Lower respiratory viral genomics study
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ABSTRACT:
PROVIDER: PRJEB86108 | ENA |
REPOSITORIES: ENA
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Project description:LPS-induced IFN networks predict severe viral Lower Respiratory tract Infections in infancy
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Project description:Lower respiratory tract microbiome in critically ill patients
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Project description:Bovine respiratory epithelial cells have different susceptibility to bovine
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derived from the upper respiratory tract. Pre-infection with virus of lower
respiratory tract with increased adherence of P. multocida; this was not the
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2020-07-28 | PXD019509 | JPOST Repository
Project description:Viral infections affecting the upper or lower respiratory tract induce mucin production in the epithelial surfaces of the respiratory cells. However, a little is known about how mucins are produced on the surfaces of respiratory epithelial cells and affects viral replication. In the course of the investigation of the cellular responses in the early stage of Influenza A virus (IAV) infection, we found that two miRNAs, miR-221 and miR-17-3p, which target the mRNA of GalNAc transferase 3 (GALNT3), are rapidly down-regulated as early as 1.5 h post-infection.
2015-12-08 | GSE57508 | GEO
Project description:Viral infections affecting the upper or lower respiratory tract induce mucin production in the epithelial surfaces of the respiratory cells. However, a little is known about how mucins are produced on the surfaces of respiratory epithelial cells and affects viral replication. In the course of the investigation of the cellular responses in the early stage of Influenza A virus (IAV) infection, we found that two miRNAs, miR-221 and miR-17-3p, which target the mRNA of GalNAc transferase 3 (GALNT3), are rapidly down-regulated as early as 1.5 h post-infection. To understand the early host cell responses to the IAV infection, we performed miRNA microarray analysis using a human alveolar adenocarcinoma cell line, A549 cells, infected with influenza A/Puerto Rico/8/34 H1N1 (PR8) virus. We isolated the cellular RNAs at 0.5, 1.5 and 4.5 h post-infection and detected significant changes in the global profile of miRNA expression after infection with IAV. mouse embryonic fibroblasts. Each sample was run in duplicate.
2015-12-08 | E-GEOD-57508 | biostudies-arrayexpress
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: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:Lower respiratory tract infections are among the top five leading causes of human death. Fighting these infections is therefore a world health priority. Searching for induced alterations in host gene expression shared by several relevant respiratory pathogens represent an alternative to identifying new targets for wide-range host-oriented therapeutics. With this aim, alveolar macrophages were independently infected with three unrelated bacterial (Streptococcus pneumoniae, Klebsiella pneumoniae and Staphylococcus aureus) and two dissimilar viral (respiratory syncytial virus and influenza A virus) respiratory pathogens which are nevertheless highly relevant for human health. Cells were also activated with bacterial lipopolysaccharide (LPS) as a prototypical pathogen-associated molecular pattern. Patterns of differentially expressed cellular genes shared by the indicated pathogens were searched by microarray analysis. Most of the commonly up-regulated genes were related to the innate immune response and/or apoptosis, with Toll-like, RIG-I-like and NOD-like receptors among the top ten signaling pathways with over-expressed genes. These results identify new potential broad-spectrum targets to fight the important human infections caused by the bacteria and viruses studied here.
2017-03-01 | GSE88825 | GEO
Project description:Validation of Gene Array to Predict Bacterial Co-infection In Adults Hospitalized with Viral Lower Respiratory Tract Infections (LRTI)
| PRJNA353736 | ENA