A host gene expression approach for identifying triggers of asthma exacerbations

Abstract

Rationale Asthma exacerbations often occur due to infectious triggers, but determining whether infection is present and whether it is bacterial or viral remains clinically challenging. A diagnostic strategy that clarifies these uncertainties could enable personalized asthma treatment and mitigate antibiotic overuse. Objectives To explore the performance of validated peripheral blood gene expression signatures in discriminating bacterial, viral, and noninfectious triggers in subjects with asthma exacerbations. Methods Subjects with suspected asthma exacerbations of various etiologies were retrospectively selected for peripheral blood gene expression analysis from a pool of subjects previously enrolled in emergency departments with acute respiratory illness. RT-PCR quantified 87 gene targets, selected from microarray-based studies, followed by logistic regression modeling to define bacterial, viral, or noninfectious class. The model-predicted class was compared to clinical adjudication and procalcitonin. Results Of 46 subjects enrolled, 7 were clinically adjudicated as bacterial, 18 as viral, and 21 as noninfectious. Model prediction was congruent with clinical adjudication in 15/18 viral and 13/21 noninfectious cases, but only 1/7 bacterial cases. None of the adjudicated bacterial cases had confirmatory microbiology; the precise etiology in this group was uncertain. Procalcitonin classified only one subject in the cohort as bacterial. 47.8% of subjects received antibiotics. Conclusions Our model classified asthma exacerbations by the underlying bacterial, viral, and noninfectious host response. Compared to clinical adjudication, the majority of discordances occurred in the bacterial group, due to either imperfect adjudication or model misclassification. Bacterial infection was identified infrequently by all classification schemes, but nearly half of subjects were prescribed antibiotics. A gene expression-based approach may offer useful diagnostic information in this population and guide appropriate antibiotic use.

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections

“…For example, regression and decision tree classifiers trained using data collected on one type of microarray and tested in another type perform poorly, arguably at least in part due to inadequate cross-platform normalization 13,17 . Even models tested in data from the same technical platform can be prone to overfitting due to the lack of adequate representation of clinical heterogeneity in the training data 18 . We have repeatedly demonstrated that leveraging biological and technical heterogeneity across a large number of studies taken from diverse clinical backgrounds and profiled using different platforms increases generalizability required for clinical translation [11][12][13]17,[19][20][21][22] .…”

Prospective Validation of a Rapid Host Gene Expression Test to Discriminate Bacterial From Viral Respiratory Infection

“…This process has been conducted using slower high-complexity research tools, such as low-density array cards (TaqMan; Thermo Fisher Scientific) or nanostring arrays (NanoString Technologies). 28 , 32 , 33 , 34 There has been more limited progress in the development of rapid sample-to-answer tests. Sutherland et al 17 described a host gene expression test for the diagnosis of tuberculosis using a molecular analyzer (GeneXpert System; Cepheid).…”

“…focused on the narrower indication of acute (Յ7 days) febrile respiratory illness suspected to be caused by bacterial or viral infection.Measuring gene expression simply, rapidly, and reliably requires the convergence of multiple technologies, including blood cell lysis, RNA purification, RNA preservation or rapid processing, and quantitative real-time reverse transcription amplification (emerging technologies could substitute for some of these elements). This process has been conducted using slower high-complexity research tools, such as low-density array cards (TaqMan; Thermo Fisher Scientific) or nanostring arrays (NanoString Technologies) 28,[32][33][34]. There has been more limited progress in the development of rapid sample-to-answer tests.…”

A comparison of host response strategies to distinguish bacterial and viral infection

“…Since there is no gold standard to define whether ARI is bacterial, viral, or non-infectious, panel adjudication served as the reference standard [ 18 , 23 , 25 , 26 ]. Adjudications were performed by specialists in emergency medicine, infectious diseases, pulmonary medicine, or hospital medicine based on chart reviews performed >28 days after enrollment, and before measuring procalcitonin, the protein panel, or the mRNA panel.…”