. Black line rep-
resents the average fold-change of all genes in the
modules, averaged over 8 Ad5 nAb ≤ 200 subjects.
Green lines represent the average fold-change of all
genes in the module for the two Ad5 nAb > 200
subjects. (B) Attenuated induction of critical innate
immunity pathways in Ad5 nAb > 200 compared
with Ad5 nAb ≤ 200 subjects. Nodes are colored
according to average extent of induction in Ad5
nAb ≤ 200 subjects at 24 h (Left) and average extent
of response attenuation in Ad5 nAb > 200 subjects
compared with Ad5 nAb ≤ 200 subjects (Right).
Edges represent protein–protein interactions be-
tween nodes obtained from InnateDB (65). (C)
Representative attenuation of MRKAd5/HIV serum
cytokine induction in Ad5 nAb > 200 subjects. Se-
rum IP-10/CXCL10 concentrations are contrasted
between Ad5 nAb ≤ 200 (n = 27) and Ad5 nAb > 200
(n = 6) individuals. Shading represents the inter-
quartile ranges with the 75th percentile shown on
top and the 25th percentile shown below the me-
dian line. *P < 0.05 after Hochberg adjustment.
Preexisting Ad5 neutralizing antibodies at-
Zak et al.PNAS
| Published online November 14, 2012
skewing TF KLF4 (35), macrophage inhibitor PSTPIP2 (36), and
the PD-1 death receptor ligand PDCD1LG2. The preferential
induction of three transcription factors by MRKAd5, IRF1,
KLF4, and STAT5A suggested that these factors may partly
account for the PBMC transcriptome induced by MRKAd5. By
mining published ChIP-Seq datasets (37–39), we confirmed that
several MRKAd5-specific genes are direct targets of these tran-
scription factors (Fig. 6D), and that these transcription factors
potentially coregulate each other. Although there were no genes
preferentially induced by YF-17D in vivo that validated in vitro,
we identified a robust gene set, predominantly consisting of
magnitudes of CD8+T-cell responses induced by MRKAd5/HIV. (A) Network
depiction of serum cytokines and chemokines with 24-h vaccine-induced fold-
changes that discriminate subjects who develop CD8+T-cell responses (28 d
after vaccination) from those who do not. Node sizes indicate predictive ac-
curacy of the factors individually (percentages inside nodes), node color, and
intensity indicate positive (pink) or negative (blue) correlations between in-
duction levels of the factor and CD8+T-cell response magnitudes. The pres-
ence of an edge between nodes indicates that the two cytokines in
combination have increased predictive accuracy compared with either cyto-
kine alone. Edge width is proportional to predictive accuracy of the pair-wise
signature of the connected nodes (red percentages). (B) ROC for predicting
which subjects will develop CD8+T-cell responses, based on 24-h vaccine in-
duced fold-changes of GRO and MCP-2 in a LDA model. The red line indicates
the tradeoff in false positive rate required as the true positive rate is in-
creased. Prediction accuracies were estimated from over 60 rounds of
eightfold cross-validation. AUC, area under the curve. (C) ROC for predicting
which subjects will develop high magnitude CD8+T-cell responses, based on
24-h vaccine induced fold-changes of IL-28A and RANTES in a LDA model.
Notation is as in B.
Vaccine-induced serum cytokineand chemokineresponsespredictthe
associated with the immunogenicity of MRKAd5/HIV. (A) The 209 genes with
72-h MRKAd5/HIV-induced expression responses that are positively (Upper)
or negatively (Lower) associated with the magnitude of MRKAd5/HIV-in-
duced CD8+T-cell responses. Subjects with low and high magnitude CD8+
T-cell responses are on the Left and Right halves of the heatmap, respectively.
Gene selection criteria: FDR ≤ 20% and |average log2(fold-change)| ≥ 0.5.
Rows are scaled as in Fig. 1A. Pink intensity indicates up-regulation compared
with prevaccination, cyan indicates down-regulation. (B) Seventy-two hour
MRKAd5/HIV-induced expression responses of two representative genes,
stratified by the magnitude of the CD8+T-cell responses observed in the same
subjects. (C) Protein–protein interaction network emphasizing links between
CD8+T-cell response-associated genes identified in Fig. 5A (triangles) and
constituents of functional gene modules differentially regulated by MRKAd5/
HIV (circles), colored according to module associations.
Systems analysis identifies innate immune response genes that are
| www.pnas.org/cgi/doi/10.1073/pnas.1208972109 Zak et al.
members of the “Interferon response” modules (including STAT1,
STAT2, IRF7, and IFI27), that was induced by both vaccines in
vitro and in vivo (Dataset S1, tab 15).
