Stofoverdracht in een turbulente vloeistof-vloeistof dispersie in een geroerd vat

Applied SciencesKramers Laboratorium voor Fysische Technologi

Identification of Novel PAMP-Triggered Phosphorylation and Dephosphorylation Events in <i>Arabidopsis thaliana</i> by Quantitative Phosphoproteomic Analysis

Signaling cascades rely strongly on protein kinase-mediated substrate phosphorylation. Currently a major challenge in signal transduction research is to obtain high confidence substrate phosphorylation sites and assign them to specific kinases. In response to bacterial flagellin, a pathogen-associated molecular pattern (PAMP), we searched for rapidly phosphorylated proteins in <i>Arabidopsis thaliana</i> by combining multistage activation (MSA) and electron transfer dissociation (ETD) fragmentation modes, which generate complementary spectra and identify phosphopeptide sites with increased reliability. Of a total of 825 phosphopeptides, we identified 58 to be differentially phosphorylated. These peptides harbor kinase motifs of mitogen-activated protein kinases (MAPKs) and calcium-dependent protein kinases (CDPKs), as well as yet unknown protein kinases. Importantly, 12 of the phosphopeptides show reduced phosphorylation upon flagellin treatment. Since protein abundance levels did not change, these results indicate that flagellin induces not only various protein kinases but also protein phosphatases, even though a scenario of inhibited kinase activity may also be possible

Interactome of the Amyloid Precursor Protein APP in Brain Reveals a Protein Network Involved in Synaptic Vesicle Turnover and a Close Association with Synaptotagmin‑1

Knowledge of the protein networks interacting with the amyloid precursor protein (APP) <i>in vivo</i> can shed light on the physiological function of APP. To date, most proteins interacting with the APP intracellular domain (AICD) have been identified by Yeast Two Hybrid screens which only detect direct interaction partners. We used a proteomics-based approach by biochemically isolating tagged APP from the brains of transgenic mice and subjecting the affinity-purified complex to mass spectrometric (MS) analysis. Using two different quantitative MS approaches, we compared the protein composition of affinity-purified samples isolated from wild-type mice versus transgenic mice expressing tagged APP. This enabled us to assess truly enriched proteins in the transgenic sample and yielded an overlapping set of proteins containing the major proteins involved in synaptic vesicle endo- and exocytosis. Confocal microscopy analyses of cotransfected primary neurons showed colocalization of APP with synaptic vesicle proteins in vesicular structures throughout the neurites. We analyzed the interaction of APP with these proteins using pulldown experiments from transgenic mice or cotransfected cells followed by Western blotting. Synaptotagmin-1 (Stg1), a resident synaptic vesicle protein, was found to directly bind to APP. We fused Citrine and Cerulean to APP and the candidate proteins and measured fluorescence resonance energy transfer (FRET) in differentiated SH-SY5Y cells. Differentially tagged APPs showed clear sensitized FRET emission, in line with the described dimerization of APP. Among the candidate APP-interacting proteins, again only Stg1 was in close proximity to APP. Our results strongly argue for a function of APP in synaptic vesicle turnover <i>in vivo.</i> Thus, in addition to the APP cleavage product Aβ, which influences synaptic transmission at the postsynapse, APP interacts with the calcium sensor of synaptic vesicles and might thus play a role in the regulation of synaptic vesicle exocytosis

Interactome of the Amyloid Precursor Protein APP in Brain Reveals a Protein Network Involved in Synaptic Vesicle Turnover and a Close Association with Synaptotagmin‑1

Knowledge of the protein networks interacting with the amyloid precursor protein (APP) <i>in vivo</i> can shed light on the physiological function of APP. To date, most proteins interacting with the APP intracellular domain (AICD) have been identified by Yeast Two Hybrid screens which only detect direct interaction partners. We used a proteomics-based approach by biochemically isolating tagged APP from the brains of transgenic mice and subjecting the affinity-purified complex to mass spectrometric (MS) analysis. Using two different quantitative MS approaches, we compared the protein composition of affinity-purified samples isolated from wild-type mice versus transgenic mice expressing tagged APP. This enabled us to assess truly enriched proteins in the transgenic sample and yielded an overlapping set of proteins containing the major proteins involved in synaptic vesicle endo- and exocytosis. Confocal microscopy analyses of cotransfected primary neurons showed colocalization of APP with synaptic vesicle proteins in vesicular structures throughout the neurites. We analyzed the interaction of APP with these proteins using pulldown experiments from transgenic mice or cotransfected cells followed by Western blotting. Synaptotagmin-1 (Stg1), a resident synaptic vesicle protein, was found to directly bind to APP. We fused Citrine and Cerulean to APP and the candidate proteins and measured fluorescence resonance energy transfer (FRET) in differentiated SH-SY5Y cells. Differentially tagged APPs showed clear sensitized FRET emission, in line with the described dimerization of APP. Among the candidate APP-interacting proteins, again only Stg1 was in close proximity to APP. Our results strongly argue for a function of APP in synaptic vesicle turnover <i>in vivo.</i> Thus, in addition to the APP cleavage product Aβ, which influences synaptic transmission at the postsynapse, APP interacts with the calcium sensor of synaptic vesicles and might thus play a role in the regulation of synaptic vesicle exocytosis

