Project description:Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.
Project description:Chemical cross-linking mass spectrometry (CXMS) has emerged as a powerful technology to analyze protein complex structure and interaction. However, the spectral fragmentation behavior and spectral data retrieval of cross-linked peptides are more complex than single peptides. In this study, we designed and synthesized a trehalose-based MS-cleavable cross-linker, Trehalose Disuccinimidyl Ester (TDS), which possesses a CID/HCD-cleavable glycosidic bond and has good bioorthogonality and amphipathicity. Using TDS, the cross-linked peptides were simplified into conventional single peptides via the selective cleavage between glycosidic and peptide bonds under individual MS collision energy, which enhances the matching degree and retrieval throughput of spectral identification. The deep coverage of the TDS method facilitated the accurate resolution of the structural dynamics of purified proteins with different physicochemical properties and yeast 26S proteasome complex. Additionally, the bioorthogonality and amphipathicity of TDS enabled the cross-linking reaction to occur in vivo without the introduction of any organic solvent. Through coinciding with this feature and MS-cleavable capacity, TDS provided us a high throughput snapshot of the structural architecture of protein complex in live cells. These results provide a promising TDS toolkit to study CXMS and decipher the protein conformations and interactions with high accuracy and easy portability for cross-linker design.
Project description:Cross-linking mass spectrometry is a powerful method for the investigation of protein-protein interactions from highly complex samples. XL-MS combined with tandem mass tag labeling holds the promise of large-scale PPI quantification. However, a robust and efficient TMT-based XL-MS quantification method has not yet been established due to the lack of a benchmarking dataset and thorough evaluation of various MS parameters. To tackle these limitations, we generate a two-interactome dataset by spiking-in TMT-labeled cross-linked E. coli lysate into TMT-labeled cross-linked HEK293T lysate using a defined mixing scheme. Using this benchmarking dataset, we assess the efficacy of cross-link identification and accuracy of cross-link quantification using different MS acquisition strategies. For identification, we compare various MS2- and MS3-based XL-MS methods, and optimize stepped HCD energies for TMT-labeled cross-links. We observed a need for notably higher fragmentation energies compared to unlabeled cross-links. For quantification, we assess the quantification accuracy and dispersion of MS2-, MS3- and synchronous precursor selection-MS3-based methods. We show that a stepped HCD-MS2 method with stepped collision energies 36-42-48 provides a vast number of quantifiable cross-links with high quantification accuracy. This widely applicable method paves the way for multiplexed quantitative PPI characterization from complex biological systems.
Project description:An X-ray crystallography structure of the trimeric nuclear export complex of CRM1, SNP1 and RanGTP is available on the PDB database with the ID 3GJX. To get an XL-MS dataset of a clean sample of a stable protein complex for benchmarking purposes, another sample from the protein expression, purification and complex assembly employed in the crystallography study was also cross-linked and analyzed by mass spectrometry. The tools OpenPepXL, pLink 2, Kojak, StavroX, XiSearch and xQuest were used to analyze this dataset with comparable search settings.
Project description:Sustained smouldering, or low grade, activation of myeloid cells is a common hallmark of several chronic neurological diseases, including multiple sclerosis (MS). Distinct metabolic and mitochondrial features guide the activation and the diverse functional states of myeloid cells. However, how these metabolic features act to perpetuate neuroinflammation is currently unknown. Using a multiomics approach, we identified a new molecular signature that perpetuates the activation of myeloid cells through mitochondrial complex I (CI) activity driving reverse electron transport (RET) and the production of reactive oxygen species (ROS). Blocking CI activity in pro-inflammatory microglia protected the central nervous system (CNS) against neurotoxic damage and improved functional outcomes in animal disease models in vivo. Our data show that mitochondrial CI in microglia is a potential new therapeutic target to foster neuroprotection in smouldering inflammatory CNS disorders.
Project description:This dataset has been generated to identify promoter regions in the chicken genome to distinguish active and inactive genes. We focussed our analyses on actively transcribed tRNA and mRNAs genes. Chicken liver was cross-linked to capture histone-DNA interactions. Sequencing libraries were prepared from H3K4me3-precipitated DNA and input control.