To generate hypotheses about differences in innate immune
activation that may result at the actual sites of MRKAd5 or YF-
17D vaccination, we also performed a direct comparison between
the in vitro responses to the two vaccines, without constraining
them by the in vivo results. Unexpectedly, the innate activation
profiles of MRKAd5 and YF-17D differed more strongly in vitro
than we had originally observed in vivo, with 349 and 313 genes
being preferentially induced by MRKAd5 and YF-17D, re-
spectively, compared with 226 genes being robustly induced in
common (Dataset S1, tab 16). Similar differences in the down-
regulated gene sets were observed, with 190 and 229 genes being
preferentially down-regulated by MRKAd5 and YF-17D, re-
spectively, compared with 137 genes being down-regulated in
(CXCL9/10/11), MHC genes, and T-cell–associated cytokines
(IFNG, IL-2, and IL-7) but YF-17D preferentially induced the
IFNA family of antiviral cytokines and several neutrophil chemo-
kines (IL-8, CXCL2, -3, -5, and -6). Canonical pathwayenrichment
analysis reinforced the differences between the two vaccines, with
innate immune responses in PBMC with distinct kinetics in vivo. The number of genes significantly up-regulated (upper half) or down-regulated (lower half) in
response to vaccination with MRKAd5/HIV (red, present study) or YF-17D [blue (19)] are plotted. Significant differential expression defined as FDR ≤ 10%, |
average[log2(fold-change)]| > 0.5; n = 7 (MRKAd5/HIV), n = 25 (YF-17D). (B) MRKAd5/HIV regulates a broader spectrum of gene modules in vivo than does YF-
17D. Each line represents the average fold-change of all genes in a module for a given individual (red lines, MRKAd5/HIV vaccines; blue lines, YF-17D
vaccinees). Modules labeled in red differ significantly between MRKAd5/HIV and YF-17D in vivo (FDR ≤ 10%), modules labeled with asterisks differ signif-
icantly between MRKAd5 and YF-17D in vivo and in vitro (FDR ≤ 10%). (C) Average expression responses in vivo and in vitro of 43 genes preferentially induced
by MRKAd5. Genes associated with functional gene modules are indicated. Rows are scaled as in Fig. 1A. Pink intensity indicates up-regulation compared with
controls, cyan indicates down-regulation. FDR < 10%, |average log2(fold-change)|>0.5 for induction in response to MRKAd5 and comparing MRKAd5 to YF-
17D, in vivo and in vitro. (D) Putative transcriptional regulatory network controlling innate immune responses preferentially induced by MRKAd5. Squares
indicate transcription factors preferentially induced by MRKAd5 in vivo and in vitro; circles indicate target genes preferentially induced (pink) or repressed
(light blue) by MRKAd5 in vivo and in vitro. Lines indicate protein-DNA transcription factor–target gene interactions identified from published ChIP-seq
datasets [blue, KLF4 targets (38); purple, IRF1 targets (37); orange, STAT5A targets (39)]. Pink edges indicate IRF1 target genes identified by conventional
methods (66–68). (E) Expression responses of CRIP3 and NPB (72–168 h postvaccination) are negatively associated with CD8+T-cell response magnitudes
induced by MRKAd5/HIV and YF-17D. Shown are log2(fold-changes) compared with prevaccination of the two genes for both vaccines, stratified by the
magnitudes of vaccine-induced CD8+T-cell responses observed in the same subjects. Lines indicate mean values.
MRKAd5 induces more extensive innate immune activation than the gold-standard yellow fever vaccine, YF-17D. (A) MRKAd5/HIV and YF-17D trigger
Zak et al. PNAS
| Published online November 14, 2012
MRKAd5-specific gene enrichments including “Antigen Pre-
sentation Pathway” and “T Helper Cell Differentiation” and YF-
17D–specific gene enrichments, including “Systemic Lupus Eryth-
ematosus Signaling” and several IL-17 associated pathways (Dataset
S1, tab 17). These results indicate that greater specificity in vaccine-
rather than systemic responses.
Finally, we evaluated whether the transcriptional signatures
associated with enhanced CD8+T-cell responses induced by
MRKAd5/HIV (Fig. 5A) were also associated with enhanced
CD8+T-cell responses to YF-17D, despite the numerous differ-
ence between the vaccines. By reanalyzing published YF-17D
transcriptome and longitudinal CD8+T-cell response data (19)
using the approach implemented above, we identified two genes,
CRIP3 and NPB, with vaccine-induced expression responses that
were consistently associated with impaired CD8+T-cell responses
to both vaccines (Fig. 6E). Strengthening the associations, the
subjects was always between that of high and low CD8+response
subjects, even though the data for the moderate subjects was not
used in the gene selection.
