Browsing by Subject "BACTERIAL"
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Item Open Access 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 LRATIONALE: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.Item Open Access Loss of deep roots limits biogenic agents of soil development that are only partially restored by decades of forest regeneration(Elementa, 2018-01-01) Billings, SA; Hirmas, D; Sullivan, PL; Lehmeier, CA; Bagchi, S; Min, K; Brecheisen, Z; Hauser, E; Stair, R; Flournoy, R; De Richter, DB© 2018 The Author(s). Roots and associated microbes generate acid-forming CO2 and organic acids and accelerate mineral weathering deep within Earth's critical zone (CZ). At the Calhoun CZ Observatory in the USA's Southern Piedmont, we tested the hypothesis that deforestation-induced deep root losses reduce root- and microbially-mediated weathering agents well below maximum root density (to 5 m), and impart land-use legacies even after ∼70 y of forest regeneration. In forested plots, root density declined with depth to 200 cm; in cultivated plots, roots approached zero at depths >70 cm. Below 70 cm, root densities in old-growth forests averaged 2.1 times those in regenerating forests. Modeled root distributions suggest declines in density with depth were steepest in agricultural plots, and least severe in old-growth forests. Root densities influenced biogeochemical environments in multiple ways. Microbial community composition varied with land use from surface horizons to 500 cm; relative abundance of root-associated bacteria was greater in old-growth soils than in regenerating forests, particularly at 100-150 cm. At 500 cm in old-growth forests, salt-extractable organic C (EOC), an organic acid proxy, was 8.8 and 12.5 times that in regenerating forest and agricultural soils, respectively. The proportion of soil organic carbon comprised of EOC was greater in old-growth forests (20.0 ± 2.6%) compared to regenerating forests (2.1 ± 1.1) and agricultural soils (1.9 ± 0.9%). Between 20 and 500 cm, [EOC] increased more with root density in old-growth relative to regenerating forests. At 300 cm, in situ growing season [CO2] was significantly greater in old-growth forests relative to regenerating forests and cultivated plots; at 300 and 500 cm, cultivated soil [CO2] was significantly lower than in forests. Microbially-respired δ13C-CO2 suggests that microbes may rely partially on crop residue even after ∼70 y of forest regeneration. We assert that forest conversion to frequently disturbed ecosystems limits deep roots and reduces biotic generation of downward-propagating weathering agents.Item Open Access Rapid, Sample-to-Answer Host Gene Expression Test to Diagnose Viral Infection(Open Forum Infectious Diseases, 2019-11-01) Tsalik, Ephraim L; Khine, Ayeaye; Talebpour, Abdossamad; Samiei, Alaleh; Parmar, Vilcy; Burke, Thomas W; Mcclain, Micah T; Ginsburg, Geoffrey S; Woods, Christopher W; Henao, Ricardo; Alavie, TinoAbstract Background Distinguishing bacterial, viral, or other etiologies of acute illness is diagnostically challenging with significant implications for appropriate antimicrobial use. Host gene-expression offers a promising approach although no clinically useful tests have yet been developed to accomplish this. Here, Qvella’s FAST™ HR process was developed to quantify previously identified host gene-expression signatures in whole blood in <45 minutes. Methods Whole blood was collected from 128 human subjects (mean age 47, range 18-88) with clinically adjudicated, microbiologically confirmed viral infection, bacterial infection, non-infectious illness, or healthy controls. Stabilized mRNA was released from cleaned and stabilized RNA-surfactant complexes using e-lysisTM, an electrical process providing a qRT-PCR-ready sample. Threshold cycle values (CT) for 10 host response targets were normalized to HPRT1 expression, a control mRNA. The transcripts in the signature were specifically chosen to discriminate viral from non-viral infection (bacterial, non-infectious illness, or healthy). Classification accuracy was determined using cross-validated sparse logistic regression. Results Reproducibility of mRNA quantification was within 1 cycle as compared to the difference seen between subjects with viral vs. non-viral infection (up to 5.0 normalized CT difference). Classification of 128 subjects into viral or non-viral etiologies demonstrated 90.6% overall accuracy compared to 82.0% for procalcitonin (p=0.06). FASTTM HR achieved rapid and accurate measurement of the host response to viral infection in less than 45 minutes. Conclusions These results demonstrate the ability to translate host gene expression signatures to clinical platforms for use in patients with suspected infection.