Conditions: Influenza
Keywords: Fluad, MF59, Fluzone, HDFlu, Influenza, Vaccine
Allocation: RANDOMIZED | Intervention model: PARALLEL
Enrollment: 241
Healthy volunteers: yes

Brief summary:
The purpose of this research study is to better understand the immune response to the Adjuvanted Subunit flu vaccine (MF59) and the High Dose flu vaccine (HDFlu) in people 65 years of age and older. The research team will be studying why immune response diminishes as people get older in both men and women. The ultimate goal is to understand how flu immunity develops after vaccination. This information may lead to the development of more effective flu vaccines in the future.

Detailed description:
The overall goal of this proposal is to determine how vaccine type, sex, and gene expression influence both innate and T helper cell immune responses using systems biology and bioinformatics as tools to comprehensively assess the human transcriptome. We will evaluate sex-dependent immune responses to two unique influenza vaccines in a population of older adults; the recently FDA-licensed MF59-adjuvanted influenza subunit vaccine and the high-dose split virion influenza virus vaccine. The influence of sex on immune response to vaccination has been observed across multiple vaccines (including standard dose influenza vaccines, but the mechanisms are unknown, it affects all age groups regardless of hormonal status, and existing studies focus almost exclusively on humoral immune responses. Relatively little is known about the effect of sex on innate and T helper responses following vaccination and we are unaware of any studies evaluating the effect of sex on immune responses to adjuvanted influenza vaccine. The presence of adjuvant (MF59Flu) or higher antigen (Ag) dose leads to greater immunogenicity through mechanisms that have not been fully deciphered and are likely to be different. Further, a direct comparison of innate and T helper immune responses between adjuvanted and high dose influenza vaccines has not been reported.

The study design will include 200 generally healthy individuals (ages ≥65) who meet all inclusion criteria. 100 subjects will receive each vaccine with equal sex representation in each subgroup (a factorial design for sex by vaccine type). Subjects will undergo venipuncture for blood samples (\~100 mL each, sufficient for the proposed assays) before vaccination and at three timepoints after vaccination (Day 1, Day 8, Day 28).

The clinical characterization of our study subjects will include demographic information, height, weight, BMI, waist circumference, medications, and medical conditions that do not meet exclusion criteria (see Protection of Human Subjects). We will also quantify blood leukocyte populations (CBC, WBC differential).

Immunosenescence and cytomegalovirus (CMV) infection can affect influenza vaccine-induced immune responses. We will evaluate whether CMV seropositivity or other measures of immunosenescence are associated with immune response and whether they interact with vaccine type/sex.

We will monitor/characterize transcriptional changes (mRNA and miRNA) as well as measures of immune function (cytokine secretion, leukocyte surface phenotype, hemagglutination inhibiting antibody titer, and memory B cell ELISPOT) at each time point.

Arms:
arm 1: Subjects receive a single dose of the Fluad influenza vaccine. arm 2: Subjects receive a single dose of the Fluzone High-Dose influenza vaccine.

Interventions: intervention 1: Fluad Vaccine intervention 2: Fluzone High-Dose
Intervention descriptions: intervention 1: FLUAD is an inactivated influenza vaccine indicated for active immunization against influenza. disease caused by influenza virus subtypes A and type B contained in the vaccine. FLUAD is. approved for use in persons 65 years of age and older. intervention 2: FLUZONE® HIGH-DOSE vaccine is indicated for people 65 years of age and older to help prevent influenza disease caused by influenza A and B strains contained in the vaccine.

Protocol document:
SAP_003.pdf: 
Transcriptomic Signatures of Influenza Vaccine 
Responses 
 
NCT03603509  
 
May 19, 2019 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


Analysis Approach. Subjects will be randomly assigned to vaccine type. For all laboratory assays, 
randomized complete or incomplete block designs that include technical replicates and laboratory 
controls will be utilized to ensure sample and assay processing are balanced with respect to collection 
date, vaccine, and sex, keeping specimens from the same subject adjacent to one another. Data will be 
scrutinized to identify potential biases, batch effects, or failed assays. We will normalize mRNA-
sequence data by Conditional Quantile Normalization (CQN), adjusting for library size, gene length, and 
GC content. Our primary aim is to test the hypotheses that vaccine type (Fluad vs Fluzone) is associated 
with the change in immune response as measured by hemagglutination inhibition (HAI) Antibody titer. 
To test this hypothesis, we will use the Wilcoxon Rank Sum test to test for differences between the 
vaccine types in the change from baseline HAI to Day 28 HAI Ab titers. 

Prot_001.pdf: 
 
 
Transcriptomic Signatures of Influenza Vaccine 
Responses 
 
NCT03603509 
 
May 19, 2019 


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IRB Minimal Risk Protocol Template  
 
General Study Information 
 
Principal Investigator: Richard Kennedy, PhD       
 
 
 
 
 
 
 
 
Study Title: Transcriptomic Signatures of Influenza Vaccine Responses 
 
Protocol version number and date:  Version 7, 05/19/2019    
 
IRB #17-010601 
 
Research Question and Aims 
 
Specific aims and Hypotheses: 
 
Specific Aim 1: Identify Innate Cell Transcriptomic Signatures Associated with Sex and Immune 
Response to Influenza Vaccines in the Elderly. To do this we will:  
• Aim 1.1 Test the hypotheses that vaccine type (MF59Flu vs HDFlu) and/or sex are associated with variations 
in innate cell immune outcomes (e.g., APC activation phenotype and cytokine/chemokine secretion).  
• Aim 1.2 Test the hypothesis that the increased Ag dose in HDFlu results in greater activation/suppression of 
the innate genes/genesets previously associated with immune responses to SDFlu. We will also test the 
hypothesis that the MF59 adjuvant results in activation/suppression of additional specific innate genes/genesets 
compared to HDFlu (see Overview and Rationale of Specific Aim 1).  
• Aim 1.3 Test the hypothesis that innate immune cell transcriptomic signatures mediate the association of 
vaccine type (or sex) with innate cell immune outcomes.  
• Aim 1.4 Test the hypothesis that the innate cell immune outcomes and transcriptomic signatures from Aim 1.3 
will predict markers of humoral immunity (HAI Ab and B cell ELISPOT response).  
 
Specific Aim 2: Identify CD4+ T Helper (Th) Cell Transcriptomic Signatures Associated with Sex and 
Immune Response to Influenza Vaccines in the Elderly. To do this we will:  
• Aim 2.1 Test the hypotheses that vaccine type (MF59Flu vs HDFlu) and/or sex are associated with variations 
in Th cell immune outcomes (e.g., Th phenotype and cytokine production).  
• Aim 2.2 Test the hypothesis that the increased Ag dose in HDFlu results in greater activation/suppression of 
the Th cell genes/genesets previously associated with immune responses to SDFlu. We will also test the 
hypothesis that the MF59 adjuvant results in activation/suppression of additional specific genes/genesets in 
CD4+ Th cells compared to HDFlu (see Overview and Rationale of Specific Aim 2).  
• Aim 2.3 Test the hypothesis that Th cell transcriptomic signatures mediate the association of vaccine type (or 
sex) with Th cell immune outcomes.  
• Aim 2.4 Test the hypothesis that the Th cell immune outcomes and transcriptomic signatures from Aim 2.3 
will predict markers of humoral immunity (HAI Ab and B cell ELISPOT response).  
 
Background: 


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Scientific Premise of the Proposed Research. While older populations are at the highest health risk from 
seasonal influenza, these populations have poor immune responses to standard-dose seasonal influenza vaccine 
(SDFlu).1, 2 Our study is focused on two influenza vaccines for use in subjects ≥65 years of age: MF59Flu, an 
adjuvanted subunit vaccine (licensed in late 2015);3 and HDFlu, a split-virus vaccine containing 4x the 
hemagglutinin protein (licensed in 2009).4, 5 These vaccines have demonstrated greater immunogenicity than 
SDFlu in older populations, but little is known about the underlying mechanisms.5-16 For these two vaccines, 
systems biology studies designed to identify the mechanisms for improved immune response have never 
been conducted in older individuals, nor have sex-based differences been adequately studied. The goals of 
this proposed project are to examine inter-individual variations in innate and T helper (Th) responses, and 
identify transcriptomic signatures associated with immune responses (including correlates of protective 
immunity) to influenza vaccines in older individuals.  
 
This is important for the following reasons:  
A. Public Health Importance. Seasonal influenza A is believed to kill between 3,000–49,000 annually,17 
resulting in over 250,000 excess hospitalizations and annual costs of >$90 billion in the US.18 Influenza 
morbidity and mortality increase significantly with age.19, 20 More than 90% of influenza-associated deaths 
occur in individuals ≥65 years of age, and are predominantly associated with influenza A/H3N2.21 During 
the 2014–2015 influenza season, adults age ≥65 years had an influenza-related hospitalization rate of 314 per 
100,000 people—a record high since surveillance began in 2005.19, 20 The efficacy of influenza vaccines is 
diminished in older adults and varies widely (15% to 75%), averaging <50%.1, 2, 22, 23 Considering the 
unprecedented population growth in persons ≥65 years of age and the significant public health impact of 
influenza, it is imperative that influenza vaccine-induced immunity in older adults be better 
understood.24-26 Due to in-creased mortality in the elderly and cognizant of budgetary limitations, we will 
focus on influenza A/H3N2.  
 
B. Knowledge Gap—Immune Response Differences to Adjuvanted vs. High-Dose Influenza Vaccines.  
HDFlu contains 60ug HA per influenza strain, and has been the focus of high-quality studies, including a study 
of 31,989 older individuals (≥ 65 years of age) that demonstrated higher immunogenicity (ratio of GMT 1.8) 
and 24% greater protection against influenza illness than SDFlu (15ug HA/strain/dose).5, 13, 14  
MF59Flu also induces higher immunogenicity and longer persistence of Ab titers than non-adjuvanted SDFlu.6-
12, 15, 27 A recent large study in 7,082 individuals (≥ 65 years of age) demonstrated significantly higher 
immunogenicity (p<0.001, seroconversion and HAI GMT) of MF59Flu vaccine compared to SDFlu 
(particularly against A/H3N2).15 A single small systems biology study comparing MF59Flu and SDFlu in 
immunoimmature children (14- to 24-months-old, n=90) identified significantly higher transcriptional responses 
to MF59Flu and identified early innate response signatures correlated with Day 28 Ab titers.28 These include 
M16 (a module associated with TLR and inflammatory signaling); M11 (a module regulating monocyte 
function); M75 (a module controlling IFN-induced antiviral response); M156 (a module associated with Ab 
secreting cells); and S3 (a module with genes involved in immunoglobulin production). These genesets and 
those identified in other systems biology studies of influenza vaccine response will be evaluated for their 
influence on immune responses to MF59Flu and HDFlu in this proposal.  
 
The underlying immunologic mechanisms for the improved immunogenicity of MF59Flu and HDFlu are 
largely unknown for older adults. Our proposal will answer key questions (as illustrated in Figure 1) 
regarding innate and Th immune responses to these vaccines, the underlying transcriptomic signatures, 
and their impact on humoral immunity in older adults. At a minimum, this proposal will fill the knowledge 


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gap by examining whether the higher immunogenicity of these vaccines is a result of greater 
activation/suppression of genes/genesets (mRNA and miRNA) previously associated with immune responses to 
SDFlu, or additional unsuspected genes/genesets controlling immune responses.  
 
C. Knowledge Gap—Sex-Based Differences in Immune Responses to Influenza Vaccines. Studies of 
Multiple vaccines (influenza, yellow fever, MMR, hepatitis A and B, herpes simplex [HSV] 2, rabies, 
smallpox, and dengue) demonstrate significantly higher Ab responses in females than males, as reported in a 
series of high-quality papers.16, 29-38 Sex-based differences in humoral immune responses are observed prior 
to puberty, during the reproductive years, and after reproductive senescence,29-38 suggesting that sex hormones 
are not the necessary—or sole—mediators of sex differences in humoral immune responses to vaccines.39, 40 
Despite significant evidence of immune response differences between the sexes, most vaccine studies do 
not analyze immune response outcome data by sex.41, 42  
Across a cohort of 556 older (ages 50–64) and 558 younger (ages 18–49) subjects, the SDFlu vaccine induced 
>1.5-fold higher A/H3N2-specific HAI Ab titers in women than men.29 Similarly, a study of SDFlu and HDFlu 
vaccine responses in 414 elderly subjects (ages 65–95) demonstrated higher rates of seroconversion in females 
than in males (p<0.05).31 However, no significant differences in Ab measures were found in cohorts of 494 and 
158 older adults after receiving SDFlu,43, 44 and sex was also not reliably associated with seroprotection 
and/or Ab titers in children and young adults receiving MF59Flu.45 These publications demonstrate in-
consistent—and sometimes conflicting—findings regarding sex-based effects on immune responses to 
influenza vaccine. Further research is needed, which is a stated priority for NIH research.  
Additionally, new vaccines containing the MF59 adjuvant are likely to alter the transcriptomic signatures 
associated with vaccine responses in elderly populations, and potentially alter the influence of sex on those 
responses. The current lack of knowledge is a critical barrier to understanding poor vaccine responses in the 
elderly, and such knowledge is foundational to the future development of new influenza vaccines.  
 
D. Impact of this Work. Given the substantially diminished efficacy of influenza vaccines with age and the 
importance of developing improved influenza vaccines,46 data from our studies could be used to inform directed 
and rational development of next-generation influenza vaccines and/or therapeutics47-57—although the 
development of such a vaccine is not the purpose of this proposal. Age-related immune dysfunction 
(immunosenescence) might be overcome by adjuvant stimulation of innate and/or Th cell-specific genes, which 
may be different in males and females (see Expected Results). For example, a TLR4 agonist GLA-SE has been 
shown to enhance Th1 responses to influenza vaccine in older adults.58 The identification of critical chemokine 
pathways involving CCR5 and its ligands (CCL5) (see Preliminary Data #7), predictive of influenza vaccine-
induced humoral immunity, may support the usage of CCR5 small molecule agonists/antagonists approaches in 
vaccines to modulate inflammatory response and T cell chemotaxis/activation for optimal Ab response.44, 59-62 
 
Study Design and Methods 
 
Methods:   
 
Subject Recruitment, Enrollment and Screening  
 
We will screen and recruit 400 generally healthy males and females ages 65 and older who meet all inclusion 
and exclusion criteria and are willing to receive a flu vaccine.  
 


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We will also screen and recruit 20 generally healthy males and females ages 18-40 as a control group. These 
subjects will only have a baseline blood draw (no flu vaccine administration and no follow-study visits). 
 
Eligible subjects who consent and enroll into the study will undergo a baseline blood draw (up to 108 mL) and 
then be randomly assigned to receive either the MF59 (Fluad) flu vaccine (N=200) or the High Dose (Fluzone) 
flu vaccine (N=200).  Subjects in the control group will undergo the baseline blood draw only (up to 108 mL).  
They will NOT receive a flu vaccine. The following sections describe the remaining study visits for the subjects 
who are ages 65 and older. 
 
Subjects will then be asked to come back for three additional blood draws after their flu vaccination: Day 1, 
Day 8, and Day 28 after vaccination. Each blood draw will be up to 105mL of blood.  
 
Flu vaccination can occur up to 2 weeks after the baseline blood draw. The Day 1 blood draw will occur the 
following day after vaccination. The Day 8 blood draw will occur 7-10 days after the vaccination. The Day 28 
blood draw will occur 25-31 days after vaccination.   
 
During the Baseline visit we will collect patient demographic information, height, weight, BMI, medication 
history, medical information, and information on alcohol and smoking use. Patients will also be offered the 
CDC’s influenza vaccine-specific Vaccine Information Sheet that is routinely provided to Mayo patients. 
 
During the 3 follow-up visits we will collect weight, medication history and medical information. 
 
Subjects will be remunerated $40 for each blood draw visit they complete. 
 
We will also mail subjects, roughly a year after participation, 3 different sleep questionnaires as a way to assess 
sleep hygiene and immune response.  
 
 
 
 
 
Resources: Potential participants will be identified through routine clinical appointments, advertising with 
flyers, brochures, an EMR search to identify mail-merge candidates, and existing lists/databases of individuals 
interested in being contacted about participation in research studies. Subject recruitment and advertisement will 
occur on Mayo campus and at various sites off campus (e.g., retirement centers). Permission will be obtained 
from each location before recruitment begins. We will also use newspaper and social media advertisements.   
 
Based on a 2016 census by the United States Government, there were between 30,447 - 54,748 people 65 years 
of age or older in Olmsted County.  
 
 This project has been funded through the NIH through an R01 award.  
 
The majority of the laboratory assays will be performed in the Vaccine Research Group Laboratory which 
occupies approximately 3,500 square feet on the sixth floor of the Mayo Clinic Guggenheim Research Building. 
Equipment not in this laboratory is readily available in institutional core laboratories and is freely shared. 


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  (1a)  This is a multisite study involving Mayo Clinic and non Mayo Clinic sites. When checked, describe in 
detail the research procedures or activities that will be conducted by Mayo Clinic study staff. 
 
  (1b)  Mayo Clinic study staff will be engaged in research activity at a non Mayo Clinic site.  When checked, 
provide a detailed description of the activity that will be conducted by Mayo Clinic study staff. 
 
Subject Information 
 
Target accrual: 420.  This includes 400 subjects ages 65 and older (over-recruiting by 10% to account for 
subject drop-out and insufficient cell recovery from biospecimens) and 20 younger subjects (18-40 years of age) 
as a control group. 
 
Subject population:  Potential participants will be identified through routine clinical appointments, advertising 
with flyers, brochures, an EMR search to identify mail-merge candidates, and existing lists/databases of 
individuals interested in being contacted about participation in research studies. Subject recruitment and 
advertisement will occur on Mayo campus and at various sites off campus (e.g., retirement centers). Permission 
will be obtained from each location before recruitment begins.   
Inclusion Criteria:  
• Male or female adults ages 18-40 or  65 and older at the time of enrollment 
• Eligible to receive Fluad® (MF59Flu) or Fluzone® (HDFlu) if age 65 or older 
• No history of anaphylactic reaction to gelatin, neomycin, or other vaccine component 
• Not pregnant 
• No immunosuppression or immunodeficiency 
• No acute illness at time of vaccination 
• Determined by medical history and clinical judgment to be eligible for the study, by being generally 
healthy, with no autoimmune or immunosuppressive conditions and having stable current medical 
conditions (subjects with preexisting stable disease, defined as disease not requiring significant change 
in therapy or hospitalization for worsening disease 12 weeks before receipt of study vaccine, will be 
eligible. A change in dose or therapy within a category (e.g., change from one nonsteroidal anti-
inflammatory drug to another) is allowed. A change to a new therapy category (e.g., surgery or addition 
of a new pharmacological class) is only allowed if it is not caused by worsening disease. A change to a 
new therapy category caused by worsening disease is considered significant and therefore ineligible for 
enrollment. 
 