Taking these data together, we have defined the early innate
immune response to the MRKAd5/HIV vaccine, identified an
attenuated innate response in individuals with Ad5 nAb, and
defined innate response signatures that predict CD8+ T-cell
responses to Gag. These data suggest previously unexplored
targets for enhancing the immunogenicity of next-generation
Systems biology analysis can contribute to rational vaccine design
in four major ways. First, it can enable the identification of cor-
regulatory networks within cells that lead to the desired host im-
regimen to favor desirable responses; and finally, it can supply
tools to glean insight from failed candidate vaccines. We believe
highly immunogenic but nonefficacious MRKAd5/HIV vaccine.
Despite inducing T-cell responses at a high frequency,
MRKAd5/HIV neither reduced HIV-1 acquisition nor lowered
viral loads postacquisition in two independent clinical trials (3, 5).
Furthermore, Ad5seropositivemale vaccine recipients in theStep
study showed an increased rate of HIV-1 acquisition, making the
influence of Ad5 nAbs on vaccine responses an area of intense
research (3, 4, 7, 8, 40–42). Although additional factors likely
played a role in acquisition (43–45), the effect of Ad5 nAbs was
significant and has been supported in the nonhuman primate SIV
challenge model. One current hypothesis is that antibody-medi-
ated internalization of Ad5 results in increased dendritic cell ac-
study, we found no evidence for enhanced or prolonged systemic
innate immune responses in volunteers with preexisting Ad5 nAb.
Instead, we observed attenuation of the overall transcriptional
response (Fig. 3 A–C), which we confirmed at the protein level by
Our results are compatible with the suggestion that Ad5 nAb may
effectively lower the dose of the vaccine detected by the innate
immune system(8,46)andare consistentwiththereducedvaccine
immunogenicity seen in vaccine recipients with nAb titers >200
(3). Our results are also compatible with a model in which nAbs
negatively regulate innate signaling pathways. The latter hypoth-
esis is of interest given the possibility that the opsonized vector
could interact with Fc receptors on antigen presenting cells; an
event that might result in an inappropriate context for pre-
sentation of the vaccine-encoded antigens. Regardless of the
precise mechanism, our observations highlight the impact of pre-
existing type-specific immunity to the vector on vaccine responses
and open new avenues for mechanistic studies into the effects of
this important variable.
Few vaccine vectors in development match Ad5 in terms of the
magnitude and frequency of vaccine insert-specific CD8+T cells
they induce (Fig. S3) (3). Ad5-induced CD8+T cells are func-
tional in some settings, because they are essential to the efficacy
of Ad5-vectored Ebola vaccines in the nonhuman primate model
(10) and also appear to have exerted selective pressure on
infecting HIV-1 in the Step Study (6). Results in the murine
model, however, show that Ad5 induced CD8+T cells may not
properly differentiate into memory cells required for protective
responses.†These contrasting results suggest that although the
strong Ad5-induced CD8+T-cell response may be sufficient for
vaccine efficacy in some systems, increases in the efficacy of Ad5-
based vaccines may be achieved if the quality of the induced
CD8+T cells is optimized.
To determine how the magnitude and quality of vaccine-in-
duced T-cell responses are shaped and may ultimately be opti-
mized by activation of innate pathways, we performed two
hypothesis-generating analyses. First, we evaluated innate im-
mune response signatures that were associated with vaccine-in-
duced CD8+T-cell magnitude, and second, we compared its
innate activation profile with that of the highly efficacious yellow
fever vaccine YF-17D.
In the signature analyses, we found that serum induction of the
two chemokines, MCP-2 and MCP-1, 24 h postvaccination,
predicted whether or not a subject would develop a measureable
CD8+T-cell response 4 wk postvaccination (Fig. 4 A and B, and
Fig. S4 A and B). Predictive accuracy was increased to nearly
90% by coupling these chemokines with proinflammatory cyto-
kines (Fig. 4 A and B, and Fig. S4 A and B). Although additional
studies are required to confirm this result, a role for these che-
mokines in CD8+T-cell responses is supported by the strong T-
cell chemoattractant function they exhibit (47) and the reported
CD8+T-cell adjuvant activity of MCP-1 (48). Furthermore,
there was an indication in our data that subjects with the highest
CD8+T-cell responses were those who either strongly down-
regulated RANTES or up-regulated IL-28A (Fig. 4C,and Fig. S4
C and D). One hypothesis is that strong down-regulation of se-
rum RANTES postvaccination may indicate increased uptake by
migrating inflammatory cells, but increased serum IL-28A may
indicate an immunogenic role for the antiviral activities of this
cytokine (49, 50). The transcriptional CD8+signature analysis
also revealed many genes exhibiting responses to MRKAd5/HIV
at the 72-h timepoint that were significantly associated with
CD8+T-cell response magnitudes (Fig. 5 A and B), including
several that are nearest neighbors of CD8+T-cell response-as-
sociated module genes (Fig. 5C). One compelling component of
the gene signature was EAT-2 (Fig. 5B), which was recently
reported to enhance the frequency of vaccine induced T cells
when encoded in an Ad5 vector (30). Despite the striking dif-
ferences in innate response kinetics between the vaccines, we
also tested whether the CD8+signature for MRKAd5/HIV was
associated with CD8+T-cell response magnitudes for YF-17D.
