Transcriptome networks identify mechanisms of viral and nonviral asthma exacerbations in children

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Respiratory infections are common precursors to asthma exacerbations in children, but molecular immune responses that determine whether and how an infection causes an exacerbation are poorly understood. By using systems-scale network analysis, we identify repertoires of cellular transcriptional pathways that lead to and underlie distinct patterns of asthma exacerbation. Specifically, in both virus-associated and nonviral exacerbations, we demonstrate a set of core exacerbation modules, among which epithelial-associated SMAD3 signaling is upregulated and lymphocyte response pathways are downregulated early in exacerbation, followed by later upregulation of effector pathways including epidermal growth factor receptor signaling, extracellular matrix production, mucus hypersecretion, and eosinophil activation. We show an additional set of multiple inflammatory cell pathways involved in virus-associated exacerbations, in contrast to squamous cell pathways associated with nonviral exacerbations. Our work introduces an in vivo molecular platform to investigate, in a clinical setting, both the mechanisms of disease pathogenesis and therapeutic targets to modify exacerbations.

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Fig. 1: Study design and primary and secondary endpoints.
Fig. 2: Pulmonary function declines during colds that lead to an exacerbation.
Fig. 3: Four core exacerbation modules are upregulated in cold events that lead to exacerbations.
Fig. 4: Longitudinal dynamics of module expression show patterns of sequential module activation.
Fig. 5: The ratio of expression for the ‘type 2 inflammation’ and ‘type I IFN response’ modules predicts exacerbation risk.
Fig. 6: Systemic corticosteroids affect only a subset of exacerbation modules.
Fig. 7: Network overview of modular expression patterns demonstrates co-associated biological pathways.

Data availability

The study data and R code to support the findings of this study have been made publicly available. The raw RNA-seq fastq data and minimum information about a high-throughput nucleotide sequencing experiment (MINSEQE) have been deposited to the Gene Expression Omnibus (GEO) with accession numbers GSE115824, GSE115770, and GSE115823. All metadata from the study cohort have been deposited to ImmPort with accession number SDY1387.

Code availability

The R code for all analyses in this manuscript has been annotated and deposited as open-source code in GitHub at


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This project has been funded in whole or in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, US Department of Health and Human Services, under contract numbers 1UM1AI114271 awarded to W.W.B. and UM2AI117870 awarded to Rho, Inc. Additional support comes from CTSA UL1TR000150 and UL1TR001422 to J.A.P., 5UL1TR001425 to G.K.K.H., NCRR/NIH UL1TR000451 to M.A.G. and R.S.G., CTSI 1UL1TR001430 to G.T.O., CCTSI UL1TR001082 to A.H.L., 5UM1AI114271 to W.W.B. and J.E.G., NCATS/NIH UL1 TR001876 to S.J.T., and UL1TR002345 to L.B.B. We are grateful to all participants and their families who took part in this study. We thank all of the investigators and staff of the National Institutes of Allergy and Infectious Diseases Inner City Asthma Consortium. We thank the Benaroya Research Institute Genomics and Bioinformatics cores for assistance with sample handling, data generation, and RNA sequencing alignment. We would like to thank P. Woodruff, J. Boyce, and S. Durham for assistance with methodological development as well as advice and discussion.

Author information

M.C.A. contributed to study design, RNA-seq data generation, and data analysis and wrote the manuscript. M.A.G. contributed to study design, cell enumeration, and sample collection. E.W. contributed to study design and data analysis. D.C.B. contributed to study design and data analysis. B.S. contributed to cell enumeration. A.H.L. contributed to study design and sample collection. B.J. contributed to data analysis. R.S.G. contributed to sample collection. G.T.O. contributed to sample collection. J.A.P. contributed to sample collection. C.M.K. contributed to sample collection. G.K.K.H. contributed to sample collection. E.M.Z. contributed to sample collection. C.C.J. contributed to sample collection. S.J.T. contributed to sample collection. M.K. contributed to sample collection. L.B.B. contributed to sample collection. A.B. contributed to study design and sample collection. S.M.S. contributed to study coordination. S.P. contributed to RNA-seq data generation. J.E.G. contributed to virology. P.J.G. contributed to study design and study coordination. L.M.W. contributed to study coordination. A.T. contributed to study design and study coordination. W.W.B. contributed to study design, study coordination, and writing of the manuscript. D.J.J. contributed to study design, study coordination, virology, and writing of the manuscript.

Correspondence to Matthew C. Altman.

