Transcriptomic signatures of neonatal acute respiratory distress syndrome in a prospective cohort of respiratory distress

Abstract

Neonatal acute respiratory distress syndrome (NARDS) is challenging to differentiate from other respiratory conditions, and gestational age (GA) may influence gene expression. This study characterized whole blood transcriptomic profiles of NARDS in a pilot cohort of 48 neonates with respiratory distress, demonstrating a significant GA-dependent modulation of gene expression. Functional analyses revealed prominent involvement of interferon-related pathways in NARDS, with greater suppression in neonates born before 34 weeks. Immune cell infiltration was observed in term or late preterm neonates but was absent in more preterm cases. Machine learning identified three key predictive genes, among which ALOX15 and PTGDR2 were validated in an independent cohort, with area under the curve ranging from 0.68 to 0.83 across different GAs. The gene changes were also confirmed in a neonatal lipopolysaccharide-induced lung injury mouse model. These findings highlight the potential predictive and therapeutic value of ALOX15 and PTGDR2 for NARDS.

Keywords: Machine learning; Transcriptomics.

Graphical abstract

Figure 1

The schematic overview of this study

Figure 2

Evaluation of gene expression variance caused by GA and NARDS signature genes screening (A) Principal component analysis (PCA) of variance between NARDS and control. The ellipses represent 95% confidence intervals for each cluster. the first principal component (PC1) explained 31% of the total variance, while the second principal component (PC2) accounted for an additional 9% of the variance. (B) Number of differentially expresses genes (DEGs) (p < 0.05, |log Foldchange| > 1) in NARDS and control. (C) Number of DEGs (p < 0.05, |log Foldchange| > 1) in C-MA, N-LA, LA-MA comparisons. (D) Gene expression of whole blood in relation to gestational age. (E) Volcano plots of DEGs in NARDS and control after adjustment by gestational age. (F) LASSO coefficient computation. The vertical dashed line shows the ideal lambda value and 11 was selected in this study. (G) 10-fold cross-validation for LASSO model parameter adjustment. Each curve represents a gene. (H) The correlation between the erro rate and the number of trees in the random forest, the error rate stabilized when the number of trees reached 100 to 200. (I) The area under the curve (AUC) of the XGBoost model was 0.812, indicating a relative strong classification performance. (J) A Venn diagram illustration of the intersection of genes selected by LASSO, random forest, and XGBoost algorithms. (K) A Venn diagram illustration of the intersection of genes of three machine algorithms and overlapped DEGs in C-MA and N-LA. NARDS: neonatal acute respiratory distress syndrome.

Figure 3

Functional enrichment analysis of genes expressed in subgroup of NARDS and control (A) Results of Reactome pathway analysis of DEGs in group C and MA presented in a barplot. (B) Results of Reactome pathway analysis of DEGs in group N and C presented in a barplot. (C) Results of GSEA analysis of ranked gene lists of N-C, N-LA, C-MA, LA-MA comparison groups in a bubble plot, with bubble size representing the absolute value of enrichment score (ES) (larger circles indicate larger ES) and color representing the adjusted p-value (darker blue shades indicate smaller p-values). (D–F) Significant enrichment in interferon-related pathways of GSEA analysis. (D) Interferon-gamma response was activated in group LA compared with group N. (F) Interferon-alpha/gamma response was suppressed in MA compared with group C, while (F) activated when compared with group LA. (G) Expression pattern of interferon-related genes in different subgroups in a heatmap. NARDS: neonatal acute respiratory distress syndrome, GSEA: gene set enrichment analysis, DEG: differentially expresses genes.

Figure 4

Immune cell infiltration analysis and correlation analysis (A–C) Boxplot showing the difference in immune cell infiltration between C-MA, N-LA, LA-MA. p values were calculated using the non-parametric Mann-Whitney U test. The significance levels are: ∗p < 0.05, ∗∗p < 0.01, ns (not significant). (D) Spearman correlation analysis of the expression levels of three key genes (ALOX15, LIRB3, PTGDR2) with 22 immune cells. The color intensity reflects the magnitude of the correlation coefficient. Blue indicates a negative correlation and red indicates a positive correlation. ∗ indicates p < 0.05.

Figure 5

Validation and diagnostic efficacy of signature genes (A–C) Relative mRNA levels of ALOX15, PTGDR2, LIRB3 in the peripheral blood of validation cohort. Data are presented as the means ± SEM and error bars represent SEM. Statistical differences between groups were analyzed using the non-parametric Mann-Whitney U test. ∗ Indicates a difference between groups. ∗p < 0.05, ∗∗p < 0.01, ns (not significant). (D and E) ROC curves indicate the predictive performance of ALOX15, PTGDR2 and the combined two genes in NARDS subgroups. NARDS: neonatal acute respiratory distress syndrome, ROC: receiver operating characteristic. SEM: standard error of the mean.

Figure 6

Validation of ALOX15 and PTGDR2 in neonatal ALI model (A and B) ALOX15 and PTGDR2 mRNA levels in peripheral blood. (C and D) ALOX15 and PTGDR2 mRNA levels in lung tissue. Data are presented as the means ± SEM and error bars represent SEM (n = 5). Statistical differences were analyzed using a two-tailed, unpaired Student's t-test. ∗ Indicates a difference between groups.∗∗p < 0.01,∗∗∗p < 0.001, ns (not significant) versus control group. ALI: acute lung injury. SEM: standard error of the mean.