 
 
 
• Patients with diabetes mellitus are eligible for inclusion if they have had a hemoglobin A1c 
measurement of <8.0 within the past 6 months prior to enrollment. These hemoglobin A1c 
measurements are recommended at least twice yearly by the American Diabetes Association (ADA), and 
the target levels here are representative of the goals of the ADA. These hemoglobin A1c levels will 
ensure that these participants have good glycemic control. (American Diabetes Association. American 


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Diabetes Association Position Statement: Standards of Medical Care in Diabetes— 2015. Diabetes Care 
2015;38(Suppl. 1): S1–S94) 
• Able to follow study procedures in the opinion of the investigator 
• Expected to be available for the duration of the study 
• Weighs >110 lbs 
Exclusion Criteria:   
• Known or suspected immunodeficiency or receiving treatment with immunosuppressive therapy 
including cytotoxic agents (e.g., for cancer, HIV, or autoimmune disease).  
• Subjects on corticosteroids will be excluded if ≥ 20mg of Prednisone (or equivalent drug) has been (or 
will be) administered daily for 2 weeks or more. Subjects will be eligible if corticosteroid therapy  has 
been discontinued for at least 30 days. 
• Serious chronic medical conditions including metastatic malignancy, severe chronic obstructive 
pulmonary disease requiring supplemental oxygen, end-stage renal disease with or without dialysis, 
clinically unstable cardiac disease, or any other disorder that, in the investigator’s opinion, precludes the 
subject from participating in the study. Diabetic patients will be excluded if they do not have a 
hemoglobin A1c measurement within the past 6 months or if they had a hemoglobin A1c measurement 
of an A1c >8.0 
• Receipt of any blood products, including immunoglobulin, within 6 months of study enrollment. 
• Current anticoagulant therapy or a history of bleeding diathesis that would contraindicate intramuscular 
(IM) injection. (Note: antiplatelet drugs such as aspirin and clopidogrel are permitted.) 
• Receipt of any vaccines within the past 30 days prior to enrollment 
• Receipt of the current seasonal influenza vaccine other than in this study 
• Acute illness within the last 30 days 
• Blood donation within the last 56 days prior to study enrollment and within 56 days following the last 
study visit 
• Pregnancy, Nursing or trying to conceive at the time of the study or for 28 days following the baseline 
visit 
• Any condition (e.g. allergic reaction, Guillain-Barre Syndrome) that precludes their receipt of the 
influenza vaccine 
• Currently taking antibiotics to treat a serious infection. Preventative use of antibiotics (i.e. oral surgery) 
is not an exclusion criterion.  
• Diagnosis of a cognitive disorder (e.g. Alzheimer’s, Dementia) 
• Anemia 
• Any medical condition that would, in the opinion of the investigator, interfere with the evaluation of the 
study objectives 
 
Visit Schedule for subjects 18-40 
 
 
Baseline 
Visit - Vaccination 
 
Review of 
Eligibility/Ineligibility 
X 


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Demographics 
X 
Study Questionnaire 
X 
Medication Review 
X 
Height/Weight 
X 
Informed Consent 
X 
Blood Draw – 108ml 
Xb,  
                               bFollowing standard practice, subjects will be provided juice, water and cookies after providing their blood sample 
 
Visit Schedule for subjects 65 and older 
 
 
Baseline 
Visit - 
Vaccination 
Post-Vaccination Visit  
 
Day 1 
Day 8 (-1 
day/+2    days) 
Day 28(±3 
days) 
Review of 
Eligibility/Ineligibility 
X 
 
 
 
Demographics 
X 
 
 
 
Study Questionnaire 
X 
 
 
 
Medication Review 
X 
X 
X 
X 
Height 
X 
 
 
 
Weight 
X 
X 
X 
X 
Informed Consent 
X 
 
 
 
Blood Draw – 108ml 
Xb,  
 
 
 
Blood Draw – 105ml 
 
Xb 
Xb 
Xb 
Flu vaccine 
            X 
 
 
 
Adverse Events 
            X 
X 
X 
X 
                              bFollowing standard practice, subjects will be provided juice, water and cookies after providing their blood sample 
 
Blood will either be collected using a blood collection bag or with tubes. The blood bag collection is the 
preferred method.  
 
Blood bag collection 
• Baseline visit: 108 mL collected in the blood bag. 5 mL will be separated into a redtop tube x1 and 3 mL 
into an EDTA tube x1 
• Day 1 visit:  105 mL collected in the blood bag. 5 mL will be separated into a redtop tube x1  
• Day 8 visit:  105 mL collected in the blood bag. 5 mL will be separated into a redtop tube x1 
• Day 28 visit: 105 mL collected in the blood bag. 5 mL will be separated into a redtop tube x1  
 
Biospecimen tube collection 
• Baseline visit: 5 mL redtop tube x1; 10 mL green top sodium heparin tubes x10; 3 mL EDTA tube x1 
• Day 1 visit: 5 mL redtop tube x1; 10 mL green top sodium heparin tubes x10  
• Day 8 visit: 5 mL redtop tube x1; 10 mL green top sodium heparin tubes x10  
• Day 28 visit: 5 mL redtop tube x1; 10 mL green top sodium heparin tubes x10 
 


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Experimental Design: 
 
Below, we briefly describe the assays we will use to monitor/characterize transcriptional changes and specific 
innate immune response variables at three important time points as in-formed by other studies and our 
preliminary data: Baseline (Day 0) and Days 1 and 8 after vaccination. The baseline blood draw allows us to 
ascertain each subject’s pre-existing immune status and provides a baseline upon which to evaluate 
immunologic changes caused by the vaccine.  The Day 1 blood draw allows us to examine early, innate immune 
responses.  The Day 8 sample will be used to characterize plasmablast responses which are directly responsible 
for humoral immunity and the development of protective Ab titers.  The Day 28 blood draw will allow us to 
examine the peak adaptive immune response. 
The assays will be performed either on T- and B-cell depleted PBMCs isolated at each timepoint (for APC im-
munophenotyping, mRNA, and miRNA) or on T- and B-cell depleted PBMCs stimulated in vitro with influenza 
A/H3N2 (for cytokine/chemokine secretion). This will allow us to capture the in vivo and in vitro effects of 
these two vaccines.  
 
The clinical characterization of our study subjects will include demographic information, height, weight, 
medications, and medical conditions that do not meet exclusion criteria. We will also run a CBC to quantify 
blood leukocyte populations. 
 
Secreted Cytokine/Chemokine Mediators. Meso Scale Discovery kits will be used to detect cytokines and 
chemokines in culture supernatants before and 24 hours after in vitro stimulation with influenza A/H3N2.  
Detection of Functional Innate Cell Subsets by Flow Cytometry. Cellular phenotypes of cells (before and 24 
hours after in vitro stimulation with influenza virus) will be characterized in Baseline, Day 1, and Day 8 
PBMCs by flow cytometry.259 We will examine the frequency of immune cell types, and characterize their 
activation by detecting the presence of cell surface markers, co-stimulatory molecules, and activation markers.  
Influenza A/H3N2 Ab Assay. The HAI Ab assay will be performed using standard protocols.262, 359, 369-
373 Given the known issues with some A/H3N2 strains, we will utilize influenza antigen matching the vaccine 
strain and test the antigen for neuraminidase binding to RBCs.374, 375  
CMV Serostatus. CMV Ab titers (IgG) will be measured by multiplex flow-based immunoassay (Bio-Rad). 
B Cell ELISPOT. Influenza-specific Ab-secreting cells/plasmablasts and memory IgG-like B cells will be 
quantified in PBMCs using ELISPOTPLUS for Human IgG kits (Mabtech) with (for memory B cells) or 
without (plasmablasts) R848/IL-2 pre-stimulation.262, 361  
mRNA Transcriptomics.141 mRNASeq will be performed in Mayo Clinic’s Medical Genome Facility on 
RNA samples extracted from PBMCs harvested at Baseline, Day 1 and Day 8. RNA libraries will be 
multiplexed with six samples per lane. Flow cells will be sequenced as 51x2 paired-end reads on an Illumina 
HiSeq. Base-calling will be performed using Illumina’s RTA version 1.17.21.3. We will use StringTie376, 377 
to process the resulting BAM files and sQTLseekeR378 to evaluate alternately spliced transcripts.  
miRNA Sequencing. RNA libraries will be prepared with RNA extracted from Baseline, Day 1, and Day 8-
harvested PBMCs (NEBNext Multiplex Small RNA Kit, New England Biolabs). Following multiplex adapter 
ligation (24 samples/lane) and library enrichment by PCR, equimolar amounts of each library will be pooled, 
purified, and loaded onto flow cells for sequencing (Illumina HiSeq 2000, Illumina cBot and cBot Paired end 
cluster kit version 3). Bioinformatics analysis will use adapter trimmed reads and miRDeep2.379 
 
Research Activity 


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Check all that apply and complete the appropriate sections as instructed.  
  
1. 
  Drug & Device:  Drugs for which an investigational new drug application is not required. Device for 
which (i) an investigational device exemption application is not required; or the medical device is 
cleared/approved for marketing and being used in accordance with its cleared/approved labeling. (Specify in 
the Methods section) 
 
2. 
  Blood:  Collection of blood samples by finger stick, heel stick, ear stick, or venipuncture.  
 
3. 
  Biological specimens other than blood:  Prospective collection of human biological specimens by 
noninvasive means that may include: urine, sweat, saliva, buccal scraping, oral/anal/vaginal swab, sputum, 
hair and nail clippings, etc. 
 
4. 
  Tests & Procedures:  Collection of data through noninvasive tests and procedures routinely employed 
in clinical practice that may include: MRI, surface EEG, echo, ultrasound, moderate exercise, muscular 
strength & flexibility testing, biometrics, cognition testing, eye exam, etc.  (Specify in the Methods section) 
 
5. 
  Data (medical record, images, or specimens):  Research involving use of existing and/or prospectively 
collected data. 
 
6. 
  Digital Record:  Collection of electronic data from voice, video, digital, or image recording. (Specify in 
the Methods section) 
 
7. 
  Survey, Interview, Focus Group:  Research on individual or group characteristics or behavior, survey, 
interview, oral history, focus group, program evaluation, etc.  (Specify in the Methods section) 
 
 
 NIH has issued a Certificate of Confidentiality (COC).  When checked, provide the institution and 
investigator named on the COC and explain why one was requested. ________________________ 
 
Biospecimens – Categories 2 and 3 
 
(2)  Collection of blood samples.  
a. From healthy, non-pregnant, adult subjects who weigh at least 110 pounds. For a minimal risk 
application, the amount of blood drawn from these subjects may not exceed 550ml in an 8 week period 
and collection may not occur more frequently than 2 times per week. 
 
Volume per blood draw (Baseline only): ___108__ml   
Volume per blood draw (Follow-up visits): ___105__ml   
 
Frequency of blood draw: Four blood draws total. Blood draw #1 can occur the day of or up to 2 weeks prior to 
receiving the flu vaccination.  Blood draw #2 will occur the following day after vaccination. Blood draw #3 will 
occur 7-9 days after the vaccination. Blood draw #4 will occur 25-31 days after vaccination.   


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b. From other adults and children considering age, weight, and health of subject. For a minimal risk 
application, the amount of blood drawn from these subjects may not exceed the lesser of 50 ml or 3 ml 
per kg in an 8 week period, and collection may not occur more frequently than 2 times per week.   
Volume per blood draw: _____ml 
Frequency of blood draw (e.g. single draw, time(s) per week, per year, etc.) ___________  
 
(3)  Prospective collection of biological specimens other than blood: ______________________________ 
 
Review of medical records, images, specimens – Category 5 
 
For review of existing data: provide a date range or an end date for when the data was generated. The end date 
can be the date this application was submitted to the IRB.   
Date Range:  
 
Check all that apply (data includes medical records, images, specimens).  
 
  (5a)  Only data that exists before the IRB submission date will be collected.   
 
 
  (5b)  The study involves data that exist at the time of IRB submission and data that will be generated after 
IRB submission. Include this activity in the Methods section.  
Examples 
• The study plans to conduct a retrospective chart review and ask subjects to complete a questionnaire.  
• The study plans to include subjects previously diagnosed with a specific disease and add newly 
diagnosed subjects in the future.  
 
  (5c)  The study will use data that have been collected under another IRB protocol. Include in the Methods 
section and enter the IRB number from which the research material will be obtained. When appropriate, note 
when subjects have provided consent for future use of their data and/or specimens as described in this protocol.  
 
 
Enter one IRB number per line, add more lines as needed 
 
 Data    
 Specimens  
 Data & Specimens  ______________________________________ 
 
 Data    
 Specimens  
 Data & Specimens  ______________________________________ 
 
 Data    
 Specimens  
 Data & Specimens  ______________________________________ 
 
  (5d)  This study will obtain data generated from other sources. Examples may include receiving data from 
participating sites or an external collaborator, accessing an external database or registry, etc.  Explain the source 
and how the data will be used in the Methods section.  
 


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  (6)  Video audio recording: 
 
HIPAA Identifiers and Protected Health Information (PHI) 
Protected health information is medical data that can be linked to the subject directly or through a combination 
of indirect identifiers.  
 
Recording identifiers (including a code) during the conduct of the study allows you to return to the medical 
record or data source to delete duplicate subjects, check a missing or questionable entry, add new data points, 
etc. De-identified data is medical information that has been stripped of all HIPAA identifiers so that it cannot be 
linked back to the subject. De-identified data is rarely used in the conduct of a research study involving a chart 
review.   
 
Review the list of subject identifiers below and, if applicable, check the box next to each HIPAA identifier 
being recorded at the time of data collection or abstraction.  Identifiers apply to any subject enrolled in the 
study including Mayo Clinic staff, patients and their relatives and household members.  
 
Internal refers to the subject’s identifier that will be recorded at Mayo Clinic by the study staff. 
External refers to the subject’s identifier that will be shared outside of Mayo Clinic. 
 
Check all that apply: 
INTERNAL 
EXTERNAL 
Name 
X 
 
Mayo Clinic medical record or patient registration number, lab accession, 
specimen or radiologic image number  
X 
 
Subject ID, subject code or any other person-specific unique identifying 
number, characteristic or code that can link the subject to their medical data   
X 
 
Dates: All elements of dates [month, day, and year] directly related to an 
individual, their birth date, date of death, date of diagnosis, etc.   
Note: Recording a year only is not a unique identifier.  
X 
 
Social Security number 
X 
 
Medical device identifiers and serial numbers 
 
 
Biometric identifiers, including finger and voice prints, full face photographic 
images and any comparable images 
 
 
Web Universal Resource Locators (URLs), Internet Protocol (IP) address 
numbers, email address 
 
 
Street address, city, county, precinct, zip code, and their equivalent geocodes 
X 
 
Phone or fax numbers 
X 
 
Account, member, certificate or professional license numbers, health 
beneficiary numbers 
 
 
Vehicle identifiers and serial numbers, including license plate numbers 
 
 
Check ‘None’ when none of the identifiers listed above will be recorded, 
maintained, or shared during the conduct of this study.  (exempt category 4) 
 None 
 None 
Data Analysis 
Data Analysis Plan:  


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The planned analyses for Aims 1 and 2 will follow the same strategies, so for Aim 2 analyses we point out the 
differences needed to adapt the analyses for Aim 2.  
Aim 2.1 Test the hypotheses that vaccine type (MF59Flu vs HDFlu) or sex is associated with variations in 
Th cell immune outcomes: The analysis plans for Aim 1.1 will be followed, but using the Th phenotype and 
cytokine production outcomes, evaluating change from Baseline to Days 8 and 28.  
Aim 2.2 Test the hypothesis that the expression of specific genes is associated with differences in Th cell 
immune outcomes: Analyses will parallel those for Aim 1.2, but use differential Th cell-specific gene and 
miRNA expression (change from Baseline to Days 8 and 28) as independent variables to evaluate for their 
association with change in Th cell immune responses.  
Aim 2.3 Test the hypothesis that Th cell transcriptomic signatures mediate the association of vaccine type 
(or sex) with Th cell immune outcomes: Analyses will parallel those described in Specific Aim 1.3. 
Aim 2.4 Test the hypothesis that Th cell immune outcomes and transcriptomic signatures from Aim 2.3 
will predict markers of humoral immunity (HAI Ab and B cell ELISPOT response): The analysis plans 
closely follow the mediation analyses described in Aim 1.4. We will test the hypothesis that Th immune 
response and their associated genes (Aim 2.3), predict humoral immunity (HAI Ab titer and B cell ELISPOT 
response) at Day 28. We will also test the hypothesis that additional genes mediate humoral immunity 
independently of Th responses. To achieve this, we will treat Th responses as adjusting covariates, using the 
regression model: By adjusting for Th response, we seek genes/genesets that are only associated with humoral 
immune response.  
 
Power: See Specific Aim 1. We anticipate stronger signals in the transcriptomic data from purified CD4+ T 
cells compared to PBMCs. The increased signal to noise ratio will allow for detection of smaller effects.  
 
Potential Limitations and Alternative Strategies for Aims 1 and 2.  
False-positive associations will be controlled by our clearly defined analysis plan, the use of false-discovery 
rates to assess significance, and geneset/pathway analysis to maximize power and minimize false discovery.  
Statistical power to detect effects is driven by our sample size of 400 subjects, which is larger than most 
systems biology studies, including our prior study of SDFlu in older subjects where we and others were able to 
successfully identify transcriptomic signatures of vaccine response.16, 44, 59-61, 260, 261, 263, 264, 275, 280, 
437 Accumulation of information across multiple genes increases the percent of variability explained by the 
model and hence increases the likelihood of detecting effects. Our analysis approach will also test the 
association of each innate and Th cell immune response with the expression of all genes in a specified geneset 
using kernel methods.438-441 Advantages of kernel methods are the following: 1) they allow for correlations 
among the different genes in a geneset; 2) they are robust to when some genes are positively associated with a 
response, others are negatively associated with a response, and other genes in the geneset are not associated; 3) 
and they allow adjustment for covariates. Finally, we will evaluate data-driven genesets using WGCNA to 
group genes into biological modules,118 which is an approach we have successfully used (see Preliminary 
Data).  
Separate mediation analyses will be performed for mRNA and miRNA. If we find both mRNA and miRNA to 
be separate mediators in our initial analyses, we will evaluate their joint effects as a pathway of mediators using 
techniques developed by VanderWeele.106 Other confounding factors (e.g., immunosenescence, underlying 
medical conditions, obesity, and CMV serostatus) will be evaluated in our analyses as potential confounders.  
 
Expected Results and Impact of Findings for Specific Aims 1 and 2. Our publications44, 59, 258, 259, 263-
265, 442, 443 and preliminary data indicate a high likelihood of identifying not only significant vaccine type- 


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and sex-specific differences in immune responses, but also transcriptomic signatures associated with those 
differences. Furthermore, the preliminary data from our mediation analysis support our hypothesis that changes 
in gene expression influence the effect of sex on immune response. We therefore anticipate identifying 
comparable sex-specific differences along with the genes/genesets mediating those responses (i.e., 
transcriptomic signatures), following receipt of MF59Flu and HDFlu. Pinpointing the innate and/or Th cell 
responses associated with humoral immunity to these vaccines facilitates our understanding of the critical 
pathways vaccines must trigger in order to elicit protective antibody titers.  
Our data will also provide new insights into why men and women respond differently to influenza vaccines, and 
highlight the specific genetic pathways associated with MF59- and sex-based immune response differences. 
Aims 1.1 and 2.1 may provide additional correlates of protection that can be used in conjunction with HAI titer 
to more accurately assess immune status. Understanding what genes and genesets are associated with the 
increased immunogenicity of MF59Flu and HDFlu (Aims 1.2 and 2.2) will inform functional studies verifying 
the mechanistic relationship between gene expression and immune function. For example, if TLR4 and TLR8 
activation are strongly associated with increased APC activation and HAI titers, it suggests the use of a 
combination of GLA-SE (a TLR4 agonist) and CpG (a TLR8/9 agonist) might make an effective adjuvant. As 
another example, MF59 has been shown to enhance APC recruitment to the Ag site and transport of Ag to the 
draining lymph nodes. Due to reasons that are currently poorly understood, those processes are followed by 
enhanced Th cell responses and more robust (in terms of both titer and breadth of targets) Ab responses. 
Evaluating the transcriptomic changes occurring in innate and Th cells after MF59Flu vaccination is likely to 
identify gene expression patterns occurring in the APCs and responding T cells, providing critical clues 
regarding the nature of the interactions between these cell types, and may highlight the receptor-ligand pairs and 
downstream signaling cascades responsible for enhanced T cell responses. Such information allows for directed 
and informed engineering of novel vaccine candidates and adjuvants. In the unlikely event that we do not 
identify vaccine type or sex-dependent differences, this finding will be interesting its own right, suggesting that 
further studies be conducted to find additional variables that do impact innate and/or Th responses to influenza 
A/H3N2. 
 
