Browsing by Author "Mazur, Anna"

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    A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study.
    (The Lancet. Infectious diseases, 2020-09-24) McClain, Micah T; Constantine, Florica J; Nicholson, Bradly P; Nichols, Marshall; Burke, Thomas W; Henao, Ricardo; Jones, Daphne C; Hudson, Lori L; Jaggers, L Brett; Veldman, Timothy; Mazur, Anna; Park, Lawrence P; Suchindran, Sunil; Tsalik, Ephraim L; Ginsburg, Geoffrey S; Woods, Christopher W
    BACKGROUND:Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present. METHODS:In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18-25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0-4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay. FINDINGS:Between Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92-0·96]), as well as at 1 day (0·87 [95% CI 0·84-0·90]), 2 days (0·85 [0·82-0·88]), and 3 days (0·74 [0·71-0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding. INTERPRETATION:Transcriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases. FUNDING:US Defense Advanced Research Projects Agency.
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    A host gene expression approach for identifying triggers of asthma exacerbations.
    (PloS one, 2019-01) Lydon, Emily C; Bullard, Charles; Aydin, Mert; Better, Olga M; Mazur, Anna; Nicholson, Bradly P; Ko, Emily R; McClain, Micah T; Ginsburg, Geoffrey S; Woods, Chris W; Burke, Thomas W; Henao, Ricardo; Tsalik, Ephraim L
    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.
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    Host gene expression classifiers diagnose acute respiratory illness etiology.
    (Sci Transl Med, 2016-01-20) Tsalik, Ephraim L; Henao, Ricardo; Nichols, Marshall; Burke, Thomas; Ko, Emily R; McClain, Micah T; Hudson, Lori L; Mazur, Anna; Freeman, Debra H; Veldman, Tim; Langley, Raymond J; Quackenbush, Eugenia B; Glickman, Seth W; Cairns, Charles B; Jaehne, Anja K; Rivers, Emanuel P; Otero, Ronny M; Zaas, Aimee K; Kingsmore, Stephen F; Lucas, Joseph; Fowler, Vance G; Carin, Lawrence; Ginsburg, Geoffrey S; Woods, Christopher W
    Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.