Post-Transcriptional Coordination of the Arabidopsis Iron Deficiency Response is Partially Dependent on the E3 Ligases RING DOMAIN LIGASE1 (RGLG1) and RING DOMAIN LIGASE2 (RGLG2)*
2015; Elsevier BV; Volume: 14; Issue: 10 Linguagem: Inglês
10.1074/mcp.m115.048520
ISSN1535-9484
AutoresI-Chun Pan, Huei‐Hsuan Tsai, Ya-Tan Cheng, Tuan‐Nan Wen, Thomas J. Buckhout, Wolfgang Schmidt,
Tópico(s)Iron Metabolism and Disorders
ResumoAcclimation to changing environmental conditions is mediated by proteins, the abundance of which is carefully tuned by an elaborate interplay of DNA-templated and post-transcriptional processes. To dissect the mechanisms that control and mediate cellular iron homeostasis, we conducted quantitative high-resolution iTRAQ proteomics and microarray-based transcriptomic profiling of iron-deficient Arabidopsis thaliana plants. A total of 13,706 and 12,124 proteins was identified with a quadrupole-Orbitrap hybrid mass spectrometer in roots and leaves, respectively. This deep proteomic coverage allowed accurate estimates of post-transcriptional regulation in response to iron deficiency. Similarly regulated transcripts were detected in only 13% (roots) and 11% (leaves) of the 886 proteins that differentially accumulated between iron-sufficient and iron-deficient plants, indicating that the majority of the iron-responsive proteins was post-transcriptionally regulated. Mutants harboring defects in the RING DOMAIN LIGASE1 (RGLG1)1 and RING DOMAIN LIGASE2 (RGLG2) showed a pleiotropic phenotype that resembled iron-deficient plants with reduced trichome density and the formation of branched root hairs. Proteomic and transcriptomic profiling of rglg1 rglg2 double mutants revealed that the functional RGLG protein is required for the regulation of a large set of iron-responsive proteins including the coordinated expression of ribosomal proteins. This integrative analysis provides a detailed catalog of post-transcriptionally regulated proteins and allows the concept of a chiefly transcriptionally regulated iron deficiency response to be revisited. Protein data are available via ProteomeXchange with identifier PXD002126. Acclimation to changing environmental conditions is mediated by proteins, the abundance of which is carefully tuned by an elaborate interplay of DNA-templated and post-transcriptional processes. To dissect the mechanisms that control and mediate cellular iron homeostasis, we conducted quantitative high-resolution iTRAQ proteomics and microarray-based transcriptomic profiling of iron-deficient Arabidopsis thaliana plants. A total of 13,706 and 12,124 proteins was identified with a quadrupole-Orbitrap hybrid mass spectrometer in roots and leaves, respectively. This deep proteomic coverage allowed accurate estimates of post-transcriptional regulation in response to iron deficiency. Similarly regulated transcripts were detected in only 13% (roots) and 11% (leaves) of the 886 proteins that differentially accumulated between iron-sufficient and iron-deficient plants, indicating that the majority of the iron-responsive proteins was post-transcriptionally regulated. Mutants harboring defects in the RING DOMAIN LIGASE1 (RGLG1)1 and RING DOMAIN LIGASE2 (RGLG2) showed a pleiotropic phenotype that resembled iron-deficient plants with reduced trichome density and the formation of branched root hairs. Proteomic and transcriptomic profiling of rglg1 rglg2 double mutants revealed that the functional RGLG protein is required for the regulation of a large set of iron-responsive proteins including the coordinated expression of ribosomal proteins. This integrative analysis provides a detailed catalog of post-transcriptionally regulated proteins and allows the concept of a chiefly transcriptionally regulated iron deficiency response to be revisited. Protein data are available via ProteomeXchange with identifier PXD002126. With the exception of some bacteria that substitute manganese for iron or that rely on obligate parasitism (1.Archibald F.S. Fridovich I. Manganese and defenses against oxygen-toxicity in Lactobacillus plantarum.J. 