G3 Genes Genomes GeneticsGeneticsPeer-reviewed journal article
Escherichia coli Metabolite Profiling Leads to the Development of an RNA Interference Strain for Caenorhabditis elegans
Isaiah A.A. NeveiD, Jessica N. SowaiD, Chih-Chun LiniD, Priya SivaramakrishnaniD, Christophe HermaniD, Youqiong YeiD, Leng HaniD, Meng Carla WangiD
Abstract
The relationship of genotypes to phenotypes can be modified by environmental inputs. Such crucial environmental inputs include metabolic cues derived from microbes living together with animals. Thus the analysis of genetic effects on animals physiology can be confounded by variations in the metabolic profile of microbes. Caenorhabditis elegans exposed to distinct bacterial strains and species exhibit phenotypes different at cellular, developmental and behavioral levels. Here we reported metabolomic profiles of three Escherichia coli strains, B strain OP50, K-12 strain MG1655, and B-K-12 hybrid strain HB101, and also different mitochondrial and fat storage phenotypes of C. elegans exposed to MG1655 and HB101 versus OP50. We found that these metabolic phenotypes of C. elegans are not correlated with overall metabolic patterning of bacterial strains, but their specific metabolites. In particular, the fat storage phenotype is traced to the betaine level in different bacterial strains. HT115 is another K-12 E. coli strain that is commonly utilized to elicit an RNA interference response, and we showed that C. elegans exposed to OP50 and HT115 exhibit differences in mitochondrial morphology and fat storage levels. We thus generated an RNA interference competent OP50 (iOP50) strain that can robustly and consistently knockdown endogenous C. elegans genes in different tissues. Together, these studies suggest the importance of specific bacterial metabolites in regulating the hosts physiology, and provide a tool to prevent confounding effects when analyzing genotype-phenotype interactions under different bacterial backgrounds.
- organism: C. elegans
- organism: E. coli
- assay: metabolomics
- research focus: gene regulatory network inference
- research focus: cell type annotation
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: experimental assay
- Citations
- 54
- FWCI
- 1.46
- Field percentile
- top 21%
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