Reanalysis of the published YF-17D dataset (19) identified two
genes, CRIP3 and NPB, with induction patterns that were asso-
ciated with the CD8+T-cell responses of both MRKAd5/HIV
and YF-17D (Fig. 6E). Roles for both of these genes in vaccine
mechanisms are plausible, given the function of CRIP3 in thymic
cellularity (51) and the high expression levels of NPB in lym-
phoid tissues (52).
In the comparative analysis, we found a striking difference in
the temporal innate immune activation profile of MRKAd5/HIV
and YF-17D (Fig. 6A) that is consistent with, but not completely
explained by, the dosage and pharmacokinetics of the vaccines:
although replication-incompetent MRKAd5/HIV is present at
the highest levels immediately after injection (53), live-attenu-
ated YF-17D takes 5–7 d to reach maximal titers in the host (31).
Unexpectedly, the innate immune response to MRKAd5/HIV
†Sarkar S, et al, Keystone Symposia on Molecular and Cellular Biology, October 27–
November 1, 2010, Seattle, WA.
| www.pnas.org/cgi/doi/10.1073/pnas.1208972109 Zak et al.
was much more extensive than that induced by YF-17D (Fig. 6 A
and B). Although the peak response to MRKAd5/HIV involved
induction of over a dozen functional modules, the peak response
to YF-17D involved induction of only two. In vitro stimulation
experiments with the two vaccines identified which of the innate
immune response differences observed in vivo are because of
cell-intrinsic differences in innate immune signaling (Fig. 6 C and
D). MRKAd5 preferentially induced a transcriptional regulatory
network involving three transcription factors, IRF1, KLF4, and
STAT5A, in vivo and in vitro (Fig. 6 C and D). Activation of the
IRF1 network may play a part in the strong CD8+T-cell immu-
nogenicity of MRKAd5/HIV, given the role of this transcription
factor in regulating MHC class I presentation and CD8+T-cell
responses (54). Interestingly, a number of the MRKAd5-specific
genes are also associated with immunoregulatory functions.
Among these, KLF4 promotes anti-inflammatory “M2” and
inhibits proinflammatory “M1” macrophage polarization (35),
and PDCD1LG2 and IDO1 suppress T-cell activation through
a variety of mechanisms (55, 56). Further study will determine
whether pharmacological inhibition of these molecules will lead
to enhanced Ad5-induced T-cell functionality. Finally, direct
comparison between MRKAd5- and YF-17D–induced innate im-
mune responses in vitro revealed additional functionally relevant
differences between the vaccines, suggesting that profiling of local
responses may complement measurements of systemic responses
obtained by from blood cell transcriptomes.
Our comprehensive analysis of the immediate systemic re-
sponse following vaccination with MRKAd5 provides fresh un-
derstanding of vaccine-induced innate immune activation, how it
is modulated by preexisting immunity, and how it relates to the
subsequent adaptive immune responses. Such understanding will
play an important role in the development of a highly efficacious
Materials and Methods
Subjects. We enrolled 35 healthy HIV-1-uninfected adults [median age 37 y
(range 20–50); 21 female; 28 Caucasian, 7 African American]. Eleven subjects
were Ad5Positiveand 24 were Ad5Neg(Fig. S2). Microarrays were run on five
males and five females [median age 33 y (range 22–43); nine Caucasians].
Female participants were counseled to use birth control and avoid preg-
nancy during the study. All participants provided written informed consent,
and each of the four United States trial sites obtained approval for the study
through their institutional review boards.
Study Design. HVTN 071 was a Phase 1b multicenter, open-label trial
(ClinicalTrials.gov #NCT00486408). At the start of the trial (day 0), all volunteers
were intramuscularly vaccinated with 1.5 × 1010genomes of the previously-
described MRKAd5/HIV vaccine (3); 24 received a second vaccination at day 28
before all MRKAd5/HIV vaccinations were suspended (4). Blood was collected
immediately before vaccination and at 4–6, 24, 72, and 168 h postvaccination
for 11 individuals. Serum was obtained from an additional 24 individuals at the
prevaccination and 24-h time points.
