Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Mar 1;25(4):104007.
doi: 10.1016/j.isci.2022.104007. eCollection 2022 Apr 15.

Aberrant newborn T cell and microbiota developmental trajectories predict respiratory compromise during infancy

Andrew McDavid  1 , Nathan Laniewski  2 , Alex Grier  3 , Ann L Gill  2 , Haeja A Kessler  2 , Heidie Huyck  4 , Elizabeth Carbonell  4 , Jeanne Holden-Wiltse  1 , Sanjukta Bandyopadhyay  1 , Jennifer Carnahan  4 , Andrew M Dylag  4 , David J Topham  2 , Ann R Falsey  5 , Mary T Caserta  4 , Gloria S Pryhuber  4 , Steven R Gill  2 , Kristin M Scheible  4
Affiliations

Aberrant newborn T cell and microbiota developmental trajectories predict respiratory compromise during infancy

Andrew McDavid et al. iScience. .

Abstract

Neonatal immune-microbiota co-development is poorly understood, yet age-appropriate recognition of - and response to - pathogens and commensal microbiota is critical to health. In this longitudinal study of 148 preterm and 119 full-term infants from birth through one year of age, we found that postmenstrual age or weeks from conception is a central factor influencing T cell and mucosal microbiota development. Numerous features of the T cell and microbiota functional development remain unexplained; however, by either age metric and are instead shaped by discrete perinatal and postnatal events. Most strikingly, we establish that prenatal antibiotics or infection disrupt the normal T cell population developmental trajectory, influencing subsequent respiratory microbial colonization and predicting respiratory morbidity. In this way, early exposures predict the postnatal immune-microbiota axis trajectory, placing infants at later risk for respiratory morbidity in early childhood.

