Abstract

   Influenza, a communicable disease, affects thousands of people
   worldwide. Young children, elderly, immunocompromised individuals and
   pregnant women are at higher risk for being infected by the influenza
   virus. Our study aims to highlight differentially expressed genes in
   influenza disease compared to influenza vaccination, including
   variability due to age and sex. To accomplish our goals, we conducted a
   meta-analysis using publicly available microarray expression data. Our
   inclusion criteria included subjects with influenza, subjects who
   received the influenza vaccine and healthy controls. We curated 18
   microarray datasets for a total of 3,481 samples (1,277 controls, 297
   influenza infection, 1,907 influenza vaccination). We pre-processed the
   raw microarray expression data in R using packages available to
   pre-process Affymetrix and Illumina microarray platforms. We used a
   Box-Cox power transformation of the data prior to our down-stream
   analysis to identify differentially expressed genes. Statistical
   analyses were based on linear mixed effects model with all study
   factors and successive likelihood ratio tests (LRT) to identify
   differentially-expressed genes. We filtered LRT results by disease
   (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile
   cutoff to identify biologically significant genes. Furthermore, we
   assessed age and sex effects on the disease genes by filtering for
   genes with a statistically significant (Bonferroni adjusted p < 0.05)
   interaction between disease and age, and disease and sex. We identified
   4,889 statistically significant genes when we filtered the LRT results
   by disease factor, and gene enrichment analysis (gene ontology and
   pathways) included innate immune response, viral process, defense
   response to virus, Hematopoietic cell lineage and NF-kappa B signaling
   pathway. Our quantile filtered gene lists comprised of 978 genes each
   associated with influenza infection and vaccination. We also identified
   907 and 48 genes with statistically significant (Bonferroni adjusted p
   < 0.05) disease-age and disease-sex interactions, respectively. Our
   meta-analysis approach highlights key gene signatures and their
   associated pathways for both influenza infection and vaccination. We
   also were able to identify genes with an age and sex effect. This gives
   potential for improving current vaccines and exploring genes that are
   expressed equally across ages when considering universal vaccinations
   for influenza.

   Keywords: influenza, vaccinations, immunity, aging, meta-analysis,
   micro-arrays, gene-expression

1. Introduction

   The influenza virus, a respiratory pathogen, is responsible for
   seasonal influenza (also known as the flu), influenza pandemics and
   high rates of morbidity and mortality worldwide ([27]1). The influenza
   virus infects the upper respiratory tract by invading the epithelial
   cells, releasing viral RNA, replicating and spreading throughout the
   respiratory tract while also causing inflammation ([28]2). Influenza is
   a highly contagious disease and spreads easily via contact with an
   infected person's nasal discharges and cough droplets ([29]3). The main
   virulence factors are haemagglutinin (HA) and neuraminidase (NA)
   ([30]2). These surface glycoproteins are also important for determining
   the sub-type of the influenza virus. The influenza virus can also
   reduce host gene expression through their viral proteins ([31]4,
   [32]5). The viral proteins affect transcription and translation in the
   host which reduces the production of host proteins and promotes immune
   system evasion for the virus ([33]4, [34]5). The virus interferes with
   host gene expression to promote viral gene expression, and this affects
   the immune system of the host by reducing the expression of immune
   components such as the major histocompatibility (MHC) molecules antigen
   presentation, and interferon and cytokine signaling pathways ([35]4,
   [36]6).