Endpoints: 
Secreted cytokine levels 
Detection of Functional Th Cell Subsets 
mRNA and miRNA Sequencing 
 
References: 
 
1.   
Govaert TM, Thijs CT, Masurel N, Sprenger MJ, Dinant GJ, Knottnerus JA. The efficacy of influenza 
vaccination in elderly individuals. A randomized double-blind placebo-controlled trial. JAMA 1994; 
272:1661-5.  
2. 
Lambert ND, Ovsyannikova IG, Pankratz VS, Jacobson RM, Poland GA. Understanding the immune 
response to seasonal influenza vaccination in older adults: a systems biology approach. Expert Rev 
Vaccines 2012;11:985-94. PMCID: PMC3514506  
3. 
Fellner C. Pharmaceutical Approval Update. PT 2016;41:26-59. PMCID: 4699482 
4. 
Sullivan SJ, Jacobson R, Poland GA. Advances in the vaccination of the elderly against influenza: role 
of a high-dose vaccine. Expert Rev Vaccines 2010;9:1127-33.  
5. 
DiazGranados CA, Dunning AJ, Kimmel M, Kirby D, Treanor J, Collins A, Pollak R, Christoff J, Earl J, 
Landolfi V, Martin E, Gurunathan S, Nathan R, Greenberg DP, Tornieporth NG, Decker MD, Talbot HK. 


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 14 of 37 
 
Efficacy of high-dose versus standard-dose influenza vaccine in older adults. N Engl J Med 
2014;371:635-45.  
6. 
Khurana S, Verma N, Yewdell JW, Hilbert AK, Castellino F, Lattanzi M, Del GG, Rappuoli R, Golding H. 
MF59 adjuvant enhances diversity and affinity of antibody-mediated immune response to pandemic 
influenza vaccines. Sci Transl Med 2011;3:85ra48.  
7. 
Ansaldi F, Zancolli M, Durando P, Montomoli E, Sticchi L, Del Giudice G, Icardi G. Antibody response 
against heterogeneous circulating influenza virus strains elicited by MF59- and non-adjuvanted 
vaccines during seasons with good or partial matching between vaccine strain and clinical isolates. 
Vaccine 2010;28:4123-9.  
8. 
Minutello M, Senatore F, Cecchinelli G, Bianchi M, Andreani T, Podda A, Crovari P. Safety and 
immunogenicity of an inactivated subunit influenza virus vaccine combined with MF59 adjuvant 
emulsion in elderly subjects, immunized for three consecutive influenza seasons. Vaccine 1999;17:99-
104.  
9. 
Scheifele DW, McNeil SA, Ward BJ, Dionne M, Cooper C, Coleman B, Loeb M, Rubinstein E, 
McElhaney J, Hatchette T, Li Y, Montomoli E, Schneeberg A, Bettinger JA, Halperin SA. Safety, 
immunogenicity, and tolerability of three influenza vaccines in older adults: results of a randomized, 
controlled comparison. Hum Vaccin Immunother 2013;9:2460-73.  
10. 
Van Buynder PG, Konrad S, Van Buynder JL, Brodkin E, Krajden M, Ramler G, Bigham M. The 
comparative effectiveness of adjuvanted and unadjuvanted trivalent inactivated influenza vaccine (TIV) 
in the elderly. Vaccine 2013;31:6122-8.  
11. 
Seo YB, Choi WS, Lee J, Song JY, Cheong HJ, Kim WJ. Comparison of the immunogenicity and safety 
of the conventional subunit, MF59-adjuvanted, and intradermal influenza vaccines in the elderly. Clin 
Vaccine Immunol 2014;21:989-96.  
12. 
Del Giudice G, Hilbert AK, Bugarini R, Minutello A, Popova O, Toneatto D, Schoendorf I, Borkowski A, 
Rappuoli R, Podda A. An MF59-adjuvanted inactivated influenza vaccine containing A/Panama/1999 
(H3N2) induced broader serological protection against heterovariant influenza virus strain 
A/Fujian/2002 than a subunit and a split influenza vaccine. Vaccine 2006;24:3063-5.  
13. 
DiazGranados CA, Dunning AJ, Robertson CA, Talbot HK, Landolfi V, Greenberg DP. Efficacy and 
immunogenicity of high-dose influenza vaccine in older adults by age, comorbidities, and frailty. 
Vaccine 2015;33:4565-71.  
14. 
Falsey AR, Treanor JJ, Tornieporth N, Capellan J, Gorse GJ. Randomized, double-blind controlled 
phase 3 trial comparing the immunogenicity of high-dose and standard-dose influenza vaccine in adults 
65 years of age and older. J Infect Dis 2009;200:172-80.  
15. 
Frey SE, Reyes MR, Reynales H, Bermal NN, Nicolay U, Narasimhan V, Forleo-Neto E, Arora AK. 
Comparison of the safety and immunogenicity of an MF59(R)-adjuvanted with a non-adjuvanted 
seasonal influenza vaccine in elderly subjects. Vaccine 2014;32:5027-34.  
16. 
Furman D, Hejblum BP, Simon N, Jojic V, Dekker CL, Thiebaut R, Tibshirani RJ, Davis MM. Systems 
analysis of sex differences reveals an immunosuppressive role for testosterone in the response to 
influenza vaccination. Proc Natl Acad Sci USA 2014;111:869-74.  
17. 
Estimates of Deaths Associated with Seasonal Influenza-United States, 1976-2007. MMWR 
2010;59(33):1057-62.  
18. 
Poland GA. Valuing influenza vaccine: medical, economic, and social benefits. Clin Infect Dis 2009; 
48:299-301.  
19. 
Centers for Disease Control and Prevention. 2014-2015 Influenza Season Week 15 ending April 18, 
2015. http://www.cdc.gov/flu/weekly/. Date accessed: June 1, 2017. 
20. 
D'Mello T, Brammer L, Blanton L, Kniss K, Smith S, Mustaquim D, Steffens C, Dhara R, Cohen J, 
Chaves SS, Finelli L, Bresee J, Wallis T, Xu X, Abd Elal AI, Gubareva L, Wentworth D, Villanueva J, 
Katz J, Jernigan D. Update: Influenza activity--United States, September 28, 2014-February 21, 2015. 
MMWR 2015;64:206-12.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 15 of 37 
 
21. 
Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, Fukuda K. Mortality 
associated with influenza and respiratory syncytial virus in the United States. JAMA 2003;289:179-86.  
22. 
Jefferson T, Di PC, Al-Ansary LA, Ferroni E, Thorning S, Thomas RE. Vaccines for preventing influenza 
in the elderly. Cochrane Database Syst Rev 2010;2:CD004876.  
23. 
Osterholm MT, Kelley NS, Sommer A, Belongia EA. Efficacy and effectiveness of influenza vaccines: a 
systematic review and meta-analysis. Lancet Infect Dis 2012;12(1):36-44.  
24. 
Hill T, Platzer A, Reyes C. Influenza deaths in spite of immunization and prophylaxis. Clin Infect Dis 
2005;40:492-3.  
25. 
Poland GA, Jacobson RM, Ovsyannikova IG. Influenza virus resistance to antiviral agents: a plea for 
rational use. Clin Infect Dis 2009;48:1254-6. PMCID: PMC2831648 
26. 
Poland GA, Mulligan MJ. The imperative of influenza vaccines for elderly individuals-an evolving story. 
J Infect Dis 2009;200:161-3. PMCID: PMC2843138  
27. 
Baldo V, Baldovin T, Floreani A, Carraro AM, Trivello R. MF59-adjuvanted influenza vaccine confers 
superior immunogenicity in adult subjects (18-60 years of age) with chronic diseases who are at risk of 
post-influenza complications. Vaccine 2007;25:3955-61.  
28. 
Nakaya HI, Clutterbuck E, Kazmin D, Wang L, Cortese M, Bosinger SE, Patel NB, Zak DE, Aderem A, 
Dong T, Del Giudice G, Rappuoli R, Cerundolo V, Pollard AJ, Pulendran B, Siegrist CA. Systems 
biology of immunity to MF59-adjuvanted versus nonadjuvanted trivalent seasonal influenza vaccines in 
early childhood. Proc Natl Acad Sci USA 2016;113:1853-8.  
29. 
Engler RJ, Nelson MR, Klote MM, VanRaden MJ, Huang CY, Cox NJ, Klimov A, Keitel WA, Nichol KL, 
Carr WW, Treanor JJ. Half- vs full-dose trivalent inactivated influenza vaccine (2004-2005): age, dose, 
and sex effects on immune responses. Arch Intern Med 2008;168:2405-14.  
30. 
Cook IF, Barr I, Hartel G, Pond D, Hampson AW. Reactogenicity and immunogenicity of an inactivated 
influenza vaccine administered by intramuscular or subcutaneous injection in elderly adults. Vaccine 
2006;24:2395-402.  
31. 
Couch RB, Winokur P, Brady R, Belshe R, Chen WH, Cate TR, Sigurdardottir B, Hoeper A, Graham IL, 
Edelman R, He F, Nino D, Capellan J, Ruben FL. Safety and immunogenicity of a high dosage trivalent 
influenza vaccine among elderly subjects. Vaccine 2007;25:7656-63.  
32. 
Fang JWS, Lai CL, Chung HT, Wu PC, Lau JYN. Female children respond to recombinant hepatitis B 
vaccine with a higher titre than male. J Trop Pediatr 1994;40:104-7.  
33. 
Stanberry LR, Spruance SL, Cunningham AL, Bernstein DI, Mindel A, Sacks S, Tyring S, Aoki FY, 
Slaoui M, Denis M, Vandepapeliere P, Dubin G, GlaxoSmithKline Vaccine Efficacy Study G. 
Glycoprotein-D-adjuvant vaccine to prevent genital herpes. N Engl J Med 2002;347:1652-61.  
34. 
Kennedy RB, Ovsyannikova IG, Pankratz VS, Vierkant RA, Jacobson RM, Ryan MA, Poland GA. 
Gender effects on humoral immune responses to smallpox vaccine. Vaccine 2009;27:3319-23. PMCID: 
PMC2831636  
35. 
Veit O, Niedrig M, Chapuis-Taillard C, Cavassini M, Mossdorf E, Schmid P, Bae HG, Litzba N, Staub T, 
Hatz C, Furrer H. Immunogenicity and safety of yellow fever vaccination for 102 HIV-infected patients. 
Clin Infect Dis 2009;48:659-66.  
36. 
Kanesa-Thasan N, Sun W, Ludwig GV, Rossi C, Putnak JR, Mangiafico JA, Innis BL, Edelman R. 
Atypical antibody responses in dengue vaccine recipients. Am J Trop Med Hyg 2003;69:32-8.  
37. 
Lorenzo ME, Hodgson A, Robinson DP, Kaplan JB, Pekosz A, Klein SL. Antibody responses and cross 
protection against lethal influenza A viruses differ between the sexes in C57BL/6 mice. Vaccine 2011; 
29:9246-55.  
38. 
Klein SL, Hodgson A, Robinson DP. Mechanisms of sex disparities in influenza pathogenesis. J Leukoc 
Biol 2012;92:67-73.  
39. 
Klein SL, Jedlicka A, Pekosz A. The Xs and Y of immune responses to viral vaccines. Lancet Infect Dis. 
2010;10:338-49.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 16 of 37 
 
40. 
Klein SL, Pekosz A. Sex-based biology and the rational design of influenza vaccination strategies. J 
Infect Dis 2014;209 Suppl 3:S114-9.  
41. 
Beery AK, Zucker I. Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev 2011; 
35:565-72.  
42. 
Klein SL. Immune cells have sex and so should journal articles. Endocrinology 2012;153:2544-50.  
43. 
Talbot HK, Coleman LA, Zhu Y, Spencer S, Thompson M, Cheng PY, Sundaram ME, Belongia EA, 
Griffin MR. Factors associated with maintenance of antibody responses to influenza vaccine in older, 
community-dwelling adults. BMC Infect Dis 2015;15:195.  
44. 
Ovsyannikova IG, Salk HM, Kennedy RB, Haralambieva IH, Zimmermann MT, Grill DE, Oberg AL, 
Poland GA. Gene signatures associated with adaptive humoral immunity following seasonal influenza 
A/H1N1 vaccination. Genes Immun 2016;17:371-9. PMCID: PMC5133148  
45. 
Zuccotti GV, Pariani E, Scaramuzza A, Santoro L, Giani E, Macedoni M, Gazzarri A, Anselmi G, 
Amendola A, Zanetti A. Long-lasting immunogenicity and safety of a 2009 pandemic influenza A(H1N1) 
MF59-adjuvanted vaccine when co-administered with a 2009-2010 seasonal influenza vaccine in young 
patients with type 1 diabetes mellitus. Diabet Med 2011;28:1530-6.  
46. 
Gallaher WR. Towards a sane and rational approach to management of Influenza H1N1 2009. Virol J 
2009;6:51.  
47. 
Poland GA, Jacobson RM, Ovsyannikova IG. Trends affecting the future of vaccine development and 
delivery: The role of demographics, regulatory science, the anti-vaccine and consumer culture and 
vaccinomics. Vaccine 2009;27:3240-4. PMCID: PMC2693340 
48. 
Poland GA, Oberg AL. Vaccinomics and bioinformatics: accelerants for the next golden age of 
vaccinology. Vaccine 2010;28:3509-10. PMCID: PMC2860731  
49. 
Poland GA, Ovsyannikova IG, Jacobson RM, Smith DI. Heterogeneity in vaccine immune response: the 
role of immunogenetics and the emerging field of vaccinomics. Clin Pharmacol Ther 2007;82:653-64.  
50. 
Poland GA. Pharmacology, vaccinomics, and the second golden age of vaccinology. Clin Pharmacol 
Ther 2007; 82:623-6.  
51. 
Poland GA, Ovsyannikova IG, Jacobson RM. Personalized vaccines: the emerging field of vaccinomics. 
Expert Opin Biol Ther 2008;8:1659-67. PMCID: PMC2831634  
52. 
Poland GA, Ovsyannikova IG, Jacobson RM. Vaccinomics and Personalized Vaccinology. The Jordan 
Report: U.S. Department of Health and Human Services 2012:3-18. 
53. 
Poland GA, Ovsyannikova IG, Kennedy RB, Haralambieva IH, Jacobson RM. Vaccinomics and a new 
paradigm for the development of preventive vaccines against viral infections. Omics 2011; 15:625-36. 
PMCID: PMC3166201  
54. 
Poland GA, Kennedy RB, Ovsyannikova IG. Vaccinomics and personalized vaccinology: Is science 
leading us toward a new path of directed vaccine development and discovery? PLoS Pathog 
2011;7:e1002344. PMCID: PMC3248557 
55. 
Ovsyannikova IG, Poland GA. Vaccinomics: current findings, challenges and novel approaches for 
vaccine development. AAPS 2011;13:438-44. PMCID: PMC3160164  
56. 
Haralambieva IH, Poland GA. Vaccinomics, predictive vaccinology and the future of vaccine 
development. Future Microbiol 2010;5:1757-60.  
57. 
Nakaya HI, Li S, Pulendran B. Systems vaccinology: learning to compute the behavior of vaccine 
induced immunity. Wiley Interdiscip Rev Syst Biol Med 2012;4:193-205.  
58. 
Behzad H, Huckriede AL, Haynes L, Gentleman B, Coyle K, Wilschut JC, Kollmann TR, Reed SG, 
McElhaney JE. GLA-SE, a synthetic toll-like receptor 4 agonist, enhances T-cell responses to influenza 
vaccine in older adults. J Infect Dis 2012;205:466-73.  
59. 
Ovsyannikova IG, Oberg AL, Kennedy RB, Zimmermann MT, Haralambieva IH, Goergen KM, Grill DE, 
Poland GA. Gene signatures related to HAI response following influenza A/H1N1 vaccine in older 
individuals. Heliyon 2016;2:e00098. PMCID: PMC4946173 


