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61.
Emilie Boulanger Alicia Dalongeville Marco Andrello David Mouillot Stéphanie Manel 《Ecography》2020,43(8):1167-1179
Current approaches that compare spatial genetic structure of a given species and the dispersal of its mobile phase can detect a mismatch between both patterns mainly due to processes acting at different temporal scales. Genetic structure result from gene flow and other evolutionary and demographic processes over many generations, while dispersal predicted from the mobile phase often represents solely one generation on a single time-step. In this study, we present a spatial graph approach to landscape genetics that extends connectivity networks with a stepping-stone model to represent dispersal between suitable habitat patches over multiple generations. We illustrate the approach with the case of the striped red mullet Mullus surmuletus in the Mediterranean Sea. The genetic connectivity of M. surmuletus was not correlate with the estimated dispersal probability over one generation, but with the stepping-stone estimate of larval dispersal, revealing the temporal scale of connectivity across the Mediterranean Sea. Our results highlight the importance of considering multiple generations and different time scales when relating demographic and genetic connectivity. The spatial graph of genetic distances further untangles intra-population genetic structure revealing the Siculo-Tunisian Strait as an important corridor rather than a barrier for gene flow between the Western- and Eastern Mediterranean basins, and identifying Mediterranean islands as important stepping-stones for gene flow between continental populations. Our approach can be easily extended to other systems and environments. 相似文献
62.
Eric Dumonteil Henry Pronovost Eli F. Bierman Anna Sanford Alicia Majeau Ryan Moore Claudia Herrera 《Molecular ecology》2020,29(19):3747-3761
Integrating how biodiversity and infectious disease dynamics are linked at multiple levels and scales is highly challenging. Chagas disease is a vector‐borne disease, with specificities of the triatomine vectors and Trypanosoma cruzi parasite life histories resulting in a complex multihost and multistrain life cycle. Here, we tested the hypothesis that T. cruzi transmission cycles are shaped by triatomine host communities and gut microbiota composition by comparing the integrated interactions of Triatoma sanguisuga in southern Louisiana with feeding hosts, T. cruzi parasite and bacterial microbiota in two habitats. Bugs were collected from resident's houses and animal shelters and analysed for genetic structure, blood feeding sources, T. cruzi parasites, and bacterial diversity by PCR amplification of specific DNA markers followed by next‐generation sequencing, in an integrative metabarcoding approach. T. sanguisuga feeding host communities appeared opportunistic and defined by host abundance in each habitat, yielding distinct parasite transmission networks among hosts. The circulation of a large diversity of T. cruzi DTUs was also detected, with TcII and TcV detected for the first time in triatomines in the US. The bacterial microbiota was highly diverse and varied significantly according to the DTU infecting the bugs, indicating specific interactions among them in the gut. Expanding such studies to multiple habitats and additional triatomine species would be key to further refine our understanding of the complex life cycles of multihost, multistrain parasites such as T. cruzi, and may lead to improved disease control strategies. 相似文献
63.
H. Lisle Gibbs Libia Sanz Alicia Prez Alexander Ochoa Alyssa T.B. Hassinger Matthew L. Holding Juan J. Calvete 《Molecular ecology》2020,29(15):2871-2888
Understanding how interspecific interactions mould the molecular basis of adaptations in coevolving species is a long‐sought goal of evolutionary biology. Venom in predators and venom resistance proteins in prey are coevolving molecular phenotypes, and while venoms are highly complex mixtures it is unclear if prey respond with equally complex resistance traits. Here, we use a novel molecular methodology based on protein affinity columns to capture and identify candidate blood serum resistance proteins (“venom interactive proteins” [VIPs]) in California Ground Squirrels (Otospermophilus beecheyi) that interact with venom proteins from their main predator, Northern Pacific Rattlesnakes (Crotalus o. oreganus). This assay showed that serum‐based resistance is both population‐ and species‐specific, with serum proteins from ground squirrels showing higher binding affinities for venom proteins of local snakes compared to allopatric individuals. Venom protein specificity assays identified numerous and diverse candidate prey resistance VIPs but also potential targets of venom in prey tissues. Many specific VIPs bind to multiple snake venom proteins and, conversely, single venom proteins bind multiple VIPs, demonstrating that a portion of the squirrel blood serum “resistome” involves broad‐based inhibition of nonself proteins and suggests that resistance involves a toxin scavenging mechanism. Analyses of rates of evolution of VIP protein homologues in related mammals show that most of these proteins evolve under purifying selection possibly due to molecular constraints that limit the evolutionary responses of prey to rapidly evolving snake venom proteins. Our method represents a general approach to identify specific proteins involved in co‐evolutionary interactions between species at the molecular level. 相似文献
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65.