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Competing interests

M.A.G. reports consulting fees from the American Academy of Allergy, Asthma, and Immunology and the American Academy of Pediatrics. A.H.L. reports consulting fees from Merck Sharp & Dohme and reports data-monitoring committee membership for an asthma study conducted by GlaxoSmithKline. R.S.G. reports employment as a special government employee with the Center for Biologics Evaluation and Research and consulting fees from the Consulting Massachusetts Medical Society. G.T.O. reports consulting fees from AstraZeneca and reports a grant from Janssen Pharmaceuticals paid to his employing institution. J.A.P. reports provision of study drugs from GlaxoSmithKline, Teva, Merck, Boehringer-Ingelheim, and Genentech/Novartis for research studies outside of the scope of the submitted work. C.M.K. reports consulting fees from GlaxoSmithKline. E.M.Z. reports consulting fees from Wayne State University. S.J.T. reports consulting fees from Novartis, grants from PCORI, the Fight for Children Foundation, EJF Philanthropies, and NIH/NHLBI, and royalties from Uptodate. M.K. reports consulting fees from Novartis. L.B.B. reports consulting fees from Aerocrine, GlaxoSmithKline, Genentech/Novartis, Merck, Cephalon, DBV Technologies, Teva, Boehringer-Ingelheim, AstraZeneca, WebMD/Medscape, Sanofi, Vectura, and Circassia. J.E.G. reports consulting fees from Janssen, Regeneron, and PReP Biosciences and travel expenses from Boehringer-Ingelheim. W.W.B. reports consulting fees from Boston Scientific, ICON, Novartis, GlaxoSmithKline, Genentech, Roche, Boehringer-Ingelheim, Sanofi Genzyme, AstraZeneca, Teva, 3M, PrEPBiopharm, Circassia, Regeneron, Peptinnovate, and Elsevier. D.J.J. reports consulting fees from Novartis, GlaxoSmithKline, Boehringer-Ingelheim, Pfizer, Commense, and Vectura and a grant from NIH/NHLBI. M.C.A., D.C.B., E.W., B.S., B.J., G.K.K.H., C.C.J., A.B., S.M.S., S.P., P.J.G., L.M.W., and A.T. have nothing to disclose.

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Integrated supplementary information

Supplementary Figure 1 Analysis overview.

(a) Cell-associated gene coexpression modules were generated using samples collected from the first fall enrollment season, which included 42 participants capturing 59 cold events. Cell deconvolution was performed to assign genes to cell types, and then WGCNA was used to generate coexpression modules. Module networks were determined by assessing known gene-gene interactions and annotated using public databases. (b) These modules were validated as coherent using the full dataset of 106 participants and 154 cold events. The modules were then used for the comparison analyses. The primary analysis compared Ex+ events (47) to Ex- events (107) controlling for cell differentials and presence or absence of a virus-associated with the event (virus status), visit, and library depth with a random effect included for participant. The validation of the primary analysis was done through independent analyses of paired samples (19 participants who had one Ex+ and one Ex- event) and unpaired samples (87 participants who collectively had 28 Ex+ and 88 Ex- events). The secondary analysis compared V+ Ex+ (33), V+ Ex- (69), V-Ex+ (14), and V-Ex- (38) events. The longitudinal analysis used LOESS regression to estimate the longitudinal dynamics of module expression per day. The corticosteroid analysis compared the expression of modules before and after initiation of systemic corticosteroids in the Ex+ group. All results were merged to generate overview bipartite networks demonstrating the global signals of module expression specific to V+ Ex+ events, V-Ex+ events, and the effects of systemic corticosteroids.

Supplementary Figure 2 Two core exacerbation modules are downregulated in cold events that lead to exacerbation.

(a) The expression levels of the 2 nasal gene expression modules that decreased in Ex+ events compared to Ex- events. FDR values from top to bottom are 1.38E-05, 4.09E-04. Expression levels represent the log base 2 of the geometric mean of the normalized expression of all genes within the module. Shown are group mean values, interquartile ranges, and all data points. Sample sizes are: Ex+ at baseline (n = 38); Ex+ at 0–3 days (n = 44); Ex+ at 4–6 days (n = 11); Ex- at baseline (n = 68); Ex- at 0–3 days (n = 97); Ex- at 4–6 days (n = 95). 18 participants who had one Ex+ and one Ex- event have the same measurement shown in both Ex+ at baseline and Ex- at baseline. All measurements shown are otherwise biologically independent. Comparisons were performed using a weighted linear model and empirical Bayes method, including terms for exacerbation status, cell percentages, presence or absence of virus, visit, and library depth with a random effect included for participant. (b) Gene-gene association networks for each of the modules demonstrate significant interaction networks. Genes are represented as circular nodes, and known gene-gene interactions from STRING as connecting edges. The size of each node is proportional to the number of interactions. STRING enrichment p-values for both networks are <1.0E-16.

Supplementary Figure 3 Principal component analysis (PCA) shows important sources of variability in the transcriptome.

