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Integrating pulmonary and systemic transcriptomes to characterize lung injury after pediatric hematopoietic stem cell transplant
Emma M. Pearce, … , Joseph L. DeRisi, Matt S. Zinter
Emma M. Pearce, … , Joseph L. DeRisi, Matt S. Zinter
Published July 22, 2025
Citation Information: JCI Insight. 2025;10(17):e194440. https://doi.org/10.1172/jci.insight.194440.
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Clinical Research and Public Health Immunology Inflammation Pulmonology

Integrating pulmonary and systemic transcriptomes to characterize lung injury after pediatric hematopoietic stem cell transplant

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Abstract

Hematopoietic stem cell transplantation (HCT) is a potentially life-saving therapy but can lead to lung injury due to chemoradiation toxicity, infection, and immune dysregulation We previously showed that bronchoalveolar lavage (BAL) transcriptomes representing pulmonary inflammation and cellular injury can phenotype post-HCT lung injury and predict mortality. To test whether peripheral blood might be a suitable surrogate for BAL, we compared 210 paired BAL and blood transcriptomes obtained from 166 pediatric patients with HCT at 27 hospitals. BAL and blood RNA abundance showed minimal correlation at the level of individual genes, gene set enrichment scores, imputed cell fractions, and T and B cell receptor clonotypes. Instead, we identified significant site-specific transcriptional programs. In BAL, pathways related to immunity, hypoxia, and epithelial mesenchymal transition were tightly coexpressed and linked to mortality. In contrast, in blood, expression of endothelial injury, DNA repair, and cellular metabolism pathways was associated with mortality. Integration of paired BAL and blood transcriptomes dichotomized patients into 2 groups with significantly different rates of hypoxia and clinical outcomes within 1 week of BAL. These findings reveal a compartmentalized injury response, where BAL and blood transcriptomes provide distinct but complementary insights into local and systemic mechanisms of post-HCT lung injury.

Authors

Emma M. Pearce, Erica Evans, Madeline Y. Mayday, Gustavo Reyes, Miriam R. Simon, Jacob Blum, Hanna Kim, Jessica Mu, Peter J. Shaw, Courtney M. Rowan, Jeffery J. Auletta, Paul L. Martin, Caitlin Hurley, Erin M. Kreml, Muna Qayed, Hisham Abdel-Azim, Amy K. Keating, Geoffrey D.E. Cuvelier, Janet R. Hume, James S. Killinger, Kamar Godder, Rabi Hanna, Christine N. Duncan, Troy C. Quigg, Paul Castillo, Nahal R. Lalefar, Julie C. Fitzgerald, Kris M. Mahadeo, Prakash Satwani, Theodore B. Moore, Benjamin Hanisch, Aly Abdel-Mageed, Dereck B. Davis, Michelle P. Hudspeth, Greg A. Yanik, Michael A. Pulsipher, Christopher C. Dvorak, Joseph L. DeRisi, Matt S. Zinter

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Figure 4

Differential gene coexpression by survival status.

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Differential gene coexpression by survival status.
(A) Network of genes ...
(A) Network of genes coexpressed in BAL of nonsurvivors (right) but not coexpressed in survivors (left). Examples of hubs genes (CEACAM6, CXCL17, NFAM1) are shown. Examples of differentially coexpressed genes linked to each hub gene are shown (e.g., CEACAM6-FN1 coexpression) to illustrate differential gene-expression. To the right, pathway enrichment for hub genes are shown. (B) Network of genes coexpressed in peripheral blood of nonsurvivors (right) but not coexpressed in survivors (left). Examples of hub genes (ZNF707, GARRE1, TMEM86B) are shown. Examples of differentially coexpressed genes linked to each hub gene are shown (e.g., ZNF707-BUB1B coexpression) to illustrate differential gene-expression. To the right, pathway enrichment for hub genes are shown.

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