Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases
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ABSTRACT:
PROVIDER: PRJNA454300 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7119040_1.fastq.gz | Fastqsanger.gz | |||
| SRR7119040_2.fastq.gz | Fastqsanger.gz | |||
| SRR7119050_1.fastq.gz | Fastqsanger.gz | |||
| SRR7119050_2.fastq.gz | Fastqsanger.gz | |||
| SRR7119060_1.fastq.gz | Fastqsanger.gz |
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Project description:Misdiagnosis of enteric fever is a major global health problem resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine-learning algorithm to host gene expression profiles, we identified a diagnostic signature, which could distinguish culture-confirmed enteric fever cases from other febrile illnesses (area under receiver operating characteristic curve >95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host-response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR highlighting their utility as PCR-based diagnostic for use in endemic settings.
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Project description:In this study, whole blood samples were used to determine the gene expression of febrile culture confirmed enteric fever cases (ST = S. Typhi; SPT = S. Paratyphi), febrile culture negative individuals presenting to hospital in Kathamandu (sEF = suspected enteric fever), and healthy community controls (CTRL).
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Project description:Misdiagnosis of enteric fever is a major global health problem, resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine learning algorithm to host gene expression profiles, we identified a diagnostic signature, which could distinguish culture-confirmed enteric fever cases from other febrile illnesses (area under receiver operating characteristic curve > 95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR, highlighting their utility as PCR-based diagnostics for use in endemic settings.
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Project description:Whole blood transcriptional profiles of patients with (1) active pulmonary ['AdjuVIT active TB' and 'New active pulmonary'] and (2) extrapulmonary TB ['New active-extrapulmonary'] at time of diagnosis, (3) long-term recovery after treatment for active pulmonary TB ['AdjuVIT active TB'], (4) febrile illnesses presenting to hospital ['Fever mixed infection'] and (5) febrile pneumonia ['Fever pneumonia'] before antibiotic treatment, and (6) healthy vounteers. Each array sample represents a separate individual in each group. This submission includes two human whole genome Agilent Array Designs: A-MEXP-2104 and A-AGIL-28. Each of the individual raw array files are included as well as a single processed file representing the data matrix of all the merged and normalised data for the probes that are shared by the two array designs.
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Project description:ABSTRACT Background. Acute Kawasaki disease (KD) is difficult to distinguish from other acute rash/fever illnesses, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed. Methods. We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with three illnesses that resemble KD. Results. Genes associated with platelet and neutrophil activation were expressed at higher levels in KD patients than in patients with acute adenovirus infections or systemic adverse drug reactions but not in patients with scarlet fever; genes associated with B cell activation were also expressed at higher levels in KD patients than in controls. A striking absence of interferon-stimulated gene expression in the KD patients was confirmed in an independent cohort of KD subjects. We successfully predicted the diagnosis in 21 of 23 KD patients and 7 of 8 adenovirus patients using a set of 38 gene transcripts. Conclusions. These findings provide insight into the molecular features that distinguish KD from other febrile illnesses, and support the feasibility of developing novel diagnostic reagents for KD based on the host response. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: One of Kawasaki Disease (KD) or control (C) of Scarlet fever (C-sf), adenovirus infection (C-ai) or drug reaction (C-dr) disease_state_design
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Project description:ABSTRACT Background. Acute Kawasaki disease (KD) is difficult to distinguish from other acute rash/fever illnesses, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed. Methods. We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with three illnesses that resemble KD. Results. Genes associated with platelet and neutrophil activation were expressed at higher levels in KD patients than in patients with acute adenovirus infections or systemic adverse drug reactions but not in patients with scarlet fever; genes associated with B cell activation were also expressed at higher levels in KD patients than in controls. A striking absence of interferon-stimulated gene expression in the KD patients was confirmed in an independent cohort of KD subjects. We successfully predicted the diagnosis in 21 of 23 KD patients and 7 of 8 adenovirus patients using a set of 38 gene transcripts. Conclusions. These findings provide insight into the molecular features that distinguish KD from other febrile illnesses, and support the feasibility of developing novel diagnostic reagents for KD based on the host response. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: One of Kawasaki Disease (KD) or control (C) of Scarlet fever (C-sf), adenovirus infection (C-ai) or drug reaction (C-dr)
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