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 17 of 37 
 
60. 
Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, Means AR, Kasturi SP, 
Khan N, Li GM, McCausland M, Kanchan V, Kokko KE, Li S, Elbein R, Mehta AK, Aderem A, Subbarao 
K, Ahmed R, Pulendran B. Systems biology of seasonal influenza vaccination in humans. Nat Immunol 
2011;12:786-95.  
61. 
Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, Schmidt DS, Johnson SE, 
Milton A, Rajam G, Kasturi S, Carlone GM, Quinn C, Chaussabel D, Palucka AK, Mulligan MJ, Ahmed 
R, Stephens DS, Nakaya HI, Pulendran B. Molecular signatures of antibody responses derived from a 
systems biology study of five human vaccines. Nat Immunol 2014;15:195-204.  
62. 
Oh JZ, Ravindran R, Chassaing B, Carvalho FA, Maddur MS, Bower M, Hakimpour P, Gill KP, Nakaya 
HI, Yarovinsky F, Sartor RB, Gewirtz AT, Pulendran B. TLR5-mediated sensing of gut microbiota is 
necessary for antibody responses to seasonal influenza vaccination. Immunity 2014;41:478-92.  
63. 
Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, Pirani A, Gernert K, Deng J, Marzolf 
B, Kennedy K, Wu H, Bennouna S, Oluoch H, Miller J, Vencio RZ, Mulligan M, Aderem A, Ahmed R, 
Pulendran B. Systems biology approach predicts immunogenicity of the yellow fever vaccine in 
humans. Nat Immunol 2009;10:116-25.  
64. 
Davis MM, Tato CM, Furman D. Systems immunology: just getting started. Nat Immunol 2017;18:725-
32.  
65. 
Hung IF, Zhang AJ, To KK, Chan JF, Li P, Wong TL, Zhang R, Chan TC, Chan BC, Wai HH, Chan LW, 
Fong HP, Hui RK, Kong KL, Leung AC, Ngan AH, Tsang LW, Yeung AP, Yiu GC, Yung W, Lau JY, 
Chen H, Chan KH, Yuen KY. Topical imiquimod before intradermal trivalent influenza vaccine for 
protection against heterologous non-vaccine and antigenically drifted viruses: a single-centre, double-
blind, randomised, controlled phase 2b/3 trial. Lancet Infect Dis 2016;16:209-18.  
66. 
Hung IF, Zhang AJ, To KK, Chan JF, Li C, Zhu HS, Li P, Chan TC, Cheng VC, Chan KH, Yuen KY. 
Immunogenicity of intradermal trivalent influenza vaccine with topical imiquimod: a double blind 
randomized controlled trial. Clin Infect Dis 2014;59:1246-55.  
67. 
Poland GA, Kennedy RB, McKinney BA, Ovsyannikova IG, Lambert ND, Jacobson RM, Oberg AL. 
Vaccinomics, adversomics, and the immune response network theory: Individualized vaccinology in the 
21st century. Semin Immunol 2013;25:89-103. PMCID: PMC3752773  
68. 
Poland GA, Ovsyannikova IG, Jacobson RM. Vaccine immunogenetics: bedside to bench to population. 
Vaccine 2008;26:6183-8. PMCID: PMC2614670  
69. 
Ovsyannikova IG, Jacobson RM, Dhiman N, Vierkant RA, Pankratz VS, Poland GA. Human leukocyte 
antigen and cytokine receptor gene polymorphisms associated with heterogeneous immune responses 
to mumps viral vaccine. Pediatrics 2008;121:e1091-e9. PMCID: PMC2668976  
70. 
Dhiman N, Ovsyannikova IG, Oberg AL, Grill DE, Jacobson RM, Poland GA. Immune activation at 
effector and gene expression levels after measles vaccination in healthy individuals: a pilot study. Hum 
Immunol 2005;66:1125-36.  
71. 
Targonski PV, Jacobson RM, Poland GA. Immunosenescence: Role and measurement in influenza 
vaccine response among the elderly. Vaccine 2007;25:3066-9.  
72. 
Poland GA, Jacobson RM, Targonski PV. Avian and pandemic influenza: An overview. Vaccine 
2007;25:3057-61.  
73. 
Poland GA. Vaccines against avian influenza: a race against time. N Engl J Med 2006;354:1411-3.  
74. 
Targonski PV, Caldwell CR, Strausbauch M, Wettstein P, Poland GA, Tangalos EG. White Blood Cell 
Telomerase Activity and Incident Respiratory Illness Among Community-Dwelling Elderly Vaccinated 
Against Seasonal Influenza. Clin Pharmacol Ther 2007;82:694-9.  
75. 
Boyce TG, Poland GA. Promises and challenges of live-attenuated intranasal influenza vaccines across 
the age spectrum: a review. Biomed Pharmacother 2000;54:210-8.  
76. 
Frey S, Poland G, Percell S, Podda A. Comparison of the safety, tolerability, and immunogenicity of a 
MF59-adjuvanted influenza vaccine and a non-adjuvanted influenza vaccine in non-elderly adults. 
Vaccine 2003;21:4234-7.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 18 of 37 
 
77. 
Goronzy JJ, Fulbright JW, Crowson CS, Poland GA, O'Fallon WM, Weyand CM. Value of 
immunological markers in predicting responsiveness to influenza vaccination in elderly individuals. J 
Virol 2001;75:12182-7.  
78. 
Jacobson RM, Poland GA. Universal vaccination of healthy children against influenza. A role for the 
cold-adapted intranasal influenza vaccine. Pediatr Drugs 2002;4:65-71.  
79. 
Margolis KL, Nichol KL, Poland GA, Pluhar RE. Frequency of adverse reactions to influenza vaccine in 
the elderly. A randomized, placebo-controlled trial. JAMA 1990;264:1139-41.  
80. 
Nichol KL, Korn JE, Margolis KL, Poland GA, Petzel RA, Lofgren RP. Achieving the national health 
objective for influenza immunization: Success of an institution-wide vaccination program. Am J Med 
1990;89:156-60.  
81. 
Ofstead C, Tucker SJ, Beebe TJ, Poland GA. Influenza Vaccination Among Registered Nurses: 
Information Receipt, Knowledge, and Decision-Making at an Institution With a Multifaceted Educational 
Program. Infection Control and Hosp Epidemiol 2008;29:99-106.  
82. 
Poland GA. Health care workers and influenza vaccine: first do no harm, then do the right thing. J Am 
Pharm Assoc 2004;44:637-8.  
83. 
Poland GA, Rottinghaus ST, Jacobson RM. Influenza vaccines: a review and rationale for use in 
developed and underdeveloped countries. Vaccine 2001;19:2216-20.  
84. 
Poland GA, Couch R. Intranasal influenza vaccine. Adding to the armamentarium for influenza control. 
JAMA 1999;282:182-4.  
85. 
Poland GA. Lessons from the influenza vaccine recall of 1996-1997. JAMA 1997;278:1022-3.  
86. 
Poland GA, Tosh P, Jacobson RM. Requiring influenza vaccination for health care workers: seven 
truths we must accept. Vaccine 2005;23:2251-5.  
87. 
Sambhara S, Poland GA. Avian influenza vaccines: what's all the flap? Lancet 2006;367:1636-8.  
88. 
Sambhara S, Bridges CB, Poland GA. H5N1 vaccine hits the target, but not the bull's eye. Lancet Infect 
Dis 2007;7:503-5.  
89. 
Targonski PV, Poland GA. Intranasal cold-adapted influenza virus vaccine combined with inactivated 
influenza virus vaccines. An extra boost for the elderly? Drugs Aging 2004;21:349-59.  
90. 
Tosh PK, Boyce TG, Poland GA. Flu myths: dispelling the myths associated with live attenuated 
influenza vaccine. Mayo Clin Proc 2008;83:77-84.  
91. 
Tosh P, Jacobson RM, Poland GA. Mandatory influenza vaccination for health care workers: a timely 
step forward. Md Med 2006;Winter:21-3.  
92. 
Tosh PK, Poland GA. Emerging Vaccines for Influenza. Expert Opin Emerg Drugs 2008;13:21-40.  
93. 
Tucker SJ, Poland GA, Jacobson RM. Requiring influenza vaccination for health care workers. Am J 
Nurs 2008;108:32-4.  
94. 
Kasten MJ, Poland GA. Influenza vaccination and the elderly: Pandemic preparedness. Drugs and 
Aging 2008;25:179-86.  
95. 
Poland GA, Ovsyannikova IG, Jacobson RM. Immunogenetics of seasonal influenza vaccine response. 
Vaccine 2008;26S:D35-D40. PMCID: PMC2610683  
96. 
Ovsyannikova IG, Jacobson RM, Vierkant RA, Pankratz VS, Poland GA. HLA supertypes and immune 
responses to measles-mumps-rubella viral vaccine: Findings and implications for vaccine design. 
Vaccine 2007;25:3090-100.  
97. 
Tosh PK, Ovsyannikova IG, Sambhara S, Barry MA, Gray GC, Poland GA. Vaccine Development: The 
Development of Avian Influenza Vaccines for Human Use. In: Schaechter M, editor. Encyclopedia of 
Microbiology: Oxford: Elsevier; 2009. p. 775-95. 
98. 
McElhaney JE, Zhou X, Talbot HK, Soethout E, Bleackley RC, Granville DJ, Pawelec G. The unmet 
need in the elderly: how immunosenescence, CMV infection, co-morbidities and frailty are a challenge 
for the development of more effective influenza vaccines. Vaccine 2012;30:2060-7.  
99. 
Nepom GT, Domeier ME, Ou D, Kovats S, Mitchell LA, Tingle AJ. Recognition of contiguous allele-
specific peptide elements in the rubella virus E1 envelope protein. Vaccine 1997;15:648-52.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 19 of 37 
 
100. 
Ou D, Chong P, Tingle AJ, Gillam S. Mapping T-cell epitopes of rubella virus structural proteins E1, E2, 
and C recognized by T-cell lines and clones derived from infected and immunized populations. J Med 
Virol 1993;40:175-83.  
101. 
Tang YW, Li H, Wu H, Shyr Y, Edwards KM. Host single-nucleotide polymorphisms and altered 
responses to inactivated influenza vaccine. J Infect Dis 2007;196:1021-5.  
102. 
Ovsyannikova IG, Johnson KL, Bergen HR, III, Poland GA. Mass spectrometry and peptide-based 
vaccine development. Clin Pharmacol Ther 2007;82:644-52.  
103. 
Kennedy RB, Poland GA. The Top Five "Game Changers" in Vaccinology: Toward Rational and 
Directed Vaccine Development. Omics 2011;15:533-7. PMCID: PMC3166183  
104. 
Oberg AL, Kennedy RB, Li P, Ovsyannikova IG, Poland GA. Systems biology approaches to new 
vaccine development. Curr Opin Immunol 2011;23:436-43. PMCID: PMC3129601 
105. 
McElhaney JE, Kuchel GA, Zhou X, Swain SL, Haynes L. T-Cell Immunity to Influenza in Older Adults: 
A Pathophysiological Framework for Development of More Effective Vaccines. Front Immunol 2016; 
7:41.  
106. 
Vanderweele TJ. Explanation and Causal Inference. New York: Oxford University Press; 2015. 
107. 
VanderWeele TJ, Vansteelandt S. Conceptual issues concerning mediation, interventions and 
composition. Stat Interface 2009;2:457-68.  
108. 
Imai K, Keele L, Yamamoto T. Identification, Inference and Sensitivity Analysis for Causal Mediation 
Effects. Stat Sci 2010;25:51-71.  
109. 
MacKinnin D. An Introduction to Statistical Mediation Analysis. New York: Lawrence Erlbaum 
Associates; 2008. 
110. 
VanderWeele TJ. Marginal structural models for the estimation of direct and indirect effects. 
Epidemiology 2009;20:18-26.  
111. 
Wang J, Spitz MR, Amos CI, Wilkinson AV, Wu X, Shete S. Mediating effects of smoking and chronic 
obstructive pulmonary disease on the relation between the CHRNA5-A3 genetic locus and lung cancer 
risk. Cancer 2010;116:3458-62.  
112. 
Ishii T, Wakabayashi R, Kurosaki H, Gemma A, Kida K. Association of serotonin transporter gene 
variation with smoking, chronic obstructive pulmonary disease, and its depressive symptoms. J Hum 
Gen 2011;56:41-6.  
113. 
Gu J, Chen M, Shete S, Amos CI, Kamat A, Ye Y, Lin J, Dinney CP, Wu X. A genome-wide association 
study identifies a locus on chromosome 14q21 as a predictor of leukocyte telomere length and as a 
marker of susceptibility for bladder cancer. Cancer Prev Res 2011;4:514-21.  
114. 
Wang J, Spitz MR, Amos CI, Wu X, Wetter DW, Cinciripini PM, Shete S. Method for evaluating multiple 
mediators: mediating effects of smoking and COPD on the association between the CHRNA5-A3 
variant and lung cancer risk. PLos ONE 2012;7:e47705.  
115. 
VanderWeele TJ, Asomaning K, Tchetgen Tchetgen EJ, Han Y, Spitz MR, Shete S, Wu X, Gaborieau 
V, Wang Y, McLaughlin J, Hung RJ, Brennan P, Amos CI, Christiani DC, Lin X. Genetic variants on 
15q25.1, smoking, and lung cancer: an assessment of mediation and interaction. Am J Epidemiol 2012; 
175:1013-20.  
116. 
MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol 2007;58:593-614.  
117. 
Chaussabel D, Quinn C, Shen J, Patel P, Glaser C, Baldwin N, Stichweh D, Blankenship D, Li L, 
Munagala I, Bennett L, Allantaz F, Mejias A, Ardura M, Kaizer E, Monnet L, Allman W, Randall H, 
Johnson D, Lanier A, Punaro M, Wittkowski KM, White P, Fay J, Klintmalm G, Ramilo O, Palucka AK, 
Banchereau J, Pascual V. A modular analysis framework for blood genomics studies: application to 
systemic lupus erythematosus. Immunity 2008;29:150-64.  
118. 
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC 
Bioinformatics 2008;9:559.  
119. 
Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq 
data. BMC Bioinformatics 2013;14:7.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 20 of 37 
 
120. 
Duraisingham SS, Rouphael N, Cavanagh MM, Nakaya HI, Goronzy JJ, Pulendran B. Systems biology 
of vaccination in the elderly. Curr Top Microbiol Immunol 2013;363:117-42.  
121. 
Bernstein A, Pulendran B, Rappuoli R. Systems vaccinomics: the road ahead for vaccinology. Omics 
2011;15:529-31.  
122. 
Buonaguro L, Pulendran B. Immunogenomics and systems biology of vaccines. Immunol Rev 
2011;239:197-208.  
123. 
Pulendran B, Li S, Nakaya HI. Systems vaccinology. Immunity 2010;33:516-29.  
124. 
Pulendran B. Learning immunology from the yellow fever vaccine: innate immunity to systems 
vaccinology. Nat Rev Immunol 2009;9:741-7.  
125. 
Pulendran B, Miller J, Querec TD, Akondy R, Moseley N, Laur O, Glidewell J, Monson N, Zhu T, Zhu H, 
Staprans S, Lee D, Brinton MA, Perelygin AA, Vellozzi C, Brachman P, Jr., Lalor S, Teuwen D, Eidex 
RB, Cetron M, Priddy F, del Rio C, Altman J, Ahmed R. Case of yellow fever vaccine--associated 
viscerotropic disease with prolonged viremia, robust adaptive immune responses, and polymorphisms 
in CCR5 and RANTES genes. J Infect Dis 2008;198:500-7.  
126. 
Kohl P, Noble D. Systems biology and the virtual physiological human. Mol Syst Biol 2009;5:292.  
127. 
Rief DM, White BC, Moore JH. Integrated analysis of genetic, genomic and proteomic data. Expert Rev 
Proteomics 2004;1:67-75.  
128. 
Wang E, Marincola FM. Bottom up: a modular view of immunology. Immunity 2008;29:9-11.  
129. 
Furman D, Jojic V, Kidd B, Shen-Orr S, Price J, Jarrell J, Tse T, Huang H, Lund P, Maecker HT, Utz 
PJ, Dekker CL, Koller D, Davis MM. Apoptosis and other immune biomarkers predict influenza vaccine 
responsiveness. Mol Syst Biol 2013;9:659.  
130. 
Tan Y, Tamayo P, Nakaya H, Pulendran B, Mesirov JP, Haining WN. Gene signatures related to B-cell 
proliferation predict influenza vaccine-induced antibody response. Eur J Immunol 2014;44:285-95.  
131. 
Obermoser G, Presnell S, Domico K, Xu H, Wang Y, Anguiano E, Thompson-Snipes L, Ranganathan 
R, Zeitner B, Bjork A, Anderson D, Speake C, Ruchaud E, Skinner J, Alsina L, Sharma M, Dutartre H, 
Cepika A, Israelsson E, Nguyen P, Nguyen QA, Harrod AC, Zurawski SM, Pascual V, Ueno H, Nepom 
GT, Quinn C, Blankenship D, Palucka K, Banchereau J, Chaussabel D. Systems scale interactive 
exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal 
vaccines. Immunity 2013;38:831-44.  
132. 
Jackson KJ, Liu Y, Roskin KM, Glanville J, Hoh RA, Seo K, Marshall EL, Gurley TC, Moody MA, 
Haynes BF, Walter EB, Liao HX, Albrecht RA, Garcia-Sastre A, Chaparro-Riggers J, Rajpal A, Pons J, 
Simen BB, Hanczaruk B, Dekker CL, Laserson J, Koller D, Davis MM, Fire AZ, Boyd SD. Human 
responses to influenza vaccination show seroconversion signatures and convergent antibody 
rearrangements. Cell Host Microbe 2014;16:105-14.  
133. 
Price JV, Jarrell JA, Furman D, Kattah NH, Newell E, Dekker CL, Davis MM, Utz PJ. Characterization 
of influenza vaccine immunogenicity using influenza antigen microarrays. PLos ONE 2013;8:e64555.  
134. 
Jiang N, He J, Weinstein JA, Penland L, Sasaki S, He XS, Dekker CL, Zheng NY, Huang M, Sullivan M, 
Wilson PC, Greenberg HB, Davis MM, Fisher DS, Quake SR. Lineage structure of the human antibody 
repertoire in response to influenza vaccination. Sci Transl Med 2013;5:171ra19.  
135. 
Bucasas KL, Franco LM, Shaw CA, Bray MS, Wells JM, Nino D, Arden N, Quarles JM, Couch RB, 
Belmont JW. Early patterns of gene expression correlate with the humoral immune response to 
influenza vaccination in humans. J Infect Dis 2011;203:921-9.  
136. 
Brandes M, Klauschen F, Kuchen S, Germain RN. A systems analysis identifies a feedforward 
inflammatory circuit leading to lethal influenza infection. Cell 2013;154:197-212.  
137. 
Gottschalk RA, Martins AJ, Sjoelund VH, Angermann BR, Lin B, Germain RN. Recent progress using 
systems biology approaches to better understand molecular mechanisms of immunity. Semin Immunol 
2013;25:201-8.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 21 of 37 
 
138. 
Weekes MP, Tomasec P, Huttlin EL, Fielding CA, Nusinow D, Stanton RJ, Wang EC, Aicheler R, 
Murrell I, Wilkinson GW, Lehner PJ, Gygi SP. Quantitative temporal viromics: an approach to 
investigate host-pathogen interaction. Cell 2014;157:1460-72.  
139. 
Su LF, Han A, McGuire HM, Furman D, Newell EW, Davis MM. The promised land of human 
immunology. Cold Spring Harb Symp Quant Biol 2013;78:203-13.  
140. 
Kennedy RB, Oberg AL, Ovsyannikova IG, Haralambieva IH, Grill DE, Poland GA. Transcriptomic 
profiles of high and low antibody responders to smallpox vaccine. Genes Immun 2013;14:277-85. 
PMCID: PMC3723701  
141. 
Haralambieva IH, Oberg AL, Ovsyannikova IG, Kennedy RB, Grill DE, Middha S, Bot BM, Wang VW, 
Smith DI, Jacobson RM, Poland GA. Genome-wide characterization of transcriptional patterns in high 
and low antibody responders to rubella vaccination. PLos ONE 2013;8:e62149. PMCID: PMC3641062 
142. 
Haralambieva IH, Kennedy RB, Ovsyannikova IG, Whitaker JA, Poland GA. Variability in Humoral 
Immunity to Measles Vaccine: New Developments. Trends Mol Med 2015;21:789-801. PMCID: 
PMC4679650 
143. 
Klein SL, Poland GA. Personalized vaccinology: one size and dose might not fit both sexes. Vaccine 
2013;31:2599-600.  
144. 
Ovsyannikova IG, Haralambieva IH, Vierkant RA, O'Byrne MM, Poland GA. Associations between 
polymorphisms in the antiviral TRIM genes and measles vaccine immunity. Hum Immunol 2013; 
74:768-74. PMCID: PMC3644318 
145. 
Haralambieva IH, Ovsyannikova IG, Pankratz VS, Kennedy RB, Jacobson RM, Poland GA. The genetic 
basis for interindividual immune response variation to measles vaccine: new understanding and new 
vaccine approaches. Expert Rev Vaccines 2013;12:57-70. PMCID: PMC3570049 
146. 
White SJ, Haralambieva IH, Ovsyannikova IG, Vierkant RA, O'Byrne MM, Poland GA. Replication of 
associations between cytokine and cytokine receptor single nucleotide polymorphisms and measles-
specific adaptive immunophenotypic extremes. Hum Immunol 2012;73:636-40. PMCID: PMC3368081 
147. 
White SJ, Boldt KL, Holditch SJ, Poland GA, Jacobson RM. Measles, mumps, and rubella. Clin Obstet 
Gynecol 2012;55:550-9. PMCID: PMC3334858 
148. 
Umlauf BJ, Haralambieva IH, Ovsyannikova IG, Kennedy RB, Pankratz VS, Jacobson RM, Poland GA. 
Associations between demographic variables and multiple measles-specific innate and cell-mediated 
immune responses after measles vaccination. Viral Immunol 2012;25:29-36. PMCID: PMC3271368 
149. 
Poland GA, Jacobson RM. The re-emergence of measles in developed countries: time to develop the 
next-generation measles vaccines? Vaccine 2012;30:103-4. PMCID: PMC3905323 
150. 
Phelan DM, Poland GA. HLA-DR specific monoclonal antibodies block lymphoproliferative response to 
measles vaccine in vitro: a pilot study. Vaccine 2012;30:6628-31. PMCID: PMC3473139 
151. 
Ovsyannikova IG, Pankratz VS, Vierkant RA, Jacobson RM, Poland GA. Consistency of HLA 
associations between two independent measles vaccine cohorts: a replication study. Vaccine 
2012;30:2146-52. PMCID: PMC3288791 
152. 
Ovsyannikova IG, Haralambieva IH, Vierkant RA, O'Byrne MM, Jacobson RM, Poland GA. Effects of 
vitamin A and D receptor gene polymorphisms/haplotypes on immune responses to measles vaccine. 
Pharmacogenet Genomics 2012;22:20-31. PMCID: PMC3237827  
153. 
Kennedy RB, Ovsyannikova IG, Haralambieva IH, O'Byrne MM, Jacobson RM, Pankratz VS, Poland 
GA. Multigenic control of measles vaccine immunity mediated by polymorphisms in measles receptor, 
innate pathway, and cytokine genes. Vaccine 2012;30:2159-67. PMCID: PMC3288471  
154. 
Jacobson RM, Ovsyannikova IG, Vierkant RA, Pankratz VS, Poland GA. Independence of measles-
specific humoral and cellular immune responses to vaccination. Hum Immunol 2012; 73:474-9. PMCID: 
PMC3338862 
155. 
Ovsyannikova IG, Haralambieva IH, Vierkant RA, Pankratz VS, Poland GA. The role of polymorphisms 
in toll-like receptors and their associated intracellular signaling genes in measles vaccine immunity. 
Hum Genetics 2011;130:547-61. PMCID: PMC3924423 


