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 PMID:34125894  

A genetic titration of membrane composition in Caenorhabditis elegans reveals its importance for multiple cellular and physiological traits.

Ranjan Devkota | Delaney Kaper | Rakesh Bodhicharla | Marcus Henricsson | Jan Borén | Marc Pilon
Genetics | 2021

Communicating editor: B. Grant The composition and biophysical properties of cellular membranes must be tightly regulated to maintain the proper functions of myriad processes within cells. To better understand the importance of membrane homeostasis, we assembled a panel of five Caenorhabditis elegans strains that show a wide span of membrane composition and properties, ranging from excessively rich in saturated fatty acids (SFAs) and rigid to excessively rich in polyunsaturated fatty acids (PUFAs) and fluid. The genotypes of the five strain are, from most rigid to most fluid: paqr-1(tm3262); paqr-2(tm3410), paqr-2(tm3410), N2 (wild-type), mdt-15(et14); nhr-49(et8), and mdt-15(et14); nhr-49(et8); acs-13(et54). We confirmed the excess SFA/rigidity-to-excess PUFA/fluidity gradient using the methods of fluorescence recovery after photobleaching (FRAP) and lipidomics analysis. The five strains were then studied for a variety of cellular and physiological traits and found to exhibit defects in: permeability, lipid peroxidation, growth at different temperatures, tolerance to SFA-rich diets, lifespan, brood size, vitellogenin trafficking, oogenesis, and autophagy during starvation. The excessively rigid strains often exhibited defects in opposite directions compared to the excessively fluid strains. We conclude that deviation from wild-type membrane homeostasis is pleiotropically deleterious for numerous cellular/physiological traits. The strains introduced here should prove useful to further study the cellular and physiological consequences of impaired membrane homeostasis.

Pubmed ID: 34125894

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Pilon (tool)

RRID:SCR_014731

Software tool to automatically improve draft assemblies and find variation among strains, including large event detection. FASTA files of genome along with one or more BAM files of reads aligned as input. Read alignment analysis is used to identify inconsistencies between input genome and evidence in reads, then attempts to make improvements to genome.

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