Christophe J. Queval Antony Fearns Laure Botella Alicia Smyth Laura Schnettger Morgane Mitermite Esen Wooff Bernardo Villarreal-Ramos Waldo Garcia-Jimenez Tiaan Heunis Matthias Trost Dirk Werling Francisco J. Salguero Stephen V. Gordon Maximiliano G. Gutierrez 《PLoS pathogens》2021,17(3)
The Mycobacterium tuberculosis complex (MTBC) is a group of related pathogens that cause tuberculosis (TB) in mammals. MTBC species are distinguished by their ability to sustain in distinct host populations. While Mycobacterium bovis (Mbv) sustains transmission cycles in cattle and wild animals and causes zoonotic TB, M. tuberculosis (Mtb) affects human populations and seldom causes disease in cattle. The host and pathogen determinants underlying host tropism between MTBC species are still unknown. Macrophages are the main host cell that encounters mycobacteria upon initial infection, and we hypothesised that early interactions between the macrophage and mycobacteria influence species-specific disease outcome. To identify factors that contribute to host tropism, we analysed blood-derived primary human and bovine macrophages (hMϕ or bMϕ, respectively) infected with Mbv and Mtb. We show that Mbv and Mtb reside in different cellular compartments and differentially replicate in hMϕ whereas both Mbv and Mtb efficiently replicate in bMϕ. Specifically, we show that out of the four infection combinations, only the infection of bMϕ with Mbv promoted the formation of multinucleated giant cells (MNGCs), a hallmark of tuberculous granulomas. Mechanistically, we demonstrate that both MPB70 from Mbv and extracellular vesicles released by Mbv-infected bMϕ promote macrophage multinucleation. Importantly, we extended our in vitro studies to show that granulomas from Mbv-infected but not Mtb-infected cattle contained higher numbers of MNGCs. Our findings implicate MNGC formation in the contrasting pathology between Mtb and Mbv for the bovine host and identify MPB70 from Mbv and extracellular vesicles from bMϕ as mediators of this process. 相似文献
66.
67.
Isolated mitochondria respiring on physiological substrates, both in state 4 and 3, are reported to be or not to be a source of reactive oxygen species (ROS). The cause of these discrepancies has been investigated. As protein concentration was raised in in vitro assays at 37°C, the rate of H2O2 release by rat heart mitochondria supplemented with pyruvate/malate or with succinate (plus rotenone) was shown to increase (0.03–0.15?mg?protein/ml), to decrease (0.2–0.5?mg?protein/ml) and to be negligible (over 0.5?mg?protein/ml). The inhibition of mitochondrial respiration (with rotenone or antimycin A) or the increase in the oxygen concentration dissolved in the assay medium allowed an enhancement of ROS production rate throughout the studied range of protein concentrations. In mitochondria respiring in state 3 on pyruvate/malate or on succinate (plus rotenone), ROS release vanished for protein concentrations over 0.5 or 0.2?mg/ml, respectively. However, ROS production rates measured with low protein concentrations (below 0.1?mg/ml) or in oxygen-enriched media were similar or even slightly higher in the active respiratory state 3 than in the resting state 4 for both substrates. Consequently, these findings indicate that isolated mitochondria, respiring in vitro under conditions of forward electron transport, release ROS with Complex I- and II-linked substrates in the resting condition (state 4) and when energy demand is maximal (state 3), provided that there is sufficient oxygen dissolved in the medium. 相似文献
68.
