Immunopeptidomics: Reading the Immune Signal That Defines Self From Nonself

Thibault, Pierre; Perreault, Claude

doi:10.1016/j.mcpro.2022.100234

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…However, mass spectrometry is not always sufficient to define the full repertoire of small peptides loaded onto MHC molecules. Moreover, the proinflammatory environment of the spinal cord and the dysregulated protein metabolism of motor neurons in ALS may promote the activation of the IP and the membrane presentation of antigenic peptides recognized as non-self by CD8+ T cells, which then activates a cytotoxic autoimmune response [22][23][24][25]. Taken together, the data confirm that future analysis of the MHC-I immunopeptidomes from neurological cells in different cellular states and in diseased tissues is of great interest.…”

Section: Mhc Peptides Techniquementioning

confidence: 57%

“…Misfolded proteins that cannot be degraded by the constitutive proteasome are directed toward the INF-γ-activated IP [23]. Misfolded proteins in the motor neurons may trigger inflammation through the release of danger-associated molecular pattern molecules (DAMPs), including ROS, HMGB1 and HSPs, which activate glial and immune cells to produce inflammatory cytokines including IFN-γ [22,24]. IFN-γ may then induce the upregulation of the IP in the motor neurons.…”

Section: Misfolded Proteins In Neuronal Diseasementioning

confidence: 99%

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Proteomic Analysis of Human iPSC-Derived Neural Stem Cells and Motor Neurons Identifies Proteasome Structural Alterations

Álvarez,

Tirado-Herranz,

Alvarez-Palomo

et al. 2023

Cells

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Background: Proteins targeted by the ubiquitin proteasome system (UPS) are identified for degradation by the proteasome, which has been implicated in the development of neurodegenerative diseases. Major histocompatibility complex (MHC) molecules present peptides broken down by the proteasome and are involved in neuronal plasticity, regulating the synapse number and axon regeneration in the central or peripheral nervous system during development and in brain diseases. The mechanisms governing these effects are mostly unknown, but evidence from different compartments of the cerebral cortex indicates the presence of immune-like MHC receptors in the central nervous system. Methods: We used human induced pluripotent stem cells (iPSCs) differentiated into neural stem cells and then into motor neurons as a developmental model to better understand the structure of the proteasome in developing motor neurons. We performed a proteomic analysis of starting human skin fibroblasts, their matching iPSCs, differentiated neural stem cells and motor neurons that highlighted significant differences in the constitutive proteasome and immunoproteasome subunits during development toward motor neurons from iPSCs. Results: The proteomic analysis showed that the catalytic proteasome subunits expressed in fibroblasts differed from those in the neural stem cells and motor neurons. Western blot analysis confirmed the proteomic data, particularly the decreased expression of the β5i (PSMB8) subunit immunoproteasome in MNs compared to HFFs and increased β5 (PSMB5) in MNs compared to HFFs. Conclusion: The constitutive proteasome subunits are upregulated in iPSCs and NSCs from HFFs. Immunoproteasome subunit β5i expression is higher in MNs than NSCs; however, overall, there is more of a constitutive proteasome structure in MNs when comparing HFFs to MNs. The proteasome composition may have implications for motor neuron development and neurodevelopmental diseases that warrant further investigation.

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Section: Mhc Peptides Techniquementioning

confidence: 57%

Section: Misfolded Proteins In Neuronal Diseasementioning

confidence: 99%

Proteomic Analysis of Human iPSC-Derived Neural Stem Cells and Motor Neurons Identifies Proteasome Structural Alterations

Álvarez,

Tirado-Herranz,

Alvarez-Palomo

et al. 2023

Cells

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“…Different MHC-I alleles have different peptide-binding specificities, which include differences in binding motifs and peptide length distributions (Gfeller et al, 2018b; Tadros et al, 2023; Trolle et al, 2016). This results in distinct repertoires of MHC-I ligands in different individuals and different species (Gfeller, Liu, et al, 2023; Neefjes et al, 2011; Thibault & Perreault, 2022; Vyas et al, 2008). Over the last decade, mass spectrometry-based MHC-I peptidomics has emerged as the leading source of information about MHC-I binding specificities.…”

Section: Introductionmentioning

confidence: 99%

Predicting MHC-I ligands across alleles and species: How far can we go?

Tadros,

Racle,

Gfeller

2024

Preprint

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CD8+ T-cell activation is initiated by the recognition of epitopes presented on class I major histocompatibility complex (MHC-I) molecules. Identifying such epitopes is useful for molecular understanding of cellular immune responses and can guide the development of personalized vaccines for various diseases including cancer. Here, we capitalize on high-quality MHC-I peptidomics data available from different species and an expanded architecture of our MHC-I ligand predictor (MixMHCpred) to carefully explore how much predictions can be extrapolated to MHC-I alleles without known ligands. Our results reveal high prediction accuracy for most MHC-I alleles in human and in laboratory mouse strains, but significantly lower accuracy in other species. Our work further outlines some of the molecular determinants of MHC-I ligand predictions accuracy across alleles and species. Robust benchmarking on external data shows that the pan-allele version of MixMHCpred (v3.0) outperforms other state-of-the-art MHC-I ligand predictors and can be used for CD8+ T-cell epitope predictions.

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“…The Ribosome profiling (Ribo-seq) technique sequences ribosome-protected RNA fragments and thus enables the prediction of thousands of AltORFs with bioinformatics pipelines ( 16 , 17 ). Currently, mass spectrometry (MS) is considered as the only method that allows direct identification of AltProts ( 18 , 19 ). However, only tens to hundreds of AltProts per sample can be identified by MS ( 2 , 20 , 21 , 22 ).…”

mentioning

confidence: 99%

“…The big difference in the identification number between the two methods calls for urgent improvement on the MS-based methodologies to detect AltProts. The discovery of AltProts by MS is challenging partly due to their short length and interference from large canonical proteins ( 19 , 23 ). Another major obstacle is the lack of well-established AltProt databases ( 9 ).…”

mentioning

confidence: 99%