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Record W7022823391

Session 4

2017· article· en· W7022823391 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsnot available
Fundersnot available
KeywordsProteogenomicsContext (archaeology)HyporeflexiaLimiting
DOInot available

Abstract

fetched live from OpenAlex

7.1 On the hunt for cancer neoantigens: is mass spectrometry the solution? Joshua Elias(1), Niclas Olsson(1), Michael Khodadoust(1), Lisa Wagar(1), Kavya Swaminathan(1), Michael R. Green(2), Mark M. Davis(1), Ron Levy(1), Ash A. Alizadeh(1) (1)Stanford University, Stanford, CA, USA; (2)University of Nebraska, Omaha, NE, USA Immunotherapies targeting cancer-specific antigens (“neoantigens”) presented by Major Histocompatibility Complexes (MHC) have high potential for improving rates of long-term, disease-free survival. Immunization against cancer neoantigens predicted from tumor mutations has shown early success in melanoma treatment, and promises a new era of effective therapies with greatly reduced adverse responses. Despite these positive results, it remains unclear whether the same approach will apply to tumors with much lower mutation burdens than melanoma. We sought to identify cancer neoantigens from one such cancer type, mantle cell lymphoma. We undertook an integrated genomic and proteomic strategy which interrogated antigen peptides presented by MHC-class I and class II. Peptides bound to MHC were purified via immunoprecipitation followed by identification using mass spectrometry. Mass spectra were searched against patient-specific proteome databases generated by whole exome sequencing and targeted immunoglobulin gene sequencing. This approach was applied to systematically characterize 36,500 immunopeptides from 17 patients' tumor specimens. Despite identifying hundreds of somatic mutations from these specimens by exome sequencing, we could not reliably measure any as presented antigens. We noted, however, 52 neoantigenic peptides derived from the lymphoma immunoglobulin (Ig) heavy or light chain variable regions were almost exclusively presented by MHC-II. T-cells recovered from two patients were specific for Ig-derived neoantigens. Following ex vivo activation and expansion, the T-cells were remarkably able to mediate killing of autologous lymphoma cells. These results demonstrate that combining MHC isolation, peptide identification, and exome sequencing is an effective platform to uncover tumor neoantigens. Application of this strategy to MCL implicates immunoglobulin neoantigens as targets for lymphoma immunotherapy, and suggests similar strategies could be possible in other types of lymphoma. 7.2 Improvement of sensitivity and comprehensiveness of proteomic analyses using a novel FAIMS interface Pierre Thibault(1), Sibylle Pfammatter(1), Eric Bonneil(1), Michael Belford(2), Satendra Prasad(2), Jean-Jacques Dunyach(2) (1)Institute for Research in Immunology and Cancer, Université de Montréal, Quebec, Canada; (2)ThermoFisher Scientific, Toronto, ON, Canada Despite remarkable advances in MS sensitivity and resolution, the depth of proteomic analyses is often limited by the overwhelming proportion of confounding background ions that compromise the identification and quantification of low abundance peptides. To extend the sensitivity of proteomic analyses, we developed a new high field asymmetric waveform ion mobility spectrometry (FAIMS) interface that can be coupled to the Orbitrap Tribrid mass spectrometers. The interface has several advantages over previous FAIMS devices including ease of operation, robustness, and high ion transmission. Replicate LC-FAIMS-MS/MS analyses (N>100) of on protein digests provided stable ion current over extended time periods with uniform peptide identification on more than 10,000 distinct peptides. LC-MS/MS analyses of tryptic digests of HEK293 cells performed with and without FAIMS enabled the identification of 12783 peptides (3017 proteins) and 6253 peptides (2197 proteins), respectively. Improvement in sensitivity enabled the identification low abundance peptides, and extended the limit of detection by one order of magnitude. The distribution of precursor ion fraction (PIF) for an isolation window of 1.2 Th ranged from 0.25 to 0.90 without FAIMS and from 0.67 to 0.99 for FAIMS. The reduced precursor ion co-selection observed using FAIMS resulted in a 2-fold gain in peptide identification, and 30% reduction in identification score values for a false discovery rate of 1% compared to non-FAIMS analyses. The reduction in chimeric MS/MS spectra using FAIMS also improved the precision and the number of quantifiable peptides when using isobaric labeling. We compared TMT-based quantitation for LC-MS/MS analyses performed using synchronous precursor selection (SPS) and LC-FAIMS-MS/MS to profile the temporal changes in protein abundance of HEK293 cells following heat shock for periods up to 10h. FAIMS provided a three-fold increase in the number of quantifiable peptides compared to non-FAIMS experiments (7754 peptides from 3800 proteins for FAIMS vs. 2038 peptides from 1583 proteins with SPS). LC-FAIMS-MS/MS analyses enabled the identification of 17 heat shock proteins (HSPs) showing a progressive in abundance over time compared to only 7 HSPs when the same analyses were performed using LC-MS/MS with SPS. Altogether, the enhancement in ion transmission and duty cycle of the new FAIMS interface extended the depth and comprehensiveness of proteomic analyses and improved the precision of quantitative measurements. 