   Influenza is a global health burden, and as a preventative method
   vaccinations are offered annually. Vaccines are modified annually
   because the influenza virus strains change and mutate every season
   ([37]7). The influenza vaccinations target the viral strains and
   sub-types that researchers predict would be most prevalent each flu
   season ([38]3, [39]8). Furthermore, there are groups in the population
   who are considered at a higher risk for influenza infection, and they
   include young children, elderly, individuals who are immunocompromised,
   and females who are pregnant ([40]3). The Centers for Disease Control
   and Prevention (CDC) has estimated, for the 2017–2018 season for
   influenza, 959,000 hospitalizations and over 79,000 deaths ([41]3). 90%
   of the deaths during the 2017–2018 flu season were within the elderly
   population, while about 48,000 of the hospitalizations were in children
   ([42]3). These estimates highlight that young children and especially
   the elderly are at higher risks for influenza and severe infections
   that can lead to hospitalization or death. Additionally, the CDC has
   recommended varying dosages for each vaccine for different age groups
   due to age-dependent immune responses ([43]3, [44]9). Due to a decrease
   in efficacy of the influenza vaccines in the 65 and older population,
   they receive different dosages compared to younger age groups, in order
   to elicit a beneficial immune response ([45]3, [46]9). Contrasting
   between changes in gene expression due to immunosenescence in healthy
   subjects and the age-dependent immune responses to diseases such as
   influenza can help our understanding of how responses to different
   diseases vary with age. Due to the influenza virus constantly changing
   and the efficacy of the vaccine being dependent on one's age,
   researchers have started efforts to develop a universal vaccine
   ([47]10–[48]12). The goal is for such a universal vaccine to provide
   protection to all influenza strains ([49]13). One approach, is to
   implement the use of highly conserved influenza peptides in vaccine
   formulations ([50]12, [51]13).

   Previous studies have investigated global blood gene expression to
   compare influenza disease to other respiratory diseases to assess
   severity and pathogenesis ([52]14). For example, influenza has been
   shown to induce a stronger immune response than respiratory syncytial
   virus by producing more respiratory cytokines ([53]14, [54]15). Studies
   also explored responses to vaccinations to highlight gene signatures.
   In our meta-analysis, our aim was to combine publicly available
   influenza microarray data to identify the effects of disease state
   (control, influenza infection, and vaccination), age and sex on gene
   expression. We explored gene expression variation in blood for 3,481
   samples (1,277 controls, 297 influenza infected, 1,907 influenza
   vaccinated) to identify genes and their pathways in influenza
   ([55]Figures 1, [56]2). This is to the best of our knowledge, the
   largest meta-analysis (18 datasets) to explore blood expression changes
   in influenza infection and vaccination. Our results provide gene
   signatures and pathways that can be targeted to improve influenza
   treatment and vaccinations. We also highlight disease associated genes
   that have interactions with age and sex, that can be used to further
   explore improving vaccinations, and aid efforts in identifying
   potential gene targets toward developing universal vaccinations to help
   reduce the burden of influenza.

Figure 1.

   [57]Figure 1
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   Meta-analysis workflow to assess gene expression variation in influenza
   disease and vaccination. (A) Main steps. (B) Data pre-processing in R.
   (C) Downstream analysis. (D) post-hoc analysis.

Figure 2.

   [59]Figure 2
   [60]Open in a new tab

   Preferred reporting items for systematic reviews and meta-analyses
   (PRISMA) checklist.

2. Methods

   We curated 18 influenza-related microarray datasets from public
   database repositories ([61]Table 1) to investigate changes in gene
   expression due to disease status, sex, and age. The 18 datasets were
   from Affymetrix and Illumina microarray platforms ([62]Table 1). We
   modified and implemented the data-analysis pipeline outlined by Brooks
   et al. ([63]29). To achieve our goal, after curating the datasets, we
   used the R programming language ([64]30) to pre-process the raw gene
   expression data and to fit linear mixed effects models to determine
   statistically significant differentially expressed genes by factor
   ([65]Figure 1). In addition, we identified genes that varied in
   expression due to disease status, sex, and age, and we also determined
   which gene ontology (GO) terms and pathways enrichment based on these
   gene sets ([66]Figure 1).

Table 1.

   Demographics of curated influenza microarray datasets.
   Accession number Controls Influenza disease Influenza vaccine Sex (M/F)
   Age range Platform References