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 22 of 37 
 
156. 
Ovsyannikova IG, Haralambieva IH, Vierkant RA, O'Byrne MM, Jacobson RM, Poland GA. The 
association of CD46, SLAM, and CD209 cellular receptor gene SNPs with variations in measles 
vaccine-induced immune responses--a replication study and examination of novel polymorphisms. Hum 
Hered 2011;72:206-23. PMCID: PMC3242703 
157. 
Jacobson RM, Ovsyannikova IG, Vierkant RA, Shane PV, Poland GA. Human leukocyte antigen 
associations with humoral and cellular immunity following a second dose of measles-containing 
vaccine: Persistence, dampening, and extinction of associations found after a first dose. Vaccine 
2011;29:7982-91. PMCID: PMC3319093 
158. 
Haralambieva IH, Ovsyannikova IG, Umlauf BJ, Vierkant RA, Pankratz SV, Jacobson RM, Poland GA. 
Genetic polymorphisms in host antiviral genes: associations with humoral and cellular immunity to 
measles vaccine. Vaccine 2011;29:8988-97. PMCID: PMC3941984 
159. 
Haralambieva IH, Ovsyannikova IG, O'Byrne M, Pankratz VS, Jacobson RM, Poland GA. A large 
observational study to concurrently assess persistence of measles specific B-cell and T-cell immunity in 
individuals following two doses of MMR vaccine. Vaccine 2011;29:4485-91. PMCID: PMC3117252  
160. 
Haralambieva IH, Ovsyannikova IG, Kennedy RB, Vierkant RA, Pankratz SV, Jacobson RM, Poland 
GA. Associations between single nucleotide polymorphisms and haplotypes in cytokine and cytokine 
receptor genes and immunity to measles vaccination. Vaccine 2011;29:7883-95. PMCID: PMC3191314  
161. 
Yoo KH, Agarwal K, Butterfield M, Jacobson RM, Poland GA, Juhn YJ. Assessment of humoral and 
cell-mediated immune response to measles-mumps-rubella vaccine viruses among patients with 
asthma. Allergy Asthma Proc 2010;31:499-506. PMCID: PMC3941466  
162. 
Haralambieva IH, Ovsyannikova IG, Dhiman N, Vierkant RA, Jacobson RM, Poland GA. Differential 
cellular immune responses to wild-type and attenuated edmonston tag measles virus strains are 
primarily defined by the viral phosphoprotein gene. J Med Virol 2010;82:1966-75. PMCID: 
PMC3924428 
163. 
Haralambieva IH, Ovsyannikova IG, Vierkant RA, Poland GA. Development of a novel efficient 
fluorescence-based plaque reduction microneutralization assay for measles immunity. Clin Vaccine 
Immunol 2008;15:1054-9. PMCID: PMC2446644  
164. 
Dhiman N, Ovsyannikova IG, Vierkant RA, Ryan JE, Pankratz VS, Jacobson RM, Poland GA. 
Associations between SNPs in toll-like receptors and related intracellular signaling molecules and 
immune responses to measles vaccine: preliminary results. Vaccine 2008;26:1731-6. PMCID: 
PMC2292110  
165. 
Dhiman N, Ovsyannikova IG, Vierkant RA, Pankratz VS, Jacobson RM, Poland GA. Associations 
between cytokine/cytokine receptor SNPs and humoral immunity to measles, mumps and rubella in a 
Somali population. Tissue Antigens 2008;72:211-20. PMCID: PMC2595143 
166. 
St.Sauver JL, Jacobson RM, Jacobsen SJ, Vierkant RA, Ovsyannikova IG, Dhiman N, Poland GA. 
Assessing participation bias in a population-based study of measles-mumps-rubella vaccine immunity 
in children and adolescents. Paediatr Perinat Epidemiol 2007;21:376-84.  
167. 
Ovsyannikova IG, Dhiman N, Jacobson RM, Vierkant RA, Pankratz VS, Poland GA. HLA homozygosity 
does not adversely affect measles vaccine-induced cytokine responses. Virology 2007;364:87-94.  
168. 
Dhiman N, Ovsyannikova IG, Cunningham JM, Vierkant RA, Kennedy RB, Pankratz VS, Poland GA, 
Jacobson RM. Associations between measles vaccine immunity and single nucleotide polymorphisms 
in cytokine and cytokine receptor genes. J Infect Dis 2007;195:21-9.  
169. 
Dhiman N, Cunningham JM, Jacobson RM, Vierkant RA, Wu Y, Ovsyannikova IG, Pankratz VS, Poland 
GA. Variations in measles vaccine-specific humoral immunity by polymorphisms in SLAM and CD46 
measles virus receptors. J Allergy Clin Immunol 2007;120:666-72.  
170. 
St.Sauver JL, Jacobsen SJ, Jacobson RM, Vierkant RA, Ovsyannikova IG, Dhiman N, Poland GA. 
Assessing volunteer and recruitment bias in a population-based study of measles-mumps-rubella 
vaccine immunity in children and adolescents. Paediatr Perinat Epidemiol 2006;21:376-84.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 23 of 37 
 
171. 
Ovsyannikova IG, Vierkant RA, Poland GA. Importance of HLA-DQ and HLA-DP polymorphisms in 
cytokine responses to naturally processed HLA-DR-derived measles virus peptides. Vaccine 
2006;24:5381-9.  
172. 
Ovsyannikova IG, Pankratz VS, Vierkant RA, Jacobson RM, Poland GA. Human leukocyte antigen 
haplotypes in the genetic control of immune response to measles-mumps-rubella vaccine. J Infect Dis 
2006;193:655-63.  
173. 
Ovsyannikova IG, Pankratz SV, Vierkant R, Jacobson RM, Poland GA. Human leukocyte antigen 
haplotypes in the genetic control of immune response to measles-mumps-rubella vaccine. J Infect Dis 
2006;193:655-63.  
174. 
Ovsyannikova IG, Jacobson RM, Ryan JE, Vierkant RA, Pankratz VS, Poland GA. Human Leukocyte 
Antigen and Interleukin 2, 10 and 12p40 cytokine responses to measles: Is there evidence of the HLA 
effect? Cytokine 2006;36:173-9.  
175. 
Juhn YJ, Kita H, Lee LA, Swanson RJ, Smith R, Bagniewski SM, Weaver AL, Pankratz VS, Jacobson 
RM, Poland GA. Childhood asthma and measles vaccine response. Ann Allergy Asthma Immunol 2006; 
97:469-76.  
176. 
Bratberg JP, Pinsky NA, Nysse LJ, Sohni Y, Vierkant RA, Poland GA. Measles antibody variation 
among different gamma globulin preparations. Infect Dis Clin Practice 2006;14:281-2.  
177. 
Sauver JL, Dhiman N, Ovsyannikova IG, Jacobson RM, Vierkant RA, Pankratz SV, Jacobsen SJ, 
Poland GA. Extinction of the human leukocyte antigen homozygosity effect after two doses of the 
measles-mumps-rubella vaccine. Hum Immunol 2005;66:788-98.  
178. 
Ryan JE, Ovsyannikova IG, Poland GA. Detection of measles virus-specific interferon-gamma-
secreting T-cells by ELISPOT. Methods Mol Biol 2005;302:207-18.  
179. 
Ryan JE, Ovsyannikova IG, Dhiman N, Pinsky NA, Vierkant RA, Jacobson RM, Poland GA. Inter-
operator variation in ELISPOT analysis of measles virus-specific IFN-gamma secreting T cells. Scand J 
Clin Lab Invest 2005;65:681-90.  
180. 
Ovsyannikova IG, Ryan JE, Vierkant RA, Pankratz VS, Jacobson RM, Poland GA. Immunologic 
significance of HLA class I genes in measles virus-specific IFN-gamma and IL-4 cytokine immune 
responses. Immunogenetics 2005;57:828-36.  
181. 
Ovsyannikova IG, Johnson KL, Naylor S, Poland GA. Identification of HLA-DRB1-bound self-peptides 
following measles virus infection. J Immunol Methods 2005; 297:153-67.  
182. 
Ovsyannikova IG, Jacobson RM, Vierkant RA, Jacobsen SJ, Pankratz VS, Poland GA. Human 
leukocyte antigen class II alleles and rubella-specific humoral and cell-mediated immunity following 
measles-mumps-rubella-II vaccination. J Infect Dis 2005;191:515-9.  
183. 
Ovsyannikova IG, Jacobson RM, Ryan JE, Vierkant RA, Pankratz VS, Jacobsen SJ, Poland GA. HLA 
class II alleles and measles virus-specific cytokine immune response following two doses of measles 
vaccine. Immunogenetics 2005; 56:798-807.  
184. 
Johnson KL, Ovsyannikova IG, Poland G, Muddiman DC. Identification of class II HLA-DRB1*03-bound 
measles virus peptides by 2D-liquid chromatography tandem mass spectrometry. J Proteome Res 
2005;4:2243-9.  
185. 
Howe RC, Ovsyannikova IG, Pinsky NA, Poland GA. Identification of Th0 cells responding to measles 
virus. Hum Immunol2005;66:104-15.  
186. 
Howe RC, Dhiman N, Ovsyannikova IG, Poland GA. Induction of CD4 T cell proliferation and Th1-like 
cytokine responses  in vitro  to measles virus. Clin Exp Immunol 2005;140:333-42.  
187. 
Dhiman N, Ovsyannikova IG, Ryan JE, Jacobson RM, Vierkant RA, Pankratz VS, Jacobsen SJ, Poland 
GA. Correlations among measles virus-specific antibody, lymphoproliferation and Th1/Th2 cytokine 
responses following measles-mumps-rubella-II (MMR-II) vaccination. Clin Exp Immunol 2005;142:498-
504.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 24 of 37 
 
188. 
Dhiman N, Ovsyannikova IG, Jacobson RM, Vierkant RA, Pankratz VS, Jacobsen SJ, Poland GA. 
Correlates of lymphoproliferative responses to measles, mumps, and rubella (MMR) virus vaccines 
following MMR-II vaccination in healthy children. Clin Immunol 2005;115:154-61.  
189. 
Ovsyannikova IG, Ryan JE, Howe RC, Pinsky NA, Vierkant RA, Pankratz VS, Jacobson RM, Poland 
GA. Presence of memory T-cell responses directed against HLA class II measles virus peptides post 
measles vaccination. Int J Infect Dis 2004;8:S224-S5.  
190. 
Ovsyannikova IG, Poland GA, Easler NJ, Vierkant RA. Influence of HLA-DRB1 alleles on 
lymphoproliferative responses to a naturally processed and presented measles virus phosphoprotein in 
measles immunized individuals. Hum Immunol 2004;65:209-17.  
191. 
Ovsyannikova IG, Johnson KL, Vierkant RA, Muddiman DC, Poland GA. HLA polymorphisms and T cell 
cytokine responses to processed HLA class II measles virus peptides: the rationale for a measles 
peptide vaccine. Clin Microbiol Infect 2004;10:54.  
192. 
Ovsyannikova IG, Johnson KL, Muddiman DC, Vierkant RA, Poland GA. Identification and 
characterization of novel, naturally processed measles virus class II HLA-DRB1 peptides. J Virol 
2004;78:42-51.  
193. 
Ovsyannikova IG, Jacobson RM, Vierkant RA, Pankratz SV, Jacobsen SJ, Poland GA. Associations 
between human leukocyte antigen (HLA) alleles and very high levels of measles antibody following 
vaccination. Vaccine 2004;22:1914-20.  
194. 
Jacobson RM, Poland GA. The genetic basis for measles vaccine failure. Acta Paediatrica 2004; Suppl 
445:43-7.  
195. 
Dhiman N, Ovsyannikova IG, Howe RC, Ryan JE, Jacobson RM, Poland GA. Interleukin-4 induced by 
measles virus and measles-derived peptides as measured by IL-4 receptor-blocking ELISA. J Immunol 
Methods 2004;287:217-25.  
196. 
Dhiman N, Jacobson RM, Poland GA. Measles virus receptors: SLAM and CD46. Rev Med Virol 
2004;14:217-29.  
197. 
St Sauver JL, Schaid DJ, Vierkant RA, Jacobson RM, Jacobsen SJ, Ovsyannikova IG, Poland GA. 
Associations between measles antibody levels and the protein structure of class II human leukocyte 
antigens. Hum Immunol 2003;64:696-707.  
198. 
Pinsky NA, Huddleston JM, Jacobson RM, Wollan PC, Poland GA. Effect of multiple freeze-thaw cycles 
on detection of measles, mumps, and rubella virus antibodies. Clin Diag Lab Immunol 2003;10:19-21.  
199. 
Ovsyannikova IG, Reid KC, Jacobson RM, Oberg AL, Klee GG, Poland GA. Cytokine production 
patterns and antibody response to measles vaccine. Vaccine 2003;21:3946-53.  
200. 
Ovsyannikova IG, Johnson KL, Naylor S, Poland GA. Identification and immunogenicity of a naturally 
processed measles virus peptide eluted from class II HLA-DRB1*0301. Clin Microbiol Infect 2003;9:17.  
201. 
Ovsyannikova IG, Johnson KL, Naylor S, Muddiman DC, Poland GA. Naturally processed measles 
virus peptide eluted from class II HLA-DRB1*03 recognized by T lymphocytes from human blood. 
Virology 2003;312:495-506.  
202. 
Ovsyannikova IG, Dhiman N, Jacobson RM, Vierkant RA, Poland GA. Frequency of measles virus-
specific CD4+ and CD8+ T cells in subjects seronegative or highly seropositive for measles vaccine. 
Clin Diag Lab Immunol 2003;10:411-6.  
203. 
Jacobson RM, Poland GA, Vierkant RA, Pankratz VS, Schaid DJ, Jacobsen SJ, Sauver JL, Moore SB. 
The association of class I HLA alleles and antibody levels following a single dose of measles vaccine. 
Hum Immunol 2003;64:103-9.  
204. 
Dhiman N, Ovsyannikova IG, Pinsky NA, Vierkant RA, Jacobsen SJ, Jacobson RM, Poland GA. Lack of 
association between transporter associated with antigen processing (TAP) and HLA-DM gene 
polymorphisms and antibody levels following measles vaccination. Eur J Immunogenet 2003;30:195-
200.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 25 of 37 
 
205. 
Dhiman N, Bonilla RG, Jacobson RM, O'Kane D, Poland GA. Differential HLA gene expression in 
measles vaccine seropositive and seronegative subjects: a pilot study. Scand J Infect Dis 2003; 
35:332-6.  
206. 
St Sauver JL, Ovsyannikova IG, Jacobson RM, Jacobsen SJ, Vierkant RA, Schaid DJ, Pankratz VS, 
Green EM, Poland GA. Associations between human leukocyte antigen homozygosity and antibody 
levels to measles vaccine. J Infect Dis 2002;185:1545-9.  
207. 
Sauver JL, Jacobson RM, Vierkant RA, Jacobsen SJ, Green EM, Poland GA. Association of parental 
vaccination reports with measles, mumps, and rubella protective antibody levels: Comparison of Somali 
immigrant, Hispanic migrant, and US children in Rochester, Minn. Mayo Clin Proc 2002;77:241-5.  
208. 
Poland GA, Jacobson RM, Vierkant RA, Colbourne SA, Thampy AM, Pankratz VS, Jacobsen SJ. Effect 
of differing immunization policies on circulating measles antibody levels in US and Canadian children. 
Mayo Clin Proc 2002;77:446-51.  
209. 
St.Sauver JL, Jacobson RM, Vierkant RA, Jacobsen SJ, Green EM, Schaid DJ, Poland GA. 
Correlations between measles, mumps, and rubella serum antibody levels in Olmsted County school 
children. Vaccine 2001;19:1363-8.  
210. 
Poland GA, Ovsyannikova IG, Jacobson RM, Vierkant RA, Jacobsen SJ, Pankratz VS, Schaid DJ. 
Identification of an association between HLA class II alleles and low antibody levels after measles 
immunization. Vaccine 2001;20:430-8.  
211. 
Ovsyannikova IG, Johnson KL, Naylor S, Poland GA. Analysis of naturally processed peptides bound to 
HLA-DR3 alleles influenced by measles vaccine virus (MVV) infection. Mol Aspects Viral Immunity 
2001;220:63.  
212. 
Ovsyannikova IG, Jacobson RM, Vierkant R, Schaid DJ, Pankratz SV, Jacobsen SJ, Poland GA. The 
association of class II HLA alleles and antibody levels after a single dose of measles immunization. 
Gene Ther 2001;8:S10.  
213. 
Grote D, Russell SJ, Cornu TI, Cattaneo R, Vile R, Poland GA, Fielding AK. Live attenuated measles 
virus induces regression of human lymphoma xenografts in immunodeficient mice. Blood 
2001;97:3746-54.  
214. 
Pardi DS, Tremaine WJ, Sandborn WJ, Loftus EV, Poland GA. Early measles virus infection is 
associated with the development of inflammatory bowel disease. Am J Gastroenterol 2000; 95:1480-5.  
215. 
Ovsyannikova IG, Sohni Y, Shitaye H, Jacobson R, Vierkant R, Schaid D, Jacobsen S, Poland G. 
Association of class ii HLA-DQ alleles and antibody response after measles immunizatdion: Preliminary 
results. J Allergy Clin Immunol 2000;105:S178.  
216. 
Ovsyannikova IG, Johnson KL, Naylor S, Poland GA. Isolation and rapid identification of an abundant 
self-peptide from class II HLA-DRB1*0401 alleles induced by measles vaccine virus infection. J 
Immunol Methods 2000;246:1-12.  
217. 
Poland GA, Jacobson RM, Colbourne SA, Thampy AM, Lipsky JJ, Wollan PC, Roberts P, Jacobsen SJ. 
Measles antibody seroprevalence rates among immunized Inuit, Innu and Caucasian subjects. Vaccine 
1999;17:1525-31.  
218. 
Poland GA. Immunogenetic mechanisms of antibody response to measles vaccine:the role of the HLA 
genes. Vaccine 1999;17:1719-25.  
219. 
Pardi DS, Tremaine WJ, Sandborn WJ, Loftus EV, Jr., Poland GA, Melton LJ, III. Perinatal exposure to 
measles virus is not associated with the development of inflammatory bowel disease. Inflamm Bowel 
Dis 1999;5:104-6.  
220. 
Ovsyannikova IG, Poland GA, Jacobson RM, Vierkant R, Schaid DJ, Jacobsen SJ. Association of HLA-
DQB 1 alleles and antibody (Ab) levels after measles immunization: preliminary results. Clin Microbiol 
Infect1999;5 (Suppl) 3:126.  
221. 
Grizzard T, Poland G, Jacobson R, Vierkant R. Low measles antibody levels are associated with class I 
HLA homozygosity in Hispanics. Clin Microbiol Infect 1999;5 (Suppl) 3:125.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 26 of 37 
 
