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J Proteomics. 2012 August 3; 75(15-10): 4632–4646.
PMCID: PMC3426930

Proteomic profiling of ATM kinase proficient and deficient cell lines upon blockage of proteasome activity[star]

Abstract

Ataxia Telangiectasia Mutated (ATM) protein kinase is a key effector in the modulation of the functionality of some important stress responses, including DNA damage and oxidative stress response, and its deficiency is the hallmark of Ataxia Telangiectasia (A-T), a rare genetic disorder. ATM modulates the activity of hundreds of target proteins, essential for the correct balance between proliferation and cell death. The aim of this study is to evaluate the phenotypic adaptation at the protein level both in basal condition and in presence of proteasome blockage in order to identify the molecules whose level and stability are modulated through ATM expression. We pursued a comparative analysis of ATM deficient and proficient lymphoblastoid cells by label-free shotgun proteomic experiments comparing the panel of proteins differentially expressed. Through a non-supervised comparative bioinformatic analysis these data provided an insight on the functional role of ATM deficiency in cellular carbohydrate metabolism's regulation. This hypothesis has been demonstrated by targeted metabolic fingerprint analysis SRM (Selected Reaction Monitoring) on specific thermodynamic checkpoints of glycolysis. This article is part of a Special Issue entitled: Translational Proteomics.

Keywords: ATM kinase, Proteasome, Shotgun, Carbohydrate metabolism

Graphical abstract

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Highlights

► Identification of the repertoire of protein adaptions in A-T cells ► Evident impairment of carbohydrate metabolism in absence of ATM expression ► ATM deficiency affects the control of protein stability

1. Introduction

Ataxia Telangiectasia (A-T) is an autosomal recessive disorder. The clinical hallmark of the disease is progressive neuromotor dysfunction resulting from gradual cerebellar cortical atrophy and Purkinje cells degeneration. Additional important features are telangiectasia in the eyes, thymic degeneration, immune deficiency, premature aging and higher predisposition to develop lymphomas and leukemia [1,2]. Moreover, A-T patients are highly sensitive to ionizing radiations (IR) [3]. A-T is linked to mutations of the ATM (Ataxia Telangiectasia Mutated) gene on both alleles, which ultimately impair the production of a functional ATM protein [4,5], a serine/threonine-PI3-kinase like protein originally identified as a central player of the DNA damage response (DDR) network [2]. ATM kinase mainly exerts its function through the phosphorylation of several protein substrates. These include proteins involved in cell cycle control, DNA repair and apoptosis, such as p53, Chk2, MRN complex, Rad51 and many more [3]. Following DNA damage, ATM molecule is (auto)phosphorylated on Ser 1981 and phosphorylation on this site has been proposed as a signature of an activation event [6]. ATM is activated mainly by DNA double strand breaks (DSBs), a cytotoxic lesion induced by IR, radiomimetic agents, such as neocarzinostatin (NCS), reactive oxygen species accompanying normal metabolism, and it triggers cell cycle arrest and DNA repair as well as, depending on the cellular context and on the severity of the damage, programmed cell death or apoptosis [7]. Increasing evidence suggests that the ubiquitin system plays a crucial role in the DDR providing a timed and coordinated degradation or relocalization or activation of regulatory proteins essential for the execution of the DDR [8]. In fact, cellular homeostasis maintenance and ability of adaptation to the environment depend on degradation of regulatory proteins. Furthermore, more recently non‐degradative ubiquitylation of DNA repair proteins has been shown to play an essential role in the DDR. This post-translational modification of key DDR molecules provides direct and indirect routes to damage site recognition for DNA repair proteins [9]. Phosphorylation-dependent or independent ubiquitylation and deubiquitylation affect protein localization and pathway activation/inactivation and are signals regulating the multiple mechanisms allowing for DDR transient activity [10].

The ubiquitin–proteasome (Ub–P) system plays a key role in maintaining the integrity of cellular proteome and in protecting cells from protein damage. Accumulation of damaged proteins can interfere with normal cellular processes and may directly induce cell death. Under normal circumstances, ubiquitylation of proteins acts as a quality control mechanism, marking and destroying improperly manufactured proteins. Indeed upon cellular stresses such as metal and oxidants exposure or heat shock, there is a significant increase of ubiquitylated proteins level in the cell, and aberrations in this pathway are implicated in the pathogenesis of several diseases, including many neurodegenerative disorders [11,12].

In this scenario, it has been demonstrated that Ub–P is also induced in response to ATM kinase activation. NCS treatment endogenously increases ubiquitin conjugates in lymphoblastoid cells. A-T cells show an attenuated ability to mount the ubiquitylation response to stress, supporting a role of ATM in modulating the ubiquitylation machinery [13]. ATM modulates the activity of E3 ubiquitin ligases, affecting indirectly the stability of target proteins: for example the E3 ubiquitin ligases MDM2 and COP1 have been identified as ATM substrates and their ATM-dependent phosphorylation results in the inhibition of their enzymatic activity which in turn triggers p53 stabilization [14–16]. Recently, Stagni and colleagues have shown that ATM modulates the proteasome dependent down-regulation of c-FLIP therefore affecting death receptor induced apoptosis [17,18]. Furthermore it has been shown that ATM activity triggers NEMO ubiquitylation and NF-κB activation modulating the TNFα response [19]. A recent paper demonstrates how protein proteasome-mediated degradation is negatively affected in A-T cells due to the ATM impairment of ISG15 (an ubiquitin-like protein) pathway [20]. Importantly, proteomic approaches aimed to deciphering ATM substrates identified more than 700 proteins as novel ATM targets among which the Ub–P system is highly represented [21–23]. Moreover, these studies suggested that ATM may importantly contribute to several cellular functions beside DNA damage response. Indeed, DDR independent roles of ATM as cytoplasmic protein involved in different biochemical phenomena are starting to emerge linking ATM deficiency to increased oxidative stress, neurodegeneration, metabolic dysregulation and oncogenesis (reviewed in [24]). ATM participates in maintenance of cellular redox homeostasis by up-regulation of antioxidants, increasing production of reductive precursors and decreasing reactive oxygen species (ROS) production by mitochondria. Accordingly, the absence of a functional ATM results in a continuous state of oxidative stress causing adverse effects on particularly sensitive cells as neurons [25,26]. Moreover, an intrinsic up-regulation of ROS and mitochondria dysfunction are exhibited by ATM deficient lymphoblastoid cells [27] and Cheema and colleagues reported that ATM controls oxidative stress by regulating purine, pyrimidine and urea cycle pathways [28]. Interestingly, H2O2 dependent ATM Cys-2991 dimer formation was proposed as oxidation activation mechanism different from the classic Ser-1981 autophosphorylation occurring after the DBSs [29]. Other evidences supported ATM role in regulation of metabolic signaling pathways. ATM participates in insulin signaling through phosphorylation of eIF-4E-binding protein 1 [30] and glucose metabolism is affected by ATM activity as the levels of Insulin-like growth factor 1 receptor (IGFR1) are reduced in ATM-deficient cells [31] and translocation of the cell-surface Glucose transporter 4 (GLUT4) is regulated indirectly by ATM in response to insulin stimulation [32]. Moreover, a link between ATM and the pentose phosphate pathway has been provided [33] and ATM activity modulates metabolic syndrome [34,35].