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To dissect post-transcriptional responses of Arabidopsis to iron deficiency and to determine the possible impact of RGLG on the regulation of these responses, we conducted genome-wide proteomic and transcriptomic surveys of leaves and roots from iron-sufficient and iron-deficient Col-0 wild-type plants and rglg1 rglg2 double mutants. This analysis showed that post-transcriptional regulation has a stronger influence on the proteomic readout than transcriptional control, affecting proteins that in turn control post-transcriptional processes. In particular, the expression of ribosomal proteins is strongly post-transcriptionally affected by iron deficiency, putatively leading to a bias in translation by prioritizing subsets of mRNAs that are critical to the acclimation to low iron availability. RGLG has a dramatic influence on the proteomic profile of iron-deficient plants, affecting both protein abundance and, most likely as a secondary affect, the transcriptional profile by targeting transcription factors and other proteins involved in transcriptional regulation. The combined analysis permits an integration of several regulatory layers involved in adapting plants to low iron availability and underscores the importance of protein turnover in this process. Arabidopsis thaliana (L.) Heynh, Columbia (Col-0) ecotype was used as the wild-type control. rglg1 rglg2 mutants were kindly provided by A. Bachmair, University of Vienna. Plants were grown in a growth chamber on medium as described by Estelle and Somerville (44.Estelle M.A. Somerville C. Auxin-resistant mutants of Arabidopsis thaliana with an altered morphology.Mol. Gen. Genet. 1987; 206: 200-206Crossref Scopus (460) Google Scholar). Seeds were surface sterilized and germinated on a media containing, KNO3 (5 mm), MgSO4 (2 mm), Ca(NO3)2 (2 mm), KH2PO4 (2.5 mm), H3BO3 (70 μm), MnCl2 (14 μm), ZnSO4 (1 μm), CuSO4 (0.5 μm), CoCl2 (0.01 μm), Na2MoO4 (0.2 μm), and FeEDTA (40 μm), solidified with 0.4% Gelrite pure (Kelco), 1.5% sucrose and 1 g/L MES. The pH was adjusted to 5.5 with KOH. Seeds were sown on Petri plates and stratified for 1 day in 4 °C in the dark before being transferred to a growth chamber and grown at 21 °C under continuous illumination (50 μmol m−2 s−1). After 10 d of precultivation, plants were transferred to fresh agar medium either with 40 μm FeEDTA (+Fe plants) or without Fe and with 100 μm 3-(2-pyridyl)-5,6-diphenyl-1,2,4-triazine sulfonate (-Fe plants). For iron quantification and determination of trichome density, plants were grown for 14 d on media supplemented with either 40 μm or 0.5 μm FeEDTA. Roots from control and iron-deficient plants (13-day-old) were ground in liquid nitrogen and suspended in 10× volume of precooled acetone (−20 °C) containing 10% (w/v) TCA and 0.07% (v/v) 2-mercaptoethanol. Proteins were then thoroughly mixed and precipitated for 2 h at −20 °C. Proteins were collected by centrifuging at 35,000 × g (JA-20 108 rotor; Beckman J2-HS) at 4 °C for 30 min. The supernatant was carefully removed, and the protein pellets were washed twice with cold acetone containing 0.07% (v/v) 2-mercaptoethanol and 1 mm phenylmethanesulfonyl fluoride and a third time with cold acetone without 2-mercaptoethanol. Protein pellets were dried by lyophilization and stored at −80 °C or immediately extracted using protein extraction buffer composed of 8 m urea, 50 mm Tris, pH 8.5, for 1 h at 6 °C under constant shaking. Protein extracts were centrifuged at 19,000 × g for 20 min at 10 °C. The supernatant was then collected, and the protein concentration was determined using a protein assay kit (Pierce). Total protein (100 μg) was reduced by adding dithiothreitol to a final concentration of 10 mm and incubated for 1 h at room temperature. Subsequently, iodoacetamide was added to a final concentration of 50 mm, and the mixture was incubated for 30 min at room temperature in the dark. Then, dithiothreitol (30 mm) was added to the mixture to consume any free iodoacetamide by incubating the mixture for 1 h at room temperature in the dark. Proteins were then diluted by 50 mm Tris, pH 8.5, to reduce the urea concentration to 4 m and digested with 0.5 μg of Lys-C (Wako) for 4 h at room temperature. The Lys-C digested protein solution was further digested with 20 μg of modified trypsin (Promega) at 37 °C overnight after the solution was further