Project description:As virus diseases cannot be controlled by traditional plant protection methods the risk of their spread have to be minimized on vegetatively propagated plants, such as grapevine. Metagenomics approaches used for virus diagnostics, offer a unique opportunity to reveal the presence of all viral pathogens in the investigated plant, why their usage can reduce the risk of using infected material for a new plantation. Here we used a special field, deep sequencing of virus derived small RNAs, of this high throughput method for virus diagnostics and determined viromes of vineyards in Hungary. With NGS of virus derived small RNAs we could detect not only the viruses tested routinely, but also new ones, which have never been described in Hungary before. Virus presence didn’t correlated with the age of the plantation, moreover phylogenetic analysis of the identified virus isolates suggests that infections mostly caused by the usage of infected propagating material. Our results, validated by other molecular methods, highlighted further questions to be answered before these method can be introduced as a routine, reliable test for grapevine virus diagnostics.
Project description:This series includes a 32-array training dataset used to evaluate E-Predict normalization and similarity metric parameters as well as 13 microarrays used as examples in (Urisman, et. al 2005). Training data set includes 15 independent HeLa RNAhybridizations (microarrays 1-15), 10 independent nasal lavage samples positive for Respiratory Syncytial virus (microarrays 16-25), and 7 independent nasal lavage samples positive for Influenza A virus (microarrays 26-32). Examples iclude a serum sample positive for Hepatitis B virus (microarray 33), a nasal lavage sample positive for both Influenza A virus and Respiratory Syncytial virus (microarray 34), and culture samples of 11 distinct Human Rhinovirus serotypes (microarrays 35-45). Keywords = virus detection, E-Predict, species identification, metagenomics
Project description:This series includes a 32-array training dataset used to evaluate E-Predict normalization and similarity metric parameters as well as 13 microarrays used as examples in (Urisman, et. al 2005). Training data set includes 15 independent HeLa RNAhybridizations (microarrays 1-15), 10 independent nasal lavage samples positive for Respiratory Syncytial virus (microarrays 16-25), and 7 independent nasal lavage samples positive for Influenza A virus (microarrays 26-32). Examples iclude a serum sample positive for Hepatitis B virus (microarray 33), a nasal lavage sample positive for both Influenza A virus and Respiratory Syncytial virus (microarray 34), and culture samples of 11 distinct Human Rhinovirus serotypes (microarrays 35-45). Keywords = virus detection, E-Predict, species identification, metagenomics Keywords: other
Project description:We have performed RNA sequencing on kidneys from inclusion body nephropathy-affected mice and compared the data to healthy, uninfected controls. Using a metagenomics approach, we report the identification of the disease causing agent as an atypical virus, mouse kidney parvovirus (MKPV), belonging to a divergent genus of the Parvoviridae. The RNA sequencing also enabled us to assess the host response to MKPV-infection.
2018-07-27 | GSE117710 | GEO
Project description:Plant virus metagenomic data 2022
Project description:Plants in their natural and agricultural environments are continuously exposed to a plethora of diverse microorganisms resulting in microbial colonization of plants in the rhizosphere. This process is believed to be accompanied by an intricate network of ongoing simultaneous interactions. In this study, we compared transcriptional patterns of Arabidopsis thaliana roots and shoots in the presence and absence of whole microbial communities extracted from compost soil. The results show a clear growth promoting effect of Arabidopsis shoots in the presence of soil microbes compared to axenically grown plants under identical conditions. Element analyses showed that iron uptake was facilitated by these mixed microbial communities which also lead to transcriptional downregulation of genes required for iron transport. In addition, soil microbial communities suppressed the expression of marker genes involved in oxidative stress/redox signalling, cell wall modification and plant defense. While most previous studies have focussed on individual plant-microbe interactions, our data suggest that multi-species transcriptional profiling, using simultaneous plant and metatranscriptomics coupled to metagenomics may be required to further increase our understanding of the intricate networks underlying plant-microbe interactions in their diverse environments.
Project description:70mer probes were designed to detect plant viruses infection in genus level. This microarray platform is able to detect 169 plant virus species of 13 virus genera.
Project description:70mer probes were designed to detect plant viruses infection in genus level. This microarray platform is able to detect 169 plant virus species of 13 virus genera. Virus sampels were extracted from infected plant hosts. Genomic RNA was extracted and hybridized to the microarray.
Project description:Plants in their natural and agricultural environments are continuously exposed to a plethora of diverse microorganisms resulting in microbial colonization of plants in the rhizosphere. This process is believed to be accompanied by an intricate network of ongoing simultaneous interactions. In this study, we compared transcriptional patterns of Arabidopsis thaliana roots and shoots in the presence and absence of whole microbial communities extracted from compost soil. The results show a clear growth promoting effect of Arabidopsis shoots in the presence of soil microbes compared to axenically grown plants under identical conditions. Element analyses showed that iron uptake was facilitated by these mixed microbial communities which also lead to transcriptional downregulation of genes required for iron transport. In addition, soil microbial communities suppressed the expression of marker genes involved in oxidative stress/redox signalling, cell wall modification and plant defense. While most previous studies have focussed on individual plant-microbe interactions, our data suggest that multi-species transcriptional profiling, using simultaneous plant and metatranscriptomics coupled to metagenomics may be required to further increase our understanding of the intricate networks underlying plant-microbe interactions in their diverse environments. Four samples were analysed in total. One corresponded to a pooled sample of RNA extracted from root tissues of 60 plants. The other three were biological replicates from shoot tissues, each of which contained 20 plants. Controls were used as reference and corresponded to tissues of plants grown in sterile conditions.