Alicia L. Richards Catherine E. Vincent Adrian Guthals Christopher M. Rose Michael S. Westphall Nuno Bandeira Joshua J. Coon 《Molecular & cellular proteomics : MCP》2013,12(12):3812-3823
We report the use of neutron-encoded (NeuCode) stable isotope labeling of amino acids in cell culture for the purpose of C-terminal product ion annotation. Two NeuCode labeling isotopologues of lysine, 13C615N2 and 2H8, which differ by 36 mDa, were metabolically embedded in a sample proteome, and the resultant labeled proteins were combined, digested, and analyzed via liquid chromatography and mass spectrometry. With MS/MS scan resolving powers of ∼50,000 or higher, product ions containing the C terminus (i.e. lysine) appear as a doublet spaced by exactly 36 mDa, whereas N-terminal fragments exist as a single m/z peak. Through theory and experiment, we demonstrate that over 90% of all y-type product ions have detectable doublets. We report on an algorithm that can extract these neutron signatures with high sensitivity and specificity. In other words, of 15,503 y-type product ion peaks, the y-type ion identification algorithm correctly identified 14,552 (93.2%) based on detection of the NeuCode doublet; 6.8% were misclassified (i.e. other ion types that were assigned as y-type products). Searching NeuCode labeled yeast with PepNovo+ resulted in a 34% increase in correct de novo identifications relative to searching through MS/MS only. We use this tool to simplify spectra prior to database searching, to sort unmatched tandem mass spectra for spectral richness, for correlation of co-fragmented ions to their parent precursor, and for de novo sequence identification.The ability to make de novo sequence identifications directly from tandem mass spectra has long been a holy grail of the proteomic community. Such a capability would wean the field from its reliance upon sequenced genome databases. Even for organisms with fully annotated genomes, events such as single nucleotide polymorphisms, alternative splicing, gene fusion, and a host of other genomic transformations can result in altered proteomes. These alterations can vary from cell to cell and individual to individual. Thus, one could argue that the most valuable proteomic information, the individual and cellular proteome variation from the genome, remains elusive (1). This problem has received considerable attention; that said, it is not easy to de novo correlate spectrum to sequence in a large-scale, automated fashion (2–6). Improvements in mass accuracy have helped, but routine, reliable de novo sequencing without database assistance is not standard (7–10).A primary means to facilitate de novo spectral interpretation is the simple annotation of m/z peaks in tandem mass spectra as either N- or C-terminal. We and others have investigated this seemingly simple first step. Real-world spectra, however, are complex. Difficulties often arise in determining the charge state of the fragment or in differentiating between fragment ions and peaks arising from neutral loss, internal fragmentation, or spectral noise, both electronic and chemical. Several strategies have focused on product ion annotation. These approaches have included manipulation of the N-terminus basicity combined with electron transfer dissociation (ETD)1 (11–13). This approach can yield mostly N-terminal fragments for peptides having only two charges. However, it requires both ETD and the protease LysN. Other methods have used differential labeling of N- and C-terminal peptides to shift either one or the other product ion series, by either metabolic or chemical means (14–18). Metabolic incorporation of amino acids is an efficient method of introducing distinctive labels that eliminates in vitro labeling, but this method requires that the sample be amenable to cell culture (19, 20). Additionally, it may be difficult to achieve complete labeling in complex systems. Several other approaches