Molecular & Cellular Proteomics最新文献

Understanding neuronal differentiation and function requires precise proteomic characterization of distinct neuron types, yet existing methods face challenges in specificity and sensitivity. Here, we combine Methionine Analog-based Cell-Specific Proteomics and Interactomics (MACSPI) and SILAC to achieve neuron type-specific proteomic profiling in Caenorhabditis elegans. We demonstrate the utility of the methods by profiling and comparing the proteomes of two neuron types, namely the eight dopaminergic neurons (DA) and the six touch receptor neurons (TRNs). By expressing in these neurons an engineered methionyl-tRNA synthetase that can attach a methionine analog with a chemical handle to the synthesizing proteins, we chemically label the proteomes of specific neurons in complex tissues and isolate the labeled proteins from the whole-animal lysates through click chemistry and affinity purification. Thus, our approach does not require physical isolation of the neurons through cell sorting. Quantitative mass spectrometry studies through SILAC enable proteomic profiling and comparison of DA and TRNs and reveal distinct functional signatures between these two neuron types, with DA neurons showing enrichment in synaptic and metabolic pathways, while TRNs were characterized by cytoskeletal and signaling components. Moreover, we observed a weak correlation between protein abundance and mRNA levels, underscoring the importance of proteomic measurements. Our study establishes MACSPI-SILAC as a versatile platform for cell-type-specific proteomics in multicellular organisms, bridging a critical gap between transcriptomic and functional analyses. The proteomic datasets provide a resource for exploring the mechanisms of neuronal fate specification and cellular differentiation.

MiPEPs are microproteins encoded by primary transcripts of microRNAs (pri-miRNAs). Initially identified in plants, we recently characterized a miPEP in Drosophila melanogaster, named miPEP8, which is involved in the regulation of wing size. However, mechanisms at play are unknown. In the present study, we take advantage of the Drosophila cell line Schneider 2 (S2) to further investigate miPEP8 function at the molecular level. Overexpressing miPEP8 in S2 cells induced a reduction of cell size as well as an increase of the proportion of cells in the G1 phase of the cell cycle and a decrease of the autophagic flux. A proteomics analysis revealed that miPEP8 overexpression in S2 cells induces the upregulation of several proteins including the autophagosome cargo protein ref(2)P (the orthologue of the human p62/Sequestosome 1 protein). The interactome of miPEP8 was generated and revealed interactions between this miPEP8 and the mTORC1/autophagy pathway. Bioinformatics analysis identified a short linear motif (SLiM) on miPEP8 sequence. Mutation of this SLiM prevented the interaction between ref(2)P/p62 and miPEP8. Mutation of the SLiM also reverted the smaller cell size phenotype observed when overexpressing miPEP8 in S2 cells. RNA interference targeting ref(2)P/p62 reversed the cell size phenotype, suggesting that this protein plays a role in the regulation of cell size in Drosophila. Finally, the cell size phenotype was also observed in vivo on wings of flies either mutated or overexpressing miPEP8.

Loss of the protein scaffold Coiled-coil and C2 domain containing 1A (CC2D1A) leads to intellectual disability (ID), autism spectrum disorder (ASD), and other neurodevelopmental presentations in humans. CC2D1A interactions have been studied in different cell lines proposing diverse roles in endolysosomal maturation and intracellular signaling, but the composition and function of the CC2D1A interactome remain poorly understood, especially in the brain. We performed comprehensive proteomic analyses to characterize CC2D1A binding partners, first comparing immunoprecipitations with three different anti-CC2D1A antibodies in HEK293 cells and then probing the mouse hippocampus. In HEK cells, Gene Ontology (GO) analysis revealed broad interaction networks in the nucleus, mitochondrion, and cytoplasmic vesicles with a variety of functions unified by the best characterized CC2D1A interactor, the ESCRT III component CHMP4B, and reflecting the pleiotropic role of CC2D1A in membrane trafficking and protein signaling. In the hippocampus, using stringent criteria, we identified 41 high-confidence interactors in addition to CHMP4B revealing roles for protein translation, cytoskeletal organization, and synaptic function. The HEK studies had also pointed to CC2D1B, the only paralog of CC2D1A, as an interactor. We confirmed that not only the two proteins can bind in the brain, but also localize in different synaptic compartments, showing that CC2D1A is uniquely enriched in the post-synapse. This supports a unique function of CC2D1A in regulation of synaptic transmission that could explain the more severe cognitive deficits in humans and mice upon its loss. To our knowledge these findings provide the most comprehensive characterization of the CC2D1A interactome to date, elucidating novel, multifaceted, and dynamic cellular functions, providing potential implications for its role in neurodevelopmental disorders.

