Rapid clinical diagnosis of lower respiratory infection
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB39606 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:BACKGROUND. Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging since non-infectious respiratory illnesses appear clinically similar and existing microbiologic tests are often falsely negative or detect incidentally-carried microbes common in children. These challenges result in antimicrobial overuse and adverse patient outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and precision treatment remains unclear. METHODS. We used tracheal aspirate RNA-sequencing to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a random forest gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n=117) or of non-infectious respiratory failure (n=50). We then developed a classifier that integrates the: i) host LRTI probability, ii) abundance of respiratory viruses, and iii) dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm. RESULTS. The host classifier achieved a median AUC of 0.967 by 5-fold cross-validation, driven by activation markers of T cells, alveolar macrophages and the interferon response. The integrated classifier achieved a median AUC of 0.986 and significantly increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n=94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those. CONCLUSIONS. Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.
2022-12-06 | GSE212532 | GEO
Project description:Lower respiratory tract microbiome in critically ill patients
| PRJEB64676 | ENA
Project description:Leveraging the pulmonary immune response and microbiome for improved lower respiratory tract infection diagnosis in critically ill children
| PRJNA875913 | ENA
Project description:Background: Distinguishing between bacterial and viral lower respiratory tract infections (LRTI) in hospitalized patients remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI. Methods: We performed whole blood transcriptional analysis in a cohort of 118 adult patients (median [IQR] age, 61 [50-76] years) hospitalized with bacterial, viral or viral-bacterial LRTI, and 40 age-matched healthy controls (60 [46-70] years). We applied class comparisons, modular analysis and class prediction algorithms to identify distinct biosignatures for bacterial and viral LRTI, which were validated in an independent group of patients. Results: Patients were classified as bacterial (B, n=22), viral (V, n=71) and bacterial-viral LRTI (BV, n=25) based on comprehensive microbiologic testing. Compared with healthy controls statistical group comparisons (p<0.01; with multiple test corrections) identified 3,376 differentially expressed genes in patients with B-LRTI; 2,391 in V-LRTI, and 2,628 in BV-LRTI. Independent of etiologic pathogen, patients with LRTI demonstrated overexpression of innate immunity and underexpression of adaptive immunity genes. Patients with B-LRTI showed significant overexpression of inflammation (B>BV>V) and neutrophils (B>BV>V) while those with V-LRTI displayed significantly greater overexpression of interferon genes (V>BV>B). The K-Nearest Neighbors (K-NN) algorithm identified 10 classifier genes that discriminated patients with bacterial vs viral LRTI with 97% [95%CI: 84-100] sensitivity and 92% [77-98] specificity. In comparison, procalcitonin classified bacterial vs viral LRTI with 38% [18-62] sensitivity and 91% [76-98] specificity. Conclusions: Transcriptional profiling can be used as a helpful tool for the diagnosis of adults hospitalized with LRTI. 158 samples, no replicates; bacterial LRTI n=22, viral LRTI n=71, bacterial-viral coinfections n=25, and healthy controls n=40
2015-09-14 | E-GEOD-60244 | biostudies-arrayexpress
Project description:Bovine respiratory epithelial cells have different susceptibility to bovine
respiratory syncytial virus infection. The cells derived from the lower
respiratory tract were significantly more susceptible to the virus than those
derived from the upper respiratory tract. Pre-infection with virus of lower
respiratory tract with increased adherence of P. multocida; this was not the
case for upper tract. However, the molecular mechanisms of enhanced
bacterial adherence are not completely understood. To investigate whether
virus infection regulates the cellular adherence receptor on bovine trachea-,
bronchus- and lung-epithelial cells, we performed proteomic analyses.
2020-07-28 | PXD019509 | JPOST Repository
Project description:Assessment of host gene expression is an emerging tool for the diagnosis of human infections. We compared nasal and blood samples for evaluation of the host transcriptomic response in children with acute respiratory syncytial virus (RSV), symptomatic and asymptomatic picornavirus (PV) infection, and virus-negative asymptomatic controls (Ctrls). RNA was extracted from nasal and blood samples and analyzed by microarray. Despite generally lower quality of nasal RNA, the number of genes detected in each sample type was equivalent. Nasal gene expression signal derived mainly from epithelial cells but also included a leukocyte contribution that was higher in samples from symptomatic children. The number of genes with increased expression in virus-infected children was comparable in nasal and blood samples, while nasal samples also had large numbers of genes with decreased expression, including many genes associated with ciliary function and assembly. Compared to symptomatic children, those with asymptomatic PV had fewer genes with increased or decreased expression in both sample types. Genes with increased expression in comparisons of symptomatic children versus Ctrls included genes associated with components of innate immunity and apoptosis. Children with RSV but not PV also had increased expression of genes related to the cell cycle. Using nested leave-one-pair-out cross-validation and supervised principal components analysis, we defined sets of genes whose expression patterns accurately classified subjects, with high area-under-the-curve values in receiver operating characteristic analysis. Our results support use of nasal samples to augment pathogen-based tests to diagnose viral respiratory infection.
2019-01-01 | GSE117827 | GEO
Project description:Pairs of SARS-CoV-2 genomes from upper and lower respiratory tract of different individuals
| PRJEB42830 | ENA
Project description:the metagenomic sequencing data of 450 respiratory samples Metagenome
| PRJNA818777 | ENA
Project description:Analysis of transcriptional profiles in whole blood from children < 2 years of age (and healthy matched controls) with RSV, rhinovirus and influenza infection. The hypothesis tested is that transcriptional profile heterogeneity will reflect patient clinical heterogeneity and that RSV infection induces a distinct host response compared with influenza and rhinovirus infection Total RNA extracted from whole blood (lysed in Tempus tubes) drawn from individual pediatric patients with acute RSV, influenza and Rhinovirus lower respiratory tract infection. A total of 241 samples are analyzed: 135 with acute RSV LRTI, 30 with Rhinovirus LRTI, 16 with influenza LRTI, 39 age-sex matched healthy controls and 21 samples obtained one month after the acute hospitalization in children with RSV. Samples GSM1226237-GSM1226272, which were hybridized to Platform GPL10558, were normalized separately from the other Samples in this Series, which were hybridized to Platform GPL6884. 'GSE38900_non-normalized_GSM1226237-GSM1226272.txt.gz' includes the non-normalized data for Samples GSM1226237-GSM1226272; 'GSE38900_non-normalized.txt.gz' includes the non-normalized data for the other Samples.
2013-11-12 | E-GEOD-38900 | biostudies-arrayexpress