Microarrays and Quantitative Real-Time PCR. PBMC were isolated from blood
as previously described (57). RNA was extracted from PBMC using the RNeasy
Protect Cell protocol (Qiagen). Before labeling, the integrity of samples was
checked using an Agilent 2100 Bioanalyzer.
Affymetrix Exon Arrays. RNA expression for the in vivo study and one in vitro
study was analyzed using the Human Exon ST 1.0 microarray platform (Affy-
metrix) essentially as described in ref. 17. Forinvivo profiling, 50 samples were
analyzed: five time points (prevaccination and 6, 24, 72 and 168 h post-
vaccination) for 10 subjects (three Ad5-seropositive and seven Ad5-seroneg-
ative). For in vitro profiling, eight samples were analyzed: PBMC obtained
from four Ad5 seronegative donors stimulated for 24 h with MRKAd5 empty
vector at 20,000 particles per cell and GTS buffer mock control.
Agilent3′Arrays. RNAfromtheMRKAd5vs.YF-17D comparativeinvitrostudy
was analyzed using the Agilent SurePrint G3 Human GE 8 × 60K microarray
platform (Agilent Technologies), essentially as described in ref. 17. For
comparative in vitro profiling, 16 samples were analyzed: PBMC obtained
from four Ad5 seronegative donors stimulated for 24 h with MRKAd5 empty
vector at 60,000 particles per cell and GTS buffer mock control or YF-17D at
30 particles per cell and DMEM 2% (vol/vol) FCS buffer mock control.
Microarray data analysis procedures are described in the SI Materials and
Methods. Quantitative real-time PCR was performed as described in ref. 17.
Multiplex Cytokine Analysis. Serum cytokine analysis was performed using the
Lincoplex High Sensitivity kit (Millipore Cat# HSCYTO-60SPMX13) and regular
sensitivity kits (Millipore Cat# MPXHCYTO60KPMX42, MPXHCYP2:00 PMX23,
and MPXHCYP3-PMX9), according to the manufacturer’s instructions (Linco/
Millipore), and samples were analyzed on a Luminex 200 (Luminex). PBMC
supernatants were assayed similarly for a subset of the analytes. Data were
analyzed using a custom in-house export and quality control program in
conjunction with the Ruminex program (58).
Enumeration and Phenotyping of Fresh Blood Cell Populations. Trucount tubes
(BD) were stained with CD45-ΑPC, CD14-PE, CD3-PerCp, and CD8-FITC (all
from BD). For further phenotyping, whole blood was diluted 1:10 in Pharm-
lyse RBC lysis buffer (BD), incubated 10 min at room temperature and
centrifuged at 750 × g for 5 min. RBC lysis was repeated and cells were
resuspended in cold PBS; 2 × 106to 6 × 106cells were stained with Aqua Vi-
ability Dye (Invitrogen), followed by one of three antibody mixtures (details
provided upon request). Cells were fixed with PBS containing 1% para-
formaldehyde and stored at 4 °C until analysis by flow cytometry. All samples
from one volunteer were analyzed together within 7 d of staining.
Statistical Analysis of Cell Concentration and Multiplex Cytokine Data. Analysis
of analytes altered after vaccination was performed by running a mixed
model with normally distributed errors and an unstructured covariance
matrix, with cytokine or cell concentration as the dependent variable and
categorical sampling time as the independent variable, allowing random
intercepts for each participant for each immunization series and each cy-
tokine or cell type. Sex and age were included as possible confounders. P
values for time were adjusted within a given vaccine series using the
Hochberg method (59). Methods for identification of serum analyte profiles
that were predictive of CD8+T-cell responses are provided in the SI Materials
In Vitro PBMC Stimulations. One-millionPBMCfromhealthyAd5-seronegative
individuals were stimulated with MRKAd5 empty vector or GTS buffer mock
control (60) or YF-17D or DMEM with 2% (vol/vol) FCS mock control [courtesy
of Charles Rice, Rockefeller University, New York (61–63)] in RPMI containing
10% (vol/vol) FCS, penicillin and streptomycin at a range of multiplicities of
infection. After 24 h, cell-culture supernatants were harvested for multiplex
cytokine analysis and cells were frozen in RLT buffer (Qiagen) containing
β-mercaptoethanol, for RNA purification and microarray analysis.
