Homo sapiens
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PROVIDER: PRJNA318887 | ENA |
REPOSITORIES: ENA
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Project description:Diagnosis of childhood bacterial and viral infection using host RNA expression - Meningococcal validation cohort
| PRJNA319053 | ENA
Project description:Multisystem inflammatory syndrome in children (MIS-C) occurs in some children approximately 2-6 weeks following infection with SARS-CoV-2. Clinical symptoms are highly overlapping with Kawasaki disease (KD) and bacterial (DB) and viral (DV) infections, making diagnosis particularly challenging. Host whole blood transcriptomics can reveal specific combinations of host genes whose expression patterns can distinguish between disease groups of interest. We performed whole blood RNA-Sequencing of individuals with MIS-C, KD, bacterial and viral infections to identify a number of host genes that, when combined, could be used to diagnose MIS-C. This data contains processed data only. Raw data will be available via a controlled access archive.
2023-06-27 | E-MTAB-12793 | biostudies-arrayexpress
Project description:<p><strong>BACKGROUND:</strong> There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.</p><p><strong>METHODS:</strong> Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with acute viral infections, including COVID-19, bacterial infections, inflammatory conditions and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.</p><p><strong>FINDINGS:</strong> 3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI: 0.708-0.915). Transcripts of viperin and <em>CMPK2</em>, enzymes responsible for ddhCTP synthesis, were amongst the 5 genes most highly correlated with ddhC abundance.</p><p><strong>CONCLUSIONS: </strong>The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes, viperin and <em>CMPK2</em> are implicated in ddhC production <em>in vivo</em>. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness and acute infection management.</p><p><strong>FUNDING:</strong> National Institute for Health Research Imperial Biomedical Research Centre; Medical Research Council.</p>
2022-02-10 | MTBLS718 | MetaboLights
Project description:Host gene expression profiling of fungal (candidemia), bacterial, and viral infection, and non-infectious illness [validation cohort]
| PRJNA735656 | ENA
Project description:Rationale: Patients in the intensive care unit (ICU) are frequently exposed to unnecessary antibiotics. Markers of the host response to infection may aid pneumonia diagnosis and avoid antibiotic-induced complications. Objective: To assess the host response to suspected bacterial pneumonia through assessment of alveolar neutrophilia and transcriptomic profiling of alveolar macrophages. Methods: We determined the test characteristics of BAL neutrophilia for the diagnosis of bacterial pneumonia in 3 cohorts of mechanically ventilated patients. In one cohort, we also isolated alveolar macrophages from BAL fluid and used the transcriptome to identify signatures of bacterial pneumonia. Finally, we developed a humanized mouse model of Pseudomonas aeruginosa pneumonia to determine if pathogen-specific signatures can be identified in human alveolar macrophages. Measurements and Main Results: BAL neutrophilia was highly sensitive for bacterial pneumonia in both the retrospective (N = 851) and validation cohorts (N = 76 and N = 79) with a negative predictive value of over 90% when BAL neutrophil percentage was less than 50%. A transcriptional signature of bacterial pneumonia was present in both resident and recruited macrophages. Gene signatures from both cell types identified patients with bacterial pneumonia with test characteristics similar to BAL neutrophilia. Conclusions: A BAL neutrophil percentage of less than 50% is highly sensitive for bacterial pneumonia. Informative transcriptomic signatures can be generated from BAL fluid obtained during routine clinical care in the ICU. The identification of novel host response biomarkers is a promising approach to aid the diagnosis and treatment of pneumonia.
2018-11-06 | GSE121871 | GEO
Project description:Bacterial pneumonia is still a major cause of morbidity and mortality worldwide. One of the reasons for this may be the lack of accurate diagnostic tests that results in delayed identification of the causative agent and subsequent delay in initiating appropriate therapy. Therefore, there is an urgent need for new diagnostic tools for the rapid identification of the causative agent in bacterial pneumonia. Host biomarkers for early identification of etiology agents in bacterial pneumonia could assist in the development of those new diagnostic tools. The existing biomarkers such as procalcitonin and C-reactive protein for diagnosis of bacterial pneumonia are rather unspecific inflammatory markers and are not discriminatory between different infectious pathogens. In this regard, the objective of this study was the identification of host biomarkers which could distinguish pneumococcal pneumonia from staphylococcal pneumonia in an experimental murine infection model using RNA-Sequencing.
2018-06-22 | E-MTAB-5815 | biostudies-arrayexpress
Project description:<p>Accurate tests for microbiologic diagnosis of lower respiratory tract infections (LRTI) are needed. Gene expression profiling of whole blood represents a powerful new approach for analysis of the host response during respiratory infection that can be used to supplement pathogen detection testing. Using qPCR, we prospectively validated the differential expression of 10 genes previously shown to discriminate bacterial and non-bacterial LRTI confirming the utility of this approach. In addition, a novel approach using RNAseq analysis identified 141 genes differentially expressed in LRTI subjects with bacterial infection. Using "pathway-informed" dimension reduction, we identified a novel 11 gene set (selected from lymphocyte, α-linoleic acid metabolism, and IGF regulation pathways) and demonstrated a predictive accuracy for bacterial LRTI (nested CV-AUC=0.87). RNAseq represents a new and an unbiased tool to evaluate host gene expression for the diagnosis of LRTI.</p>
| phs001248 | dbGaP
Project description:Introduction: Diagnosis of severe influenza pneumonia remains challenging because of the lack of correlation between presence of influenza virus and patient’s clinical status. We conducted gene expression profiling in the whole blood of critically ill patients to identify a gene signature that would allow clinicians to distinguish influenza infection from other causes of severe respiratory failure (e.g. bacterial pneumonia, non-infective systemic inflammatory response syndrome). Methods: Whole blood samples were collected from critically ill individuals and assayed on Illumina HT-12 gene expression beadarrays. Differentially expressed genes were determined by linear mixed model analysis and over-represented biological pathways determined using GeneGo MetaCore. Results: The gene expression profile of H1N1 influenza A pneumonia was distinctly different from bacterial pneumonia and systemic inflammatory response syndrome. The influenza gene expression profile is characterized by up-regulation of genes from cell cycle regulation, apoptosis and DNA-damage response pathways. In contrast, no distinctive gene-expression signature was found in patients with bacterial pneumonia or systemic inflammatory response syndrome. The gene expression profile of influenza infection persisted through five days of follow-up. Furthermore, in patients with primary H1N1 influenza A infection who subsequently developed bacterial co-infection, the influenza gene-expression signature remained unaltered, despite the presence of a super-imposed bacterial infection. Conclusions: The whole blood expression profiling data indicates that the host response to influenza pneumonia is distinctly different from that caused by bacterial pathogens. This information may speed up identification of the cause of infection in patients presenting with severe respiratory failure, allowing appropriate patient care to be undertaken more rapidly. Daily PAXgene samples for up to 5 days for; influenza A pneumonia patients (n=8), bacterial pneumonia patients (n=16), mixed bacterial and influenza A pneumonia patients (n=3), systemic inflammatory response patients (SIRS, n=13). Days 1 and 5 PAXgene samples for healthy control individuals
2012-08-14 | E-GEOD-40012 | biostudies-arrayexpress
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:Previously developed gene expression signatures had high predictive accuracy at distinguishing viral and bacterial infection in a South Asian cohort with ARI caused by typical and atypical pathogens.
2020-07-06 | GSE149947 | GEO