Overall, these data confirm that ATM deficiency affects the cellular proteome composition resulting in multiple defective signaling pathways. Therefore, we developed a non-targeted proteomic investigation to analyze the profile of proteins whose levels change in response to ATM expression in order to elucidate the role of ATM in the control of protein quality and stability. To this aim, protein expression profiling was also assessed in the presence of the proteasome inhibitor MG132 to highlight those proteins whose expression is modulated by ATM most likely through the ubiquitin–proteasome system. Our investigation was pursued by the use of isotope-free shotgun proteomics approach that provides a relatively high-throughput assessment of changes in protein expression, which may act as a molecular remnant of ATM activity mechanism, and generates raw data for unsupervised data-mining of functional biological process. This approach allowed us to obtain an overview on the role of ATM in the modulation of the proteome, thereby offering a better understanding of its physiologic and pathologic implication.

2. Materials and methods

2.1. Cell lines, antibodies and reagents

L6pCDNA (L6), L6-Flag-ATM-wt (L6ATM), GM-03189 and HeLa cell lines were cultured as described previously [17,18,36]. Anti-Mono- and polyubiquitinylated conjugates (clone FK2) mAb was purchased from ENZO Life Sciences (Farmingdale, NY, USA). Anti-ATM (clone 2C1) mAb, anti-Lamin B (M-20) Ab, anti Matrin-3 (C-20) Ab, anti PGAM1 (6) mAb, anti-PGK1 (Y-12) Ab, anti PKM2 (H-59) Ab, anti-Stat1 (M-22) Ab and anti-T-Plastin (A-3) mAb were obtained from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Anti-hnRNP F+H (1G11) mAb was obtained by Abcam (Cambridge, UK) and anti-GLRX1 (AF3119) Ab from R&D Systems Inc. (Minneapolis, MN, USA). Anti-β-Actin (clone AC-15) mAb and anti-α-Tubulin (Clone DM 1A) mAb were purchased from Sigma-Aldrich (Saint Louis, Missouri, USA). ShATM construct and its control (shSc) were described elsewhere [18]. The proteasome inhibitor Z-Leu–Leu–Leu-al (MG132), DMSO, Trizma base, Urea, CHAPS, Iodoacetamide (IAA), DTT, [Glu1]-Fibrinopeptide B, Ammonium acetate, Methanol, Ethanol, Acetone and standard compounds (glucose 6–phosphate, fructose 1,6-bisphosphate, glyceraldehyde 3–phosphate, pyruvate, lactate) were purchased from Sigma. Sequence grade trypsin was purchased from Promega (Madison, WI, USA). Water ultra-gradient, Acetonitrile ultra-gradient (ACN), TFA and Formic Acid (FA) were purchased by Romil (Cambridge, UK).

2.2. Immunoblotting

Protein extracts were obtained by lysing and sonicating cells in 6 M Urea, 100 mM Tris pH 7.5 and 0.5% CHAPS. Protein concentration was determined by the Bio-Rad Protein Assay (Bio-Rad Laboratories, Hercules, CA, USA). Equal amounts of proteins were resolved by 1-D-SDS-PAGE and blotted onto nitrocellulose membranes using a Hoefer SemiPhor semidry transfer unit (Amersham Biosciences, Uppsala, Sweden). Blots were incubated with the indicated primary antibodies, extensively washed and, after incubation with horseradish peroxidase (HRP)-labeled goat anti-mouse or anti-rabbit (Bio-Rad Laboratories) or bovine anti-goat Ab (Santa Cruz), developed with the ECL plus chemiluminescence's detection system (GE Healthcare, UK). The band intensities were quantified and normalized with those of α-Tubulin using the image analysis software: ImageQuant™ TL (GE Healthcare). Three independent experiments were done for each detected protein.

2.3. Expression analysis by nLC-MSE

Proteins extracted from 10 × 106 L6 and L6ATM cells, treated with 10 μM MG132 or 1:1000 DMSO (control) for 2 hours, were quantified by Bio-Rad assay. Three different experiments were performed and four protein pools were obtained (L6, L6MG132, L6ATM and L6ATMMG132), collecting 50 μg of protein from each experiment. Proteins pools were precipitated adding a cold mix of Ethanol, Methanol and Acetone (ratio 2:1:1, v/v), and redissolved in 6 M Urea, 100 mM Tris pH 7.5. After reduction with 10 mM DTT and alkylation with 20 mM IAA, protein samples were digested 100:1 (w/w) with sequence grade trypsin at 37 °C overnight. The reaction was stopped by adding a final concentration of 0.1% TFA. Samples were diluted with 0.1% FA, 3% ACN at a concentration of 0.86 μg/μl, and 1.72 μg of protein digestion were loaded on column for peptide separation. Prior of loading, 100 fmol/μl Saccharomyces cerevisiae Enolase digestion (Waters, Milford, MA, USA) was added to samples as internal standard. Peptides were trapped on a 5 μm Symmetry C18 trapping column 180 μm × 20 mm (Waters) and separated using a 180 min RP gradient at 300 nl/min (3 to 40% ACN over 125 min) on a nanoACQUITY UPLC System (Waters), utilizing a 1.7 μm BEH 130 C18 NanoEase 75 μm × 25 cm nanoscale LC column (Waters). The lock mass ([Glu1]-Fibrinopeptide B, 500 fmol/μl) was delivered from the auxiliary pump of the UPLC System with a constant flow rate of 250 nl/min. The separated peptides were mass analyzed by a hybrid quadrupole orthogonal acceleration time-of-flight mass spectrometer (Q-Tof Premier, Waters Corp., Manchester, UK) directly coupled to the chromatographic system and programmed to step between low (4 eV) and high (15–40 eV) collision energies on the gas cell, using a scan time of 1.5 s per function over 50–1990 m/z (Expression analysis [37,38]). Three continuum LC-MS data for each pool were processed for qualitative and quantitative analysis using the software ProteinLynx Global Server (version 2.4, PLGS, Waters). Protein identifications were obtained with the embedded ion accounting algorithm of the software and searching a human database (UniProtKB/Swiss-Prot Protein Knowledgebase, release 2011_06 of 31-May-11 containing 529056 sequence entries; taxonomical restrictions: Homo sapiens, 20235 sequence entries) to which data from S. cerevisiae Enolase was appended. The search parameters were automatic tolerance for precursor ions and for product ions, minimum 3 fragment ions matched per peptide, minimum 7 fragment ions matched per protein, minimum 2 peptide matched per protein, 1 missed cleavage, carbamydomethylation of cysteine as fixed modification and oxidation of methionine as variable modification. The false positive rate estimated was under 4%, as previously described [39]. Quantitative analyses have been performed by data independent alternate scanning expression algorithm. Identified proteins were normalized against P00924 entry (S. cerevisiae Enolase) while the most reproducible peptides for retention time and intensity deriving from Enolase digestion (756.4604 m/z, 807.4302 m/z, 814.4923 m/z, 1159.6069 m/z, 1159.6069 m/z, 1755.9429 m/z) were used to normalize the EMRTs table, that is the list of peptide (Exact Masses paired to their Retention Times). In fact, the processing of the two mass spectrometric data functions, low energy and elevated energy, plus data of the reference lock mass, provides a time-aligned inventory of accurate mass-retention time components for both the low and elevated-energy (EMRT). The entire differentially expressed proteins data set was filtered by considering only those identifications from the alternate scanning LC-MSE data with identified peptides exhibiting good replication rate (at least two out of three injections) and with a probability of upregulation (p-value) lower than 0.05 and upper than 0.95 associated to the relative protein fold change. Furthermore, the significance of regulation level was determined at 30% fold change, that is an average relative fold change between − 0.30 and 0.30 on a natural log scale, which is typically 2–3 times higher than the estimated error on the intensity measurement (for more details see Supplementary Information Data S1).

