Multilaboratory Assessment of a New Reference Material for Quality Assurance of Cell-Free Tumor DNA Measurements.

We conducted a multilaboratory assessment to determine the suitability of a new commercially available reference material with 40 cancer variants in a background of wild-type DNA at four different variant allele frequencies (VAFs): 2%, 0.50%, 0.125%, and 0%. The variants include single nucleotides, insertions, deletions, and two structural variations selected for their clinical importance and to challenge the performance of next-generation sequencing (NGS) methods. Fragmented DNA was formulated to simulate the size distribution of circulating wild-type and tumor DNA in a synthetic plasma matrix. DNA was extracted from these samples and characterized with different methods and multiple laboratories. The various extraction methods had differences in yield, perhaps because of differences in chemistry. Digital PCR assays were used to measure VAFs to compare results from different NGS methods. Comparable VAFs were observed across the different NGS methods. This multilaboratory assessment demonstrates that the new reference material is an appropriate tool to determine the analytical parameters of different measurement methods and to ensure their quality assurance.

A Transcriptomic Method to Determine Airway Immune Dysfunction in T2-High and T2-Low Asthma.

BACKGROUND: Type 2 (T2) inflammation drives airway dysfunction in many patients with asthma; yet, we lack a comprehensive understanding of the airway immune cell types and networks that sustain this inflammation. Moreover, defects in the airway immune system in patients with asthma without T2 inflammation are not established. OBJECTIVES: To determine the gene networks that sustain T2 airway inflammation in T2-high asthma and to explore the gene networks that characterize T2-low asthma. METHODS: Network analysis of sputum cell transcriptome expression data from 84 subjects with asthma and 27 healthy control subjects was used to identify immune cell type-enriched networks that underlie asthma subgroups. RESULTS: Sputum T2 gene expression was characterized by an immune cell network derived from multiple innate immune cells, including eosinophils, mast cells/basophils, and inflammatory dendritic cells. Clustering of subjects within this network stratified subjects into T2-high and T2-low groups, but it also revealed a subgroup of T2-high subjects with uniformly higher expression of the T2 network. These "T2-ultrahigh subjects" were characterized clinically by older age and more severe airflow obstruction and pathologically by a second T2 network derived from T2-skewed, CD11b+/CD103-/IRF4+ classical dendritic cells. Subjects with T2-low asthma were differentiated from healthy control subjects by lower expression of a cytotoxic CD8+ T-cell network, which was negatively correlated with body mass index and plasma IL-6 concentrations. CONCLUSIONS: Persistent airway T2 inflammation is a complex construct of innate and adaptive immunity gene expression networks that are variable across individuals with asthma and persist despite steroid treatment. Individuals with T2-low asthma exhibit an airway deficiency in cytotoxic T cells associated with obesity-driven inflammation.

Computational Analysis of RNA-Seq Data from Airway Epithelial Cells for Studying Lung Disease.

Airway epithelial cells (AECs) play a central role in the pathogenesis of many lung diseases. Consequently, advancements in our understanding of the underlying causes of lung diseases, and the development of novel treatments, depend on continued detailed study of these cells. Generation and analysis of high-throughput gene expression data provide an indispensable tool for carrying out the type of broad-scale investigations needed to identify the key genes and molecular pathways that regulate, distinguish, and predict distinct pulmonary pathologies. Of the available technologies for generating genome-wide expression data, RNA sequencing (RNA-seq) has emerged as the most powerful. Hence many researchers are turning to this approach in their studies of lung disease. For the relatively uninitiated, computational analysis of RNA-seq data can be daunting, given the large number of methods and software packages currently available. The aim of this chapter is to provide a broad overview of the major steps involved in processing and analyzing RNA-seq data, with a special focus on methods optimized for data generated from AECs. We take the reader from the point of obtaining sequence reads from the lab to the point of making biological inferences with expression data. Along the way, we discuss the statistical and computational considerations one typically confronts during different phases of analysis and point to key methods, software packages, papers, online guides, and other resources that can facilitate successful RNA-seq analysis.

IL1RL1 asthma risk variants regulate airway type 2 inflammation.

Genome-wide association studies of asthma have identified genetic variants in the IL1RL1 gene, but the molecular mechanisms conferring risk are unknown. IL1RL1 encodes the ST2 receptor (ST2L) for IL-33 and an inhibitory decoy receptor (sST2). IL-33 promotes type 2 inflammation, which is present in some but not all asthmatics. We find that two single nucleotide polymorphisms (SNPs) in IL1RL1 - rs1420101 and rs11685480 - are strongly associated with plasma sST2 levels, though neither is an expression quantitative trait locus (eQTL) in whole blood. Rather, rs1420101 and rs11685480 mark eQTLs in airway epithelial cells and distal lung parenchyma, respectively. We find that the genetically determined plasma sST2 reservoir, derived from the lung, neutralizes IL-33 activity, and these eQTL SNPs additively increase the risk of airway type 2 inflammation among asthmatics. These risk variants define a population of asthmatics at risk of IL-33-driven type 2 inflammation.

Alternative splicing of interleukin-33 and type 2 inflammation in asthma.

Type 2 inflammation occurs in a large subgroup of asthmatics, and novel cytokine-directed therapies are being developed to treat this population. In mouse models, interleukin-33 (IL-33) activates lung resident innate lymphoid type 2 cells (ILC2s) to initiate airway type 2 inflammation. In human asthma, which is chronic and difficult to model, the role of IL-33 and the target cells responsible for persistent type 2 inflammation remain undefined. Full-length IL-33 is a nuclear protein and may function as an "alarmin" during cell death, a process that is uncommon in chronic stable asthma. We demonstrate a previously unidentified mechanism of IL-33 activity that involves alternative transcript splicing, which may operate in stable asthma. In human airway epithelial cells, alternative splicing of the IL-33 transcript is consistently present, and the deletion of exons 3 and 4 (Δ exon 3,4) confers cytoplasmic localization and facilitates extracellular secretion, while retaining signaling capacity. In nonexacerbating asthmatics, the expression of Δ exon 3,4 is strongly associated with airway type 2 inflammation, whereas full-length IL-33 is not. To further define the extracellular role of IL-33 in stable asthma, we sought to determine the cellular targets of its activity. Comprehensive flow cytometry and RNA sequencing of sputum cells suggest basophils and mast cells, not ILC2s, are the cellular sources of type 2 cytokines in chronic asthma. We conclude that IL-33 isoforms activate basophils and mast cells to drive type 2 inflammation in chronic stable asthma, and novel IL-33 inhibitors will need to block all biologically active isoforms.