ACKNOWLEDGMENTS. We thank the HVTN 071 Protocol Team, Michael
Robertson, Youyi Fong, Greg Spies, Jennifer Vogt, Jim Simandl, Don Carter,
Stephen Voght, Lamar Fleming, Marcus Altfeld, and Galit Alter for their
assistance, and the James B. Pendleton Charitable Trust for their generous
equipment donation. This work was supported by National Institutes of Health
Grants UM1 AI068618 and U01 AI069481 (to M.J.M.); the Bill and Melinda
Gates Foundation Collaboration for AIDS Vaccine Discovery Grant 38645 (to
M.J.M.); and National Institutes of Health Grant T32 AI007140 (to E.A.-N.).
1. Rerks-Ngarm S, et al.; MOPH-TAVEG Investigators (2009) Vaccination with ALVAC and
AIDSVAX to prevent HIV-1 infection in Thailand. N Engl J Med 361(23):2209–2220.
2. Pulendran B, Ahmed R (2011) Immunological mechanisms of vaccination. Nat
3. McElrath MJ, et al.; Step Study Protocol Team (2008) HIV-1 vaccine-induced immunity
in the test-of-concept Step Study: A case-cohort analysis. Lancet 372(9653):1894–
4. Buchbinder SP, et al.; Step Study Protocol Team (2008) Efficacy assessment of a cell-
mediated immunity HIV-1 vaccine (the Step Study): A double-blind, randomised,
placebo-controlled, test-of-concept trial. Lancet 372(9653):1881–1893.
5. Gray GE, et al.; HVTN 503/Phambili study team (2011) Safety and efficacy of the HVTN
503/Phambili study of a clade-B-based HIV-1 vaccine in South Africa: A double-blind,
randomised, placebo-controlled test-of-concept phase 2b study. Lancet Infect Dis 11
6. Rolland M, et al. (2011) Genetic impact of vaccination on breakthrough HIV-1
sequences from the STEP trial. Nat Med 17(3):366–371.
7. Hutnick NA, et al. (2009) Baseline Ad5 serostatus does not predict Ad5 HIV vaccine-
induced expansion of adenovirus-specific CD4+ T cells. Nat Med 15(8):876–878.
8. O’Brien KL, et al. (2009) Adenovirus-specific immunity after immunization with an
Ad5 HIV-1 vaccine candidate in humans. Nat Med 15(8):873–875.
Zak et al. PNAS
| Published online November 14, 2012
9. Qureshi H, et al. (2012) Low-dose penile SIVmac251 exposure of rhesus macaques Download full-text
infected with adenovirus type 5 (Ad5) and then immunized with a replication-
defective Ad5-based SIV gag/pol/nef vaccine recapitulates the results of the phase IIb
step trial of a similar HIV-1 vaccine. J Virol 86(4):2239–2250.
10. Sullivan NJ, et al. (2011) CD8+ cellular immunity mediates rAd5 vaccine protection
against Ebola virus infection of nonhuman primates. Nat Med 17(9):1128–1131.
11. Mu J, et al. (2009) Immunization with a bivalent adenovirus-vectored tuberculosis
vaccine provides markedly improved protection over its monovalent counterpart
against pulmonary tuberculosis. Mol Ther 17(6):1093–1100.
12. Andersen-Nissen E, Heit A, McElrath MJ (2012) Profiling immunity to HIV vaccines
with systems biology. Curr Opin HIV AIDS 7(1):32–37.
13. Zak DE, Aderem A (2012) Overcoming limitations in the systems vaccinology
approach: A pathway for accelerated HIV vaccine development. Curr Opin HIV AIDS 7
14. Gilchrist M, et al. (2006) Systems biology approaches identify ATF3 as a negative
regulator of Toll-like receptor 4. Nature 441(7090):173–178.
15. Ramsey SA, et al. (2008) Uncovering a macrophage transcriptional program by
integrating evidence from motif scanning and expression dynamics. PLOS Comput
16. Litvak V, et al. (2009) Function of C/EBPdelta in a regulatory circuit that discriminates
between transient and persistent TLR4-induced signals. Nat Immunol 10(4):437–443.
17. Zak DE, et al. (2011) Systems analysis identifies an essential role for SHANK-associated
RH domain-interacting protein (SHARPIN) in macrophage Toll-like receptor 2 (TLR2)
responses. Proc Natl Acad Sci USA 108(28):11536–11541.
18. Gaucher D, et al. (2008) Yellow fever vaccine induces integrated multilineage and
polyfunctional immune responses. J Exp Med 205(13):3119–3131.
19. Querec TD, et al. (2009) Systems biology approach predicts immunogenicity of the
yellow fever vaccine in humans. Nat Immunol 10(1):116–125.