Proteins extracted from 10 × 106 L6 and L6ATM cells, treated with 10 μM MG132 or 1:1000 DMSO (control) for 2 hours, were quantified by Bio-Rad assay. Three different experiments were performed and four protein pools were obtained (L6, L6MG132, L6ATM and L6ATMMG132), collecting 50 μg of protein from each experiment. Proteins pools were precipitated adding a cold mix of Ethanol, Methanol and Acetone (ratio 2:1:1, v/v), and redissolved in 6 M Urea, 100 mM Tris pH 7.5. After reduction with 10 mM DTT and alkylation with 20 mM IAA, protein samples were digested 100:1 (w/w) with sequence grade trypsin at 37 °C overnight. The reaction was stopped by adding a final concentration of 0.1% TFA. Samples were diluted with 0.1% FA, 3% ACN at a concentration of 0.86 μg/μl, and 1.72 μg of protein digestion were loaded on column for peptide separation. Prior of loading, 100 fmol/μl Saccharomyces cerevisiae Enolase digestion (Waters, Milford, MA, USA) was added to samples as internal standard. Peptides were trapped on a 5 μm Symmetry C18 trapping column 180 μm × 20 mm (Waters) and separated using a 180 min RP gradient at 300 nl/min (3 to 40% ACN over 125 min) on a nanoACQUITY UPLC System (Waters), utilizing a 1.7 μm BEH 130 C18 NanoEase 75 μm × 25 cm nanoscale LC column (Waters). The lock mass ([Glu1]-Fibrinopeptide B, 500 fmol/μl) was delivered from the auxiliary pump of the UPLC System with a constant flow rate of 250 nl/min. The separated peptides were mass analyzed by a hybrid quadrupole orthogonal acceleration time-of-flight mass spectrometer (Q-Tof Premier, Waters Corp., Manchester, UK) directly coupled to the chromatographic system and programmed to step between low (4 eV) and high (15–40 eV) collision energies on the gas cell, using a scan time of 1.5 s per function over 50–1990 m/z (Expression analysis [37,38]). Three continuum LC-MS data for each pool were processed for qualitative and quantitative analysis using the software ProteinLynx Global Server (version 2.4, PLGS, Waters). Protein identifications were obtained with the embedded ion accounting algorithm of the software and searching a human database (UniProtKB/Swiss-Prot Protein Knowledgebase, release 2011_06 of 31-May-11 containing 529056 sequence entries; taxonomical restrictions: Homo sapiens, 20235 sequence entries) to which data from S. cerevisiae Enolase was appended. The search parameters were automatic tolerance for precursor ions and for product ions, minimum 3 fragment ions matched per peptide, minimum 7 fragment ions matched per protein, minimum 2 peptide matched per protein, 1 missed cleavage, carbamydomethylation of cysteine as fixed modification and oxidation of methionine as variable modification. The false positive rate estimated was under 4%, as previously described [39]. Quantitative analyses have been performed by data independent alternate scanning expression algorithm. Identified proteins were normalized against P00924 entry (S. cerevisiae Enolase) while the most reproducible peptides for retention time and intensity deriving from Enolase digestion (756.4604 m/z, 807.4302 m/z, 814.4923 m/z, 1159.6069 m/z, 1159.6069 m/z, 1755.9429 m/z) were used to normalize the EMRTs table, that is the list of peptide (Exact Masses paired to their Retention Times). In fact, the processing of the two mass spectrometric data functions, low energy and elevated energy, plus data of the reference lock mass, provides a time-aligned inventory of accurate mass-retention time components for both the low and elevated-energy (EMRT). The entire differentially expressed proteins data set was filtered by considering only those identifications from the alternate scanning LC-MSE data with identified peptides exhibiting good replication rate (at least two out of three injections) and with a probability of upregulation (p-value) lower than 0.05 and upper than 0.95 associated to the relative protein fold change. Furthermore, the significance of regulation level was determined at 30% fold change, that is an average relative fold change between − 0.30 and 0.30 on a natural log scale, which is typically 2–3 times higher than the estimated error on the intensity measurement (for more details see Supplementary Information Data S1).

2.4. Bioinformatic analysis

To identify Gene Ontology (GO) classes and biologically relevant molecular pathways from our large-scale data we have analyzed the proteomics dataset by using two different bioinformatic analysis tools endowed with a comprehensive knowledgebase, such as Protein ANalysis Through Evolutionary Relationships (PANTHER) Classification System (version 7.0 http://www.pantherdb.org) [40] and Ingenuity Pathways Analysis (IPA; version 9.0, Ingenuity Systems, http://www.ingenuity.com). By PANTHER resource genes products can be categorized by their molecular functions and/or biological processes on the basis of published papers and by evolutionary relationships to predict function when interpreting experimental evidence is challenging. Ingenuity Pathways Analysis is a web-based application that enables mining, visualization and exploration of relevant functional associations significant to the experimental results. The analysis settings applied were: Reference set: Ingenuity Knowledge Base (Genes Only); Relationship to include: Direct and Indirect; Includes Endogenous Chemicals; Filter Summary: Consider all molecules and/or relationships. The most significant categories associated to the uploaded datasets were identified by calculating the related significance (p-value) statistically evaluated by the Fischer's exact test. The p-value measures the likelihood that the association between the genes/proteins in the datasets and each Canonical Pathway, Biological Function, etc., is not due to random chance alone identifying significant over-representation of molecules in association to a given process. We applied a p-value threshold of 0.05, limiting the false discovery rate (i.e., the expected fraction of false positives among significant functions) to less than 5%.

