High-Throughput Sequencing in Respiratory, Critical Care, and Sleep Medicine Research. An Official American Thoracic Society Workshop Report

Genetic Determinants in Airways Obstructive Diseases: The Case of Asthma Chronic Obstructive Pulmonary Disease Overlap

Genome-wide association studies (GWAS) of asthma and chronic obstructive pulmonary disease (COPD) with ever-increasing sample sizes have found multiple genetic loci associated with either disease. However, there are few intersecting loci between asthma and COPD. GWAS specifically focused on asthma-COPD overlap (ACO) have been limited by smaller sample sizes and the lack of a consistent definition of ACO that has also hampered clinical and epidemiologic studies. Other genomic techniques, such as gene expression profiling, are feasible with smaller sample sizes. Genetic analyses of objective measures of airway reactivity and allergy/T2 inflammation biomarkers in COPD studies may be another strategy to overcome limitations in ACO definitions.

Multiomics Analysis Identifies BIRC3 as a Novel Glucocorticoid Response-Associated Gene

BACKGROUND

Inhaled corticosteroid (ICS) response among patients with asthma is influenced by genetics, but biologically actionable insights based on associations have not been found. Various glucocorticoid response omics datasets are available to interrogate their biological effects.

OBJECTIVE

We sought to identify functionally relevant ICS response genetic associations by integrating complementary multiomics datasets.

METHODS

Variants with p-values<10^-4 from a previous ICS response genome-wide association study were re-ranked based on integrative scores determined from: i) glucocorticoid receptor (GR)- and ii) RNA polymerase II (RNAP II)-binding regions inferred from ChIP-Seq data for three airway cell types, iii) glucocorticoid response element (GRE) motifs, iv) differentially expressed genes in response to glucocorticoid exposure according to 20 transcriptomic datasets, and v) expression quantitative trait loci (eQTLs) from GTEx. Candidate variants were tested for association with ICS response and asthma in six independent studies.

RESULTS

Four variants had significant (q-value<0.05) multiomics integrative scores. These variants were in a locus consisting of 52 variants in high LD (r²≥0.8) near GR-binding sites by the gene BIRC3. Variants were also BIRC3 eQTLs in lung, and two were within/near putative GRE motifs. BIRC3 had increased RNAP II occupancy and gene expression with glucocorticoid exposure in two ChIP-Seq and 13 transcriptomic datasets. Some BIRC3 variants in the 52-variant locus were associated (p-value<0.05) with ICS response in three independent studies and others with asthma in one study.

CONCLUSION

BIRC3 should be prioritized for further functional studies of ICS response.

CLINICAL IMPLICATION

Genetic variation near BIRC3 may influence ICS response in people with asthma.

Transcriptome analysis reveals a protective role of liver X receptor alpha against silica particle-induced experimental silicosis

Silicosis, a severe and irreversible form of pulmonary fibrosis (PF) caused by long-term exposure to dust particles in production environments, is the biggest occupational health concern in China and most low-income countries. The transdifferentiation of pulmonary fibroblasts is the terminal event in silicosis, and specific transcription factors (TFs) play a crucial role in this condition. However, the relationship between TF-mediated regulation and silicosis remains unknown. We performed a transcriptomic analysis to elucidate this relationship, and our results revealed that two TFs, EGR2 and BHLHE40, were upregulated and five, i.e., TBX2, NR1H3 (LXRα), NR2F1, PPARG (PPARγ), and EPAS1, were downregulated in activated fibroblasts. Notably, PPARγ and LXRα expression was also decreased in an experimental mouse model of silicosis. The mechanism underlying these changes may involve TGF-β1 secretion from silica-exposed alveolar macrophages, causing PPARγ and LXRα downregulation, which in turn would result in aberrant α-SMA transcription. Our results suggest that LXRα is a potential target for the prevention of silicosis and PF.

A Practical Guide to the Measurement and Analysis of DNA Methylation

DNA methylation represents a fundamental epigenetic mark that is associated with transcriptional repression during development, maintenance of homeostasis, and disease. In addition to methylation-sensitive PCR and targeted deep-amplicon bisulfite sequencing to measure DNA methylation at defined genomic loci, numerous unsupervised techniques exist to quantify DNA methylation on a genome-wide scale, including affinity enrichment strategies and methods involving bisulfite conversion. Both affinity-enriched and bisulfite-converted DNA can serve as input material for array hybridization or sequencing using next-generation technologies. In this practical guide to the measurement and analysis of DNA methylation, the goal is to convey basic concepts in DNA methylation biology and explore genome-scale bisulfite sequencing as the current gold standard for assessment of DNA methylation. Bisulfite conversion chemistry and library preparation are discussed in addition to a bioinformatics approach to quality assessment, trimming, alignment, and methylation calling of individual cytosine residues. Bisulfite-converted DNA presents challenges for standard next-generation sequencing library preparation protocols and data-processing pipelines, but these challenges can be met with elegant solutions that leverage the power of high-performance computing systems. Quantification of DNA methylation, data visualization, statistical approaches to compare DNA methylation between sample groups, and examples of integrating DNA methylation data with other -omics data sets are also discussed. The reader is encouraged to use this article as a foundation to pursue advanced topics in DNA methylation measurement and data analysis, particularly the application of bioinformatics and computational biology principles to generate a deeper understanding of mechanisms linking DNA methylation to cellular function.

Application of magnetic nanoparticles in nucleic acid detection

Nucleic acid is the main material for storing, copying, and transmitting genetic information. Gene sequencing is of great significance in DNA damage research, gene therapy, mutation analysis, bacterial infection, drug development, and clinical diagnosis. Gene detection has a wide range of applications, such as environmental, biomedical, pharmaceutical, agriculture and forensic medicine to name a few. Compared with Sanger sequencing, high-throughput sequencing technology has the advantages of larger output, high resolution, and low cost which greatly promotes the application of sequencing technology in life science research. Magnetic nanoparticles, as an important part of nanomaterials, have been widely used in various applications because of their good dispersion, high surface area, low cost, easy separation in buffer systems and signal detection. Based on the above, the application of magnetic nanoparticles in nucleic acid detection was reviewed.

Airway Smooth Muscle-Specific Transcriptomic Signatures of Glucocorticoid Exposure

Glucocorticoids, commonly used asthma controller medications, decrease symptoms in most patients, but some remain symptomatic despite high-dose treatment. The physiological basis underlying the glucocorticoid response, especially in asthma patients with severe, refractory disease, is not fully understood. We sought to identify differences between the transcriptomic response of airway smooth muscle (ASM) cells derived from donors with fatal asthma and donors without asthma to glucocorticoid exposure and to compare ASM-specific changes with those observed in other cell types. In cells derived from nine donors with fatal asthma and eight donors without asthma, RNA sequencing was used to measure ASM transcriptome changes after exposure to budesonide (100 nM 24 h) or control vehicle (DMSO). Differential expression results were obtained for this dataset, as well as 13 publicly available glucocorticoid-response transcriptomic datasets corresponding to seven cell types. Specific genes were differentially expressed in response to glucocorticoid exposure (7,835 and 6,957 in ASM cells derived from donors with fatal asthma and donors without asthma, respectively; adjusted P value < 0.05). Transcriptomic changes in response to glucocorticoid exposure were similar in ASM derived from donors with fatal asthma and donors without asthma, with enriched ontological pathways that included cytokine- and chemokine-related categories. A comparison of glucocorticoid-induced changes in the nonasthma ASM transcriptome with those observed in six other cell types showed that ASM has a distinct glucocorticoid-response signature that is also present in ASM cells from donors with fatal asthma.