PCA was performed on the expression data and principal components (PCs) were correlated to clinical and demographic variables. To determine significance, Pearson correlation was calculated for continuous variables and ANOVA was run for categorical variables. Significant relationships (FDR<0.05) between variables and PCs show a circle on the heatmap to indicate magnitude of the relationship, where color represents magnitude and direction of Pearson correlation for continuous variables and darkness of gray represents the magnitude of R2 from ANOVA for categorical variables. Variables that do not have a significant relationship with PCs show the FDR value in their square on the heatmap. (a) Association heatmaps of variables for nasal samples. (b) Association heatmaps of variables for blood samples.

Supplementary Figure 4 Validation analyses demonstrate the core exacerbation modules are reproducible.

(a) Module expression levels for the 6 exacerbation core modules are shown comparing the full cohort (154 total events), the paired cohort (38 total events), and the unpaired cohort (116 total events). The paired cohort included 19 participants who experienced one Ex+ and one Ex- event during the study. The unpaired cohort compared participants who contributed only Ex+ or only Ex- events, and included the other 87 participants who collectively had 28 Ex+ and 88 Ex- events. The two groups were, by definition, independent of one another. The four core modules that were significantly higher in Ex+ events (left-most four plots) have an FDR<0.05 in each comparison. The two modules that were significantly lower in Ex+ events (right-most two plots) have an FDR<0.05 in the full and unpaired cohorts and FDRs of 0.06 (Lymphocyte proliferation) and 0.22 (BCR signaling) in the paired cohort. Expression levels represent the log base 2 of the geometric mean of the normalized expression of all genes within the module. Shown are the group mean and standard error values. (b) A sensitivity analysis demonstrates the proportion of times each module was significant using iterative boot strapping to random subsets to 80% of participants. The first 6 modules are the core modules shown in (a) and were significant in >99% of iterations. The 13 modules in blue are those that were significant in the primary analysis. Modules that were never significant are not shown. Abbreviated alphanumeric module names are listed for brevity. (c) Shown are the associated log2 fold change values for the sensitivity analysis. Calculated fold changes across all iterations are shown as box plots of the medians, interquartile ranges (boxes), and 1.5 times the interquartile ranges (whiskers) with outliers shown as points. The red line indicates the 13 modules significant in the primary analysis. Abbreviated alphanumeric module names are used for brevity.

Supplementary Figure 5 Core exacerbation modules are common to exacerbation events with or without virus present, while additional distinct patterns of module expression are unique to V+Ex+ and V-Ex+ events.

(a) The core exacerbation modules show a similar degree of altered expression in both V+Ex+ (n = 33) and V-Ex+ (n = 14) events and are significant in each subgroup comparison. (b) V+Ex+ events demonstrate specific upregulation in 9 modules that represent numerous inflammatory pathways and specific downregulation in 2 modules. (c) V-Ex+ events demonstrate specific upregulation of 3 squamous cell associated modules. Expression levels represent the geometric mean of the normalized expression of all genes within the module. Shown are subgroup mean and standard error values. All modules shown have an ANOVA FDR<0.05. FC and FDR values are listed in Table 2. This analysis included 247 unique nasal samples and 256 unique blood samples from 106 individuals who in total had 33 V+Ex+, 14 V-Ex+, 69 V+Ex- and 38 V-Ex- events.

Supplementary Figure 6 Expression profiles define clusters of biological functions.

(a) A bipartite network demonstrates the coassociations among all nasal (light orange) and blood (light purple) modules (squares) and cell percentages (circles). Edges represent significant positive Pearson correlations >0.5 (FDR<0.05) and darker edges indicate higher correlations. Nodes are clustered according to their interconnectedness. Because respiratory epithelium and squamous cells were highly correlated, they were combined for this analysis. Several co-clustered networks are outlined in colors. Nasal epithelial (brown outline), nasal and blood eosinophil (pink outline) cells and pathways cluster towards the bottom of the network. These clusters include the upregulated core modules (Green squares) including pathways of multiple epithelial functions, eosinophil activation, and Th2 inflammation. Nasal macrophage and blood monocyte cells and modules cluster with one another (dark purple outline). This cluster includes blood and nasal interferon responses, the heat shock protein response, airway macrophage pathways, and blood monocyte pathways. Nasal lymphocyte pathways are not clustered with blood lymphocytes pathways (blue). Two core modules fall within the nasal lymphocyte cluster (green squares). Nasal neutrophil and blood neutrophil pathways do not cluster with one another (light green). (b) The heatmap shows the pairwise Pearson correlation coefficients between all nasal and blood module expression levels and nasal and blood cell percentages. Red indicates positive correlation and blue indicates negative correlation. Clusters represent coassociated modules and cell types. Abbreviated alphanumeric module names are used for brevity.

Supplementary Figure 7 Module validation demonstrates that module-cell associations and module coherence are reproducible.

