Saliva omics has immense potential for non-invasive diagnostics, including monitoring very young or elderly populations, or individuals in remote locations. In this study, multiple saliva omics from an individual were monitored over three periods (100 timepoints) involving: (1) hourly sampling over 24 h without intervention, (2) hourly sampling over 24 h including immune system activation using the standard 23-valent pneumococcal polysaccharide vaccine, (3) daily sampling for 33 days profiling the post-vaccination response. At each timepoint total saliva transcriptome and proteome, and small RNA from salivary extracellular vesicles were profiled, including mRNA, miRNA, piRNA and bacterial RNA. The two 24-h periods were used in a paired analysis to remove daily variation and reveal vaccination responses. Over 18,000 omics longitudinal series had statistically significant temporal trends compared to a healthy baseline. Various immune response and regulation pathways were activated following vaccination, including interferon and cytokine signaling, and MHC antigen presentation. Immune response timeframes were concordant with innate and adaptive immunity development, and coincided with vaccination and reported fever. Overall, mRNA results appeared more specific and sensitive (timewise) to vaccination compared to other omics. The results suggest saliva omics can be consistently assessed for non-invasive personalized monitoring and immune response diagnostics.

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.

Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls, by reanalyzing pre-existing, publicly available microarray expression datasets. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we used generalized least squares models with disease state, age, sex, smoking status and study as effects that also included binary interactions, followed by likelihood ratio tests (LRT). We found 3,315 statistically significant (Storey-adjusted q-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from LRT q-value <0.05 and model estimates’ 10% two-tailed quantiles of mean differences between COPD and control), to identify 679 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the common array genes as features, which enabled prediction of disease status with 81.7% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

2Influenza, a communicable disease, affects thousands of people worldwide. Young children, 3 elderly, immunocompromised individuals and pregnant women are at higher risk for being 4 infected by the influenza virus. Our study aims to highlight differentially expressed genes in 5 influenza disease compared to influenza vaccination. We also investigate genetic variation 6 due to the age and sex of samples. To accomplish our goals, we conducted a meta-analysis 7 using publicly available microarray expression data. Our inclusion criteria included subjects with 8 influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 9 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 10 influenza vaccination). We pre-processed the raw microarray expression data in R using packages 11 available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power 12 transformation of the data prior to our down-stream analysis to identify differentially expressed 13 genes. Statistical analyses were based on linear mixed effects model with all study factors and 14 successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT 15 results by disease (Bonferroni adjusted p-value < 0.05) and used a two-tailed 10% quantile cutoff 16 to identify biologically significant genes. Furthermore, we assessed age and sex effects on the 17 disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p-value < 18 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically 19 significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis 20 (gene ontology and pathways) included innate immune response, viral process, defense response 21 to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene 22 lists comprised of 978 genes each associated with influenza infection and vaccination. We also 23 identified 907 and 48 genes with statistically significant (Bonferroni adjusted p-value < 0.05) 24 disease-age and disease-sex interactions respectively. Our meta-analysis approach highlights 25 key gene signatures and their associated pathways for both influenza infection and vaccination. 26 1 Rogers LRK et al. Variability in Influenza Infection and VaccinationWe also were able to identify genes with an age and sex effect. This gives potential for improving 27 current vaccines and exploring genes that are expressed equally across ages when considering 28 universal vaccinations for influenza. 29 30

Saliva omics, a rapidly developing field for non-invasive diagnostics, may be utilized for monitoring very young or elderly populations, as well as individuals in remote locations. In this study, multiple saliva omics from an individual were monitored over 100 timepoints, over three periods involving: (i) hourly sampling over 24 hours without intervention, (ii) hourly sampling over 24 hours including immune system activation using the standard 23-valent pneumococcal polysaccharide vaccine, (iii) daily sampling for 33 days profiling the post-vaccination response. At each timepoint total saliva transcriptome and proteome were profiled, and salivary extracellular vesicles were derived, from which small-RNA sequencing was used to determine RNA, miRNA, piRNA and bacterial RNA components. The two 24-hour periods were used in a paired analysis to reveal vaccination responses. Temporal trends were classified and collective behavior revealed broad immune-responses captured in saliva, both at the innate as well as the adaptive response time frames. 1 Year Vaccination 24 hourly samples TFH2 24 hourly samples TFH1 P P S V 2 3 Results Samples and AssaysWe followed a single individual (m, 38, Caucasian), in general good health (has reported chronic sinusitis) over the span of a year. To observe whether the effects of perturbation can be profiled in saliva we carried out the profiling over 3 time frames. In the first 24 hr time frame (TFH1) we established a baseline, obtaining a saliva sample from the subject hourly without perturbation over his standard routine. In the second 24 hr time frame (TFH2), the subject was vaccinated with pneumococcal polysaccharide vaccine (PPSV23) within 3.5 hrs of waking up (at 10.30 am), while otherwise maintaining a similar routine as in the first period (including food intake and meal timing), and again saliva samples were taken hourly. We should note here that the subject reported fever ∼7.5 hours post the vaccination (between timepoints at 5 and 6 pm), lasting for about 4 hrs (10 pm). The two time periods, TFH1 and TFH2, were treated as paired and combined in the analysis below (TF∆) to identify changes induced by the vaccination, by effectively removing daily normal routine effects for this individual. Additionally, in the third time frame (TFD) we monitored the subject daily for over a month, pre-and post-vaccination to identify potential immune changes over both innate and adaptive time frames, Fig. 1.The daily samples were all taken at 8 am, to limit variability. Saliva was sampled both for downstream total RNA profiling, mass spectrometry proteomics, as well as for extraction of extracellular vesicles which were profiled for a variate of small RNA 2/19