Automated Phosphopeptide Identification Using Multiple MS/MS Fragmentation Modes

Phosphopeptide identification is still a challenging task because fragmentation spectra obtained by mass spectrometry do not necessarily contain sufficient fragment ions to establish with certainty the underlying amino acid sequence and the precise phosphosite. To improve upon this, it has been suggested to acquire pairs of spectra from every phosphorylated precursor ion using different fragmentation modes, for example CID, ETD, and/or HCD. The development of automated tools for the interpretation of these paired spectra has however, until now, lagged behind. Using phosphopeptide samples analyzed by an LTQ-Orbitrap instrument, we here assess an approach in which, on each selected precursor, a pair of CID spectra, with or without multistage activation (MSA or MS2, respectively), are acquired in the linear ion trap. We applied this approach on phosphopeptide samples of variable proteomic complexity obtained from <i>Arabidopsis thaliana</i>. We present a straightforward computational approach to reconcile sequence and phosphosite identifications provided by the database search engine Mascot on the spectrum pairs, using two simple filtering rules, at the amino acid sequence and phosphosite localization levels. If multiple sequences and/or phosphosites are likely, they are reported in the consensus sequence. Using our program FragMixer, we could assess that on samples of moderate complexity, it was worth combining the two fragmentation schemes on every precursor ion to help efficiently identify amino acid sequences and precisely localize phosphosites. FragMixer can be flexibly configured, independently of the Mascot search parameters, and can be applied to various spectrum pairs, such as MSA/ETD and ETD/HCD, to automatically compare and combine the information provided by these more differing fragmentation modes. The software is openly accessible and can be downloaded from our Web site at http://proteomics.fr/FragMixer

Automated Phosphopeptide Identification Using Multiple MS/MS Fragmentation Modes

Phosphopeptide identification is still a challenging task because fragmentation spectra obtained by mass spectrometry do not necessarily contain sufficient fragment ions to establish with certainty the underlying amino acid sequence and the precise phosphosite. To improve upon this, it has been suggested to acquire pairs of spectra from every phosphorylated precursor ion using different fragmentation modes, for example CID, ETD, and/or HCD. The development of automated tools for the interpretation of these paired spectra has however, until now, lagged behind. Using phosphopeptide samples analyzed by an LTQ-Orbitrap instrument, we here assess an approach in which, on each selected precursor, a pair of CID spectra, with or without multistage activation (MSA or MS2, respectively), are acquired in the linear ion trap. We applied this approach on phosphopeptide samples of variable proteomic complexity obtained from <i>Arabidopsis thaliana</i>. We present a straightforward computational approach to reconcile sequence and phosphosite identifications provided by the database search engine Mascot on the spectrum pairs, using two simple filtering rules, at the amino acid sequence and phosphosite localization levels. If multiple sequences and/or phosphosites are likely, they are reported in the consensus sequence. Using our program FragMixer, we could assess that on samples of moderate complexity, it was worth combining the two fragmentation schemes on every precursor ion to help efficiently identify amino acid sequences and precisely localize phosphosites. FragMixer can be flexibly configured, independently of the Mascot search parameters, and can be applied to various spectrum pairs, such as MSA/ETD and ETD/HCD, to automatically compare and combine the information provided by these more differing fragmentation modes. The software is openly accessible and can be downloaded from our Web site at http://proteomics.fr/FragMixer

Pothos indet.

Phosphopeptide identification is still a challenging task because fragmentation spectra obtained by mass spectrometry do not necessarily contain sufficient fragment ions to establish with certainty the underlying amino acid sequence and the precise phosphosite. To improve upon this, it has been suggested to acquire pairs of spectra from every phosphorylated precursor ion using different fragmentation modes, for example CID, ETD, and/or HCD. The development of automated tools for the interpretation of these paired spectra has however, until now, lagged behind. Using phosphopeptide samples analyzed by an LTQ-Orbitrap instrument, we here assess an approach in which, on each selected precursor, a pair of CID spectra, with or without multistage activation (MSA or MS2, respectively), are acquired in the linear ion trap. We applied this approach on phosphopeptide samples of variable proteomic complexity obtained from <i>Arabidopsis thaliana</i>. We present a straightforward computational approach to reconcile sequence and phosphosite identifications provided by the database search engine Mascot on the spectrum pairs, using two simple filtering rules, at the amino acid sequence and phosphosite localization levels. If multiple sequences and/or phosphosites are likely, they are reported in the consensus sequence. Using our program FragMixer, we could assess that on samples of moderate complexity, it was worth combining the two fragmentation schemes on every precursor ion to help efficiently identify amino acid sequences and precisely localize phosphosites. FragMixer can be flexibly configured, independently of the Mascot search parameters, and can be applied to various spectrum pairs, such as MSA/ETD and ETD/HCD, to automatically compare and combine the information provided by these more differing fragmentation modes. The software is openly accessible and can be downloaded from our Web site at http://proteomics.fr/FragMixer

MOESM4 of Systematic quantitative analysis of H2A and H2B variants by targeted proteomics

Additional file 4. Details of the SRM transitions for each signature peptide. SRM assay parameters including precursor and fragment ion type, charge state, elution time as well as raw data are provided in Suppl. data. (*) Indicates peptides monitored only in their endogenous form

MOESM9 of Systematic quantitative analysis of H2A and H2B variants by targeted proteomics

Additional file 9. Rules used to select or reject peptides using their transition profiles. The validation of the best transitions was performed using a signal-to-noise ratio (> 5) and a perfect co-elution of the heavy standard peptide with the endogenous peptide. Three fragment ions (F1, F2, and F3) are represented for the heavy and the endogenous peptides. a All fragment ions can be integrated because the heavy and endogenous fragment ions co-elute in the same intensity order. b In that case, only F2 can be integrated because the ratio heavy/endogenous is different for F1 and F3. c The fragment F2 is contaminated by another analyte eluting at a slightly later time; it has to be excluded from the analysis. d Here, the signal-to-noise ratio is below five, no fragment ion can be integrated. e. The endogenous peptide traces do not co-elute with the heavy peptide traces

MOESM5 of Systematic quantitative analysis of H2A and H2B variants by targeted proteomics

Additional file 5. Composition of the mixture of standard peptides