METHODS: By using a microarray-based global gene expression profiling system, this study aimed to decipher the underlying molecular pathways that may mediate the immunosuppressive activity of umbilical cord-derived MSCs (UC-MSCs) on activated T cells.
RESULTS: In the presence of UC-MSCs, the proliferation of activated T cells was suppressed in a dose-depended manner by cell-to-cell contact mode via an active cell-cycle arrest at the G0/G1 phase of the cell cycle. The microarray analysis revealed that particularly, IFNG, CXCL9, IL2, IL2RA and CCND3 genes were down-regulated, whereas IL11, VSIG4, GFA1, TIMP3 and BBC3 genes were up-regulated by UC-MSCs. The dysregulated gene clusters associated with immune-response-related ontologies, namely, lymphocyte proliferation or activation, apoptosis and cell cycle, were further analyzed.
CONCLUSIONS: Among the nine canonical pathways identified, three pathways (namely T-helper cell differentiation, cyclins and cell cycle regulation, and gap/tight junction signalling pathways) were highly enriched with these dysregulated genes. The pathways represent putative molecular pathways through which UC-MSCs elicit immunosuppressive activity toward activated T cells. This study provides a global snapshot of gene networks and pathways that contribute to the ability of UC-MSCs to suppress activated T cells.
METHODS: We developed mouse models representing three different phenotypes of allergic airway inflammation-eosinophilic, mixed, and neutrophilic asthma via different methods of house dust mite sensitization and challenge. Transcriptomic analysis of the lungs, followed by the RT-PCR, western blot, and confocal microscopy, was performed. Primary human bronchial epithelial cells cultured in air-liquid interface were used to study the mechanisms revealed in the in vivo models.
RESULTS: By whole-genome transcriptome profiling of the lung, we found that airway tight junction (TJ), mucin, and inflammasome-related genes are differentially expressed in these distinct phenotypes. Further analysis of proteins from these families revealed that Zo-1 and Cldn18 were downregulated in all phenotypes, while increased Cldn4 expression was characteristic for neutrophilic airway inflammation. Mucins Clca1 (Gob5) and Muc5ac were upregulated in eosinophilic and even more in neutrophilic phenotype. Increased expression of inflammasome-related molecules such as Nlrp3, Nlrc4, Casp-1, and IL-1β was characteristic for neutrophilic asthma. In addition, we showed that inflammasome/Th17/neutrophilic axis cytokine-IL-1β-may transiently impair epithelial barrier function, while IL-1β and IL-17 increase mucin expressions in primary human bronchial epithelial cells.
CONCLUSION: Our findings suggest that differential expression of TJ, mucin, and inflammasome-related molecules in distinct inflammatory phenotypes of asthma may be linked to pathophysiology and might reflect the differences observed in the clinic.
METHODS: mRNA was extracted from 44 fibroadenomas and 36 giant fibroadenomas, and transcriptomic profiling was performed to identify up- and down-regulated genes in the giant fibroadenomas as compared to the fibroadenomas.
RESULTS: A total of 40 genes were significantly up-regulated and 18 genes were significantly down-regulated in the giant fibroadenomas as compared to the fibroadenomas of the breast. The top 5 up-regulated genes were FN1, IL3, CDC6, FGF8 and BMP8A. The top 5 down-regulated genes were TNR, CDKN2A, COL5A1, THBS4 and BMPR1B. The differentially expressed genes (DEGs) were found to be associated with 5 major canonical pathways involved in cell growth (PI3K-AKT, cell cycle regulation, WNT, and RAS signalling) and immune response (JAK-STAT signalling). Further analyses using 3 supervised learning algorithms identified an 8-gene signature (FN1, CDC6, IL23A, CCNA1, MCM4, FLT1, FGF22 and COL5A1) that could distinguish giant fibroadenomas from fibroadenomas with high predictive accuracy.
CONCLUSION: Our findings demonstrated that the giant fibroadenomas are biologically distinct to fibroadenomas of the breast with overexpression of genes involved in the regulation of cell growth and immune response.
RESULTS: We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.
CONCLUSIONS: Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .