Ontology highlight
ABSTRACT:
PROVIDER: PRJNA811310 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR18240600.fastq.gz | Fastqsanger.gz | |||
| SRR18240601.fastq.gz | Fastqsanger.gz | |||
| SRR18240602.fastq.gz | Fastqsanger.gz | |||
| SRR18240603.fastq.gz | Fastqsanger.gz | |||
| SRR18240604.fastq.gz | Fastqsanger.gz |
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Project description:A common technique used for sensitive and specific diagnostic virus detection in clinical samples is PCR. However, an unbiased diagnostic microarray containing probes for all human pathogens could replace hundreds of individual PCR-reactions and remove the need for a clear clinical hypothesis regarding a suspected pathogen. We have established such a diagnostic platform for unbiased random amplification and subsequent microarray identification of viral pathogens in clinical samples. We show that Phi29 polymerase-amplification of a diverse set of clinical samples generates enough viral material for successful identification by the Microbial Detection Array developed at the Lawrence Livermore National Laboratory, California, USA, demonstrating the potential of the microarray technique for broad-spectrum pathogen detection. We conclude that this method detects both DNA and RNA virus, present in the same sample, as well as differentiates between different virus subtypes. We propose this assay for unbiased diagnostic analysis of all viruses in clinical samples. 19 clinical samples were analyzed for presence of virus using the MDA microarray. One of the samples is a negative control (water). One HCV-positive serum sample is included twice (HCV+1 and HCV+2).
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Project description:To advance our understanding of cellular host-pathogen interactions, technologies that facilitate the co-capture of both host and pathogen spatial transcriptome information are needed. Here, we present an approach to simultaneously capture host and pathogen spatial gene expression information from the same formalin-fixed paraffin embedded (FFPE) tissue section using the spatial transcriptomics technology. We applied the method to COVID-19 patient lung samples and enabled the dual detection of human and SARS-CoV-2 transcriptomes at 55 µm resolution. We validated our spatial detection of SARS-CoV-2 and identified an average specificity of 94.92% in comparison to RNAScope and 82.20% in comparison to in situ sequencing (ISS). COVID-19 tissues showed an upregulation of host immune response, such as increased expression of inflammatory cytokines, lymphocyte and fibroblast markers. Our colocalization analysis revealed that SARS-CoV-2+ spots presented shifts in host RNA metabolism, autophagy, NFκB, and interferon response pathways. Future applications of our approach will enable new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes.
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Project description:A common technique used for sensitive and specific diagnostic virus detection in clinical samples is PCR. However, an unbiased diagnostic microarray containing probes for all human pathogens could replace hundreds of individual PCR-reactions and remove the need for a clear clinical hypothesis regarding a suspected pathogen. We have established such a diagnostic platform for unbiased random amplification and subsequent microarray identification of viral pathogens in clinical samples. We show that Phi29 polymerase-amplification of a diverse set of clinical samples generates enough viral material for successful identification by the Microbial Detection Array developed at the Lawrence Livermore National Laboratory, California, USA, demonstrating the potential of the microarray technique for broad-spectrum pathogen detection. We conclude that this method detects both DNA and RNA virus, present in the same sample, as well as differentiates between different virus subtypes. We propose this assay for unbiased diagnostic analysis of all viruses in clinical samples.
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Project description:The proliferative darkening syndrome (PDS) is an annually recurring disease that causes species-specific die-off of brown trout (Salmo trutta fario) with a mortality rate of near 100 % in pre-alpine rivers of central Europe. So far the etiology and causation of this disease is still unclear. The objective of this study was to identify the cause of PDS using a next-generation technology detection pipeline. Following the hypothesis that PDS is caused by an infectious agent, brown trout specimens were exposed to water from a heavily affected pre-alpine river with annual occurrence of the disease. Specimens were sampled over the entire time period from potential infection through death. Transcriptomic analysis (microarray) and RT-qPCR of brown trout liver tissue evidenced strong gene expression response of immune-associated genes. Messenger RNA of specimens with synchronous immune expression profiles were ultra-deep sequenced using next-generation sequencing technology (NGS). Bioinformatic processing of generated reads and gap-filling Sanger re-sequencing of the identified pathogen genome revealed strong evidence that a piscine-related reovirus is the causative organism of PDS. The identified pathogen is phylogenetically closely related to the family of piscine reoviruses (PRV) which are considered as the causation of different fish diseases in Atlantic and Pacific salmonid species such as Salmo salar and Onchorhynchus kisutch. This study also highlights that the approach of first screening immune responses along a timeline in order to identify synchronously affected stages in different specimens which subsequently were ultra-deep sequenced is an effective approach in pathogen detection. In particular, the identification of specimens with synchronous molecular immune response patterns combined with NGS sequencing and gap-filling re-sequencing resulted in the successful pathogen detection of PDS.
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Project description:Nodeomics: pathogen detection in vertebrate lymph nodes using meta-transcriptomics
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