used to introduce heavy isotopes onto one terminus have been investigated, including trypsin digestion in 18O water (21–23), differential isotopic esterification (24, 25), derivatization of the C-terminal carboxylate by p-bromophenethylamine (8, 26), N-terminal derivatization with sulfonic acid groups (27, 28), and formaldehyde labeling via reductive amination (29–31). These chemical modifications are introduced after cell lysis, often immediately prior to analysis. Although chemical labeling strategies can be used with a variety of samples, difficulties can arise from differences in labeling efficiency between samples, and often a clean-up step is required following labeling, which may lead to sample loss. No matter the labeling method, in this regime, the two precursors must be separately isolated, fragmented, and analyzed either together or separately. The recognition and selection of the broadly spaced doublet in real time also are necessary. These requirements have limited the utility of these approaches. Our own laboratory discovered that the c- and ●z-type product ions generated from either electron capture dissociation or ETD have distinct chemical formulae and therefore can always be distinguished based on accurate mass alone (32). The problem with this approach is that extremely high mass accuracy (<500 ppb) is required in order to distinguish these product ion types above ∼600 Da in mass. Thus, the majority of the product ions within a spectrum cannot be readily mapped to either terminus with high confidence.Despite these difficulties, we assert that robust de novo sequencing methodology would benefit greatly from a simple method that could be used to distinguish N- and C-terminal product ions with high accuracy and precision. Ideally, the approach would work regardless of the choice of proteolytic enzyme or dissociation method. Recently, we described a new technology for protein quantification called neutron encoding (NeuCode) (33). NeuCode embeds millidalton mass differences into peptides and proteins by exploiting the mass defect induced by differences in the nuclear binding energies of the various stable isotopes of common elements such as C, N, H, and O. For example, consider the amino acid lysine, which has eight additional neutrons (+8 Da). One way to synthesize this amino acid is to add six 13C atoms and two 15N atoms (+8.0142 Da). Another isotopologue could be constructed by adding eight 2H atoms (+8.0502). These two isotopologues differ by only 36 mDa; peptide precursors containing both of these amino acids would appear as a single, unresolved precursor m/z peak at a mass resolving power of less than ∼100,000. However, under high resolving powers (i.e. greater than ∼100,000 at m/z 400), this doublet is resolved. We first developed this NeuCode concept in the context of metabolic labeling, akin to stable isotope labeling with amino acids in cell culture (SILAC), except that instead of the precursor partners being separated by 4 to 8 Da, they are separated by only 6 to 40 mDa. For quantitative purposes, NeuCode promises to deliver ultraplexed SILAC (>12) without increasing spectral complexity.We reasoned that the isotopologues of Lys that permit NeuCode SILAC would generate a distinct fingerprint on C-terminal product ions. Specifically, peptides that have been labeled with NeuCode SILAC and digested with LysC uniformly contain Lys at the C terminus. Upon MS/MS, all C-terminal product ions should present as doublets (with duplex NeuCode), whereas N-terminal products will be detected as a single m/z peak. The very close m/z spacing of the NeuCode SILAC partners will ensure that each partner is always co-isolated and that the signatures are visible only upon high-resolving-power mass analysis. Here we investigate the combination of NeuCode SILAC and high-resolving-power MS/MS analysis to allow the straightforward identification of C-terminal product ions.
