RNAseq of C. elegans grown with different bacteria isolates
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA601724 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10903000.fastq.gz | Fastqsanger.gz | |||
| SRR10903001.fastq.gz | Fastqsanger.gz | |||
| SRR10903002.fastq.gz | Fastqsanger.gz | |||
| SRR10902994.fastq.gz | Fastqsanger.gz | |||
| SRR10902995.fastq.gz | Fastqsanger.gz |
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Project description:Background: Copy number variation is an important component of genetic variation in higher eukaryotes. The extent of natural copy number variation in C. elegans is unknown outside of 2 highly divergent wild isolates and the canonical N2 Bristol strain. Results: We have used array comparative genomic hybridization (aCGH) to detect copy number variation in the genomes of 12 natural isolates of Caenorhabditis elegans. Deletions relative to the canonical N2 strain are more common in these isolates than duplications, and indels are enriched in multigene families on the autosome arms. Among the strains in our study, the Hawaiian and Madeiran strains (CB4856 and JU258) carry the largest number of deletions, followed by the Vancouver strain (KR314). Overall we detected 510 different deletions affecting 1136 genes, or over 5% of the genes in the canonical N2 genome. The indels we identified had a median length of 2.7 kb. Since many deletions are found in multiple isolates, deletion loci were used as markers to derive an unrooted tree to estimate genetic relatedness among the strains. Conclusion: Copy number variation is extensive in C. elegans, affecting over 5% of the genes in the genome. The deletions we have detected in natural isolates of C. elegans contribute significantly to the number of deletion alleles available to researchers. The relationships between strains are complex and different regions of the genome possess different genealogies due to recombination throughout the natural history of the species, which may not be apparent in studies utilizing smaller numbers of genetic markers.
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Project description:Pseudomonas aeruginosa (P. aeruginosa) lung infection is a significant cause of mortality in patients with cystic fibrosis (CF). Existing experimental data in our lab showed significantly different levels of virulence of "early" and "late" P. aeruginosa infection isolates in a C. elegans slow killing model. We wished to examine the expression profile of these isolates in order to explore genes that may be responsible for the observed differences. The expression profiles of two pairs of isolates (four isolates in total) were compared to each other using the Affymetrix P. aeruginosa PAO1 genome array, to gain insight into properties mediating virulence in these isolates. Data analysis was carried out using BIOCONDUCTOR software. Keywords: Comparative strain hybridization Two pairs of isolates (four isolates in total) were compared to each other when grown on Nematode Growth Medium (NGM).
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Project description:Only recently, the natural gut microbiome of the model organism Caenorhabditis elegans has been described. C. elegans harbors a distinct gut microbiome that is shaped by environmental conditions, age, and host genotype. However, there is not much known about the genetic factors on the molecular level that the worm employs in order to keep its microbiota at bay. Previously, we have shown that TGFβ/BMP immune signaling is able to control the gut microbiome of C. elegans in particular in regards to Enterobacter species. We now aimed to identify the downstream targets of TGFβ/BMP immune signaling that implement the changes in microbiome composition. Hence, we exposed TGFβ mutants, dbl-1(nk3) and sma-3(e491), overexpression strain ctIs40 [dbl-1(+) + sur-5::GFP], and wild-type N2 to a synthetic microbiota community, CeMbio, and an E. coli OP50 control, and performed RNAseq. CeMbio is a collection of 12 diverse bacterial strains, previously isolated from wild C. elegans, C. elegans grown in microcosms, or substrates with C. elegans. Using the R packages edgeR and limma we are currently analyzing the data to understand the involvement of TGFβ/BMP signaling in host-microbiome interaction.
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