2.5. Metabolomics analysis

100 μL of a mixture of ethanol/water 80:20 were added to 20 × 106 cell pellets. Cells were sonicated for 20 min and then samples were centrifuged (25,000g, 4 °C, 20 min). Supernatants were analyzed by an LC-MS/MS system consisting of a Waters Alliance HT 2795 HPLC Separation Module coupled to a Waters Quattro Ultima Pt ESI tandem quadrupole mass spectrometer (Waters). The instrument was operated in negative electrospray ionization mode using MassLynx v. 4.0 software (Waters) and data processing was performed using QuanLynx software (Waters). For HPLC analysis, the Atlantis HILIC Silica 3 μm 2.1 × 150 mm column (Waters) was used. 25 μl of the extracted samples were injected onto the HPLC-MS/MS system. The mobile phase comprised a binary solvent system: ACN (solvent A) and water containing 50 mmol/l Ammonium acetate (solvent B). The initial solvent composition was 100% A. 100%A was maintained for 3 min; decreasing from the initial conditions to 50% A within 8.0 min, holding for 4 min before returning to the initial state at 12.0 min, allowing 4 min for column re-equilibration. The total run time was 16 min, injection-to-injection. The flow rate was 0.3 ml/min. The mass spectrometer ionization source settings were optimized for maximum precursor ion yields for each metabolite. This was achieved by infusing a 1 μg/ml methanolic solution of each individual compound. The following transitions were monitored for the metabolites of interest: glucose 6–phosphate (G 6–P) 258.80 > 96.80, cone 40 V and collision energy 13 eV; fructose 1,6–bisphosphate (F 1,6–P) 338.90 > 96.90, cone 40 V and collision energy 15 eV; glyceraldehyde 3–phosphate (G 3–P) 168.80 > 96.90, cone 40 V and collision energy 5 eV; pyruvate (P) 86.90 > 43.10, cone 40 V and collision energy 5 eV; lactate (L) 88.90 > 43.10, cone 40 V and collision energy 6 eV. The capillary voltage was 3.00 kV, source temperature was 100 °C, desolvation temperature was 400 °C, and the collision cell gas pressure was 3.5 × 10 − 3 mbar argon. The inter-channel and inter-scan delay times were 0.02 and 0.10 s, respectively. The dwell time was 0.200 s for each analyte.

3. Results

3.1. Shotgun proteome profiling by label-free nUPLC-MSE

We pursued a proteomic investigation to analyze the profile of proteins whose levels change in response to ATM expression in order to elucidate the role of ATM in the control of protein quality and stability. In the attempt to investigate the different protein profiling in presence of absence of ATM we focalized our study on two cell lines previously established [17]: lymphoblastoid ATM deficient cells from an A-T patient stably transfected with constructs that allow the expression of either FLAG-ATM-wt protein (L6-ATM-wt = L6ATM) or the empty vector as control (L6-pCDNA = L6). These two cell lines were obtained from the same lymphoblastoid clone; consequently they have the same genetic background and are specifically different only for the reconstitution of ATM expression. Therefore, the differences observed comparing these cell lines are reasonably, mainly due to the different expression of ATM protein. L6 and L6ATM cells were incubated 2 hours in the presence or in the absence of the proteasome inhibitor MG132 10 μM (Fig. 1). Two different datasets were analyzed: on the one hand a comparison between the proteome of L6ATM cell line and L6 cells (L6ATM vs L6); on the other hand a parallel between MG132 treated L6ATM cell line and MG132 treated L6 cells (L6ATM MG132 vs L6 MG132). The first dataset allowed us to investigate the differences in proteome composition only due to the presence/absence of ATM. The treatment with MG132 [41] allowed to highlight those proteins whose half-life is particularly short and their ATM dependent modulation levels over the whole proteome would be partially masked in a direct investigation. The comparative proteome analysis was performed by nano ultra performance liquid chromatography (nUPLC) coupled to MSE isotope free shotgun profiling. Using this approach, we identified a total of 123153 molecular spectral features (EMRTs) and 473 proteins across both conditions of the first dataset (L6ATM vs L6); 119759 EMRTs and 503 proteins in the second dataset (L6ATM MG132 vs L6 MG132). Quality control measures were performed on the replicates to determine the mass measurement and the chromatographic retention time analytical reproducibility of each peptide (Fig. S1). The subsequent strategy for quantifying proteome profile data for differential expression analysis relies on changes in the peptide analyte signal response from each EMRT component that directly reflect their concentrations in one sample relative to another. Applying this experimental approach the label-free shotgun analysis of the two cell lines revealed that L6ATM cells showed significantly different levels of 53 proteins compared to L6 (Tables 1, S1–S7). The proteomic analysis of the second dataset under study (MG132 treated L6ATM vs MG132 treated L6 cells) led us to identify 62 proteins differentially expressed (Tables 2, S1, S8-S13). Among these identified proteins 12 were present in both comparison condition (see Tables 1 and 2).

Fig. 1
Assessment of proteasome inhibition. L6 and L6ATM cells treated 2 hours with 10 μM MG132 or DMSO 1:1000 (control). For immunoblotting, 50 μg protein extract were separated by SDS-PAGE and transferred on nitrocellulose. ...
Table 1
Significant differentially regulated proteins in L6ATM and L6 cells comparison identified by label-free LC-MSE.
Table 2
Significant differentially regulated proteins in L6ATM MG132 and L6 MG132 treated cells identified by label-free LC-MSE.

We pursued a proteomic investigation to analyze the profile of proteins whose levels change in response to ATM expression in order to elucidate the role of ATM in the control of protein quality and stability. In the attempt to investigate the different protein profiling in presence of absence of ATM we focalized our study on two cell lines previously established [17]: lymphoblastoid ATM deficient cells from an A-T patient stably transfected with constructs that allow the expression of either FLAG-ATM-wt protein (L6-ATM-wt = L6ATM) or the empty vector as control (L6-pCDNA = L6). These two cell lines were obtained from the same lymphoblastoid clone; consequently they have the same genetic background and are specifically different only for the reconstitution of ATM expression. Therefore, the differences observed comparing these cell lines are reasonably, mainly due to the different expression of ATM protein. L6 and L6ATM cells were incubated 2 hours in the presence or in the absence of the proteasome inhibitor MG132 10 μM (Fig. 1). Two different datasets were analyzed: on the one hand a comparison between the proteome of L6ATM cell line and L6 cells (L6ATM vs L6); on the other hand a parallel between MG132 treated L6ATM cell line and MG132 treated L6 cells (L6ATM MG132 vs L6 MG132). The first dataset allowed us to investigate the differences in proteome composition only due to the presence/absence of ATM. The treatment with MG132 [41] allowed to highlight those proteins whose half-life is particularly short and their ATM dependent modulation levels over the whole proteome would be partially masked in a direct investigation. The comparative proteome analysis was performed by nano ultra performance liquid chromatography (nUPLC) coupled to MSE isotope free shotgun profiling. Using this approach, we identified a total of 123153 molecular spectral features (EMRTs) and 473 proteins across both conditions of the first dataset (L6ATM vs L6); 119759 EMRTs and 503 proteins in the second dataset (L6ATM MG132 vs L6 MG132). Quality control measures were performed on the replicates to determine the mass measurement and the chromatographic retention time analytical reproducibility of each peptide (Fig. S1). The subsequent strategy for quantifying proteome profile data for differential expression analysis relies on changes in the peptide analyte signal response from each EMRT component that directly reflect their concentrations in one sample relative to another. Applying this experimental approach the label-free shotgun analysis of the two cell lines revealed that L6ATM cells showed significantly different levels of 53 proteins compared to L6 (Tables 1, S1–S7). The proteomic analysis of the second dataset under study (MG132 treated L6ATM vs MG132 treated L6 cells) led us to identify 62 proteins differentially expressed (Tables 2, S1, S8-S13). Among these identified proteins 12 were present in both comparison condition (see Tables 1 and 2).

