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Nature Oct 2023The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we...
The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics.
Topics: Humans; ABO Blood-Group System; Biological Specimen Banks; Blood Proteins; COVID-19; Databases, Factual; Drug Discovery; Epistasis, Genetic; Fucosyltransferases; Genetic Predisposition to Disease; Genomics; Health; Plasma; Proprotein Convertase 9; Proteome; Proteomics; Public-Private Sector Partnerships; Quantitative Trait Loci; United Kingdom; Galactoside 2-alpha-L-fucosyltransferase
PubMed: 37794186
DOI: 10.1038/s41586-023-06592-6 -
Cell Nov 2023Neurons build synaptic contacts using different protein combinations that define the specificity, function, and plasticity potential of synapses; however, the diversity...
Neurons build synaptic contacts using different protein combinations that define the specificity, function, and plasticity potential of synapses; however, the diversity of synaptic proteomes remains largely unexplored. We prepared synaptosomes from 7 different transgenic mouse lines with fluorescently labeled presynaptic terminals. Combining microdissection of 5 different brain regions with fluorescent-activated synaptosome sorting (FASS), we isolated and analyzed the proteomes of 18 different synapse types. We discovered ∼1,800 unique synapse-type-enriched proteins and allocated thousands of proteins to different types of synapses (https://syndive.org/). We identify shared synaptic protein modules and highlight the proteomic hotspots for synapse specialization. We reveal unique and common features of the striatal dopaminergic proteome and discover the proteome signatures that relate to the functional properties of different interneuron classes. This study provides a molecular systems-biology analysis of synapses and a framework to integrate proteomic information for synapse subtypes of interest with cellular or circuit-level experiments.
Topics: Animals; Mice; Brain; Mice, Transgenic; Proteome; Proteomics; Synapses; Synaptosomes
PubMed: 37918396
DOI: 10.1016/j.cell.2023.09.028 -
Nature Oct 2023High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between... (Comparative Study)
Comparative Study
High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.
Topics: Humans; Africa; Asia, Southern; Biological Specimen Banks; Blood Proteins; Datasets as Topic; Disease Susceptibility; Genome, Human; Genomics; Genotype; Iceland; Ireland; Phenotype; Plasma; Proteome; Proteomics; Quantitative Trait Loci; United Kingdom
PubMed: 37794188
DOI: 10.1038/s41586-023-06563-x -
Genome Biology Jun 2023The pathophysiological causes of kidney disease are not fully understood. Here we show that the integration of genome-wide genetic, transcriptomic, and proteomic...
BACKGROUND
The pathophysiological causes of kidney disease are not fully understood. Here we show that the integration of genome-wide genetic, transcriptomic, and proteomic association studies can nominate causal determinants of kidney function and damage.
RESULTS
Through transcriptome-wide association studies (TWAS) in kidney cortex, kidney tubule, liver, and whole blood and proteome-wide association studies (PWAS) in plasma, we assess for effects of 12,893 genes and 1342 proteins on kidney filtration (glomerular filtration rate (GFR) estimated by creatinine; GFR estimated by cystatin C; and blood urea nitrogen) and kidney damage (albuminuria). We find 1561 associations distributed among 260 genomic regions that are supported as putatively causal. We then prioritize 153 of these genomic regions using additional colocalization analyses. Our genome-wide findings are supported by existing knowledge (animal models for MANBA, DACH1, SH3YL1, INHBB), exceed the underlying GWAS signals (28 region-trait combinations without significant GWAS hit), identify independent gene/protein-trait associations within the same genomic region (INHBC, SPRYD4), nominate tissues underlying the associations (tubule expression of NRBP1), and distinguish markers of kidney filtration from those with a role in creatinine and cystatin C metabolism. Furthermore, we follow up on members of the TGF-beta superfamily of proteins and find a prognostic value of INHBC for kidney disease progression even after adjustment for measured glomerular filtration rate (GFR).
CONCLUSION
In summary, this study combines multimodal, genome-wide association studies to generate a catalog of putatively causal target genes and proteins relevant to kidney function and damage which can guide follow-up studies in physiology, basic science, and clinical medicine.