Keywords: Immunology; Microbiome.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Systemic interactions between age, T cells, and microbiota The amount of variance in composition of nasal (NAS) and rectal (REC) microbiota and T cell immune populations that are explained by the predictors: preterm birth (gestational age at birth< 37 weeks), postnatal day of life (DOL), postmenstrual age (PMA), T cell population composition, and microbiota composition. Controlling for PMA, system interdependence was diminished but remained highly significant (red). All comparisons control for preterm birth. (∗p < 10−4, ∗∗p < 10−20, multivariate ANOVA).
Figure 2
Figure 2
Early T cell development in preterm and full-term infants advances with postmenstrual age (A and B) T cells from flow cytometry performed on infants at birth, hospital discharge, and approximately one year of life were characterized by (A) phenotype (“Tphe”, unstimulated) and (B) cytokine function (“ICS”, stimulated in vitro) and clustered into subpopulations. The median fluorescence intensity of the flow parameters in the 79 clusters is shown. (C) T cell subpopulations from panels (B) and (C) that predicted sample PMA in a lasso regression are displayed, and their regression coefficients along the x axis. Populations that have inverse associations with PMA fall on the left of the dashed line, and vice versa. The x axis values indicate PMA fold-change per z-scored increase in the proportion of a subject’s cells assigned to that population. T cell phenotype subpopulations are grouped based on CCR7 and CD45RO expression (CM = central memory, EM = effector memory, N = naive, TE = terminal effector, and VM = virtual memory).
Figure 3
Figure 3
Immune State Types (ISTs) advance with postmenstrual age, and are perturbed by specific clinical exposures (A and B) T cell phenotype (A) and T cell function (B) immune state types (ISTs) were defined based on T cell population relative abundances. ISTs were enumerated according to the average postmenstrual age (PMA) at which they occur. Colors reflect relative abundances of component cell populations (rows); functional annotations and defining markers are shown in the heatmaps on the right. (C and D) The assigned IST vs PMA of sampling of (C) TPHE and (D) ICS. Each point represents a sample assigned to a given IST and is colored by gestational age at birth (GABirth) of the infant. The ANOVA coefficient of determination of PMA vs IST category is shown as the r2, whereas asterisks at the base of the dot plots indicate significant enrichment for either preterm (orange) or full-term (blue) samples within an IST, controlling for confounders and repeated measures (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, two-tailed binomial test). (E) Joint logistic regression showing the log odds and 95% confidence interval of ever being in Tphe5 or Tphe6 given exposure indicated, controlling for gestational age (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001).
Figure 4
Figure 4
Premature birth influences long-term age-related respiratory and gut microbiota community progression Microbiota community profiling was performed on (A and C) rectal and (B and D) nasal samples obtained from 159 infants during regular surveillance and acute respiratory illness. (A and B) Microbiota community state types (CSTs) were defined for each body site based on sample composition, and the relative abundances of the top 25 most abundant genera were visualized using heatmaps, with samples as columns, clustered by CST. CSTs were numbered according to average PMA of occurrence. (C and D) Samples within each CST were plotted against subjects' PMA at the time of sample collection. Each dot represents a single sample, colored by the subject’s GA. r2 values show correlations between CST and PMA. Asterisks at the base of the dot plots indicate significant enrichment for either preterm (orange) or full-term samples (blue) within a CST controlling for confounders and repeated measures (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, two-tailed binomial test).
Figure 5
Figure 5
Bidirectional associations between the microbiome and T cell expansion Microbiota duration (days spent) in microbial community state types (CSTs) was modeled as a function of T cell features, controlling for gestational age at birth, mode of delivery, human milk consumption, and antibiotic exposure using quasi-poisson regression. The log rate ratio and its 95% confidence interval is shown for associations that were significant at 10% FDR.
Figure 6
Figure 6
Mistimed Immune and Microbial Development Predict Respiratory Outcome Elastic net regression predicts a postmenstrual age (pPMA) based on both T cell populations and microbial operational taxonomic units (OTUs), separately. (A) The pPMA of a subject is plotted against the observed age (oPMA) at the time of sampling to establish an intercept at 37 weeks (left) and a slope (right), corresponding to maturity at term equivalent and rate of maturation over the first year, for both T cell and microbiota. Z-scores for each subject’s slope and intercept are indicated as color overlays (red - relatively advanced maturation at term equivalent/faster development, and blue - relatively delayed maturation at term equivalent/slower development). A “Developmental Index” (DI) was constructed using these four parameters: the z-scores of T cell and microbiota intercepts and slopes. (B) A random forest machine learning algorithm predicts persistent respiratory disease (PRD) from known risk factor clinical variables and from the T cell and microbiota-based DI. Boxplots show the area under the curve calculated for each set of variables (mean and standard error of the mean). (C) The contour graph demonstrates the two DI components, the microbiota intercept and T cell slope, with the best predictive strength for PRD risk, controlling for clinical factors. Blue = lower PRD risk, red = higher risk.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Bains I., Antia R., Callard R., Yates A.J. Quantifying the development of the peripheral naive CD4+ T-cell pool in humans. Blood. 2009;113:5480–5487. - PMC - PubMed
    1. Bischl B.L., Lang M., Kotthoff L., Schiffner J., Richter J., Studerus E., Casalicchio G., Jones Z. mlr: machine learning in R. J. Mach. Learn. Res. 2000;1:1–48.
    1. Bisgaard H., Li N., Bonnelykke K., Chawes B.L., Skov T., Paludan-Muller G., Stokholm J., Smith B., Krogfelt K.A. Reduced diversity of the intestinal microbiota during infancy is associated with increased risk of allergic disease at school age. J. Allergy Clin. Immunol. 2011;128:646–652.e1-5. - PubMed
    1. Boeckh M., Huang M., Ferrenberg J., Stevens-Ayers T., Stensland L., Nichols W.G., Corey L. Optimization of quantitative detection of cytomegalovirus DNA in plasma by real-time PCR. J. Clin. Microbiol. 2004;42:1142–1148. - PMC - PubMed
    1. Bolyen E., Rideout J.R., Dillon M.R., et al. Reproducible, interactive, scalable, and extensible microbiome data science using QIIME 2. Nature Biotechnology. 2019;37:852–857. doi: 10.1038/s41587-019-0209-9. - DOI - PMC - PubMed

LinkOut - more resources