3.2. Proteome-wide functional and pathway analysis

Bioinformatics analysis was performed in order to analyze the functions of co-expressed genes and gain insight into the stressed process related to the absence of ATM activity. High-throughput experimental techniques, such as label-free proteomics analysis, produce large amounts of data but these data are of little use if it is not possible to interpret the results in a biological context. Therefore, we have analyzed our proteomics dataset by using two bioinformatic analysis tools, such as Protein Analysis Through Evolutionary Relationships (PANTHER) classification system and Ingenuity Pathways Analysis (IPA). Using the PANTHER resource we classified biologically relevant functional annotations of the differentially expressed polypeptides. The proteins identified in the two dataset of L6ATM vs L6 and L6ATM MG132 vs L6 MG132 were examined for their known GO biological process and grouped in the respective functional category. The most represented biological process (with 25% and 21% respectively) was linked to cellular metabolism (Fig. 2). To gain deeper insight into the plausible cellular and molecular networks in which the identified proteins might be involved, we used the two experimental dataset of L6ATM vs L6 and L6ATM MG132 vs L6 MG132 regulated dependent gene products to query IPA. In fact, Ingenuity Pathway Core Analysis reveals assessment of the enriched signaling and metabolic pathways, molecular networks, and biological processes that are most significantly perturbed in the dataset of interest. This unbiased systems biology approach identified significant overrepresentation of proteins involved in Glycolysis/gluconeogenesis canonical pathway for both comparison, respectively p-value = 3.34E− 07 and p-value = 6.68E− 07. These results are based on the ATM dependent differentially expression of some glycolytic/gluconeogenetic enzymes: Enolase 2 (gamma, neuronal), Glyceraldehyde-3–phosphate dehydrogenase, Glucose-6–phosphate isomerase, phosphoglycerate mutase 1 (brain) (PGAM1), Phosphoglycerate kinase 1 (PGK1), Pyruvate kinase isozymes M1 M2 (PKM2). Moreover, in both dataset (L6ATM vs L6 and L6ATMMG132 vs L6MG132) among the top affected Molecular and Cellular Functions is the Carbohydrate Metabolism (p-value calculated for the first dataset: 7.11E− 08 to 2.15E− 02; for the second one: 6.34E− 06 to 4.43E− 02) (Tables 3 and S14).

Fig. 2
PANTHER biological process classification. Pie chart of Gene Ontology distribution terms associated to differentially regulated proteins.
Table 3
Ingenuity pathway analysis: top five canonical pathway and molecular and cellular function.

Bioinformatics analysis was performed in order to analyze the functions of co-expressed genes and gain insight into the stressed process related to the absence of ATM activity. High-throughput experimental techniques, such as label-free proteomics analysis, produce large amounts of data but these data are of little use if it is not possible to interpret the results in a biological context. Therefore, we have analyzed our proteomics dataset by using two bioinformatic analysis tools, such as Protein Analysis Through Evolutionary Relationships (PANTHER) classification system and Ingenuity Pathways Analysis (IPA). Using the PANTHER resource we classified biologically relevant functional annotations of the differentially expressed polypeptides. The proteins identified in the two dataset of L6ATM vs L6 and L6ATM MG132 vs L6 MG132 were examined for their known GO biological process and grouped in the respective functional category. The most represented biological process (with 25% and 21% respectively) was linked to cellular metabolism (Fig. 2). To gain deeper insight into the plausible cellular and molecular networks in which the identified proteins might be involved, we used the two experimental dataset of L6ATM vs L6 and L6ATM MG132 vs L6 MG132 regulated dependent gene products to query IPA. In fact, Ingenuity Pathway Core Analysis reveals assessment of the enriched signaling and metabolic pathways, molecular networks, and biological processes that are most significantly perturbed in the dataset of interest. This unbiased systems biology approach identified significant overrepresentation of proteins involved in Glycolysis/gluconeogenesis canonical pathway for both comparison, respectively p-value = 3.34E− 07 and p-value = 6.68E− 07. These results are based on the ATM dependent differentially expression of some glycolytic/gluconeogenetic enzymes: Enolase 2 (gamma, neuronal), Glyceraldehyde-3–phosphate dehydrogenase, Glucose-6–phosphate isomerase, Phosphoglycerate mutase 1 (brain) (PGAM1), Phosphoglycerate kinase 1 (PGK1), Pyruvate kinase isozymes M1 M2 (PKM2). Moreover, in both dataset (L6ATM vs L6 and L6ATMMG132 vs L6MG132) among the top affected Molecular and Cellular Functions is the Carbohydrate Metabolism (p-value calculated for the first dataset: 7.11E− 08 to 2.15E− 02; for the second one: 6.34E− 06 to 4.43E− 02) (Tables 3 and S14).

3.3. Protein expression and pathway analysis validation

To validate our results, we selected one sub-set of proteins among those identified as differentially expressed by label-free shotgun experiments and checked their expression by means of western blot analysis performed on new cellular extracts (Fig. 3). The choice was made on the basis of the pathway analysis (selecting three enzyme: PGK1, PKM2 and PGAM1) and literature available information coherent with already published paper and/or with known ATM function (selecting other six proteins: hnRNPH, GLRX1, Plastin3, STAT1, LaminB and Matrin3. For detailed literature references see subsequent Result and Discussion section). Interestingly, we confirmed several data from other proteomic based studies (Table S15) [21–23,28,42] related to ATM and performed on other cell lines. A-T cells have a high level of genome instability, and there were likely numerous genetic changes before the wild type ATM protein was added back. The consistency of our experimental with those obtained by other cell lines enforced the collected results. . Moreover, in addition of both the L6 cell lines, we performed the western blot validation on two other cell lines, characterized by distinct genetic background: GM-03189 (lymphoblastoid cell line derived from an A-T patient) and a stable interfered for ATM HeLa cell line plus the relative control. All the proteins we analyzed both through proteomic and western blot analysis exhibited the same trend in up- or down-regulation of protein expression except for Matrin 3 (Fig. 3E, F). From proteomic analysis we noticed a high increment of Matrin 3 protein levels in L6 ATM treated with MG132; on the contrary protein levels analyzed by western blot seem to be lower. This contradictory result highlights again a well-known technical issue on differences between the two different types of experimental methods we adopted (nLC-MS/MS and western blot analysis); we could explain this discrepancy pondering over the inherent errors and limits of each technique and on the quality of commercial antibodies. Moreover, the down-regulation of Plastin 3 in absence of ATM was confirmed by means of western blotting in the L6 cells but not in GM-03189 cells (Fig. 3A, B), probably due to different genetic background and phenotypic adaptation among the two lymphoblastoid cell lines.

