Molecular & Cellular Proteomics最新文献

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Donald Hunt has made seminal contributions to the fields of proteomics, immunology, epigenetics, and glycobiology. The foundation of every important work to come out of the Hunt Laboratory is de novo peptide sequencing. For decades, he taught hundreds of students, postdocs, engineers, and scientists to directly interpret mass spectral data. To honor his legacy and ensure that the art of de novo sequencing is not lost, we have adapted his teaching materials into "The Hunt Lab Guide to De Novo Peptide Sequence Analysis by Tandem Mass Spectrometry". In addition to the de novo sequencing tutorials, we present two freely available software tools that facilitate manual interpretation of mass spectra and validation of search results. The first, "Hunt Lab Peptide Fragment Calculator", calculates precursor and fragment mass-to-charge ratios for any peptide. The second program, "Predator Protein Fragment Calculator", was inspired in part by the fragment calculator developed in the Hunt Lab. Its capabilities are enhanced to facilitate interpretation of mass spectral data derived from intact proteins. We hope that the combination of these educational tools will continue to benefit students and researchers by empowering them to interpret data on their own.

Arriving at the University of Virginia in the autumn of 1969, Donald Hunt began his 50+ year career in academics with the study of organometallic chemistry, on which he had done his PhD thesis work, and mass spectrometry, to which he was introduced while a postdoc in Klaus Biemann's laboratory at the Massachusetts Institute of Technology. In the 1970s, Hunt's lab pioneered the use of negative chemical ionization (CI) to enhance sensitivity for studying organic molecules, developed a system for simultaneously obtaining positive and negative CI spectra to augment structure elucidation, and built a prototype triple quadrupole instrument so effective at collisional dissociation that its commercial counterpart became the analytical instrument of choice for mixture analysis for the next decade and beyond. Foreseeing that the future lay in the analysis of biological molecules, by the end of the decade Hunt shifted his focus to peptides. The analysis of protein fragments had suddenly become more accessible thanks to the advent of the triple quadrupole and Barber's introduction of fast atom bombardment. As the 1980s began and Hunt and his team sought to pursue larger and larger pieces of proteins, his attention turned to the development of mass spectrometers with greater mass range. While recounting their memories of these events, several of Hunt's students and colleagues pay tribute to his support for them as individuals, as well as to his infectious enthusiasm for scientific endeavors that he so generously shared.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.

De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry-based proteomics. Many methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) of de novo peptide-spectrum matches. Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide-sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species) and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide-sequencing methods on target-decoy spectra and to estimate and validate their FDRs. Our FDR estimation provides valuable information to assess the reliability of new peptides identified by de novo sequencing tools, especially when no ground-truth information is available to evaluate their accuracy. The FDR estimation can also be used to evaluate the capability of de novo peptide sequencing tools to distinguish between de novo peptide-spectrum matches and random matches. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide-sequencing methods and how their performances depend on specific applications and the types of data.

Inherited retinal degenerations (IRDs) are a leading cause of blindness among the population of young people in the developed world. Approximately half of IRDs initially manifest as gradual loss of night vision and visual fields, characteristic of retinitis pigmentosa (RP). Due to challenges in genetic testing, and the large heterogeneity of mutations underlying RP, targeted gene therapies are an impractical largescale solution in the foreseeable future. For this reason, identifying key pathophysiological pathways in IRDs that could be targets for mutation-agnostic and disease-modifying therapies (DMTs) is warranted. In this study, we investigated the retinal proteome of three distinct IRD mouse models, in comparison to sex- and age-matched wild-type mice. Specifically, we used the Pde6β^Rd10 (rd10) and Rho^P23H/WT (P23H) mouse models of autosomal recessive and autosomal dominant RP, respectively, as well as the Rpe65^-/- mouse model of Leber's congenital amaurosis type 2 (LCA2). The mice were housed at two distinct institutions and analyzed using LC-MS in three separate facilities/instruments following data-dependent and data-independent acquisition modes. This cross-institutional and multi-methodological approach signifies the reliability and reproducibility of the results. The large-scale profiling of the retinal proteome, coupled with in vivo electroretinography recordings, provided us with a reliable basis for comparing the disease phenotypes and severity. Despite evident inflammation, cellular stress, and downscaled phototransduction observed consistently across all three models, the underlying pathologies of RP and LCA2 displayed many differences, sharing only four general KEGG pathways. The opposite is true for the two RP models in which we identify remarkable convergence in proteomic phenotype even though the mechanism of primary rod death in rd10 and P23H mice is different. Our data highlights the cAMP and cGMP second-messenger signaling pathways as potential targets for therapeutic intervention. The proteomic data is curated and made publicly available, facilitating the discovery of universal therapeutic targets for RP.