Antimicrobial resistance (AMR) is an increasing challenge for the therapy of bacterial infections. Currently, patient treatment is guided by antimicrobial susceptibility testing (AST) using phenotypic assays and species identification by MALDI-ToF biotyping. Bacterial phenotype prediction using omics technologies could offer several advantages over current diagnostic methods. It would allow species identification and AST to be combined in a single measurement, it would eliminate the need for secondary cultivation and could enable the prediction of phenotypes beyond AMR, such as virulence. In this study, the potential of proteomics for clinical microbiology was evaluated in an analysis of 126 clinical isolates covering 16 species, including all ESKAPE genera and 29 of the most common AMR gene families. For this purpose, a flexible workflow was developed, which enables reporting of the AMR phenotype and the species of primary cultures within 2h. Proteomics provided high specificity (100%) and sensitivity (94.4%) for AMR detection, while allowing species identification from very large sequence databases with high accuracy. The results show that proteomics is well-suited for phenotyping clinical bacterial isolates and has the potential to become a valuable diagnostic tool for clinical microbiology in the future.

The trematode Fasciola hepatica has a complex life cycle involving two hosts. Infection of the definitive host, mainly livestock and humans, occurs through ingestion of the infective stage, the metacercariae, which excyst in the duodenum and release newly excysted juveniles (FhNEJ). These FhNEJ first interact with small intestinal epithelial cells (SIEC), penetrate across the intestinal wall, through the peritoneum, migrating over mesothelial cells (MC) towards the liver and the biliary tree, where they develop into adult worms and establish chronically. We recently described the in vitro interactions of FhNEJ with SIEC at the proteomic level. Here, we extend this analysis to FhNEJ interactions with MC and hepatic stellate cells (HSC) using a single cell type (SiCT) in vitro model combined with SWATH-MS-based proteomics. However, a weak proteomic response was observed in both the parasite and MC or HSC upon direct co-incubation, with no clear signatures of targeted immune or structural activation. Based on these findings, we developed a sequential cell type (SeCT) in vitro model in which FhNEJ were first incubated with SIEC and then with MC, to assess whether a specific sequence of host cell contacts is required to elicit detectable proteomic responses. This approach revealed a higher number of differentially expressed proteins upon interaction than in the SiCT in vitro model, particularly in the parasite, reflecting increased activity during cell-to-cell transitions, including processes such as proteolysis, which likely support tissue migration and host interaction. The results indicate that FhNEJ exhibit a dynamic response to host cell specific interactions, with early activation following SIEC interaction and subsequent attenuation after contact with MC, notably affecting focal adhesion pathways in the latter cell type. These findings support the idea that sequential host cell encounters are necessary for effective recognition and interaction by the FhNEJ.

Spatially resolved omics enable the discovery of tissue organization of biological or clinical importance. Despite the existence of several methods, performing a rational analysis including multiple algorithms while integrating different conditions such as clinical data is still not trivial. To make such investigations more accessible, we developed mosna, a Python package to analyze spatial omics data in integration with clinical or biological data, providing insight on cell interaction patterns or tissue architecture. mosna is compatible with all spatial omics techniques, it leverages tysserand to build accurate spatial networks, and is compatible with Squidpy. It proposes an analysis pipeline, in which increasingly complex features computed at each step with either the mosna- algorithms or others can be explored in integration with clinical data. The approach produces easy-to-use descriptive statistics and data visualization, while seamlessly training machine learning models and identifying variables with the most predictive power. mosna can take as input any dataset produced by spatial omics methods, including sub-cellular resolved transcriptomics (MERFISH, seqFISH, Xenium) and proteomics (CODEX, MIBI-TOF, low-plex immuno-fluorescence), as well as spot-based spatial transcriptomics (10x Visium, Slide-seq, Stereo-seq). Integration with experimental metadata or clinical data is adapted to binary conditions, such as biological treatments or response status of patients, and to survival data. We demonstrate the proposed analysis pipeline on two spatially resolved proteomic datasets and a spatial transcriptomics dataset containing either binary response to immunotherapy or survival data, and we assess the performance of the proposed niche discovering method in a manually annotated spatial transcriptomic dataset. mosna identifies features describing cellular composition and spatial patterns that can provide biological insight regarding factors that affect response to immunotherapies or survival. AVAILABILITY AND IMPLEMENTATION: mosna is made publicly available to the community, together with relevant documentation at https://mosna-documentation.readthedocs.io/en/latest/index.html and tutorials implemented as Jupyter notebooks to reproduce the result at https://github.com/AlexCoul/mosna.

A plethora of studies suggest that a high-fat diet in combination with a high amyloid load causes synaptic insulin resistance and is a risk factor for Alzheimer's disease. Our understanding of the underlying mechanisms is still fragmented. To gain new insights, we conducted integrated proteomic and phosphoproteomic profiling of hippocampal synaptosomes from wild-type and a transgenic mouse line with a high amyloid load (heterozygous TBA2.1 mice) that show no overt signs of neurodegeneration and dementia. Mice were fed with a regular or high-fat diet. Data-independent acquisition quantified over 5,400 proteins, revealing a stable synaptic proteome across conditions. However, the combination of high amyloid load and high-fat diet triggered coordinated remodeling of lipid metabolism pathways, particularly mitochondrial and peroxisomal fatty acid catabolism. Phosphoproteomic analysis showed pronounced activation of lipid- and stress-responsive kinases, including PKC-α, along with increased inhibitory phosphorylation of insulin receptor substrates (IRS1/2). In vitro experiments indicate that blocking PKC-α indeed prevents synaptic insulin resistance in primary neurons. The findings suggest that this proteomic workflow, combined with kinase pathway analysis, can reveal nodal points for interventions in a complex disease state with a trajectory to Alzheimer's disease.