Fig. 3
Immunoblotting validation of shotgun proteomic analysis. (A) Proteins levels revealed with specific antibodies in cellular extracts from L6 and L6ATM cells and a different human A-T lymphoblastoid (GM-03189) cell line characterized by distinct genetic ...

To validate our results, we selected one sub-set of proteins among those identified as differentially expressed by label-free shotgun experiments and checked their expression by means of western blot analysis performed on new cellular extracts (Fig. 3). The choice was made on the basis of the pathway analysis (selecting three enzyme: PGK1, PKM2 and PGAM1) and literature available information coherent with already published paper and/or with known ATM function (selecting other six proteins: hnRNPH, GLRX1, Plastin3, STAT1, LaminB and Matrin3. For detailed literature references see subsequent Result and Discussion section). Interestingly, we confirmed several data from other proteomic based studies (Table S15) [21–23,28,42] related to ATM and performed on other cell lines. A-T cells have a high level of genome instability, and there were likely numerous genetic changes before the wild type ATM protein was added back. The consistency of our experimental with those obtained by other cell lines enforced the collected results. . Moreover, in addition of both the L6 cell lines, we performed the western blot validation on two other cell lines, characterized by distinct genetic background: GM-03189 (lymphoblastoid cell line derived from an A-T patient) and a stable interfered for ATM HeLa cell line plus the relative control. All the proteins we analyzed both through proteomic and western blot analysis exhibited the same trend in up- or down-regulation of protein expression except for Matrin 3 (Fig. 3E, F). From proteomic analysis we noticed a high increment of Matrin 3 protein levels in L6 ATM treated with MG132; on the contrary protein levels analyzed by western blot seem to be lower. This contradictory result highlights again a well-known technical issue on differences between the two different types of experimental methods we adopted (nLC-MS/MS and western blot analysis); we could explain this discrepancy pondering over the inherent errors and limits of each technique and on the quality of commercial antibodies. Moreover, the down-regulation of Plastin 3 in absence of ATM was confirmed by means of western blotting in the L6 cells but not in GM-03189 cells (Fig. 3A, B), probably due to different genetic background and phenotypic adaptation among the two lymphoblastoid cell lines.

Western blot validation of the overexpression of three proteins belonging to the glycolysis pathway (PGK1, PGAM1 and PKM2, see Fig. 3A, B) and the interesting bioinformatics outcome strongly supports the idea that there is a modulation of the glycolytic metabolism in absence of ATM activity. To better qualify this potential metabolic shunt we decided to assess the related metabolic changes by HPLC-MS/MS analysis. In fact, it is not possible only with the performed bioinformatics analysis (GO protein annotation inferred by PANTHER and enrichment function and pathway analysis by IPA) to qualify if the identified process are down-regulated or up-regulated between samples; both bioinformatics tools are based only on the experimental proteins number (and not on their up- or down-regulation) under study by comparing the annotation terms outcome to the reference background (whole annotated functions). Five metabolites were evaluate monitoring their mass spectrometry transitions: glucose 6–phosphate (G 6–P), fructose 1,6–bisphosphate (F 1,6–P), glyceraldehyde 3–phosphate (G 3–P), pyruvate (P) and lactate (L) (Fig. 4). Levels of G 6–P, F 1,6–P and G 3–P intermediate glycolytic metabolites were higher in absence of ATM. On the contrary, levels of the end products of glycolysis (pyruvate) (approximately 1.5-fold increase), and lactate (approximately 3-fold increase) were higher in agreement with the higher expression level of PKM2 in ATM reconstituted L6 compared with the native L6 ATM deficient cell line. PKM2 is the rate-limiting enzyme of the glycolysis and catalyzes the transphosphorylation from phosphoenolpyruvate (PEP) to ADP as the last step of glycolysis to generate ATP and pyruvate. Hu et al. described an up-regulation of Pyruvate kinase isozymes M1 M2 (PKM2) in ATCL8 cells (expressing a functional ATM) compared with AT5BIVA (fibroblast cell line derived from a patient with A-T) due to 3 hours of irradiation [42]. Moreover, in another of proteomic study previously indicated, the authors isolated PKM2 through a large-scale proteomic analysis of proteins phosphorylated in response to DNA damage on consensus sites recognized by ATM and ATR [23]. Intriguingly is known in literature a nuclear translocation of PKM2 in response to different apoptotic stimuli and this nuclear translocation is sufficient to induce programmed cell death [43]. Our results and the reported published evidences confirm the hypothesis that PKM2 could be considered as one of the ATM target protein. Nevertheless, we observed an up-regulation of PKM2 in L6 ATM reconstituted cell line only in basal condition and not after the MG132 treatment. We can hypothesize that in the absence of ATM this protein is more degraded by the Ub-proteasome system and after the proteasome blockage there is an accumulation of the ubiquitylated protein in both cell line explaining the absence of different expression between the two treated cell lines in our study.

Fig. 4
Metabolomics analysis. Metabolites were extracted from L6 and L6ATM cells in presence and absence of MG132. Levels of glucose 6–phosphate (G 6–P), fructose 1,6–bisphosphate (F 1,6–P), glyceraldehyde 3–phosphate ...

The last monitored metabolite, lactate, was found more concentrated in presence of ATM (approximately 3-fold increase in basal condition and 2-fold increase after MG132 treatment, Fig. 4) according to the higher amount of its precursor pyruvate. Normally, lactate is produced in mammalian organism when the oxygen availability is decreased in a reduction reaction that produces NAD+ from NADH and H+. In our cellular system we hypothesize that the lactate higher amount depends on its functions as thermodynamic driving force to push the glycolytic step of the Glyceraldehyde 3 phosphate dehydrogenase (GAPDH) which is enzymatically acting in near equilibrium condition.