222. 
Poland GA, Jacobson RM, Schaid DJ, Moore SB, Jacobsen SJ. The association between HLA class I 
alleles and measles vaccine-induced antibody response: evidence of a significant association. Vaccine 
1998;16:1869-71.  
223. 
Poland GA, Jacobson RM, Colbourne SA, Thampy AM, Lipsky JJ, Wollan PC, Jacobsen SJ, Roberts P. 
Assessing measles vaccine immunogenicity: measles antibody seroprevalence rates among 
immunized Inuit, Innu, and Caucasian subjects. Vaccine 1998;17:1525-31.  
224. 
Poland GA. Variability in immune response to pathogens: using measles vaccine to probe 
immunogenetic determinants of response. Am J Hum Genet 1998;62:215-20.  
225. 
Hayney MS, Poland GA, Jacobson RM, Rabe D, Schaid DJ, Jacobsen SJ, Lipsky JJ. Relationship of 
HLA-DQA1 alleles and humoral antibody following measles vaccination. Int J Infect Dis 1998;2:143-6.  
226. 
Poland GA, Jacobson RM, Thampy AM, Colbourne SA, Wollan PC, Lipsky JJ, Jacobson SJ. Measles 
re-immunization in children seronegative after initial immunization. JAMA 1997; 277:1156-8.  
227. 
Poland GA, Hayney MS, Jacobson RM, Rabe D, Schaid DJ, Jacobsen SJ, Wollan PC, Lipsky JJ. The 
association between measles vaccine-induced antibody response and HLA-DQA1 alleles. In: Brown F, 
Burton D, Doherty P, Mekalanos J, Norrby E, editors. Vaccines 97: Molecular Approaches to the 
Control of Infectious Diseases: Cold Spring Harbor Laboratory Press;1997. p. 229-33. 
228. 
Hayney MS, Poland GA, Dimanlig P, Schaid DJ, Jacobson RM, Lipsky JJ. Polymorphisms of the TAP2 
gene may influence antibody response to live measles vaccine virus. Vaccine 1997;15:3-6.  
229. 
Hayney MS, Poland GA, Jacobson RM, Schaid DJ, Lipsky JJ. The influence of the HLA-DRB1 * 13 
allele on measles vaccine response. J Invest Med 1996;44:261-3.  
230. 
Anders JF, Jacobson RM, Poland GA, Jacobsen SJ, Wollan PC. Secondary failure rates of measles 
vaccines: a metaanalysis of published studies. Pediatr Infect Dis J 1996;15:62-6.  
231. 
Poland GA, Hayney MS, Schaid DJ, Jacobson RM, Lipsky JJ. Class II HLA-DR homozygosity is 
associated with non-response to measles vaccine in U.S. children. FASEB J 1995;9:A240-1397.  
232. 
Hayney MS, Poland GA, Jacobson RM, Schaid DJ, Lipsky JJ. Absence of the HLA-DRB1*13 allele may 
be a marker for measles vaccine failure. J Invest Med 1995;43:486A.  
233. 
Hayney MS, Dimanlig P, Lipsky JJ, Jacobson RM, Poland GA. TAP2 allele heterozygosity at position 
665 may predict measles vaccine antibody response. FASEB J 1995;9:A240-1398.  
234. 
Poland GA, Jacobson RM. Failure to reach the goal of measles elimination. Apparent paradox of 
measles infections in immunized persons. Arch Intern Med 1994;154:1815-20.  
235. 
Poland GA, Jacobson RM. Measles occurring in vaccinated persons: Exploring the apparent paradox. 
Clin Res 1992;40:609A.  
236. 
Poland GA, Jacobson RM, Murtaugh PA. The two-dose measles immunization policy: What will it cost? 
Clin Res 1991;39:606A.  
237. 
Poland GA, Jacobson RM, Murtaugh PA. A model of measles vaccine strategy: one or two doses? Clin 
Res 1991;39:639A.  
238. 
Jacobson RM, Poland GA, Murtaugh PA. Variables influencing the effectiveness of a two-dose measles 
vaccine strategy. Clin Res 1991;39:637A-A.  
239. 
Poland GA, Nichol KL. Medical students as sources of rubella and measles outbreaks. Arch Intern Med 
1990;150:44-6.  
240. 
Ovsyannikova IG, Jacobson RM, Dhiman N, Vierkant RA, Pankratz VS, Poland GA. Human leukocyte 
antigen and cytokine receptor gene polymorphisms associated with heterogeneous immune responses 
to mumps viral vaccine. Pediatrics 2008;121:e1091-e9. PMCID: PMC2668976  
241. 
Ovsyannikova IG, Pankrantz VS, Salk HM, Kennedy RB, Poland GA. HLA alleles associated with the 
adaptive immune response to smallpox vaccine: a replication study. Hum Genet 2014; 133:1083-92. 
PMCID: PMC4127812 
242. 
Ovsyannikova IG, Haralambieva IH, Kennedy RB, O'Byrne MM, Pankratz VS, Poland GA. Genetic 
variation in IL18R1 and IL18 genes and inteferon gamma ELISPOT response to smallpox vaccination: 
an unexpected relationship. J Infect Dis 2013;208:1422-30. PMCID: PMC3789570 


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 27 of 37 
 
243. 
Haralambieva IH, Ovsyannikova IG, Kennedy RB, Larrabee BR, Shane Pankratz V, Poland GA. Race 
and sex-based differences in cytokine immune responses to smallpox vaccine in healthy individuals. 
Hum Immunol 2013;74:1263-6. PMCID: PMC4170575  
244. 
Ovsyannikova IG, Kennedy RB, O'Byrne M, Jacobson RM, Pankratz VS, Poland GA. Genome-wide 
association study of antibody response to smallpox vaccine. Vaccine 2012;30:4182-9. PMCID: 
PMC3367131  
245. 
Ovsyannikova IG, Haralambieva IH, Kennedy RB, Pankratz VS, Vierkant RA, Jacobson RM, Poland 
GA. Impact of cytokine and cytokine receptor gene polymorphisms on cellular immunity after smallpox 
vaccination. Gene 2012;510:59-65. PMCID: PMC3463724  
246. 
Kennedy RB, Ovsyannikova IG, Shane PV, Haralambieva IH, Vierkant RA, Poland GA. Genome-wide 
analysis of polymorphisms associated with cytokine responses in smallpox vaccine recipients. Hum 
Genet 2012;131:1403-21. PMCID: PMC4170585 
247. 
Kennedy RB, Ovsyannikova IG, Pankratz VS, Haralambieva IH, Vierkant RA, Jacobson RM, Poland 
GA. Genome-wide genetic associations with IFNgamma response to smallpox vaccine. Hum Genet 
2012;131:1433-51. PMCID: PMC4170655 
248. 
Haralambieva IH, Oberg AL, Dhiman N, Ovsyannikova IG, Kennedy RB, Grill DE, Jacobson RM, 
Poland GA. High-dimensional gene expression profiling studies in high and low responders to primary 
smallpox vaccination. J Infect Dis 2012;206:1512-20. PMCID: PMC3475634  
249. 
Ovsyannikova IG, Vierkant RA, Pankratz VS, Jacobson RM, Poland GA. Human leukocyte antigen 
genotypes in the genetic control of adaptive immune responses to smallpox vaccine. J Infect Dis 
2011;203:1546-55. PMCID: PMC3096794 
250. 
Oberg AL, Dhiman N, Grill DE, Ryan JE, Kennedy RB, Poland GA. Optimizing high dimensional gene 
expression studies for immune response following smallpox vaccination using Taqman(R) Low density 
immune arrays. J Immunol Methods 2011;366:69-78. PMCID: PMC3051012  
251. 
Lane JM, Poland GA. Why not destropy the remaining smallpox virus stocks? Vaccine 2011; 29:2823-
4.  
252. 
Haralambieva IH, Ovsyannikova IG, Dhiman N, Kennedy RB, O'Byrne M, Pankrantz VS, Jacobson RM, 
Poland GA. Common SNPs/haplotypes in IL18R1 and IL18 genes are associated with variations in 
humoral immunity to smallpox vaccination in Caucasians and African-Americans. J Infect Dis 
2011;204:433-41. PMCID: PMC3132141 
253. 
Ryan JE, Dhiman N, Ovsyannikova IG, Vierkant RA, Pankratz VS, Poland GA. Response surface 
methodology to determine optimal cytokine responses in human peripheral blood mononuclear cells 
after smallpox vaccination. J Immunol Methods 2009;341:97-105. PMCID: PMC2835303  
254. 
Kennedy RB, Ovsyannikova IG, Jacobson RM, Poland GA. The immunology of smallpox vaccines. Curr 
Opin Immunol 2009;21:314-20. PMCID: PMC2826713 
255. 
Kennedy R, Poland GA. T-Cell epitope discovery for variola and vaccinia viruses. Rev Med Virol 
2007;17:93-113.  
256. 
Kennedy RB, Poland GA. The smallpox vaccine. In: Barrett AD, Stanberry LR, editors. Vaccines for 
biodefense and emerging and neglected diseases. London, UK: Academic Press; 2009. p. 681-704. 
257. 
Poland G. Smallpox vaccines: from first to second to third generation. Lancet 2005;365:362-3.  
258. 
Jacobson RM, Grill DE, Oberg AL, Tosh PK, Ovsyannikova IG, Poland GA. Profiles of influenza 
A/H1N1 vaccine response using hemagglutination-inhibition titers. Hum Vaccin Immunother 
2015;11:961-9. PMCID: PMC4514374  
259. 
Kennedy RB, Simon WL, Gibson MJ, Goergen KM, Grill DE, Oberg AL, Poland GA. The composition of 
immune cells serves as a predictor of adaptive immunity in a cohort of 50- to 74-year-old adults. 
Immunology 2016;148:266-75. PMCID: PMC4913285 
260. 
Zimmermann MT, Kennedy RB, Grill DE, Oberg AL, Goergen KM, Ovsyannikova IG, Haralambieva IH, 
Poland GA. Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict 


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 28 of 37 
 
Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune 
Cell Populations. Front Immunol 2017;8:445. PMCID: PMC5399034 
261. 
Kennedy RB, Ovsyannikova IG, Haralambieva IH, Oberg AL, Zimmermann MT, Grill DE, Poland GA. 
Immunosenescence-related transcriptomic and immunologic changes in older individuals following 
influenza vaccination. Front Immunol 2016;7:450. PMCID: PMC5089977 
262. 
Haralambieva IH, Painter SD, Kennedy RB, Ovsyannikova IG, Lambert ND, Goergen KM, Oberg AL, 
Poland GA. The Impact of Immunosenescence on Humoral Immune Response Variation after Influenza 
A/H1N1 Vaccination in Older Subjects. PLos ONE 2015;10:e0122282. PMCID: PMC4376784 
263. 
Haralambieva IH, Ovsyannikova IG, Kennedy RB, Zimmermann MT, Grill DE, Oberg AL, Poland GA. 
Transcriptional signatures of influenza A/H1N1-specific IgG memory-like B cell response in older 
individuals. Vaccine 2016;34:3993-4002. NIHMS880612  
264. 
Zimmermann MT, Oberg AL, Grill DE, Ovsyannikova IG, Haralambieva IH, Kennedy RB, Poland GA. 
System-Wide Associations between DNA-Methylation, Gene Expression, and Humoral Immune 
Response to Influenza Vaccination. PLos ONE 2016;11:e0152034. PMCID: PMC4816338 
265. 
Ovsyannikova IG, White SJ, Larrabee BR, Grill DE, Jacobson RM, Poland GA. Leptin and leptin-related 
gene polymorphisms, obesity, and influenza A/H1N1 vaccine-induced immune responses in older 
individuals. Vaccine 2014;32:881-7. PMCID: PMC3922536 
266. 
Bianchessi V, Badi I, Bertolotti M, Nigro P, D'Alessandra Y, Capogrossi MC, Zanobini M, Pompilio G, 
Raucci A, Lauri A. The mitochondrial lncRNA ASncmtRNA-2 is induced in aging and replicative 
senescence in Endothelial Cells. J Molec Cell Cardiol 2015;81:62-70.  
267. 
Galicia JC, Naqvi AR, Ko CC, Nares S, Khan AA. MiRNA-181a regulates Toll-like receptor agonist-
induced inflammatory response in human fibroblasts. Genes Immun 2014;15:333-7.  
268. 
Cichocki F, Felices M, McCullar V, Presnell SR, Al-Attar A, Lutz CT, Miller JS. Cutting edge: microRNA-
181 promotes human NK cell development by regulating Notch signaling. J Immunol 2011;187:6171-5.  
269. 
Martin J, Jenkins RH, Bennagi R, Krupa A, Phillips AO, Bowen T, Fraser DJ. Post-transcriptional 
regulation of Transforming Growth Factor Beta-1 by microRNA-744. PLos ONE 2011;6:e25044.  
270. 
Zhang X, Han X, Tang Y, Wu Y, Qu B, Shen N. miR-744 enhances type I interferon signaling pathway 
by targeting PTP1B in primary human renal mesangial cells. Sci Rep 2015;5:12987.  
271. 
Nakamura M, Kanda T, Sasaki R, Haga Y, Jiang X, Wu S, Nakamoto S, Yokosuka O. MicroRNA-122 
Inhibits the Production of Inflammatory Cytokines by Targeting the PKR Activator PACT in Human 
Hepatic Stellate Cells. PLos ONE 2015;10:e0144295.  
272. 
Ovsyannikova IG, Haralambieva IH, Kennedy RB, Goergen K, Grill DE, Oberg AL, Poland GA. Astract. 
OR.11. Pre-adaptive innate transcriptional signatures associated with immune responses after 
seasonal influenza vaccine. FOCIS Boston, MA; June 22-25, 2016. 
273. 
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, 
Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for 
interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102:15545-50.  
274. 
White SJ, Taylor MJ, Hurt RT, Jensen MD, Poland GA. Leptin-based adjuvants: an innovative approach 
to improve vaccine response. Vaccine 2013;31:1666-72. PMCID: PMC3596421 
275. 
Tsang JS, Schwartzberg PL, Kotliarov Y, Biancotto A, Xie Z, Germain RN, Wang E, Olnes MJ, 
Narayanan M, Golding H, Moir S, Dickler HB, Perl S, Cheung F. Global analyses of human immune 
variation reveal baseline predictors of postvaccination responses. Cell 2014;157:499-513.  
276. 
Crotty S. Follicular helper CD4 T cells (TFH). Ann Rev Immunol 2011;29:621-63.  
277. 
Poland GA, Ovsyannikova IG, Kennedy RB, Lambert ND, Kirkland JL. A systems biology approach to 
the effect of aging, immunosenescence and vaccine response. Curr Opin Immunol 2014;29C:62-8. 
PMCID: PMC4119552 
278. 
Trzonkowski P, Mysliwska J, Szmit E, Wieckiewicz J, Lukaszuk K, Brydak LB, Machala M, Mysliwski A. 
Association between cytomegalovirus infection, enhanced proinflammatory response and low level of 


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 29 of 37 
 
anti-hemagglutinins during the anti-influenza vaccination--an impact of immunosenescence. Vaccine 
2003;21:3826-36.  
279. 
Furman D, Jojic V, Sharma S, Shen-Orr SS, Angel CJ, Onengut-Gumuscu S, Kidd BA, Maecker HT, 
Concannon P, Dekker CL, Thomas PG, Davis MM. Cytomegalovirus infection enhances the immune 
response to influenza. Sci Trans Med 2015;7:281ra43.  
280. 
Nakaya HI, Hagan T, Duraisingham SS, Lee EK, Kwissa M, Rouphael N, Frasca D, Gersten M, Mehta 
AK, Gaujoux R, Li GM, Gupta S, Ahmed R, Mulligan MJ, Shen-Orr S, Blomberg BB, Subramaniam S, 
Pulendran B. Systems Analysis of Immunity to Influenza Vaccination across Multiple Years and in 
Diverse Populations Reveals Shared Molecular Signatures. Immunity 2015;43:1186-98.  
281. 
Aldridge JR, Jr., Moseley CE, Boltz DA, Negovetich NJ, Reynolds C, Franks J, Brown SA, Doherty PC, 
Webster RG, Thomas PG. TNF/iNOS-producing dendritic cells are the necessary evil of lethal influenza 
virus infection. Proc Natl Acad Sci USA 2009;106:5306-11.  
282. 
Snelgrove RJ, Edwards L, Rae AJ, Hussell T. An absence of reactive oxygen species improves the 
resolution of lung influenza infection. Eur J Immunol 2006;36:1364-73.  
283. 
Orange JS, Wang B, Terhorst C, Biron CA. Requirement for natural killer cell-produced interferon 
gamma in defense against murine cytomegalovirus infection and enhancement of this defense pathway 
by interleukin 12 administration. J Exp Med 1995;182:1045-56.  
284. 
Lee SH, Miyagi T, Biron CA. Keeping NK cells in highly regulated antiviral warfare. Trends Immunol 
2007;28:252-9.  
285. 
Loh J, Chu DT, O'Guin AK, Yokoyama WM, Virgin HW. Natural killer cells utilize both perforin and 
gamma interferon to regulate murine cytomegalovirus infection in the spleen and liver. J Virol 2005; 
79:661-7.  
286. 
Sato K, Hida S, Takayanagi H, Yokochi T, Kayagaki N, Takeda K, Yagita H, Okumura K, Tanaka N, 
Taniguchi T, Ogasawara K. Antiviral response by natural killer cells through TRAIL gene induction by 
IFN-alpha/beta. Eur J Immunol 2001;31:3138-46.  
287. 
Mirandola P, Ponti C, Gobbi G, Sponzilli I, Vaccarezza M, Cocco L, Zauli G, Secchiero P, Manzoli FA, 
Vitale M. Activated human NK and CD8+ T cells express both TNF-related apoptosis-inducing ligand 
(TRAIL) and TRAIL receptors but are resistant to TRAIL-mediated cytotoxicity. Blood 2004;104:2418-
24.  
288. 
Zingoni A, Sornasse T, Cocks BG, Tanaka Y, Santoni A, Lanier LL. NK cell regulation of T cell-
mediated responses. Molec Immunol 2005;42:451-4.  
289. 
French AR, Yokoyama WM. Natural killer cells and viral infections. Curr Opin Immunol 2003;15:45-51.  
290. 
Gerosa F, Baldani-Guerra B, Nisii C, Marchesini V, Carra G, Trinchieri G. Reciprocal activating 
interaction between natural killer cells and dendritic cells. J Exp Med 2002;195:327-33.  
291. 
Zingoni A, Sornasse T, Cocks BG, Tanaka Y, Santoni A, Lanier LL. Cross-talk between activated 
human NK cells and CD4+ T cells via OX40-OX40 ligand interactions. J Immunol 2004;173:3716-24.  
292. 
Wilder JA, Koh CY, Yuan D. The role of NK cells during in vivo antigen-specific antibody responses. J 
Immunol 1996;156:146-52.  
293. 
Dorner BG, Smith HR, French AR, Kim S, Poursine-Laurent J, Beckman DL, Pingel JT, Kroczek RA, 
Yokoyama WM. Coordinate expression of cytokines and chemokines by NK cells during murine 
cytomegalovirus infection. J Immunol  2004;172:3119-31.  
294. 
Raulet DH. Interplay of natural killer cells and their receptors with the adaptive immune response. Nat 
Immunol 2004;5:996-1002.  
295. 
He XS, Draghi M, Mahmood K, Holmes TH, Kemble GW, Dekker CL, Arvin AM, Parham P, Greenberg 
HB. T cell-dependent production of IFN-gamma by NK cells in response to influenza A virus. J Clin 
Invest 2004;114:1812-9.  
296. 
Long BR, Michaelsson J, Loo CP, Ballan WM, Vu BA, Hecht FM, Lanier LL, Chapman JM, Nixon DF. 
Elevated frequency of gamma interferon-producing NK cells in healthy adults vaccinated against 
influenza virus. Clin Vaccine Immunol 2008;15:120-30.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 30 of 37 
 
