Project description:Here we developed a new approach to sepsis diagnosis that integrates host transcriptional profiling with metagenomic broad-range pathogen detection from cell-free plasma RNA and DNA.
Project description:Here we developed a new approach to sepsis diagnosis that integrates host transcriptional profiling with metagenomic broad-range pathogen detection from cell-free plasma RNA and DNA.
Project description:Improved diagnostics are necessary to differentiate between multipe potential etiologies of acute illness in a hospitalized patient. The peripheral blood host gene expression response can act as a supplementary diagnostic tool and better inform the full host immune response to a pathogen. We performed RNA sequencing on peripheral blood from 48 hospitalized patients with confirmed candidemia as well as patients with other acute viral, bacterial, and non-infectious illnesses and derived pathogen class-specific gene expression classifiers.
Project description:Improved diagnostics are necessary to differentiate between multipe potential etiologies of acute illness in a hospitalized patient. The peripheral blood host gene expression response can act as a supplementary diagnostic tool and better inform the full host immune response to a pathogen. We performed RNA sequencing on peripheral blood from 48 hospitalized patients with confirmed candidemia as well as patients with other acute viral, bacterial, and non-infectious illnesses and derived pathogen class-specific gene expression classifiers.
Project description:We studied the host transcriptional response to SARS-CoV-2 by performing metagenomic sequencing of upper airway samples in 234 patients with COVID-19 (n=93), other viral (n=100) or non-viral (n=41) acute respiratory illnesses (ARIs). Compared to other viral ARIs, COVID-19 was characterized by a diminished innate immune response, with reduced expression of genes involved in toll-like receptor and interleukin signaling, chemokine binding, neutrophil degranulation and interactions with lymphoid cells. Patients with COVID-19 also exhibited significantly reduced proportions of neutrophils, macrophages, and increased proportions of goblet, dendritic and B-cells, compared to other viral ARIs. Using machine learning, we built 27-, 10- and 3-gene classifiers that differentiated COVID-19 from other acute respiratory illnesses with AUCs of 0.981, 0.954 and 0.885, respectively. Classifier performance was stable at low viral loads, suggesting utility in settings where direct detection of viral nucleic acid may be unsuccessful. Taken together, our results illuminate unique aspects of the host transcriptional response to SARS-CoV-2 in comparison to other respiratory viruses and demonstrate the feasibility of COVID-19 diagnostics based on patient gene expression.
2020-08-12 | GSE156063 | GEO
Project description:Metagenomic sequencing for rapid pathogen identification
Project description:Abstract Mutations in the gene encoding nucleophosmin (NPM1) carry prognostic value for patients with acute myeloid leukemia (AML). Various techniques are currently being used to detect these mutations in routine molecular diagnostics. Incorporation of accurate NPM1 mutation detection on a gene expression platform would enable simultaneous detection with various other expression biomarkers. Here we present an array based mutation detection using custom probes for NPM1 WT mRNA and NPM1 type A, B, and D mutant mRNA. This method was 100% accurate on a training cohort of 505 newly diagnosed unselected AML cases. Validation on an independent cohort of 143 normal karyotype AML cases revealed no false negative results, and one false positive (sensitivity 100.0%, and specificity 98.7%). Based on this, we conclude that this method provides a reliable method for NPM1 mutation detection. The method can be applied to other genes/mutations as long as the mutant alleles are sufficiently high expressed. Validation cohort of 143 AML cases analyzed using the AMLprofiler
Project description:Abstract Mutations in the gene encoding nucleophosmin (NPM1) carry prognostic value for patients with acute myeloid leukemia (AML). Various techniques are currently being used to detect these mutations in routine molecular diagnostics. Incorporation of accurate NPM1 mutation detection on a gene expression platform would enable simultaneous detection with various other expression biomarkers. Here we present an array based mutation detection using custom probes for NPM1 WT mRNA and NPM1 type A, B, and D mutant mRNA. This method was 100% accurate on a training cohort of 505 newly diagnosed unselected AML cases. Validation on an independent cohort of 143 normal karyotype AML cases revealed no false negative results, and one false positive (sensitivity 100.0%, and specificity 98.7%). Based on this, we conclude that this method provides a reliable method for NPM1 mutation detection. The method can be applied to other genes/mutations as long as the mutant alleles are sufficiently high expressed. Training cohort of 505 AML cases analyzed using the AMLprofiler
Project description:Fungal pathogens are emerging threats to global health and the rise of incidence is associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to align options for rapid and effective therapeutic treatments. For improved diagnostics, the discovery and development of biomarkers presents a promising avenue; however, this approach requires a priori knowledge of markers of infection. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass spectrometry-based proteomics to resolve the temporal and spatial proteomes of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins connected to fungal infections. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during progression of disease. Together, our systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarkers from complementary biological systems for monitoring the presence and progression of cryptococcal disease.