4. Discussion

Ataxia Telangiectasia (A-T) is a genetic disease characterized by cerebellar ataxia and immunodeficiency. A-T is linked to the loss of ATM protein function, a serine/threonine kinase central in DNA damage response. ATM modulates also the activity of E3-ubiquitin-ligases, affecting the stability of target proteins. Therefore, ATM deficiency may severely impinge on the cellular proteome composition resulting in defective signaling pathways. In fact, there are increasing evidence that this protein may have an important role in the control of target proteins of the ubiquitin system. Stagni and colleagues have recently shown that ATM modulates the proteasome dependent down-regulation of c-FLIP [17,18]. In the present study, we have pursued a comprehensive proteomic investigation to evaluate the biological effects of ATM expression on the control of protein quality and stability. To this aim, protein expression profiling were also assessed in the presence of the proteasome inhibitor MG132 to highlight those proteins whose expression is modulated by ATM most likely through the ubiquitin–proteasome system and whose half-life is particularly short and their ATM dependent modulation levels over the whole proteome would be partially masked in a direct investigation. By label-free nLC-MSE approach, a total of 53 and 62 differentially expressed proteins were identified in the two analyzed comparison (L6ATM vs L6ATM and MG132 treated L6ATM vs MG132 treated L6 cell lines, respectively). Twelve proteins are regulated in the same way in both experimental dataset and we can speculate that their expression is influenced by the presence/absence of ATM but this event occurs independently of the ubiquitin–proteasome system involvement. Remarkably one of them, Plastin 3 (PLS3), differentially regulated in both dataset of the shotgun proteomic experiments (overepressed in L6ATM cell line in presence or absence of MG132, Tables 1 and 2, Fig. 3A, B), is already known as phosphorylated upon DNA damage, probably by ATM or ATR [23], and its levels are decreased in Spinal Muscular Atrophy (SMA, a neurological disease) mouse model [44].

We analyzed by western blot other three proteins whose levels were influenced by ATM expression and MG132 treatment: STAT1, Lamin B1 and Matrin 3 (Table 2, Fig. 3E, F) to confirm the regulation observed through proteomic analysis in both L6 treated cell lines. Signal Transducer and Activator of Transcription 1 (STAT1) has been previously identified as a potential substrate of ATM in nuclear extracts from irradiated (10 Gy) HeLa cells [21] enforcing the idea that this member of the STAT protein family could be a direct target of ATM. In our study STAT1 is down-regulated after proteasome blockage in L6 ATM compared to L6, an evidence that could be eventually explained by proteasome dependent degradation of STAT1 in ATM proficient cells. In response to cytokines and growth factors, STAT family members are phosphorylated by the receptor associated kinases, and then form dimers that translocate to the cell nucleus where they act as transcription activators of a variety of genes, which is thought to be important for cell viability in response to different cell stimuli and pathogens [45]. There are some evidences in literature which shine a light on the interplay between ATM and STAT1 in the response to the DNA damage, that strengthen our findings [46,47].

Moreover, we observed a decrease of Lamin B1(LMNB1) in L6 ATM treated cells; recently Barascu and colleagues demonstrated an upregulation of Lamin B1 in A-T cells extract. The authors stressed the point that LMNB1 overexpression is sufficient to induce nuclear shape alterations and senescence in wild-type cells. A-T patients suffer from premature ageing and this observation led to the hypothesis that Lamin B1 dysregulation could account for senescence in A-T cells [48]. The authors related LMNB1 accumulation to A-T associated DDR defects, oxidative stress and nuclear shape alterations. Finally, by a systematic analysis of human protein complexes to identify chromosome segregation proteins, ATM and LMNB1 were found as bait-prey interactors from affinity-purification - mass spectrometry experiments (www.mitocheck.org) [49]; this experimental evidence adds an interesting discussion point for the possible direct interaction between ATM and LMNB1 occurring in the nuclear compartment while the highly ordered processes of chromosome segregation and cell division is ongoing. Chromosome alignment, movement and segregation during cell division involve interactions between the kinetochore and the mitotic spindle through microtubule depolymerization/assembly [50]. Notably, we revealed differential expression of tubulins and Heat Shock Proteins (HSPs) in both proteomics dataset. Although the expression of some cytoskeleton proteins and HSPs could be related to their abundance and therefore to their more easily accessible identification by mass spectrometry experiments, in our opinion the selective presence of centrosome components, like tubulins, and Hsp 70 and 90 in our cell models is tied to their function in cell cycle control, cell death and aggresome promoting formation as described in several literature papers and already observed in our previous work [51]. Mediators of stress response (i.e., Checkpoint kinase 2 and its upstream regulator ATM) indeed regulate centrosome inactivation checkpoint and use stress inducted centrosome fragmentation or amplification for eliminating damaged cells [52]. The role of HSPs in cell cycle control and in signal transduction networks has been indeed described and assigned both to Hsp 90 [53] and Hsp 70. In particular Hsp 70, as binding partners of hSNM1B/Apollo, a protein with stimulating effect on ATM substrate phosphorylation in response to DNA-damage, results indirectly implicated in the maintenance of genome stability [54]. Recent studies have also disclosed the involvement of Hsp 70 in the preservation of cytoarchitecture. Zhang X. and colleagues have indeed described the “unexpected role” of Hsp 70 in promoting aggresome formation through the interaction with the cochaperone ubiquitin ligase Carboxyl terminal of Hsp70/Hsp90 interacting protein (CHIP) [55]. In conclusion the participation of tubulins and HSPs in cell surveillance mechanisms qualified them as additional implementing checkpoints recruited and activated by stress stimuli, thereby explaining in part their differentially expressed levels in ATM absence in our proteomic observations.

Among the proteins whose levels were influenced by ATM expression and MG132 treatment and were analyzed by western blot, Matrin 3 (MATR3) has already been identified as cross-reacting protein to phospho-specific antibodies against known ATM/ATR substrates [21]. Moreover, Matrin 3 has been already described in literature as involved in early stage of DSB response [56]. In fact, treatment with the radiomimetic agent neocarzinostatin and MATR3 depletion led to abnormal accumulation of cells at the S-phase of the cell cycle. We observed an up-regulated protein expression in L6ATM treated cells by nLC-MSE approach. On the other hand, we could not confirm these data through western blot analysis, thus we could not completely rely on the proteomic evidence.

Therefore, as first conclusion we can argue that our experimental data pointed out some stimulating proteins whose expression changes depending on ATM in presence of proteasome inhibition and could be considered potential ATM activity substrate through the Ub–P system: the transcription activator STAT1 and Lamin B1.

