Increase in p21 expression independent of the p53 pathway in bleomycin-induced lung fibrosis

Although a number of animal models have been used to study the pathogenesis of lung disease, to date few studies have looked at changed in the expression of cell cycle regulatory genes. We have studied the variation in the expression of p21, p53, p27 and PCNA in bleomycin-induced lung fibrosis using animal mouse models using immuno-histochemistry and gene-expression analysis. No difference in the p53, PCNA and p27 expressions were observed from the bleomycin-induced fibrosis when compared to saline-induced non-fibrotic lungs. Although no difference in nuclear p21 expression was observed, the level of cytoplasmic p21 expression was found to be higher in fibrotic lungs at day 14 after bleomycin injection. p21 expression was found to increase independent of p53 in fibrotic lungs at 14 days after bleomycin induction.peer-reviewe

Lessons from Our Patients: Development of a Warm Autopsy Program

Kaminski and colleagues discuss the lessons they have learned in establishing a warm autopsy program to advance research on idiopathic pulmonary fibrosis

During the COVID-19 Pandemic, Lung Specialists of the World Implore You: Inhale Only Clean Air

Recent social media and lay news report that nicotine may help protect from COVID-19. However, lung specialists of the American Thoracic Society and California Thoracic Society recommend that you inhale only clean air. Research shows that exposure to smoke, vapors, and air pollution all contribute to worse outcomes in COVID-19 infection. This fact sheet summarizes some of the common public questions addressed to lung healthcare professionals

Comparison of normalization methods for CodeLink Bioarray data

BACKGROUND: The quality of microarray data can seriously affect the accuracy of downstream analyses. In order to reduce variability and enhance signal reproducibility in these data, many normalization methods have been proposed and evaluated, most of which are for data obtained from cDNA microarrays and Affymetrix GeneChips. CodeLink Bioarrays are a newly emerged, single-color oligonucleotide microarray platform. To date, there are no reported studies that evaluate normalization methods for CodeLink Bioarrays. RESULTS: We compared five existing normalization approaches, in terms of both noise reduction and signal retention: Median (suggested by the manufacturer), CyclicLoess, Quantile, Iset, and Qspline. These methods were applied to two real datasets (a time course dataset and a lung disease-related dataset) generated by CodeLink Bioarrays and were assessed using multiple statistical significance tests. Compared to Median, CyclicLoess and Qspline exhibit a significant and the most consistent improvement in reduction of variability and retention of signal. CyclicLoess appears to retain more signal than Qspline. Quantile reduces more variability than Median in both datasets, yet fails to consistently retain more signal in the time course dataset. Iset does not improve over Median in either noise reduction or signal enhancement in the time course dataset. CONCLUSION: Median is insufficient either to reduce variability or to retain signal effectively for CodeLink Bioarray data. CyclicLoess is a more suitable approach for normalizing these data. CyclicLoess also seems to be the most effective method among the five different normalization strategies examined

Can Blood Gene Expression Predict Which Patients with Multiple Sclerosis Will Respond to Interferon?

Gene expression patterns from peripheral blood cells may be useful as biomarkers for monitoring MS progression and response to therapy, argue Kaminski and Achiro

Challenges and perspectives in computational deconvolution in genomics data

Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach to estimate cell type abundances from a variety of omics data. Despite significant methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four significant challenges from availability of the reference data, generation of simulation data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies and strategies to promote rigorous benchmarking

Identification and validation of differentially expressed transcripts by RNA-sequencing of formalin-fixed, paraffin-embedded (FFPE) lung tissue from patients with Idiopathic Pulmonary Fibrosis

Background: Idiopathic Pulmonary Fibrosis (IPF) is a lethal lung disease of unknown etiology. A major limitation in transcriptomic profiling of lung tissue in IPF has been a dependence on snap-frozen fresh tissues (FF). In this project we sought to determine whether genome scale transcript profiling using RNA Sequencing (RNA-Seq) could be applied to archived Formalin-Fixed Paraffin-Embedded (FFPE) IPF tissues. Results: We isolated total RNA from 7 IPF and 5 control FFPE lung tissues and performed 50 base pair paired-end sequencing on Illumina 2000 HiSeq. TopHat2 was used to map sequencing reads to the human genome. On average similar to 62 million reads (53.4% of similar to 116 million reads) were mapped per sample. 4,131 genes were differentially expressed between IPF and controls (1,920 increased and 2,211 decreased (FDR lt 0.05). We compared our results to differentially expressed genes calculated from a previously published dataset generated from FF tissues analyzed on Agilent microarrays (GSE47460). The overlap of differentially expressed genes was very high (760 increased and 1,413 decreased, FDR lt 0.05). Only 92 differentially expressed genes changed in opposite directions. Pathway enrichment analysis performed using MetaCore confirmed numerous IPF relevant genes and pathways including extracellular remodeling, TGF-beta, and WNT. Gene network analysis of MMP7, a highly differentially expressed gene in both datasets, revealed the same canonical pathways and gene network candidates in RNA-Seq and microarray data. For validation by NanoString nCounter (R) we selected 35 genes that had a fold change of 2 in at least one dataset (10 discordant, 10 significantly differentially expressed in one dataset only and 15 concordant genes). High concordance of fold change and FDR was observed for each type of the samples (FF vs FFPE) with both microarrays (r = 0.92) and RNA-Seq (r = 0.90) and the number of discordant genes was reduced to four. Conclusions: Our results demonstrate that RNA sequencing of RNA obtained from archived FFPE lung tissues is feasible. The results obtained from FFPE tissue are highly comparable to FF tissues. The ability to perform RNA-Seq on archived FFPE IPF tissues should greatly enhance the availability of tissue biopsies for research in IPF

High Throughput Determination of TGFβ1/SMAD3 Targets in A549 Lung Epithelial Cells

Transforming growth factor beta 1 (TGFβ1) plays a major role in many lung diseases including lung cancer, pulmonary hypertension, and pulmonary fibrosis. TGFβ1 activates a signal transduction cascade that results in the transcriptional regulation of genes in the nucleus, primarily through the DNA-binding transcription factor SMAD3. The objective of this study is to identify genome-wide scale map of SMAD3 binding targets and the molecular pathways and networks affected by the TGFβ1/SMAD3 signaling in lung epithelial cells. We combined chromatin immunoprecipitation with human promoter region microarrays (ChIP-on-chip) along with gene expression microarrays to study global transcriptional regulation of the TGFβ1/SMAD3 pathway in human A549 alveolar epithelial cells. The molecular pathways and networks associated with TGFβ1/SMAD3 signaling were identified using computational approaches. Validation of selected target gene expression and direct binding of SMAD3 to promoters were performed by quantitative real time RT-PCR and electrophoretic mobility shift assay on A549 and human primary lung epithelial cells. Known TGFβ1 target genes such as SERPINE1, SMAD6, SMAD7, TGFB1 and LTBP3, were found in both ChIP-on-chip and gene expression analyses as well as some previously unrecognized targets such as FOXA2. SMAD3 binding of FOXA2 promoter and changed expression were confirmed. Computational approaches combining ChIP-on-chip and gene expression microarray revealed multiple target molecular pathways affected by the TGFβ1/SMAD3 signaling. Identification of global targets and molecular pathways and networks associated with TGFβ1/SMAD3 signaling allow for a better understanding of the mechanisms that determine epithelial cell phenotypes in fibrogenesis and carcinogenesis as does the discovery of the direct effect of TGFβ1 on FOXA2