Adaptation to starvation requires a flexible circadian clockwork in Neurospora crassa (RNAseq)
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Dec 16, 2022 version files 66.07 GB
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README.txt
Abstract
The circadian clock governs rhythmic cellular functions by driving expression of a substantial fraction of the genome and thereby significantly contributes to the adaptation to changing environmental conditions. Using the circadian model organism Neurospora crassa, we show that molecular timekeeping is robust even under severe limitation of carbon sources, however, stoichiometry, phosphorylation and subcellular distribution of the key clock components display drastic alterations. Protein kinase A, protein phosphatase 2A and glycogen synthase kinase are involved in the molecular reorganization of the clock. RNA-seq analysis reveals that the transcriptomic response of metabolism to starvation is highly dependent on the positive clock component WC-1. Moreover, our molecular and phenotypic data indicate that a functional clock facilitates recovery from starvation. We suggest that the molecular clock is a flexible network that allows the organism to maintain a rhythmic physiology and preserve fitness even under long-term nutritional stress.
Methods
Liquid cultures were grown under standard and glucose starving conditions in 12-12 hours light-dark cycles for 48 hours and samples were harvested at ZT12 (n=4 for each group). RNA was purified using the TriReagent® (Sigma Aldrich #93289) isolation reagent. Following DNase treatment RNA quality was controlled by the NanodropTM OneC spectrophotometer, the QubitTM 4.0 Fluorometer (Invitrogen) and the Agilent TapeStation 4150 System.
Library preparation and sequencing were performed by BGI Genomics, China, using PE-100 library. Sequencing quality check was performed with FastQC (Andrews, 2010). Mapping was performed with STAR (Dobin et al., 2013) to the N.c. genome from Ensembl (Neurospora_crassa.NC12.48) (Yates et al., 2020), and the indexing and duplicate filtering with samtools (Li et al., 2009). Read counting was done using HTSeq-count (Anders et al., 2015). Differential expression analysis was done with DESeq2 (Love et al., 2014) package in R (R Core Team, 2020).