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Transcriptomic analyses reveal regional signatures in lung allograft recipients

ABSTRACT: Background and objective: Long term outcomes of allograft recipients are compromised by the development of chronic lung allograft dysfunction (CLAD) or bronchiolitis obliterans syndrome (BOS) which initiates in the lung periphery. We established baseline transcriptomic profiles of both the large and small airway epithelial cells (referred as LAEC and SAEC, respectively) to identify regional differences irrespective of initiating disease. Methods: We obtained matched primary LAEC and SAEC from lung allograft recipients (n=4, 42.5±4.2 years) and established primary cultures. Bulk RNA sequencing was performed to determine differentially expressed genes. Results: We observed differences in the transcriptional program between LAEC and SAEC Transcription factors (TF) were ranked within the top ten differentially regulated genes. The most abundant TF families included C2H2-ZF, homeobox and bHLH. Upstream regulator analyses identified homeobox genes being significantly in LAEC. Protein-protein interaction network analysis emphasised the role of TFs (ISL1, MSX1, HOXA1, GATA6, ZNF423) in airway modulation. Additionally, functional enrichment analysis revealed the activation of chemotaxis, metalloendipeptidase/metallopeptidase activity and pro-inflammatory signatures (IL17 signalling and RAGE), in LAEC, while SAEC were characterised by elevated expression of surfactant metabolism related genes. Moreover, alveolar and club cells-related genes were expressed in SAEC, suggesting a lower airway-specific signature. Conclusion: Our analysis shows robust transcriptional differences between LAEC and SAEC. We suggest a potential role for homeobox TF family as well as the activation of the immune system in the biology of LAEC. Conversely, we observed an alveoli-like transcriptional signature in SAEC, including gas-exchange signals and surfactant metabolism; pathways involved in lung homeostasis.

ORGANISM(S): Homo sapiens

PROVIDER: GSE214434 | GEO | 2022/10/01

REPOSITORIES: GEO

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Project description:Small cell lung cancer (SCLC) is a subtype of lung cancer with poor prognosis due to early dissemination and rapid growth. We here analyze gene expression profile of 23 clinical SCLC samples. EZH2 was found to be highly expressed in SCLC samples compared to 42 normal tissues including the normal lung, and other PRC2 members, SUZ12 and EED, were also highly expressed in SCLC. To obtain target genes of PRC2 in SCLC, H3K27me3 mark was mapped in three SCLC cell lines, Lu130, H209 and DMS53, and compared to normal small airway epithelial cells (SAEC). Whereas H3K27me3(+) genes in SAEC were significantly overlapped with PRC-target genes in ES cells (P=1.7x10-31), genes with H3K27me3 in SCLC cell lines but not in SAEC were not significantly overlapped with PRC-target genes in ES cells (P=0.64). These genes with H3K27me3 specifically in SCLC cell lines but not in SAEC showed decreased expression, not only in SCLC cell lines but also in clinical SCLCs, and showed enrichment of GO-terms such as plasma membrane (P=8.1x10-21) and cell adhesion (P=1.7x10-8). Introduction of JUB, a gene showing specific H3K27me3 modification and the strongest repression in the three SCLC cell lines, resulted in repression of cellular growth in DMS53. In clinical SCLC cases, lower JUB level correlated to shorter survival (P=0.002), or a set of PRC target genes (JUB, EPHB4) and marker genes of classic type SCLC (GRP, ASCL1) correlated to shorter survival (P=0.0001) and classified SCLC into two groups with distinct prognosis. Growth of SCLC cell lines was repressed when treated with 3-Deazaneplanocin A, an inhibitor against PRC2. It is suggested that high expression of PRC2 in SCLC contributed to repression of genes including non-PRC-target genes in ES cells, and that the gene repression may play a role in genesis of SCLC. Gene expression in 23 clinical SCLC samples, 42 normal tissue samples, 3 small cell lung cancer (SCLC) cell lines, and normal small airway epithelial cell (SAEC) was analyzed by Affymetrix arrays. This dataset is part of the TransQST collection.

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Transcriptomic analyses reveal regional signatures in lung allograft recipients

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