297. 
Calabro S, Tortoli M, Baudner BC, Pacitto A, Cortese M, O'Hagan DT, De Gregorio E, Seubert A, Wack 
A. Vaccine adjuvants alum and MF59 induce rapid recruitment of neutrophils and monocytes that 
participate in antigen transport to draining lymph nodes. Vaccine 2011;29:1812-23.  
298. 
Julkunen I, Melen K, Nyqvist M, Pirhonen J, Sareneva T, Matikainen S. Inflammatory responses in 
influenza A virus infection. Vaccine 2000;19 Suppl 1:S32-S7.  
299. 
Stevens J, Corper AL, Basler CF, Taubenberger JK, Palese P, Wilson IA. Structure of the uncleaved 
human H1 hemagglutinin from the extinct 1918 influenza virus. Science 2004;303:1866-70.  
300. 
Gamblin SJ, Haire LF, Russell RJ, Stevens DJ, Xiao B, Ha Y, Vasisht N, Steinhauer DA, Daniels RS, 
Elliot A, Wiley DC, Skehel JJ. The structure and receptor binding properties of the 1918 influenza 
hemagglutinin. Science 2004;303:1838-42.  
301. 
Stevens J, Blixt O, Tumpey TM, Taubenberger JK, Paulson JC, Wilson IA. Structure and receptor 
specificity of the hemagglutinin from an H5N1 influenza virus. Science 2006;312:404-10.  
302. 
Shinya K, Ebina M, Yamada S, Ono M, Kasai N, Kawaoka Y. Avian flu: influenza virus receptors in the 
human airway. Nature 2006;440:435-6.  
303. 
Cinatl J, Jr., Michaelis M, Doerr HW. The threat of avian influenza a (H5N1): part II: Clues to 
pathogenicity and pathology. Med Microbiol Immunol 2007;196:191-201.  
304. 
Cinatl J, Jr., Michaelis M, Doerr HW. The threat of avian influenza A (H5N1). Part I: epidemiologic 
concerns and virulence determinants. Med Microbiol Immunol 2007;196:181-90.  
305. 
Skehel JJ, Wiley DC. Receptor binding and membrane fusion in virus entry: the influenza 
hemagglutinin. Ann Rev Biochem 2000;69:531-69.  
306. 
Daniels R, Kurowski B, Johnson AE, Hebert DN. N-linked glycans direct the cotranslational folding 
pathway of influenza hemagglutinin. Mol Cell 2003;11:79-90.  
307. 
Vigerust DJ, Shepherd VL. Virus glycosylation: role in virulence and immune interactions. Trends 
Microbiol 2007;15:211-8.  
308. 
Wagner R, Wolff T, Herwig A, Pleschka S, Klenk HD. Interdependence of hemagglutinin glycosylation 
and neuraminidase as regulators of influenza virus growth: a study by reverse genetics. J Virol 2000; 
74:6316-23.  
309. 
Olofsson S, Kumlin U, Dimock K, Arnberg N. Avian influenza and sialic acid receptors: more than 
meets the eye? Lancet Infect Dis 2005;5:184-8.  
310. 
Ha Y, Stevens DJ, Skehel JJ, Wiley DC. X-ray structures of H5 avian and H9 swine influenza virus 
hemagglutinins bound to avian and human receptor analogs. Proc Natl Acad Sci USA 2001; 98:11181-
6.  
311. 
Kobasa D, Takada A, Shinya K, Hatta M, Halfmann P, Theriault S, Suzuki H, Nishimura H, Mitamura K, 
Sugaya N, Usui T, Murata T, Maeda Y, Watanabe S, Suresh M, Suzuki T, Suzuki Y, Feldmann H, 
Kawaoka Y. Enhanced virulence of influenza A viruses with the haemagglutinin of the 1918 pandemic 
virus. Nature 2004;431:703-7.  
312. 
Glaser L, Stevens J, Zamarin D, Wilson IA, Garcia-Sastre A, Tumpey TM, Basler CF, Taubenberger 
JK, Palese P. A single amino acid substitution in 1918 influenza virus hemagglutinin changes receptor 
binding specificity. J Virol 2005;79:11533-6.  
313. 
Stevens J, Blixt O, Glaser L, Taubenberger JK, Palese P, Paulson JC, Wilson IA. Glycan microarray 
analysis of the hemagglutinins from modern and pandemic influenza viruses reveals different receptor 
specificities. J Mol Biol 2006;355:1143-55.  
314. 
Tumpey TM, Maines TR, Van Hoeven N, Glaser L, Solorzano A, Pappas C, Cox NJ, Swayne DE, 
Palese P, Katz JM, Garcia-Sastre A. A two-amino acid change in the hemagglutinin of the 1918 
influenza virus abolishes transmission. Science 2007;315:655-9.  
315. 
Auewarakul P, Suptawiwat O, Kongchanagul A, Sangma C, Suzuki Y, Ungchusak K, 
Louisirirotchanakul S, Lerdsamran H, Pooruk P, Thitithanyanont A, Pittayawonganon C, Guo CT, 
Hiramatsu H, Jampangern W, Chunsutthiwat S, Puthavathana P. An avian influenza H5N1 virus that 
binds to a human-type receptor. J Virol 2007;81:9950-5.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 31 of 37 
 
316. 
Klenk HD, Wagner R, Heuer D, Wolff T. Importance of hemagglutinin glycosylation for the biological 
functions of influenza virus. Virus Res 2002;82:73-5.  
317. 
Yang ZY, Wei CJ, Kong WP, Wu L, Xu L, Smith DF, Nabel GJ. Immunization by avian H5 influenza 
hemagglutinin mutants with altered receptor binding specificity. Science 2007;317:825-8.  
318. 
Thomas PG, Dash P, Aldridge JR, Jr., Ellebedy AH, Reynolds C, Funk AJ, Martin WJ, Lamkanfi M, 
Webby RJ, Boyd KL, Doherty PC, Kanneganti TD. The intracellular sensor NLRP3 mediates key innate 
and healing responses to influenza A virus via the regulation of caspase-1. Immunity 2009;30:566-75.  
319. 
Boehme KW, Compton T. Innate sensing of viruses by toll-like receptors. J Virol 2004;78:7867-73.  
320. 
Rassa JC, Ross SR. Viruses and Toll-like receptors. Microbes Infect 2003;5:961-8.  
321. 
Takeda K, Kaisho T, Akira S. Toll-like receptors. Ann Rev Immunol 2003;21:335-76.  
322. 
Medzhitov R. Toll-like receptors and innate immunity. Nat Rev Immunol 2001;1:135-45.  
323. 
Lund JM, Alexopoulou L, Sato A, Karow M, Adams NC, Gale NW, Iwasaki A, Flavell RA. Recognition of 
single-stranded RNA viruses by Toll-like receptor 7. Proc Natl Acad Sci USA 2004;101:5598-603.  
324. 
Diebold SS, Kaisho T, Hemmi H, Akira S, Reis e S. Innate antiviral responses by means of TLR7-
mediated recognition of single-stranded RNA. Science 2004;303:1529-31.  
325. 
Heil F, Hemmi H, Hochrein H, Ampenberger F, Kirschning C, Akira S, Lipford G, Wagner H, Bauer S. 
Species-specific recognition of single-stranded RNA via toll-like receptor 7 and 8. Science 2004; 
303:1526-9.  
326. 
Koyama S, Ishii KJ, Kumar H, Tanimoto T, Coban C, Uematsu S, Kawai T, Akira S. Differential role of 
TLR- and RLR-signaling in the immune responses to influenza A virus infection and vaccination. J 
Immunol 2007;179:4711-20.  
327. 
Miettinen M, Sareneva T, Julkunen I, Matikainen S. IFNs activate toll-like receptor gene expression in 
viral infections. Genes Immun 2001;2:349-55.  
328. 
Hammerbeck DM, Burleson GR, Schuller CJ, Vasilakos JP, Tomai M, Egging E, Cochran FR, Woulfe 
S, Miller RL. Administration of a dual toll-like receptor 7 and toll-like receptor 8 agonist protects against 
influenza in rats. Antiviral Res 2007;73:1-11.  
329. 
Wolff T, Zielecki F, Abt M, Voss D, Semmler I, Matthaei M. Sabotage of antiviral signaling and effectors 
by influenza viruses. Biol Chem 2008;389:1299-305.  
330. 
Le Goffic R, Pothlichet J, Vitour D, Fujita T, Meurs E, Chignard M, Si-Tahar M. Cutting Edge: Influenza 
A virus activates TLR3-dependent inflammatory and RIG-I-dependent antiviral responses in human 
lung epithelial cells. J Immunol  2007;178:3368-72.  
331. 
Heim MH. RIG-I: an essential regulator of virus-induced interferon production. J Hepatol 2005;42:431-3.  
332. 
Seth RB, Sun L, Chen ZJ. Antiviral innate immunity pathways. Cell Res 2006;16:141-7.  
333. 
Takeuchi O, Akira S. Recognition of viruses by innate immunity. Immunol Rev 2007;220:214-24.  
334. 
Thompson AJ, Locarnini SA. Toll-like receptors, RIG-I-like RNA helicases and the antiviral innate 
immune response. Immunol Cell Biol 2007;85:435-45.  
335. 
Onomoto K, Yoneyama M, Fujita T. Regulation of antiviral innate immune responses by RIG-I family of 
RNA helicases. Curr Top Microbiol Immunol 2007;316:193-205.  
336. 
Hornung V, Ellegast J, Kim S, Brzozka K, Jung A, Kato H, Poeck H, Akira S, Conzelmann KK, Schlee 
M, Endres S, Hartmann G. 5'-Triphosphate RNA is the ligand for RIG-I. Science 2006;314:994-7.  
337. 
Pichlmair A, Schulz O, Tan CP, Naslund TI, Liljestrom P, Weber F, Reis e S. RIG-I-mediated antiviral 
responses to single-stranded RNA bearing 5'-phosphates. Science 2006;314:997-1001.  
338. 
Saito T, Hirai R, Loo YM, Owen D, Johnson CL, Sinha SC, Akira S, Fujita T, Gale M, Jr. Regulation of 
innate antiviral defenses through a shared repressor domain in RIG-I and LGP2. Proc Natl Acad Sci 
USA 2007;104:582-7.  
339. 
Onoguchi K, Yoneyama M, Takemura A, Akira S, Taniguchi T, Namiki H, Fujita T. Viral infections 
activate types I and III interferon genes through a common mechanism. J Biol Chem 2007;282:7576-
81.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 32 of 37 
 
340. 
Mibayashi M, Martinez-Sobrido L, Loo YM, Cardenas WB, Gale M, Jr., Garcia-Sastre A. Inhibition of 
retinoic acid-inducible gene I-mediated induction of beta interferon by the NS1 protein of influenza A 
virus. J Virol 2007;81:514-24.  
341. 
Mubareka S, Palese P. Human genes and influenza. J Infect Dis 2008;197:1-3.  
342. 
Unterholzner L, Bowie AG. The interplay between viruses and innate immune signaling: Recent insights 
and therapeutic opportunities. Biochem Pharmacol 2008;75:589-602.  
343. 
Loo YM, Fornek J, Crochet N, Bajwa G, Perwitasari O, Martinez-Sobrido L, Akira S, Gill MA, Garcia-
Sastre A, Katze MG, Gale M, Jr. Distinct RIG-I and MDA5 signaling by RNA Viruses in Innate Immunity. 
J Virol 2008;82:335-45.  
344. 
Wang JP, Bowen GN, Padden C, Cerny A, Finberg RW, Newburger PE, Kurt-Jones EA. Toll-like 
receptor-mediated activation of neutrophils by influenza A virus. Blood 2008;112:2028-34.  
345. 
Forsbach A, Nemorin JG, Volp K, Samulowitz U, Montino C, Muller C, Tluk S, Hamm S, Bauer S, 
Lipford GB, Vollmer J. Characterization of conserved viral leader RNA sequences that stimulate innate 
immunity through TLRs. Oligonucleotides 2007;17:405-17.  
346. 
Yang CW, Chen SM. A comparative study of human TLR 7/8 stimulatory trimer compositions in 
influenza A viral genomes. PLoS ONE 2012;7:e30751.  
347. 
Ichinohe T. Respective roles of TLR, RIG-I and NLRP3 in influenza virus infection and immunity: impact 
on vaccine design. Exp Rev Vaccines 2010;9:1315-24.  
348. 
Ichinohe T, Lee HK, Ogura Y, Flavell R, Iwasaki A. Inflammasome recognition of influenza virus is 
essential for adaptive immune responses. J Exp Med 2009;206:79-87.  
349. 
Uematsu S, Akira S. Toll-like receptors and Type I interferons. J Biol Chem 2007;282:15319-23.  
350. 
Pasare C, Medzhitov R. Toll-like receptors and acquired immunity. Semin Immunol 2004;16:23-6.  
351. 
Heer AK, Shamshiev A, Donda A, Uematsu S, Akira S, Kopf M, Marsland BJ. TLR signaling fine-tunes 
anti-influenza B cell responses without regulating effector T cell responses. J Immunol 2007;178:2182-
91.  
352. 
Matikainen S, Siren J, Tissari J, Veckman V, Pirhonen J, Severa M, Sun Q, Lin R, Meri S, Uze G, 
Hiscott J, Julkunen I. Tumor necrosis factor alpha enhances influenza A virus-induced expression of 
antiviral cytokines by activating RIG-I gene expression. J Virol 2006;80:3515-22.  
353. 
Mosca F, Tritto E, Muzzi A, Monaci E, Bagnoli F, Iavarone C, O'Hagan D, Rappuoli R, De GE. 
Molecular and cellular signatures of human vaccine adjuvants. Proc Natl Acad Sci USA 2008; 
105:10501-6.  
354. 
O'Hagan DT, Wack A, Podda A. MF59 is a safe and potent vaccine adjuvant for flu vaccines in 
humans: what did we learn during its development? Clin Pharmacol Ther 2007;82:740-4.  
355. 
Seubert A, Monaci E, Pizza M, O'Hagan DT, Wack A. The adjuvants aluminum hydroxide and MF59 
induce monocyte and granulocyte chemoattractants and enhance monocyte differentiation toward 
dendritic cells. J Immunol 2008;180:5402-12.  
356. 
Caproni E, Tritto E, Cortese M, Muzzi A, Mosca F, Monaci E, Baudner B, Seubert A, De Gregorio E. 
MF59 and Pam3CSK4 boost adaptive responses to influenza subunit vaccine through an IFN type I-
independent mechanism of action. J Immunol 2012;188:3088-98.  
357. 
Ansaldi F, Bacilieri S, Durando P, Sticchi L, Valle L, Montomoli E, Icardi G, Gasparini R, Crovari P. 
Cross-protection by MF59-adjuvanted influenza vaccine: neutralizing and haemagglutination-inhibiting 
antibody activity against A(H3N2) drifted influenza viruses. Vaccine 2008;26:1525-9.  
358. 
Galli G, Hancock K, Hoschler K, DeVos J, Praus M, Bardelli M, Malzone C, Castellino F, Gentile C, 
McNally T, Del Giudice G, Banzhoff A, Brauer V, Montomoli E, Zambon M, Katz J, Nicholson K, 
Stephenson I. Fast rise of broadly cross-reactive antibodies after boosting long-lived human memory B 
cells primed by an MF59 adjuvanted prepandemic vaccine. Proc Natl Acad Sci USA 2009;106:7962-7. 
PMCID: 2674105 