20. Nakaya HI, et al. (2011) Systems biology of vaccination for seasonal influenza in
humans. Nat Immunol 12(8):786–795.
21. Chaussabel D, et al. (2008) A modular analysis framework for blood genomics studies:
Application to systemic lupus erythematosus. Immunity 29(1):150–164.
22. Banchereau R, et al. (2012) Host immune transcriptional profiles reflect the variability
in clinical disease manifestations in patients with Staphylococcus aureus infections.
PLoS ONE 7(4):e34390.
23. Skinner JA, et al. (2011) P01.06: Whole blood transcriptional monitoring of acute
HIV-1 infection reveals differential signatures of host immune activation. AIDS Res
Hum Retroviruses 27(10):A1–A148.
24. Sangro B, et al. (2004) Phase I trial of intratumoral injection of an adenovirus
encoding interleukin-12 for advanced digestive tumors. J Clin Oncol 22(8):1389–1397.
25. Shi C, Pamer EG (2011) Monocyte recruitment during infection and inflammation. Nat
Rev Immunol 11(11):762–774.
26. Wu Z, et al. (2008) Memory T cell RNA rearrangement programmed by
heterogeneous nuclear ribonucleoprotein hnRNPLL. Immunity 29(6):863–875.
27. Cline MS, et al. (2007) Integration of biological networks and gene expression data
using Cytoscape. Nat Protoc 2(10):2366–2382.
28. Mathew PA, et al. (2004) The LLT1 receptor induces IFN-gamma production by human
natural killer cells. Mol Immunol 40(16):1157–1163.
29. Wang N, et al. (2010) Cutting edge: The adapters EAT-2A and -2B are positive
regulators of CD244- and CD84-dependent NK cell functions in the C57BL/6 mouse. J
30. Aldhamen YA, et al. (2011) Expression of the SLAM family of receptors adapter EAT-2
as a novel strategy for enhancing beneficial immune responses to vaccine antigens. J
31. Pulendran B (2009) Learning immunology from the yellow fever vaccine: Innate
immunity to systems vaccinology. Nat Rev Immunol 9(10):741–747.
32. Querec T, et al. (2006) Yellow fever vaccine YF-17D activates multiple dendritic cell
subsets via TLR2, 7, 8, and 9 to stimulate polyvalent immunity. J Exp Med 203(2):
33. Cruz-Munoz ME, Dong Z, Shi X, Zhang S, Veillette A (2009) Influence of CRACC,
a SLAM family receptor coupled to the adaptor EAT-2, on natural killer cell function.
Nat Immunol 10(3):297–305.
34. Mellor A (2005) Indoleamine 2,3 dioxygenase and regulation of T cell immunity.
Biochem Biophys Res Commun 338(1):20–24.
35. Liao X, et al. (2011) Krüppel-like factor 4 regulates macrophage polarization. J Clin
36. Grosse J, et al. (2006) Mutation of mouse Mayp/Pstpip2 causes a macrophage
autoinflammatory disease. Blood 107(8):3350–3358.
37. Shi L, Perin JC, Leipzig J, Zhang Z, Sullivan KE (2011) Genome-wide analysis of
interferon regulatory factor I binding in primary human monocytes. Gene 487(1):
38. Chen X, et al. (2008) Integration of external signaling pathways with the core
transcriptional network in embryonic stem cells. Cell 133(6):1106–1117.
39. Liao W, et al. (2008) Priming for T helper type 2 differentiation by interleukin 2-
mediated induction of interleukin 4 receptor alpha-chain expression. Nat Immunol 9
40. Perreau M, Pantaleo G, Kremer EJ (2008) Activation of a dendritic cell-T cell axis by
Ad5 immune complexes creates an improved environment for replication of HIV in T
cells. J Exp Med 205(12):2717–2725.
41. Corey L, McElrath MJ, Kublin JG (2009) Post-step modifications for research on HIV
vaccines. AIDS 23(1):3–8.
42. Benlahrech A, et al. (2009) Adenovirus vector vaccination induces expansion of
memory CD4 T cells with a mucosal homing phenotype that are readily susceptible to
HIV-1. Proc Natl Acad Sci USA 106(47):19940–19945.
43. Barnabas RV, et al.; NIAID HIV Vaccine Trials Network (2011) Impact of herpes simplex
virus type 2 on HIV-1 acquisition and progression in an HIV vaccine trial (the Step
study). J Acquir Immune Defic Syndr 57(3):238–244.
44. Curlin ME, et al. (2011) Serological immunity to adenovirus serotype 5 is not
associated with risk of HIV infection: A case-control study. AIDS 25(2):153–158.