The disintegrin metalloprotease ADAM9 is a cell-surface protease that can shed the ectodomain of membrane protein substrates. Dysregulated ADAM9 activity has been implicated in several diseases such as solid tumors, autoimmunity, inflammatory diseases, and COVID-19. Despite its importance, the substrates and targets of ADAM9 in normal and pathological processes are poorly understood. Here, we developed an integrative proteotranscriptomics approach to systematically identify the transcriptional and post-transcriptional targets of ADAM9 in HCT116 cells, which have a stable diploid karyotype suitable for omics analyses. Using this approach, we uncovered major signaling pathways downstream of ADAM9, including the oncogenic mTOR pathway and the tumor suppressor FOXO pathway. We also identified several direct and indirect substrates for ADAM9, which may mediate the pathophysiological roles of this protease. This study provides new mechanistic insights into the function of ADAM9 as well as a method that can be applied to other membrane proteases.

Glycosaminoglycans (GAGs) are linear, negatively charged polysaccharides composed of repeating disaccharide units. Heparan sulfate (HS) and chondroitin sulfate (CS) are highly sulfated GAG classes, ubiquitously expressed in mammalian tissues, that play critical roles in cellular signaling, tissue homeostasis, and disease progression. Aside from their biological importance, the structural analysis of HS and CS remains limited to bulk tissue analysis due to their extensive heterogeneity, structural complexity, and the presence of isomers and epimers. In this work, we developed an integrated workflow combining laser microdissection (LMD), hydrophilic interaction liquid chromatography (HILIC), and cyclic ion mobility mass spectrometry (cIM-MS) for the identification and quantification of HS and CS disaccharides from small-scale and spatially resolved mouse brain tissue sections. Through sequential enzymatic digestion of HS and CS chains from the same sample, we profiled not only the common disaccharides that serve as structural signatures for HS and CS, but also rarely detected HS disaccharides containing saturated uronic acid residues, as well as lyase-resistant 3-O-sulfated HS tetrasaccharides. HILIC enabled the separation of HS and CS disaccharides based on their composition and hydrophilicity, while cIM-MS further enhanced the resolution of positional isomers. Quantitative analysis using linear calibration curves revealed disaccharide abundances in small-scale tissue sections collected by LMD. Overall, our finding highlighted the merit of the LMD-HILIC-cIM-MS workflow for HS and CS analysis in spatial GAGomics and its potential for biomarker discovery and therapeutic application studies.

Plants perceive mechanical forces through specialized phosphosignaling networks, yet how they are correlated with gravity force signaling remains unclear. To unravel the components of gravity force signalling, SILIA-based phosphoproteomics was performed on both 20s multiple inversion-treated and 30s gravistimulated aerial organs of Arabidopsis and has identified 2,733 and 2,878 phosphoproteins, respectively. Phosphoproteomic quantitation identified 34 and 52 significantly regulated phosphoprotein groups from Inversion and Gravistimulation, respectively. The Inversion-specific phosphoproteins, corresponding to the initial calcium code triggered by gravistimulation, might collectively mediate calcium signals sensed by EF-hand proteins, transduced by CPK1, and mediated by calmodulin-interacting proteins, which probably intersect with the receptor-like kinase(s)-initiated MAPK cascades via RAF15 and MKK1/2 kinases to induce gravitropic response. The Gravistimulation-specific phosphoproteins, associated with the secondary calcium code induced by gravistimulation, have the theme functions in Ca²⁺ signaling/homeostasis (ACA8, ZAC, IQD2, ANNAT1), membrane vesicle trafficking (ABCG36/C14, ARF-GAP8) and lipid signaling (PIP5K8/9), supporting PIN protein/auxin molecule transport, and auxin/stress signal transduction (TPR1), orchestrating responses environmental cues like physical force signals. Spatiotemporal analysis using immunoblots validation confirmed both treatments-associated phosphosites, pS108-PATL3 and pS107-TREPH2, as well as the Inversion-specific pS1145-ATEH2, with stem-specific phosphorylation enhancement. Crucially, phosphorylation on these representative phosphosites exhibited force-discriminatory responses. Functional validation has demonstrated the integrin-like protein GREPH1​ as a key regulator of gravitropism, with its mutants showing reduced inflorescence stem gravicurvature. Accelerated hyperphosphorylation on both pS107-TREPH2 and pS1145-ATEH2 phosphosites in greph1 mutant peaked at 20s - 50s while in WT plant the hyperphosphorylation of these phosphosites lasted from 20s to 2hr. These results established a stem-enriched unique phosphorylation for gravity force discrimination, with GREPH1 modulating spatiotemporal dynamics of some phosphoproteins and shoot gravicurvature and being a reminiscent receptor to the sediment plastid.