tetano
Editor, Senior Moderator
Front Immunol. 2019 Nov 7;10:2616. doi: 10.3389/fimmu.2019.02616. eCollection 2019. [h=1]Microarray Gene Expression Dataset Re-analysis Reveals Variability in Influenza Infection and Vaccination.[/h]
Rogers LRK[SUP]1,[/SUP][SUP]2[/SUP], de Los Campos G[SUP]2,[/SUP][SUP]3,[/SUP][SUP]4[/SUP], Mias GI[SUP]2,[/SUP][SUP]5[/SUP].
[h=3]Author information[/h] 1 Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, United States. 2 Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States. 3 Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States. 4 Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States. 5 Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States.
[h=3]Abstract[/h] 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.
Copyright ? 2019 Rogers, de los Campos and Mias.
[h=4]KEYWORDS:[/h] aging; gene-expression; immunity; influenza; meta-analysis; micro-arrays; vaccinations
PMID: 31787983 PMCID: PMC6854009 DOI: 10.3389/fimmu.2019.02616
Free PMC Article
Rogers LRK[SUP]1,[/SUP][SUP]2[/SUP], de Los Campos G[SUP]2,[/SUP][SUP]3,[/SUP][SUP]4[/SUP], Mias GI[SUP]2,[/SUP][SUP]5[/SUP].
[h=3]Author information[/h] 1 Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, United States. 2 Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States. 3 Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States. 4 Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States. 5 Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States.
[h=3]Abstract[/h] 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.
Copyright ? 2019 Rogers, de los Campos and Mias.
[h=4]KEYWORDS:[/h] aging; gene-expression; immunity; influenza; meta-analysis; micro-arrays; vaccinations
PMID: 31787983 PMCID: PMC6854009 DOI: 10.3389/fimmu.2019.02616
Free PMC Article