(a) In the full set of 374 nasal samples, all genes that had been associated to a specific cell type during module generation, had statistically significant positive Pearson correlations with the same cell type (13,672 genes). (b) In the full set of 387 blood samples, all but 5 genes that had been associated to a specific cell type during module generation, had statistically significant positive Pearson correlations with the same cell type (13,311/13,316 genes). (c) Correlation of nasal module expression levels from the full set of 374 samples with cell differentials was assessed to determine a module’s final cell assignment. 27/52 nasal modules were assigned to one or more cell types. (d) Correlation of blood module expression levels from the full set of 387 samples with cell differentials to finalize a module’s cell assignment. 16/42 blood modules were assigned to one cell type. (e) The average Pearson correlation of genes assigned to each nasal module during module construction (146 samples) versus the average Pearson correlation of genes assigned to the same module within the full set of 374 samples. (f) The average Pearson correlation of genes assigned to each blood module during module construction (176 samples) versus the average Pearson correlation of genes assigned to the same module within the full set of 387 samples.

Supplementary information

Supplementary Figures 1–7

Reporting Summary

Supplementary Table 1

Module annotation table. Listed are the 94 modules generated by using cell deconvolution and WGCNA. Each alphanumeric module name is based on a cell-association-specific numeric ranking during module generation. The sample type specifies whether the module was identified from the nasal lavage samples (Nasal) or peripheral blood samples (Blood). The cell association(s) are listed based on significant association between a module and cell percentage in the full dataset. A summary annotation of each module was derived from manual inspection of the module cell assignment, functional enrichment from DAVID, and the interaction network from STRING. The module size indicates the number of genes in a module. The STRING network P value is the significance of the network identified for each module by STRING. Listed in the remaining columns are all the top ranking pathway terms that had the most significant overlap with the module in each of the listed DAVID knowledgebase categories, along with the associated enrichment P value, enrichment Benjamini FDR, gene count overlap, and percentage of pathway for that term. The second tab ‘ENSG IDs’ lists all genes within each module according to Ensembl gene ID. The third tab ‘Gene symbols’ lists all genes within each module according to HUGO Gene Nomenclature Committee official gene symbol.

Supplementary Table 2

Virus subgroup demographics table. Of the 154 total events, 33 were classified as virus-associated exacerbation (V+Ex+) events and 69 were classified as virus-associated, no-exacerbation (V+Ex) events. V+Ex+ events showed significantly lower FEV1 % predicted values than V+Ex events. 14 events were classified as nonviral exacerbation (VEx+) events and 38 were classified as nonviral, no-exacerbation (VEx) events. VEx+ events had significantly lower FEV1 % predicted values and higher reported cold severity scores than VEx events. There was also a difference in the seasonality of VEx+ events as compared to VEx events, with more VEx events occurring in the winter and spring. Percentage (count) values are displayed for categorical variables. Median (quartile 1, quartile 3) values are displayed for continuous variables. For participants with two events meeting specified criteria, both colds are included in the table. Unless otherwise specified, all comparisons were performed by using a generalized linear mixed model with a random effect for participant to account for correlation between values from the same participant; a multinomial distribution was used for categorical variables and a normal distribution was used for continuous variables. A number sign indicates comparisons analyzed by two-sided Fisher's exact test, and an asterisk indicates comparisons analyzed by log-normal distribution.

Supplementary Table 3

Molecules and cells associated with exacerbation risk. Shown are the nasal modules and cells measured at the baseline, healthy visit that were significantly associated with time from the baseline visit to the next reported exacerbation. The difference in number of days to exacerbation is based on the interquartile range (75th percentile minus 25th percentile) of module expression. A positive number of days indicates that higher expression of this module results in a longer time to exacerbation, whereas a negative number of days indicates the opposite. Comparisons were performing by using a univariate linear model to compare time to exacerbation and each module’s expression.

Supplementary Table 4

Systemic corticosteroid signal. Shown are results comparing Ex+ events in which systemic corticosteroids were already started versus Ex+ events in which systemic corticosteroids had not yet been started at the visit 4–6 d after onset of cold symptoms. These values correspond to differences shown graphically in Fig. 6. Eight nasal modules and six blood modules were significantly downregulated in the Ex+ events started on systemic corticosteroids (fold-change values shown in blue). Four nasal modules and no blood modules were significantly upregulated in the Ex+ events on systemic corticosteroids (fold-change values shown in red). Shown are the fold-change and significance values. Additionally, nasal eosinophil, blood eosinophil, and blood lymphocyte percentages were decreased in the Ex+ events on systemic corticosteroids (blue) whereas nasal neutrophil and blood neutrophil percentages were increased (red). Shown are the differences in percentage of cell type and significance values. Comparisons were performed by using a weighted linear model and empirical Bayes method, including terms for status (Ex, Ex+ pre-CS, and Ex+ post-CS), cell percentages, presence or absence of virus, visit, and library depth with a random effect included for participant.

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