diluted with 50 mm Tris, pH 8.0, to reduce the urea concentration to less than 1 m. The resulting peptide solution was acidified with 10% trifluoroacetic acid and desalted on a C18 solid-phase extraction cartridge. Desalted peptides were then labeled with iTRAQ reagents (Applied Biosystems) according to the manufacturer's instructions. Control samples (proteins extracted from roots of control plants) were labeled with reagent 114; samples from iron-deficient roots were labeled with reagent 117. Three independent biological experiments with two technical repeats each were performed. The reaction was allowed to proceed for 1 h at room temperature. Subsequently, treated and control peptides were combined and further fractionated offline using high-resolution strong cation-exchange chromatography (PolySulfoethyl A, 5 μm, 200-Å beads). In total, 50 fractions were collected and combined into 16 final fractions. Each final fraction was lyophilized in a centrifugal speed vacuum concentrator. Samples were stored at −80 °C. Liquid chromatography was performed on a Dionex UltiMate 3000 RSLCnano System coupled to a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Scientific) equipped with a Nanospray Flex Ion Source. Peptide mixtures were loaded onto a 75 μm × 250 mm Acclaim PepMap RSLC column (Thermo Scientific) and separated using a segmented gradient in 120 min from 5 to 40% solvent B (100% acetonitrile with 0.1% formic acid) at a flow rate of 300 nl/min. Solvent A was 0.1% formic acid in water. The samples were maintained at 8 °C in the autosampler. The LTQ Orbitrap was operated in the positive ion mode with the following acquisition cycle: a full scan (m/z 350∼1600) recorded in the Orbitrap analyzer at resolution R 70,000 was followed by MS/MS of the 10 most intense peptide ions with HCD acquisition of the same precursor ion. HDC was done with a collision energy of 30%. HCD-generated ions were detected in the Orbitrap at resolution 17,500. Two search algorithms, Mascot (version 2.4, Matrix Science) and SEQUEST, which is integrated in Proteome Discoverer software (version 1.4, Thermo Scientific), were used to simultaneously identify and quantify proteins. Searches were made against the Arabidopsis protein database (TAIR10 20110103, 27416 sequences; ftp://ftp.arabidopsis.org/home/tair/Sequences/blast_datasets/TAIR10_blastsets/TAIR10 pep 20110103 representative gene model) concatenated with a decoy database containing the reversed sequences of the original database. The protein sequences in the database were searched with trypsin digestion at both ends and two missed cleavages allowed, fixed modifications of carbamidomethylation at Cys, iTRAQ 4plex at N terminus and Lys, variable modifications of oxidation at Met and iTRAQ 4plex at Tyr; peptide tolerance was set at 10 ppm, and MS/MS tolerance was set at 0.05 Da. iTRAQ 4plex was chosen for quantification during the search simultaneously. The search results were passed through additional filters, peptide confidence more than 95% (p < 0.05), before exporting the data. For protein quantitation, only unique peptides were used to quantify proteins. These filters resulted in a false discovery rate of less than 5% after decoy database searches were performed. For biological repeats, spectra from the two technical repeats were combined into one file and searched. Proteins identified and quantified in at least two biological repeats were considered to further analyze the abundance change in response to iron deficiency using a method described by Cox and Mann (45.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (6950) Google Scholar). In brief, the log2 ratios of the 9,110 (roots) and 8,303 (leaves) quantified proteins overlapping in at least two biological repeats were calculated and analyzed for normal distribution. For a given protein in one biological repeat, the ratio was calculated as the inverse log2 of the median of the log2 value of all peptide ratios and averaged across the biological replicates. Next, mean and S.D. were calculated and 95% confidence (Z score = 1.96) was used to select those proteins whose distribution was far from the main distribution. For the down-regulated proteins, the c
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