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A global non-coding RNA system modulates fission yeast protein levels in response to stress
Mitogen Activated Protein Kinase (MAPK) signaling cascades transduce information arising from events external to the cell, such as environmental stresses, to a variety of downstream effectors and transcription factors. The fission yeast stress activated MAP kinase (SAPK) pathway is conserved with the p38 and JNK pathways in humans, and comprises the MAPKKKs Win1, Wis4, the MAPKK Wis1, and the MAPK, Sty1. Sty1 and its main downstream effector Atf1 regulate a large set of core environmental stress response genes. The fission yeast genome encodes three other ATF proteins: Atf21, Atf31 and Pcr1. Among these, atf21 is specifically induced under conditions of high osmolarity. We have therefore instigated a programme to investigate the role played by non coding RNAs (ncRNAs) in response to osmotic stress challenge in wild type and atf21Δ cells. By integrating global proteomics and RNA sequencing data, we identified a systematic program in which elevated antisense RNAs arising both from ncRNAs and from 3'-overlapping convergent gene-pairs is directly associated with substantial reductions in protein levels throughout the fission yeast genome. We also found an extensive array of ncRNAs with trans associations that have the potential to influence different biological processes and stress responses in fission yeast, suggesting ncRNAs comprise additional components of the SAPK regulatory system.
Sample Processing Protocol
Protein reduction, alkylation and digestion. A total of 50ug of protein extract was reduced by addition of TCEP (tris(2-carboxyethyl)phosphine) to a final concentration of 5mM at 60 degrees celcius for 60 mins. Reduced cysteine residues were then alkylated with the addition of MMTS (methyl methanethiosulfonate) to a final concentration of 10mM followed by 10 min incubation at room temperature. 50ug of protein was then digested by addition of 5ug of sequencing grade trypsin followed by agitated incubation at 37 degrees celcius for 18hours as previously described (Bitton et al, 2010). The digests were desalted as previously described by Villen & Gygi (2008) as follows. The total 50ug peptide digest was loaded onto a SepPak C18 SPE cartridge in 1ml of 0.4% (v/v) TFA (trifluoroacetic acid), peptides were desalted by addition of 5mls 0.1% (v/v) TFA followed by 1ml 0.5% (v/v) HAcO (acetic acid). Peptides were eluted by addition of 5mls 50% (v/v) MeCN (acetonitrile), 0.5% HAcO. Desalted peptide digests were lyophilized to completeness prior to resuspension in 0.05% (v/v) TFA at a peptide concentration of 750ng per microliter for subsequent nano LC-MS/MS analysis. Nano LC-MS/MS analysis. 750 ng of peptides were separated utilising an RSLCnano HPLC (Dionex) as detailed below. Each sample was loaded onto an Acclaim PepMap C18 Nano-Trap column (300 μm internal diameter (ID) × 2 cm long, 5 μm particle size) in water, 1% (v/v) acetonitrile, and 0.05% (v/v) triflouroacetic acid at a flow rate of 8 μl/min for 5 min. Peptides were then separated using an Acclaim PepMap C18 column (75 μm ID × 100 cm long, 3 μm particle size) with a gradient of 1-35% (v/v) of acetonitrile, 0.1% formic acid over 70 min at a flow rate of 250 nl/min. The nano-liquid chromatography (nLC) effluent was sprayed directly into the LTQ-Orbitrap XL mass spectrometer (Thermo Fisher Scientific) aided by the Proxeon nano source at a voltage offset of 1.7 kV. The mass spectrometer was operated in parallel data-dependent mode where the MS survey scan was performed at a nominal resolution of 60,000 (at mass/charge (m/z) 400) in the Orbitrap analyzer in an m/z range of 400–2000. The top three precursors with charge states of 2, 3 and 4 were selected for collision-induced dissociation (CID) in the LTQ at a normalized collision energy of 35%. MS2 gas phase fractionation was employed to reduce redundancy within the dataset. Each sample was analyzed five times, in each case the MS1 data was collected in a m/z range of 400-2000 and MS/MS acquisition was permitted only for multiply charged ions in the m/z range cuts of 400-525, 525-638, 638-766, 766-963 and 963-2000. These gas phase fraction windows were calculated empirically from a pooled pilot injection to ensure each m/z window contained equal numbers of ions with charges considered for MS/MS. Dynamic exclusion was enabled to prevent the selection of a formally targeted ion for a total of 20 sec.
Data Processing Protocol
Following mass spectrometry data acquisition, the Thermo Xcalibur RAW files were imported into Progenesis LC-MS software (version 3.0; Nonlinear Dynamics) with automatic feature detection enabled. A representative reference run was selected to which all other runs were automatically aligned in a pair-wise fashion. Features (peaks) were filtered for charge state (2, 3 and 4 accepted) and elution time window. The resulting MS/MS peak lists were exported as a single Mascot generic file and loaded onto a local Mascot Server (version 2.3.0; Matrix Science). We searched these spectra against the SwissProt/UniProt 2011 database (selected for Schizosaccharomyces pombe) using the following parameters: tryptic enzyme digestion with one missed cleavage allowed, precursor mass tolerance of 10 ppm, fragment mass tolerance of 0.6 Da, oxidation of methionines and deamidation of glutamine/asparagine as variable modifications, ion score significance threshold of p ≤ 0.05. The resulting peptides were exported as an XML file from Mascot and imported back into Progenesis LC-MS to assign peptides to features. A table of all identified features along with their normalized peptide abundance in each sample was generated. In total, 7330 (out of 58211) features have peptide assignments and these peptides belong to 1167 non-redundant proteins. We calculated an abundance measure for each protein by averaging the corresponding normalized peptide abundance. This results in a matrix consisting of 1167 proteins and their respective abundance in each sample (five technical replicate injections were available for each treatment, no biological replicate). This was analyzed in the R/BioConductor package limma (Smyth, 2004) to identify stress-responsive proteins. A protein was considered as significant if its abundance showed differential changes in the stressed cells relative to unstressed cells in at least one time point at 5% FDR.
Hui Sun Leong, Cancer Research UK Manchester Institute, The University of Manchester
Crispin J Miller, RNA Biology Group, Cancer Research UK Manchester Institute, The University of Manchester, Wilmslow Road, Manchester M20 4BX, United Kingdom. ( lab head )
Leong HS, Dawson K, Wirth C, Li Y, Connolly Y, Smith DL, Wilkinson CR, Miller CJ. A global non-coding RNA system modulates fission yeast protein levels in response to stress. Nat Commun. 2014 May 23;5:3947 PubMed: 24853205
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