Sample Preparation
Saccharomyces cerevisiae strain BY4741 Lys1Δ was grown in defined synthetic complete (SC, Sunrise Science, San Diego, CA) drop-out media with either heavy 6C13/2N15 lysine (+8.0142 Da, Cambridge Isotopes, Tewksbury, MA), or heavy 8D (+8.0502 Da, Cambridge Isotopes). Cells were propagated to a minimum of 10 doublings. At mid-log phase, cells were harvested via centrifugation at 3,000 × g for 3 min and then washed three times with chilled double distilled H2O. Cell pellets were resuspended in 5 ml lysis buffer (50 mm Tris pH 8, 8 m urea, 75 mm sodium chloride, 100 mm sodium butyrate, 1 mm sodium orthovanadate, protease and phosphatase inhibitor tablet), and protein was extracted via glass bead milling (Retsch, Haan, Germany). Protein concentration was measured via BCA (Pierce). Cysteines in the yeast lysate were reduced with 5 m dithiothreitol at ambient temperature for 30 min, alkylated with 15 mm iodoacetamide in the dark at ambient temperature for 30 min, and then quenched with 5 mm dithiothreitol. 50 mm tris (pH 8.0) was used to dilute the urea concentration to 4 m. Proteins were digested with LysC (1:50 enzyme:protein ratio) at ambient temperature for 16 h. The digestion was quenched with TFA and desalted with a tC18 Sep-Pak (Waters, Etten-Leur, The Netherlands). Samples were prepared by mixing 6C13/2N15 (+8.0412 Da) and 8D (+8.0502 Da) labeled peptides in 1:1 ratios by mass. For strong cation exchange fractionation, peptides were dissolved in 400 μl of strong cation exchange buffer A (5 mm KH2PO4 and 30% acetonitrile; pH 2.65) and injected onto a polysulfoethylaspartamide column (9.4 mm × 200 mm; PolyLC) attached to a Surveyor LC quarternary pump (Thermo Electron, West Chester, PA) operating at 3 ml/min. Peptides were detected by photodiode array detector (Thermo Electron, West Chester, PA). Fractions were collected every 2 min starting at 10 min into the following gradient: 0–2 min at 100% buffer A, 2–5 min at 0%–15% buffer B (5 mm KH2PO4, 30% acetonitrile, and 350 mm KCl (pH 2.65)), and 5–35 min at 15%–100% buffer B. Buffer B was held at 100% for 10 min. Finally, the column was washed with buffer C (50 mm KH2PO4 and 500 mm KCl (pH 7.5)) and water before recalibration. Fractions were collected by hand every 2 to 3 min starting at 10 min into the gradient and were lyophilized and desalted with a tC18 Sep-Pak (Waters).LC-MS/MS
Samples were loaded onto a 15-cm-long, 75-μm capillary column packed with 5 μm Magic C18 (Michrom, Auburn, CA) particles in mobile phase A (0.2% formic acid in water). Peptides were eluted with mobile phase B (0.2% formic acid in acetonitrile) over a 120-min gradient at a flow rate of 300 nl/min. Eluted peptides were analyzed by an Orbitrap Elite mass spectrometer. For the nonfractionated samples, mass spectrometer instrument methods comprised one MS1 scan followed by data-dependent MS2 scans of the five most intense precursors. A survey MS1 scan was performed by the Orbitrap at 30,000 resolving power to identify precursors to sample for tandem mass spectrometry, and this was followed by an additional MS1 scan at 480,000 resolving power (at m/z 400; actual mass resolving power of 470,700). Data-dependent tandem mass spectrometry was performed via beam-type collisional activated dissociation (HCD) in the Orbitrap at a resolving power of 15,000, 60,000, 120,000, or 240,000 and a collision energy of 30. Preview mode was enabled, and precursors of unknown charge or with a charge of +1 were excluded from MS2 sampling. For experiments comparing the duty cycle and resolving power required in order to distinguish y-ion doublets, MS1 and MS2 target ion accumulation values were set to 5 × 105 and 5 × 104, respectively. For all other experiments, MS1 target accumulation values were set to 1 × 106 and MS2 accumulation values were set to 4 × 105. Dynamic exclusion was set to 30 s for −0.55 m/z and +2.55 m/z of selected precursors. For ETD analysis, data-dependent top-five mass spectrometry was performed at a resolving power of 240,000 (m/z 400; actual MS2 mass resolving power of 271,000) (34). ETD accumulation values were set to 1 × 106 for MS1 target accumulation