Multi-Omic Factors Associated with Frequency of Upper Respiratory Infections in Developing Infants

Abstract

Susceptibility to upper respiratory infections (URIs) may be influenced by host, microbial, and environmental factors. We hypothesized that multi-omic analyses of molecular factors in infant saliva would identify complex host-environment interactions associated with URI frequency. A cohort study involving 146 infants was used to assess URI frequency in the first year of life. Saliva was collected at 6 months for high-throughput multi-omic measurement of cytokines, microRNAs, transcripts, and microbial RNA. Regression analysis identified environmental (daycare attendance, atmospheric pollution, breastfeeding duration), microbial (Verrucomicrobia, Streptococcus phage), and host factors (miR-22-5p) associated with URI frequency (p < 0.05). These results provide pathophysiologic clues about molecular factors that influence URI susceptibility. Validation of these findings in a larger cohort could one day yield novel approaches to detecting and managing URI susceptibility in infants.

Keywords: infants; miRNA; multi-omic; upper respiratory infections; viral infections.

Figure 1

Interactions between environment, microbiome, and host immune factors determine frequency of upper respiratory infection (URI) in the first 12 months. This study utilized a multi-omics approach to test the overarching hypothesis that interactions between the exposome, microbiome, and inflammasome dictate URI frequency.

Figure 2

Exposome relationship with URI frequency. The violin plots display number of upper respiratory infections (URIs) associated with daycare attendance (F = 52.8, p < 0.001), and atmospheric pollution (F = 7.76, p = 0.006) (A). The boxes denote 95% confidence intervals, along with mean (square) and median values (black line). The scatter plot display the relationship between number of URIs and the duration of duration (months (mos)) of exclusive breastfeeding (F = 4.27, p = 0.041) (B). A trend line and 95% confidence interval (shaded area) are displayed.

Figure 3

Multi–omic relationships with URI frequency. The scatter plots display significant relationships between frequency of upper respiratory infections (URIs) in the first 12 months and levels of salivary “omic” factors. Levels of Verrucomicrobia (F = 7.77, p = 0.006) (A) and Streptococcus phage SpSL1 (F = 6.57, p = 0.011) (B) were directly associated with the number of URIs, whereas levels of miR-22-5p (F = 6.93, p = 0.009) (C) were inversely related. Haemophilus virus HP1 (F = 2.56, 0.11) (D), and TMPRSS2 (F = 2.24, p = 0.13) (E) met criteria for model inclusion (F > 2.0), but did not display significant relationships with URI frequency. Trend lines with 95% confidence intervals are displayed for each relationship.

Figure 4

Microbial factors modulate environmental impacts on URI frequency. The scatter plots show molecular features of interest that displayed significant interaction effects with environmental exposures on linear regression analysis. Streptococcus phage SpSL1 potentiated the number of URIs among infants who attended daycare (F = 12.1, p < 0.001) (A), whereas Verrucomicrobia potentiated URI frequency in infants without atmospheric pollution exposure (F = 3.21, p = 0.009) (B), and miR-22-5p levels potentiated the protective effects of breastfeeding ≥ 6 months (F = 10.8, p = 0.001) (C). Normalized, scaled read counts are displayed for Streptococcus phage SpSL1, Verrucomicrobia, and miR-22-5p. URI frequency represents the number of URIs reported in the first 12 months after birth. Shaded areas represent 95% confidence intervals.

Figure 5

Putative multi-omic mechanism for URI susceptibility. The concept diagram displays mechanisms through which molecular observations in this cohort may contribute to URI propensity. Decreased levels of miR-22-5p lead to retinoblastoma (Rb) inactivation, which may increase pro-inflammatory signaling by stimulating the interleukin-6/STAT3 pathway. Microbial diversity may be influenced by cytokine signaling or environmental exposures. Increased activity of Verrucomicrobia and Streptococcus Phage could influence epi-transcriptional regulators of the immune response (i.e., miR-22-5p) that increase URI risk. Black text denotes factors identified in this cohort, whereas gray text denotes interactions reported in the scientific literature.

Figure 6

Participant CONSORT diagram. The CONSORT diagram displays the number of participants who were screened (2487), eligible (359), consented (221), and completed the 12-month study (146).