45. Frahm N, et al. (2012) Human adenovirus-specific T cells modulate HIV-specific T cell
responses to an Ad5-vectored HIV-1 vaccine. J Clin Invest 122(1):359–367.
46. Haegel-Kronenberger H, et al. (2004) Inhibition of costimulation allows for repeated
systemic administration of adenoviral vector in rhesus monkeys. Gene Ther 11(3):
47. Loetscher P, Seitz M, Clark-Lewis I, Baggiolini M, Moser B (1994) Monocyte chemotactic
proteins MCP-1, MCP-2, and MCP-3 are major attractants for human CD4+ and CD8+
T lymphocytes. FASEB J 8(13):1055–1060.
48. Kim JJ, et al. (1998) CD8 positive T cells influence antigen-specific immune responses
through the expression of chemokines. J Clin Invest 102(6):1112–1124.
49. Ouyang W, Rutz S, Crellin NK, Valdez PA, Hymowitz SG (2011) Regulation and
functions of the IL-10 family of cytokines in inflammation and disease. Annu Rev
50. Jewell NA, et al. (2010) Lambda interferon is the predominant interferon induced by
influenza A virus infection in vivo. J Virol 84(21):11515–11522.
51. Kirchner J, Forbush KA, Bevan MJ (2001) Identification and characterization of
thymus LIM protein: Targeted disruption reduces thymus cellularity. Mol Cell Biol 21
52. Fujii R, et al. (2002) Identification of a neuropeptide modified with bromine as an
endogenous ligand for GPR7. J Biol Chem 277(37):34010–34016.
53. Sheets RL, et al. (2008) Biodistribution and toxicological safety of adenovirus type 5
and type 35 vectored vaccines against human immunodeficiency virus-1 (HIV-1),
Ebola, or Marburg are similar despite differing adenovirus serotype vector,
manufacturer’s construct, or gene inserts. J Immunotoxicol 5(3):315–335.
54. White LC, et al. (1996) Regulation of LMP2 and TAP1 genes by IRF-1 explains the
paucity of CD8+ T cells in IRF-1-/- mice. Immunity 5(4):365–376.
55. Jin HT, Ahmed R, Okazaki T (2011) Role of PD-1 in regulating T-cell immunity. Curr
Top Microbiol Immunol 350:17–37.
56. Mellor AL, Munn DH (2004) IDO expression by dendritic cells: Tolerance and
tryptophan catabolism. Nat Rev Immunol 4(10):762–774.
57. Bull M, et al. (2007) Defining blood processing parameters for optimal detection of
cryopreserved antigen-specific responses for HIV vaccine trials. J Immunol Methods
58. Defawe OD, et al. (2012) Optimization and qualification of a multiplex bead array to
assess cytokine and chemokine production by vaccine-specific cells. J Immunol
59. Hochberg Y (1988) A sharper Bonferroni procedure for multiple tests of significance.
60. Shiver JW, et al. (2002) Replication-incompetent adenoviral vaccine vector elicits
effective anti-immunodeficiency-virus immunity. Nature 415(6869):331–335.
61. Rice CM, Grakoui A, Galler R, Chambers TJ (1989) Transcription of infectious yellow
fever RNA from full-length cDNA templates produced by in vitro ligation. New Biol 1
62. Bredenbeek PJ, et al. (2003) A stable full-length yellow fever virus cDNA clone and the
role of conserved RNA elements in flavivirus replication. J Gen Virol 84(Pt 5):
63. Franco D, et al. (2010) Evaluation of yellow fever virus 17D strain as a new vector for
HIV-1 vaccine development. Vaccine 28(35):5676–5685.
64. Berry MP, et al. (2010) An interferon-inducible neutrophil-driven blood transcriptional
signature in human tuberculosis. Nature 466(7309):973–977.
65. Lynn DJ, et al. (2008) InnateDB: Facilitating systems-level analyses of the mammalian
innate immune response. Mol Syst Biol 4:218.
66. Chon SY, Hassanain HH, Pine R, Gupta SL (1995) Involvement of two regulatory
elements in interferon-gamma-regulated expression of human indoleamine 2,3-
dioxygenase gene. J Interferon Cytokine Res 15(6):517–526.
67. Ramsauer K, et al. (2007) Distinct modes of action applied by transcription factors
STAT1 and IRF1 to initiate transcription of the IFN-gamma-inducible gbp2 gene. Proc
Natl Acad Sci USA 104(8):2849–2854.
68. Conte E, et al. (2003) Involvement of interferon regulatory factor-1 in monocyte CD95
expression and CD95-mediated apoptosis. Cell Death Differ 10(5):615–617.
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