Topics: Animals; Renal Insufficiency, Chronic; Cystatin C; Proteome; Transcriptome; Creatinine; Genome-Wide Association Study; Proteomics; Kidney
PubMed: 37365616
DOI: 10.1186/s13059-023-02993-y -
Developmental Cell Aug 2023Age-associated impairments in adult stem cell functions correlate with a decline in somatic tissue regeneration capacity. However, the mechanisms underlying the...
Age-associated impairments in adult stem cell functions correlate with a decline in somatic tissue regeneration capacity. However, the mechanisms underlying the molecular regulation of adult stem cell aging remain elusive. Here, we provide a proteomic analysis of physiologically aged murine muscle stem cells (MuSCs), illustrating a pre-senescent proteomic signature. During aging, the mitochondrial proteome and activity are impaired in MuSCs. In addition, the inhibition of mitochondrial function results in cellular senescence. We identified an RNA-binding protein, CPEB4, downregulated in various aged tissues, which is required for MuSC functions. CPEB4 regulates the mitochondrial proteome and activity through mitochondrial translational control. MuSCs devoid of CPEB4 induced cellular senescence. Importantly, restoring CPEB4 expression rescued impaired mitochondrial metabolism, improved geriatric MuSC functions, and prevented cellular senescence in various human cell lines. Our findings provide the basis for the possibility that CPEB4 regulates mitochondrial metabolism to govern cellular senescence, with an implication of therapeutic intervention for age-related senescence.
Topics: Aged; Animals; Humans; Mice; Aging; Cellular Senescence; Muscle, Skeletal; Muscles; Proteome; Proteomics; RNA-Binding Proteins
PubMed: 37321216
DOI: 10.1016/j.devcel.2023.05.012 -
Molecular & Cellular Proteomics : MCP Jul 2023Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the...
Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification, and quantification, making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In in a previous review in 2017, we described technological and conceptual limitations that had held back success. We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification, and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. Shorter gradients, new scan modes, and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multiprotein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.
Topics: Humans; Proteomics; Mass Spectrometry; Biomarkers; Proteome; Body Fluids
PubMed: 37209816
DOI: 10.1016/j.mcpro.2023.100577 -
Nature Methods Oct 2023Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been...
Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.
Topics: Animals; Mice; Proteome; Mass Spectrometry; Proteomics; Laser Capture Microdissection
PubMed: 37783884
DOI: 10.1038/s41592-023-02007-6 -
Nature Chemical Biology Nov 2023Covalent chemistry represents an attractive strategy for expanding the ligandability of the proteome, and chemical proteomics has revealed numerous electrophile-reactive...
Covalent chemistry represents an attractive strategy for expanding the ligandability of the proteome, and chemical proteomics has revealed numerous electrophile-reactive cysteines on diverse human proteins. Determining which of these covalent binding events affect protein function, however, remains challenging. Here we describe a base-editing strategy to infer the functionality of cysteines by quantifying the impact of their missense mutation on cancer cell proliferation. The resulting atlas, which covers more than 13,800 cysteines on more than 1,750 cancer dependency proteins, confirms the essentiality of cysteines targeted by covalent drugs and, when integrated with chemical proteomic data, identifies essential, ligandable cysteines in more than 160 cancer dependency proteins. We further show that a stereoselective and site-specific ligand targeting an essential cysteine in TOE1 inhibits the nuclease activity of this protein through an apparent allosteric mechanism. Our findings thus describe a versatile method and valuable resource to prioritize the pursuit of small-molecule probes with high function-perturbing potential.
Topics: Humans; Cysteine; Proteomics; Gene Editing; Proteome; Neoplasms; Nuclear Proteins
PubMed: 37783940
DOI: 10.1038/s41589-023-01428-w -
Cell Reports. Medicine Sep 2023Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global...
Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.
Topics: Humans; Metabolic Syndrome; Prognosis; Proteomics; Proteome; Machine Learning
PubMed: 37652016
DOI: 10.1016/j.xcrm.2023.101172 -
Molecular Systems Biology Sep 2023Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic...
Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.
Topics: Humans; Proteome; Melanoma; Proteomics; Precision Medicine; Skin Neoplasms; Tumor Microenvironment
PubMed: 37602975
DOI: 10.15252/msb.202211503