7.3 Dynamic proteome organization and host defense during viral infection Ileana M. Cristea Princeton University, Princeton, NJ, USA Infections with the various viral pathogens that are intrinsic to our ecosystem represent a major cause of human disease and death worldwide. Among the DNA viruses that infect humans, herpesviruses are some of the largest and most ancient viruses. Therefore, through their long co-evolution with hosts, these viruses have acquired sophisticated mechanisms to manipulate cellular pathways for effective viral replication and spread. The outcomes and pathogenicity of viral infections derive from the dynamic interactions between viruses and host cells, which function either to promote virus replication or host defense against invading pathogens. As a result, viral infection triggers an impressive range of proteome changes, including alterations in protein abundances, localizations, interactions, and posttranslational modifications. Mass spectrometry-based proteomics is uniquely positioned to reveal these dynamic temporal proteome changes, thereby significantly contributing to important findings in virology. This presentation will highlight the use of proteomic methods for addressing three main questions: 1) what host cell conditions allow herpesvirus infections to be permissive, 2) how the host immune system responds to the infection, and 3) what are the mechanisms of pathogen replication and transmission? Examples will be given from our studies of innate immune response upon infection with herpesviruses, as well as of mechanisms through which viruses remodel subcellular organelle function to suppress host defense and promote viral replication. Our quantitative mass spectrometry-based proteomics, live-cell imaging, optogenetics, and CRISPR-based cellular assays underscore the value of integrative approaches to uncover complex cellular responses against pathogens. 7.4 Structural Analysis of the 26S Proteasome Complex to Understand its Function and Regulation Lan Huang, Xiaorong Wang, Clinton Yu, Scott Rychnovsky University of California, Irvine, CA, USA The ubiquitin-proteasome system (UPS) represents the major pathway for regulated degradation of intracellular proteins in eukaryotes, which plays an important role in regulating many biological processes such as cell cycle progression, signal transduction and DNA repair. The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation, and is composed of two subcomplexes, 20S core particle and 19S regulatory particle. Dysregulation of proteasomal degradation has been implicated in many human diseases, and components in the UPS have become attractive therapeutic targets in recent years. Therefore, structural analysis of the human 26S proteasome complex not only will advance our understanding of its action and regulation mechanisms, but also may uncover potential targets for future therapeutics. However, characterization of the 26S proteasome complex using conventional structural tools has been difficult due to its dynamics and heterogeneity. Recently, cross-linking mass spectrometry (XL-MS) has emerged as a powerful and alternative structural tool for studying protein-protein interactions and elucidating architectures of protein complexes. In addition to capturing protein-protein interactions in cells, XL-MS experiments allow the identification of cross-linked peptides and define protein physical contacts at the amino-acid level resolution. The information obtained can be used to derive protein interaction network topology and structural models of protein complexes. Although successful, MS detection and unambiguous identification of cross-linked has been challenging. To advance XL-MS studies, we have developed a series of sulfoxide-containing MS-cleavable cross-linking reagents (e.g. DSSO) with various functionalities to facilitate the determination of protein interaction interfaces for structural elucidation of protein complexes1. These MS-cleavable reagents have allowed us to establish a common robust XL-MS workflow that enables fast and accurate identification of cross-linked peptides using multistage tandem mass spectrometry. These XL-MS strategies have been effectively employed to define interaction and structural dynamics of the human 26S proteasome and identified cross-links between proteasome subunits and their interacting proteins under different physiological conditions. Our results have provided structural details on how protein-protein interactions are involved in modulating proteasomal functions. The methodology presented here can easily be adopted for the study of other protein complexes in vitro and in vivo. Acknowledgment: This work is supported

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.223
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0020.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.7770.680

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.242
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
Has abstractyes

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