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold standard requires 24 h to 48 h of bacteria culture prior to MALDI-TOF species identification, we propose a culture-free workflow, enabling bacterial identification and quantification in less than 4 h using 1 ml of urine. After rapid and automatable sample preparation, a signature of 82 bacterial peptides, defined by machine learning, was monitored in LC-MS, to distinguish the 15 species causing 84% of the UTIs. The combination of the sensitivity of the SRM mode on a triple quadrupole TSQ Altis instrument and the robustness of capillary flow enabled us to analyze up to 75 samples per day, with 99.2% accuracy on bacterial inoculations of healthy urines. We have also shown our method can be used to quantify the spread of the infection, from 8 × 10⁴ to 3 × 10⁷ CFU/ml. Finally, the workflow was validated on 45 inoculated urines and on 84 UTI-positive urine from patients, with respectively 93.3% and 87.1% of agreement with the culture-MALDI procedure at a level above 1 × 10⁵ CFU/ml corresponding to an infection requiring antibiotherapy.

Well-controlled metabolism is associated with high-quality oocytes and optimal development of a healthy embryo. However, the metabolic framework that controls mammalian oocyte growth remains unknown. In the present study, we comprehensively depict the temporal metabolic dynamics of mouse oocytes during in vivo growth through the integrated analysis of metabolomics and proteomics. Many novel metabolic features are discovered during this process. Of note, glycolysis is enhanced, and oxidative phosphorylation capacity is reduced in the growing oocytes, presenting a Warburg-like metabolic program. For nucleotide biosynthesis, the salvage pathway is markedly activated during oocyte growth, whereas the de novo pathway is evidently suppressed. Fatty acid synthesis and channeling into phosphoinositides are specifically elevated in oocytes accompanying primordial follicle activation; nevertheless, fatty acid oxidation is reduced in these oocytes simultaneously. Our data establish the metabolic landscape during in vivo oocyte growth and serve as a broad resource for probing mammalian oocyte metabolism.

Protein N-acetylation is one of the most abundant co- and post-translational modifications in eukaryotes, extending its occurrence to chloroplasts within vascular plants. Recently, a novel plastidial enzyme family comprising eight acetyltransferases that exhibit dual lysine and N-terminus acetylation activities was unveiled in Arabidopsis. Among these, GNAT1, GNAT2, and GNAT3 reveal notable phylogenetic proximity, forming a subgroup termed NAA90. Our study focused on characterizing GNAT1, closely related to the state transition acetyltransferase GNAT2. In contrast to GNAT2, GNAT1 did not prove essential for state transitions and displayed no discernible phenotypic difference compared to the wild type under high light conditions, while gnat2 mutants were severely affected. However, gnat1 mutants exhibited a tighter packing of the thylakoid membranes akin to gnat2 mutants. In vitro studies with recombinant GNAT1 demonstrated robust N-terminus acetylation activity on synthetic substrate peptides. This activity was confirmed in vivo through N-terminal acetylome profiling in two independent gnat1 knockout lines. This attributed several acetylation sites on plastidial proteins to GNAT1, reflecting a subset of GNAT2's substrate spectrum. Moreover, co-immunoprecipitation coupled with mass spectrometry revealed a robust interaction between GNAT1 and GNAT2, as well as a significant association of GNAT2 with GNAT3 - the third acetyltransferase within the NAA90 subfamily. This study unveils the existence of at least two acetyltransferase complexes within chloroplasts, whereby complex formation might have a critical effect on the fine-tuning of the overall acetyltransferase activities. These findings introduce a novel layer of regulation in acetylation-dependent adjustments in plastidial metabolism.

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) probes dynamic motions of proteins by monitoring the kinetics of backbone amide deuteration. Dynamic regions exhibit rapid HDX, while rigid segments are more protected. Current data readouts focus on qualitative comparative observations (such as "residues X to Y become more protected after protein exposure to ligand Z"). At present, it is not possible to decode HDX protection patterns in an atomistic fashion. In other words, the exact range of protein motions under a given set of conditions cannot be uncovered, leaving space for speculative interpretations. Amide back exchange is an under-appreciated problem, as the widely used (m-m₀)/(m₁₀₀-m₀) correction method can distort HDX kinetic profiles. Future data analysis strategies require a better fundamental understanding of HDX events, going beyond the classical Linderstrøm-Lang model. Combined with experiments that offer enhanced spatial resolution and suppressed back exchange, it should become possible to uncover the exact range of motions exhibited by a protein under a given set of conditions. Such advances would provide a greatly improved understanding of protein behavior in health and disease.