Comparison of mycobacteria growth indicator tube with BACTEC 460 for detection and recovery of mycobacteria from clinical specimens.

We compared the Mycobacteria Growth Indicator Tube (MGIT) system with the BACTEC 460 (B460) and Lowenstein Jensen (LJ) systems for the recovery of mycobacteria (acid-fast bacteria [AFB]) from 1,441 clinical specimens. Excluding 13 isolates of Mycobacterium gordonae, 178 significant AFB isolates were recovered from 113 patients. Isolates (119) of the Mycobacterium avium complex (MAC) accounted for 67% of all isolates, while isolates (30) of the Mycobacterium tuberculosis complex (MTB) accounted for 17% of isolates. The MGIT system recovered 98 (82%) MAC and 27 (90%) MTB isolates, while the B460 system recovered 101 (85%) MAC and 28 (93%) MTB isolates and the LJ system recovered 91 (76%) MAC and 25 (83%) MTB isolates. Overall, the MGIT system recovered 152 isolates of AFB (85.4% sensitivity), and the B460 and LJ systems recovered 151 (84.8% sensitivity) and 137 (76.9% sensitivity) AFB isolates, respectively. The recoveries of AFB with combinations of media were as follows: MGIT + LJ, 93.2%; B460 + LJ, 92.1%; and MGIT + B460, 96.6%. Although the sensitivity of MGIT was equivalent to that of B460, MGIT required a longer incubation (median, 11 days) than did B460 (median, 8 days) to become positive (P < 0.05)

Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease.

BackgroundBronchial airway expression profiling has identified inflammatory subphenotypes of asthma, but the invasiveness of this technique has limited its application to childhood asthma.ObjectivesWe sought to determine whether the nasal transcriptome can proxy expression changes in the lung airway transcriptome in asthmatic patients. We also sought to determine whether the nasal transcriptome can distinguish subphenotypes of asthma.MethodsWhole-transcriptome RNA sequencing was performed on nasal airway brushings from 10 control subjects and 10 asthmatic subjects, which were compared with established bronchial and small-airway transcriptomes. Targeted RNA sequencing nasal expression analysis was used to profile 105 genes in 50 asthmatic subjects and 50 control subjects for differential expression and clustering analyses.ResultsWe found 90.2% overlap in expressed genes and strong correlation in gene expression (ρ = .87) between the nasal and bronchial transcriptomes. Previously observed asthmatic bronchial differential expression was strongly correlated with asthmatic nasal differential expression (ρ = 0.77, P = 5.6 × 10(-9)). Clustering analysis identified TH2-high and TH2-low subjects differentiated by expression of 70 genes, including IL13, IL5, periostin (POSTN), calcium-activated chloride channel regulator 1 (CLCA1), and serpin peptidase inhibitor, clade B (SERPINB2). TH2-high subjects were more likely to have atopy (odds ratio, 10.3; P = 3.5 × 10(-6)), atopic asthma (odds ratio, 32.6; P = 6.9 × 10(-7)), high blood eosinophil counts (odds ratio, 9.1; P = 2.6 × 10(-6)), and rhinitis (odds ratio, 8.3; P = 4.1 × 10(-6)) compared with TH2-low subjects. Nasal IL13 expression levels were 3.9-fold higher in asthmatic participants who experienced an asthma exacerbation in the past year (P = .01). Several differentially expressed nasal genes were specific to asthma and independent of atopic status.ConclusionNasal airway gene expression profiles largely recapitulate expression profiles in the lung airways. Nasal expression profiling can be used to identify subjects with IL13-driven asthma and a TH2-skewed systemic immune response