and 4 × 105 for MS2 target accumulation. The fluoranthene reaction time was set to 100 ms. For the high-pH strong cation exchange fractions, data-dependent tandem mass spectrometry was performed via HCD at a resolving power of either 60,000 or 120,000 and a collision energy of 30. Preview mode was enabled, and precursors of unknown charge or with a charge of +1 were excluded from MS2 sampling. MS1 targets were set to 1 × 106, and MS2 accumulation values were set to 4 × 105. Dynamic exclusion was set to 45 s for −0.55 m/z and +2.55 m/z of selected precursors. Analysis by use of a wide isolation window was performed on an Orbitrap Fusion. MS1 analysis was performed at 450,000 resolving power (m/z 200), and MS2 analysis was performed at 120,000 resolving power (m/z 400). Data-dependent top-N mass spectrometry was performed, with precursors selected from sequential 25-Da windows. HCD was performed twice on the same precursor, first by use of a quadrupole isolation width of 0.7 m/z for peptide identification, and then using 25 m/z quadrupole isolation. Fragment ions were analyzed in the Orbitrap at a mass resolving power of 120,000 (m/z 400). MS1 and MS2 target accumulation values were set to 2 × 105 and 5 × 104, respectively.Data Analysis
Thermo.raw files were converted to searchable DTA text files using the Coon OMSSA Proteomic Analysis Software Suite (COMPASS) (35). DTA files containing exclusively y-ions were generated using an in-house algorithm. DTA files were searched against the UniProt yeast database (version 132) with Lys-C specificity using the Open Mass Spectrometry Search Algorithm (OMSSA), version 2.1.9 (36). Methionine oxidation was searched as a variable modification. Cysteine carbamidomethylation and the mass shift imparted by the lysine isotopolgues were searched as fixed modifications. For MS2 scans performed at a resolving power of 60,000, 120,000, or 240,000, a shift of +8.0142, representing the mass shift of the 13C615N2 isotopologue, was searched. For MS2 scans performed at 15,000 resolving power, the average shift of the 13C615N2 and 8H2 isotopologues (+8.0322) was searched. For all analyses, the precursor mass was obtained from the 480,000 MS scan. The precursor mass tolerance was defined as 50 ppm, and the fragment ion mass tolerance was set to 0.01 Da. A histogram of precursor mass error at different search tolerances is presented in supplemental Fig. S1. Using the COMPASS software suite, obtained search results were filtered to 1% FDR based on E-values. y-ion doublets were extracted from raw files using an in-house algorithm explained in the supplemental information. Briefly, an ensemble of three different machine learning models was used to score each MS/MS spectral peak for C-terminal product ion prediction. To train our ensemble learner to correctly distinguish C-terminal product ion peaks from N-terminal product ion peaks and noise peaks within our experimental MS/MS spectra, we generated a representative training set of spectral data. Instances used for training and test sets were peaks acquired only from MS/MS spectra associated with a peptide identification. Peaks with a signal-to-noise value of less than 5 were not used. Feature information for each training/testing instance was extracted from raw spectral data. Seven MS/MS spectral features were selected to generate training and test set data: (1) “has doublet” (evaluated as “true” only if a spectral peak could be found at the predicted m/z of the peak''s “heavy” partner), (2) “signal-to-noise” (discretized using a scale of 1–5 based on the peak''s signal-to-noise value), (3) “is isotope,” (4) “is neutral loss,” (5) “number of isotopes,” (6) “number of doublet isotopes,” and (7) “has neutral loss.”To evaluate NeuCode SILAC labeling for automated de novo sequencing, PepNovo+ (8) was benchmarked on y-ion predicted spectra. First, a set of identified spectra from 13,832 unique peptides (>7,400 per precursor charge 2–3) was produced to train PepNovo+ so it could learn features such as the relative peak height ranks of b/y-ions and the probability of noise at each mass interval. These training spectra were acquired under