The second interesting point of discussion concerns the significant overrepresentation of proteins involved in glycolysis/gluconeogenesis pathway and carbohydrate metabolism molecular function supporting the idea that there is an evident switch of the metabolism, and in particular of the carbohydrate process, in absence of the ATM expression. Our observations showed how expression of ATM in L6 cells drives higher expression of glycolytic enzymes (PGK1, PGAM1 and PKM2), lower intermediate glycolytic metabolites and higher pyruvate production probably by a stimulation of the cellular rate of glycolysis. The higher lactate amounts may depend consequently both on higher levels of its precursor (pyruvate) and on its function as NADH depleting compound in order to avoid the blockage of glycolysis due to the GAPDH enzymatic step which is operated in near equilibrium condition. These findings are related with the emerging role of ATM as central regulator of cellular metabolism in response to oxidative stress, linking genome stability, cell cycle and carbon catabolism [57–60]. ATM is largely nuclear, acting as modulator of the cellular response to genotoxic stress and indeed our observed up-regulation of hnRNPH in ATM cells could possibly be related to its function in maintaining the genome integrity. In fact, hnRNPH has been described as part of a rescue mechanism of p53 mRNA 3′-end processing regulation in DNA-damaged cells [61]. Moreover, there are increasing evidences that ATM deficiency is not only cause of damage response lack of function; ATM localizes predominantly in the cytoplasm in neuronal and neuron-like cells [62,63] and cytoplasmatic ATM activity is involved in insulin signaling pathways [30,32]). Cosentino et al. [33] demonstrated the link between ATM and the pentose phosphate pathway (PPP) by inducing Glucose-6-phosphate dehydrogenase (G6PD) activity. G6PD is the limiting enzyme of the PPP metabolic pathway which in turn is responsible for the production of the essential antioxidant NADPH cofactor and nucleotide synthesis required to promote DSB repair. Acting as a sensor of reactive oxygen species (ROS), ATM could possibly shift the carbohydrate metabolism from glycolysis to the oxidative PPP under stress condition like DSBs. Shifting the energy source glucose-6–phosphate from glycolysis to PPP, the energy stored in carbohydrate backbones molecules will be shifted toward NADPH production and nucleotide synthesis instead of ATP and NADH produced by glycolysis. In our study we hypothesize a shift of the glycolytic pathway itself in ATM activity absence which may be due to an impairment in the functional link between glycolysis and mitochondrial metabolism. In a recent published paper [36], Mongiardi et al. demonstrated that ATM defective cells have an impaired mitochondrial activity, a reduced response to hypoxia in terms of HIF-1α stabilization and transcription of Hypoxia-responsive genes, including PGK1 and MIF. Accordingly, we identified these two gene products as down-regulated in L6 cells respect to L6ATM. The proposed explanation relays on a blunted response to hypoxia and intracellular concentration of ROS in response to hypoxia which in turn is due to an impaired sensing of oxygen variation. On the other end, in our study, the observed up-regulation of GLRX1 in ATM deficient cells could possibly be related to an adaptive response to mitigate the challenge of redox unbalance in ATM absence, a continuous stress state leading to genomic instability, accumulation of unrepaired DNA, constant activation of the DNA repair mechanisms and impaired mitochondrial activity. The transcription factor NF- κB, which has a pivotal role in cell survival and proliferation, is subject to regulation by redox changes; this regulation relies in part on the oxidative inactivation by means of S-glutathionylation of the Inhibitory κB kinase (IKK) β-subunit of the IKK signalosome; overexpression of GLRX1 catalyzes deglutathionylation of IKKβ and enhances NF-κB activation [64]. This evidence, our observation of GLRX1 up-regulation in ATM absence and the ATM dependent NEMO ubiquitylation and NF-κB activation [19] could possibly open a new route to an interesting vision on the linkage between ATM, NF-κB, genotoxic and oxidative stress, and cellular metabolism.

The present study provides initial evidences toward a new scenario of ATM function in cellular homeostasis; we are aware of the necessity to go deep inside this issue to complete the schema of signaling pathways beyond the differences in the metabolism response correlated to the loss of function of ATM. Nevertheless, all the described evidences begin to explain the intricate scenario beyond the A-T syndrome which could be hardly understood as consequence only of the DNA damage response lack of function.

5. Conclusions

This research has led to the identification of a set of proteins whose levels and stability is modulated through ATM, therefore contributing to give insight into the molecular events of ATM deficiency relevant for neurodegeneration and immunodeficiency linked to A-T. Pattern of differentially expressed proteins in the presence and in the absence of ATM were obtained by shotgun label-free mass spectrometry characterization of lymphoblastoid ATM deficient and proficient cells. The treatment with MG132 highlights those proteins whose expression is modulated by ATM most likely through the ubiquitin–proteasome system and whose half-life is particularly short and their ATM dependent modulation levels over the whole proteome would be partially masked in a direct investigation. Our study pointed out some stimulating proteins whose expression changes could possibly depend on the ATM presence and the blockage of proteasome activity: Pyruvate kinase isozymes M1 M2, a glycolytic enzyme; Plastin 3, already known as involved neurological disease; the transcription activator STAT1 and Lamin B1. Moreover, proteomic and metabolomics data evidence a modulation of the carbohydrate metabolism in absence of ATM activity, in particular a different glycolysis rate. Our findings are related with the emerging role of ATM as central regulator of cellular carbohydrate metabolism in response to oxidative stress.

The following are the supplementary materials related to this article.

Data S1

Fig. S1
nLC-MSE data quality evaluation. Bar chart showing relative standard deviation of the mass measurements (RDS mass) (A), retention time coefficient of variation (%CV RT) (B) and binary comparison of the log intensity measurements obtained for two replicate ...
Supplemental Table S1:

Information about Proteins identified by nUPLC-MSE. In the table are listed the following parameters: the unique protein sequence identifier (Accession Number), alphanumeric characters with a naming convention (Entry Name), and the protein name (Description) provided by UniProtKB/Swiss-Prot Database; the theoretical Nominal mass and Calculated pI value of the identified protein [mW (Da) and pI (pH) respectively]; the protein identification's ProteinLynx Global Server score (PLGS score); the number of the identified peptides matching to the protein (Peptides); the number of theoretical peptides that can be generated by trypsin digestion (Theoretical Peptides); the percentage of protein sequence covered [Coverage (%)]; the measure of accuracy of identified parent peptides provided by the Root Mean Square error [Precursor RMS Mass Error (ppm)]; the number of matched fragment peptides (Products); the number of modified peptides (Modified Peptides) and the measure of accuracy of identified fragment peptides provided by the Root Mean Square error [RMS (ppm)].

Supplemental Tables S2–S13:

Information about peptides from proteins identified by nUPLC-MSE. In the table the following parameters are listed: alphanumeric characters with a naming convention (Protein Entry name), the unique protein sequence identifier (Accession) and the protein name provided by UniProtKB/SwissProt Database (Protein name); the sequential peptide number for each identified protein (Peptide rank); the detailed Peptide Modification; the identified amino acidic Peptide Sequence; the first amino acid's number of the identified precursor peptide according to the protein fasta sequence (Peptide Sequence Start); the Peptide Sequence Length; the number of identified fragment peptides for each precursor peptide (Peptide Matched Products); the peptide identification's ProteinLynx Global Server score (PLGS peptide score); the type of identified fragment ions (Peptide Matched Products String); the calculated monoisotopic peptide mass [Precursor MH+ (Da)]; the experimental precursor peptide retention time and its intensity [Precursor Retention Time (min), Precursor Intensity]; the precursor peptide charge (Precursor z); the experimental peptide mass/charge (Precursor m/z); the calculated peptide mass error [Peptide Precursor error (ppm)].

Table S14:

Ingenuity pathway analysis: carbohydrate metabolism.

Table S15:

Proteomic ATM studies in comparison.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jprot.2012.05.029.

Acknowledgements

This study was supported by Italian Telethon Foundation (“Comitato Telethon Fondazione Onlus”, GGP07252 to D.B. and A.U.) and in part by the following grants: AIRC IG10590 to D.B. and Fondazione Roma 2008, “Rete Nazionale di Proteomica” FIRB RBRN07BMCT to A.U.

Footnotes

[star]This article is part of a Special Issue entitled: Translational Proteomics.

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