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 33 of 37 
 
359. 
Ovsyannikova IG, White SJ, Albrecht RA, Garcia-Sastre A, Poland GA. Turkey versus guinea pig red 
blood cells: hemagglutination differences alter hemagglutination inhibition responses against influenza 
A/H1N1. Viral Immunol2014;27:174-8. PMCID: PMC4025626 
360. 
Salk HM, Haralambieva IH, Ovsyannikova IG, Goergen KM, Poland GA. Granzyme B ELISPOT assay 
to measure influenza-specific cellular immunity. J Immunol Meth 2013;398-399:44-50. PMCID: 
PMC3840047  
361. 
Painter SD, Haralambieva IH, Ovsyannikova IG, Grill DE, Poland GA. Detection of Influenza A/H1N1-
Specific Human IgG-Secreting B Cells in Older Adults by ELISPOT Assay. Viral Immunol 2014; 27:32-
8. PMCID: PMC3949448 
362. 
Umlauf BJ, Pinsky NA, Ovsyannikova IG, Poland GA. Detection of Vaccinia Virus-Specific IFN-g and 
IL-10 secretion from human PBMC and CD8+ T cells by ELISPOT. Handbook of ELISPOT. Ed. 
Kalyuzhny AE.  Springer, 2012. p. 199-218. 
363. 
Taylor MJ, Haralambieva IH, Vierkant RA, Ovsyannikova IG, Poland GA. Response surface 
methodology to determine optimal measles-specific cytokine responses in human peripheral blood 
mononuclear cells. J Immunol Methods 2012;382:220-3. PMCID: PMC3399242 
364. 
Greenwood NP, Ovsyannikova IG, Vierkant RA, O'Byrne MM, Poland GA. A qualitative and quantitative 
comparison of two rubella virus-specific IgG antibody immunoassays. Viral Immunol 2010;23:353-7.  
365. 
Oberg AL, Mahoney DW. Statistical methods for quantitative mass spectrometry proteomic experiments 
with labeling. BMC Bioinformatics. 2012;13 Suppl 16:S7. PMCID: PMC3489540 
366. 
Oberg AL, Vitek O. Statistical design of quantitative mass spectrometry-based proteomic experiments. 
J Proteome Res 2009;8:2144-56.  
367. 
Pinsky NA, Loepfe TR, Jacobson RM, Vierkant RA, Poland GA. Comparison of fingerstick versus 
venipuncture for antibody testing of measles and rubella. Scand J Infect Dis 2003;35:107-9.  
368. 
Xie Y. Dynamic Documents with R and knitr. 2nd ed. New York: Taylor & Francis Group; 2015. 
369. 
Wang S, Taaffe J, Parker C, Solorzano A, Cao H, Garcia-Sastre A, Lu S. Hemagglutinin (HA) proteins 
from H1 and H3 serotypes of influenza A viruses require different antigen designs for the induction of 
optimal protective antibody responses as studied by codon-optimized HA DNA vaccines. J Virol 2006; 
80:11628-37.  
370. 
Hai R, Krammer F, Tan GS, Pica N, Eggink D, Maamary J, Margine I, Albrecht RA, Palese P. Influenza 
viruses expressing chimeric hemagglutinins: globular head and stalk domains derived from different 
subtypes. J Virol 2012;86:5774-81.  
371. 
Heaton NS, Leyva-Grado VH, Tan GS, Eggink D, Hai R, Palese P. In vivo bioluminescent imaging of 
influenza a virus infection and characterization of novel cross-protective monoclonal antibodies. J Virol 
2013;87:8272-81.  
372. 
Steel J, Lowen AC, Pena L, Angel M, Solorzano A, Albrecht R, Perez DR, Garcia-Sastre A, Palese P. 
Live attenuated influenza viruses containing NS1 truncations as vaccine candidates against H5N1 
highly pathogenic avian influenza. J Virol 2009; 83:1742-53.  
373. 
Bentebibel SE, Lopez S, Obermoser G, Schmitt N, Mueller C, Harrod C, Flano E, Mejias A, Albrecht 
RA, Blankenship D, Xu H, Pascual V, Banchereau J, Garcia-Sastre A, Palucka AK, Ramilo O, Ueno H. 
Induction of ICOS+CXCR3+CXCR5+ TH cells correlates with antibody responses to influenza 
vaccination. Sci Trans Med 2013;5:176ra32.  
374. 
Kossyvakis A, Pogka V, Melidou A, Moutousi A, Gioula G, Kalliaropoulos A, Exindari M, Emmanouil M, 
Horefti E, Spala G, Meijer A, Malisiovas N, Mentis AF. Challenges in antigenic characterization of 
circulating influenza A(H3N2) viruses during the 2011-2012 influenza season: an ongoing problem? J 
Clin Microbiol 2015;53:1493-9. 
375. 
Bangaru S, Nieusma T, Kose N, Thornburg NJ, Finn JA, Kaplan BS, King HG, Singh V, Lampley RM, 
Sapparapu G, Cisneros A, 3rd, Edwards KM, Slaughter JC, Edupuganti S, Lai L, Richt JA, Webby RJ, 
Ward AB, Crowe JE, Jr. Recognition of influenza H3N2 variant virus by human neutralizing antibodies. 
JCI insight 2016;1.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 34 of 37 
 
376. 
Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq 
experiments with HISAT, StringTie and Ballgown. Nat Protocols 2016;11:1650-67.  
377. 
Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables 
improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnol 2015;33:290-5.  
378. 
Monlong J, Calvo M, Ferreira PG, Guigo R. Identification of genetic variants associated with alternative 
splicing using sQTLseekeR. Nat Comm 2014;5:4698.  
379. 
Mackowiak SD. Identification of novel and known miRNAs in deep-sequencing data with miRDeep2. 
Curr Protoc Bioinformatics 2011;Chapter 12:Unit 12 0.  
380. 
Pocock SJ, Simon R. Sequential treatment assignment with balancing for prognostic factors in the 
controlled clinical trial. Biometrics 1975;31:103-15.  
381. 
Therneau TM. How many stratification factors are "too many" to use in a randomization plan? 
Controlled Clin Trials 1993;14:98-108.  
382. 
Oberg AL, Bot BM, Grill DE, Poland GA, Therneau TM. Technical and biological variance structure in 
mRNA-Seq data: life in the real world. BMC Genomics 2012;13:304. PMCID: PMC3505161 
383. 
Oberg AL, McKinney BA, Schaid DJ, Pankratz VS, Kennedy RB, Poland GA. Lessons learned in the 
analysis of high-dimensional data in vaccinomics. Vaccine 2015;S0264-410X:00574-5. PMCID: 
PMC4581898 
384. 
Cunningham JM, Oberg AL, Borralho PM, Kren BT, French AJ, Wang L, Bot BM, Morlan BW, 
Silverstein KA, Staggs R, Zeng Y, Lamblin AF, Hilker CA, Fan JB, Steer CJ, Thibodeau SN. Evaluation 
of a new high-dimensional miRNA profiling platform. BMC Med Genomics 2009;2:57. PMCID: 
PMC2744682  
385. 
Oberg AL, Mahoney DW, Eckel-Passow JE, Malone CJ, Wolfinger RD, Hill EG, Cooper LT, Onuma OK, 
Spiro C, Therneau TM, Bergen HR, III. Statistical analysis of relative labeled mass spectrometry data 
from complex samples using ANOVA. J Proteome Res 2008;7:225-33. PMCID: PMC2528956  
386. 
Mahoney DW, Therneau TM, Heppelmann CJ, Higgins L, Benson LM, Zenka RM, Jagtap P, 
Nelsestuen GL, Bergen HR, Oberg AL. Relative quantification: characterization of bias, variability and 
fold changes in mass spectrometry data from iTRAQ-labeled peptides. J Proteome Res 2011;10:4325-
33. PMCID: PMC3166364  
387. 
Mahoney DW, Therneau TM, Anderson SK, Jen J, Kocher JP, Reinholz MM, Perez EA, Eckel-Passow 
JE. Quality assessment metrics for whole genome gene expression profiling of paraffin embedded 
samples. BMC Res Notes 2013;6:33.  
388. 
Oberg AL, Mahoney DW, Ballman KV, Therneau TM. Joint estimation of calibration and expression for 
high-density oligonucleotide arrays. Bioinformatics 2006;22:2381-7.  
389. 
Hansen KD, Irizarry RA, Wu Z. Removing technical variability in RNA-seq data using conditional 
quantile normalization. Biostatistics 2012;13:204-16.  
390. 
Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-
seq data. Genome Biol 2010;11:R25.  
391. 
Muddiman DC, Oberg AL. Statistical evaluation of internal and external mass calibration laws utilized in 
fourier transform ion cyclotron resonance mass spectrometry. Anal Chem 2005; 77:2406-14.  
392. 
Ballman KV, Grill DE, Oberg AL, Therneau TM. Faster cyclic loess: normalizing RNA arrays via linear 
models. Bioinformatics 2004;20:2778-86.  
393. 
VanderWeele TJ. Invited commentary: structural equation models and epidemiologic analysis. Am J 
Epidemiol 2012;176:608-12.  
394. 
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B 2005; 
67, part 2:301-20.  
395. 
Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human 
disease. Nat Rev Genet 2011;12:56-68.  
396. 
Loscalzo J. Systems biology and personalized medicine: a network approach to human disease. Proc 
Am Thoracic Soc 2011;8:196-8.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 35 of 37 
 
397. 
Leiserson MD, Vandin F, Wu HT, Dobson JR, Eldridge JV, Thomas JL, Papoutsaki A, Kim Y, Niu B, 
McLellan M, Lawrence MS, Gonzalez-Perez A, Tamborero D, Cheng Y, Ryslik GA, Lopez-Bigas N, 
Getz G, Ding L, Raphael BJ. Pan-cancer network analysis identifies combinations of rare somatic 
mutations across pathways and protein complexes. Nature Genet 2015;47:106-14. 
398. 
Qian Y, Besenbacher S, Mailund T, Schierup MH. Identifying disease associated genes by network 
propagation. BMC Systems Biol 2014;8 Suppl 1:S6.  
399. 
Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, Haussler D, Stuart JM. Inference of patient-
specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. 
Bioinformatics 2010;26:i237-45.  
400. 
Wang W, Baladandayuthapani V, Morris JS, Broom BM, Manyam G, Do KA. iBAG: integrative 
Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics 2013;29:149-59.  
401. 
Patil A, Nakai K. TimeXNet: identifying active gene sub-networks using time-course gene expression 
profiles. BMC Systems Biol 2014;8 Suppl 4:S2.  
402. 
Crescenzo R, Abate F, Lasorsa E, Tabbo F, Gaudiano M, Chiesa N, Di Giacomo F, Spaccarotella E, 
Barbarossa L, Ercole E, Todaro M, Boi M, Acquaviva A, Ficarra E, Novero D, Rinaldi A, Tousseyn T, 
Rosenwald A, Kenner L, Cerroni L, Tzankov A, Ponzoni M, Paulli M, Weisenburger D, Chan WC, Iqbal 
J, Piris MA, Zamo A, Ciardullo C, Rossi D, Gaidano G, Pileri S, Tiacci E, Falini B, Shultz LD, Mevellec 
L, Vialard JE, Piva R, Bertoni F, Rabadan R, Inghirami G. Convergent mutations and kinase fusions 
lead to oncogenic STAT3 activation in anaplastic large cell lymphoma. Cancer Cell 2015;27:516-32.  
403. 
Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, 
Stuart JM. The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genet 2013;45:1113-20.  
404. 
Kuperstein I, Bonnet E, Nguyen HA, Cohen D, Viara E, Grieco L, Fourquet S, Calzone L, Russo C, 
Kondratova M, Dutreix M, Barillot E, Zinovyev A. Atlas of Cancer Signalling Network: a systems biology 
resource for integrative analysis of cancer data with Google Maps. Oncogenesis 2015;4:e160.  
405. 
Cowan NJ, Chastain EJ, Vilhena DA, Freudenberg JS, Bergstrom CT. Nodal dynamics, not degree 
distributions, determine the structural controllability of complex networks. PLos ONE 2012;7:e38398.  
406. 
Zhu X, Gerstein M, Snyder M. Getting connected: analysis and principles of biological networks. Genes 
Dev 2007; 21:1010-24.  
407. 
Roy S. Systems biology beyond degree, hubs and scale-free networks: the case for multiple metrics in 
complex networks. Syst Synth Biol 2012;6:31-4.  
408. 
Hofree M, Shen JP, Carter H, Gross A, Ideker T. Network-based stratification of tumor mutations. 
Nature Meth 2013;10:1108-15.  
409. 
Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C. 
Pathway Commons, a web resource for biological pathway data. Nucl Acids Res2011;39:D685-90.  
410. 
Rolland T, Tasan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, Yi S, Lemmens I, Fontanillo C, 
Mosca R, Kamburov A, Ghiassian SD, Yang X, Ghamsari L, Balcha D, Begg BE, Braun P, Brehme M, 
Broly MP, Carvunis AR, Convery-Zupan D, Corominas R, Coulombe-Huntington J, Dann E, Dreze M, 
Dricot A, Fan C, Franzosa E, Gebreab F, Gutierrez BJ, Hardy MF, Jin M, Kang S, Kiros R, Lin GN, 
Luck K, MacWilliams A, Menche J, Murray RR, Palagi A, Poulin MM, Rambout X, Rasla J, Reichert P, 
Romero V, Ruyssinck E, Sahalie JM, Scholz A, Shah AA, Sharma A, Shen Y, Spirohn K, Tam S, 
Tejeda AO, Trigg SA, Twizere JC, Vega K, Walsh J, Cusick ME, Xia Y, Barabasi AL, Iakoucheva LM, 
Aloy P, De Las Rivas J, Tavernier J, Calderwood MA, Hill DE, Hao T, Roth FP, Vidal M. A proteome-
scale map of the human interactome network. Cell 2014;159:1212-26.  
411. 
Prasad TS, Kandasamy K, Pandey A. Human Protein Reference Database and Human Proteinpedia as 
discovery tools for systems biology. Methods Molec Biol 2009;577:67-79.  
412. 
Vinayagam A, Stelzl U, Foulle R, Plassmann S, Zenkner M, Timm J, Assmus HE, Andrade-Navarro 
MA, Wanker EE. A directed protein interaction network for investigating intracellular signal transduction. 
Sci Signal 2011;4:rs8.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 36 of 37 
 
413. 
Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. PID: the Pathway 
Interaction Database. Nucl Acids Res 2009; 37:D674-9.  
414. 
Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, 
Bork P, Jensen LJ, von Mering C. The STRING database in 2011: functional interaction networks of 
proteins, globally integrated and scored. Nucl Acids Res 2011;39:D561-8.  
415. 
Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM. Prioritizing candidate disease genes by network-
based boosting of genome-wide association data. Genome Res 2011;21:1109-21.  
416. 
Rosenbloom KR, Sloan CA, Malladi VS, Dreszer TR, Learned K, Kirkup VM, Wong MC, Maddren M, 
Fang R, Heitner SG, Lee BT, Barber GP, Harte RA, Diekhans M, Long JC, Wilder SP, Zweig AS, 
Karolchik D, Kuhn RM, Haussler D, Kent WJ. ENCODE data in the UCSC Genome Browser: year 5 
update. Nucl Acids Res 2013;41:D56-63.  
417. 
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. 
Nature 2012;489:57-74.  
418. 
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, 
Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, 
Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene 
Ontology Consortium. Nature Genet 2000;25:25-9.  
419. 
Mi H, Muruganujan A, Casagrande JT, Thomas PD. Large-scale gene function analysis with the 
PANTHER classification system. Nat Protoc 2013;8:1551-66.  
420. 
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. 
Cytoscape: a software environment for integrated models of biomolecular interaction networks. 
Genome Res 2003;13:2498-504.  
421. 
AllegroLayout. http://allegroviva.com/allegrolayout2. 2015. Date accessed: June 1, 2017. 
422. 
Longabaugh WJ. Combing the hairball with BioFabric: a new approach for visualization of large 
networks. BMC Bioinformatics 2012;13:275.  
423. 
Vehlow C, Kao DP, Bristow MR, Hunter LE, Weiskopf D, Gorg C. Visual analysis of biological data-
knowledge networks. BMC Bioinformatics 2015;16:135.  
424. 
Nocaj A. Untangling Networks. Focus on Less to See More. https://kops.uni-
konstanz.de/handle/123456789/33132. Konstanz, Germany: Universität Konstanz, 2015. Date 
accessed: June 1, 2017. 
425. 
Chang J, Cho H, Chou HH. Mango: combining and analyzing heterogeneous biological networks. 
BioData Mining 2016; 9:25.  
426. 
Yan KK, Wang D, Sethi A, Muir P, Kitchen R, Cheng C, Gerstein M. Cross-Disciplinary Network 
Comparison: Matchmaking Between Hairballs. Cell Syst 2016; 2:147-57.  
427. 
Fritz MS, Mackinnon DP. Required sample size to detect the mediated effect. Psychol Sci 2007; 
18:233-9.  
428. 
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to 
multiple testing. J R Stat Soc Ser B 1995;57:289-300.  
429. 
Bourgon R, Gentleman R, Huber W. Independent filtering increases detection power for high-
throughput experiments. Proc Natl Acad Sci USA 2010;107:9546-51.  
430. 
Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA 2003; 
100:9440-5.  
431. 
Franco LM, Bucasas KL, Wells JM, Nino D, Wang X, Zapata GE, Arden N, Renwick A, Yu P, Quarles 
JM, Bray MS, Couch RB, Belmont JW, Shaw CA. Integrative genomic analysis of the human immune 
response to influenza vaccination. eLife 2013;2:e00299.  
432. 
Henn AD, Wu S, Qiu X, Ruda M, Stover M, Yang H, Liu Z, Welle SL, Holden-Wiltse J, Wu H, Zand MS. 
High-resolution temporal response patterns to influenza vaccine reveal a distinct human plasma cell 
gene signature. Sci Rep 2013;3:2327.  


Minimal Risk Protocol Template [IRB 10429.009] 
 
Effective: 10/10/2016 
 
 
 
Page 37 of 37 
 
433. 
Zhou X, Hopkins JW, Wang C, Brahmakshatriya V, Swain SL, Kuchel GA, Haynes L, McElhaney JE. 
IL-2 and IL-6 cooperate to enhance the generation of influenza-specific CD8 T cells responding to live 
influenza virus in aged mice and humans. Oncotarget 2016;7(26):39171–39183.  
434. 
Goodwin K, Viboud C, Simonsen L. Antibody response to influenza vaccination in the elderly: a 
quantitative review. Vaccine 2006;24:1159-69.  
435. 
Spensieri F, Borgogni E, Zedda L, Bardelli M, Buricchi F, Volpini G, Fragapane E, Tavarini S, Finco O, 
Rappuoli R, Del Giudice G, Galli G, Castellino F. Human circulating influenza-CD4+ ICOS1+IL-21+ T 
cells expand after vaccination, exert helper function, and predict antibody responses. Proc Natl Acad 
Sci USA 2013;110:14330-5.  
436. 
Lefebvre JS, Lorenzo EC, Masters AR, Hopkins JW, Eaton SM, Smiley ST, Haynes L. Vaccine efficacy 
and T helper cell differentiation change with aging. Oncotarget 2016;7:33581-94.  
437. 
Thakar J, Hartmann BM, Marjanovic N, Sealfon SC, Kleinstein SH. Comparative analysis of anti-viral 
transcriptomics reveals novel effects of influenza immune antagonism. BMC Immunol 2015;16:46.  
438. 
Chen J, Chen W, Zhao N, Wu MC, Schaid DJ. Small Sample Kernel Association Tests for Human 
Genetic and Microbiome Association Studies. Genetic Epidemiol 2016;40:5-19. PMCID: PMC4679685 
439. 
Schaid DJ. Genomic Similarity and Kernel Methods II: Methods for Genomic Information. Hum Hered 
2010;70:132-40.  
440. 
Schaid DJ. Genomic Similarity and Kernel Methods I: Advancements by Building on Mathematical and 
Statistical Foundations. Hum Hered 2010;70:109-31.  
441. 
Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data 
with the sequence kernel association test. Am J Hum Genet 2011;89:82-93.  
442. 
Kennedy RB, Tosh PK, Goergen KM, Grill DE, Oberg AL, Poland GA. Statistical modeling using early 
markers of innate immunity to explain variation in humoral responses to influenza vaccine in older 
adults. Vaccine 2015;33:3682-8. PMCID: PMC4626876 
443. 
Sadarangani SP, Ovsyannikova IG, Goergen K, Grill DE, Poland GA. Vitamin D, Leptin and Impact on 
Immune Response to Seasonal Influenza A/H1N1 Vaccine in Older Persons. Hum Vaccines 
Immunother 2016;12:691-8. PMCID: PMC4964629