the 11 NeuCode yeast strong cation exchange fractions prepared as described above. Thermo raw files were converted into mzXML format using ProteoWizard v2.2.2828 (with peak-picking turned on) and identified by MS-GF+ v9358 (37) at a 1% spectrum-level FDR against the UniProt yeast database (plus isoforms), v20110729. A fixed modification of K+8.0142 was imposed along with variable modifications of oxidized Met and deamidated Asn/Gln. All MS/MS scans were searched with a 50-ppm precursor mass tolerance, the high-accuracy LTQ instrument setting, the HCD fragmentation setting, and one allowed missed Lys-C cleavage.Thermo.raw files were also converted into DTA spectra as before, except the in-house algorithm for selecting y-ion doublets was slightly altered to boost the peak height of predicted y-ions above that of other peaks (the cumulative peak height was equal to the sum of the monoisotopic doublet peaks, all isotopic doublet peaks, and two times the peak height of the base peak) and to convert their m/z to charge one. Remaining peaks not predicted to be y-ions were converted to charge one by a previously described MS/MS deconvolution tool (38). Deconvoluted DTA spectra that originated from identified MS/MS scans were then paired with the MSGF+ peptide IDs and passed to PepNovo+ for training. The resulting PepNovo+ scoring model lacked the rank-boosting component (39), which requires identified spectra from >100,000 unique peptides per precursor charge state and extensive modification of the PepNovo+ source code to train. Still, the model was sufficient to perform de novo peptide sequencing on the y-ion predicted spectra. PepNovo+ was also run on the raw MS/MS scans (mzXML spectra converted to MGF with all MS/MS peaks converted to charge one) by use of a previously trained HCD scoring model that also lacks the rank-boosting component (40). The following PepNovo+ parameters were set at all stages of training and benchmarking: fixed modification of K+8.0142; variable modifications of oxidized Met and deamidated Asn; 0.01-Da fragment mass tolerance; use of spectrum precursor charge; and use of spectrum precursor m/z. 相似文献69.
70.
Isabelle Vanessa Mohrenz Patrick Antonietti Stefan Pusch David Capper Jörg Balss Sophia Voigt Susanne Weissert Alicia Mukrowsky Jan Frank Christian Senft Volker Seifert Andreas von Deimling Donat Kögel 《Apoptosis : an international journal on programmed cell death》2013,18(11):1416-1425
Isocitrate dehydrogenase 1 (IDH1) decarboxylates isocitrate to α-ketoglutarate (α-KG) leading to generation of NADPH, which is required to regenerate reduced glutathione (GSH), the major cellular ROS scavenger. Mutation of R132 of IDH1 abrogates generation of α-KG and leads to conversion of α-KG to 2-hydroxyglutarate. We hypothesized that glioma cells expressing mutant IDH1 have a diminished antioxidative capacity and therefore may encounter an ensuing loss of cytoprotection under conditions of oxidative stress. Our study was performed with LN229 cells stably overexpressing IDH1 R132H and wild type IDH1 or with a lentiviral IDH1 knockdown. Quantification of GSH under basal conditions and following treatment with the glutathione reductase inhibitor BCNU revealed significantly lower GSH levels in IDH1 R132H expressing cells and IDH1 KD cells compared to their respective controls. FACS analysis of cell death and ROS production also demonstrated an increased sensitivity of IDH1-R132H-expressing cells and IDH1 KD cells to BCNU, but not to temozolomide. The sensitivity of IDH1-R132H-expressing cells and IDH1 KD cells to ROS induction and cell death was further enhanced with the transaminase inhibitor aminooxyacetic acid and under glutamine free conditions, indicating that these cells were more addicted to glutaminolysis. Increased sensitivity to BCNU-induced ROS production and cell death was confirmed in HEK293 cells inducibly expressing the IDH1 mutants R132H, R132C and R132L. Based on these findings we propose that in addition to its established pro-tumorigenic effects, mutant IDH1 may also limit the resistance of gliomas to specific death stimuli, therefore opening new perspectives for therapy. 相似文献