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Cited by 57Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsGenetic Associations and Epidemiology

Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping

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Suhre, Karsten, Venkataraman, Guhan Ram, Guturu, Harendra, Halama, Anna, Stephan, Nisha, Thareja, Gaurav, Sarwath, Hina, Motamedchaboki, Khatereh +4 more·2024·Nature Communications
DOI 10.1038/s41467-024-45233-yPMC PMC10837160

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Abstract

Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow ( Seer, Inc .) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis -encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.

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  • Platform-discordant signals for 2 proteins, APOL1 and PGLYRP2, had evidence of impacting specific peptides but not overall protein abundance in an MS study.

    “Platform-discordant signals for 2 proteins, APOL1 and PGLYRP2, had evidence of impacting specific peptides but not overall protein abundance in an MS study10 (Supplementary Data 4, 5, and 8);”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [10]

    The Results section on robust pQTLs (page not available) shows that a variant can affect a detected peptide without changing overall protein abundance, using F5 as an example. Our copy does not establish that this occurred for the two named proteins, APOL1 and PGLYRP2; searches for both names found no supporting passage. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Recent MS-based pQTL studies have suggested that some pQTL may reflect isoform differences.

    “Recent MS-based pQTL studies10,18 have further suggested that some pQTL may reflect isoform differences (although these studies are limited in number and generally encompass fewer proteins/participants).”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [10]

  • Comparing affinity-based pQTL to pQTL identified in MS studies may facilitate signal interpretation.

    “While MS-based pQTL studies are relatively limited in size and number (with some of the broadest studies, used for pQTL comparison here, currently including 1260 Americans and 1980 proteins18, or 325 participants and ~2800 proteins10), comparing affinity-based pQTL to pQTL identified in MS studies may facilitate signal interpretation.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [10]

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References 32 · Cited by 57

Research works printed but not linked: 26

  1. [1]Suhre K McCarthy MI Schwenk JM Genetics meets proteomics: perspectives for large population-based studies Nat. Rev. Genet. 2021 22 19 37 10.1038/s41576-020-0268-2 32860016 · DOI · Paper page
  2. [2]Suhre K Connecting genetic risk to disease endpoints through the human blood plasma proteome Nat. Commun. 2017 8 14357 10.1038/ncomms14357 28240269 PMC5333359 · DOI · Paper page
  3. [3]Sun BB Genomic atlas of the human plasma proteome Nature 2018 558 73 79 10.1038/s41586-018-0175-2 29875488 PMC6697541 · DOI · Paper page
  4. [4]Folkersen L Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals Nat. Metab. 2020 2 1135 1148 10.1038/s42255-020-00287-2 33067605 PMC7611474 · DOI · Paper page
  5. [5]Thareja, G. et al. Differences and commonalities in the genetic architecture of protein quantitative trait loci in European and Arab populations. Hum. Mol. Genet.32, 907–916 (2023). PMC9990988 36168886
  6. [6]Pietzner M Mapping the proteo-genomic convergence of human diseases Science. (NY) 2021 374 eabj1541 10.1126/science.abj1541 PMC9904207 34648354 · DOI · Paper page
  7. [7]Zhao JH Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets Nat. Immunol. 2023 24 1540 1551 10.1038/s41590-023-01588-w 37563310 PMC10457199 · DOI
  8. [11]Enroth S Johansson A Enroth SB Gyllensten U Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs Nat. Commun. 2014 5 4684 10.1038/ncomms5684 25147954 PMC4143927 · DOI
  9. [12]Meier F diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition Nat. Methods 2020 17 1229 1236 10.1038/s41592-020-00998-0 33257825 · DOI
  10. [13]Schessner JP Voytik E Bludau I A practical guide to interpreting and generating bottom-up proteomics data visualizations Proteomics 2022 22 e2100103 10.1002/pmic.202100103 35107884 · DOI
  11. [14]Aebersold R Mann M Mass spectrometry-based proteomics Nature 2003 422 198 207 10.1038/nature01511 12634793 · DOI · Paper page
  12. [15]Demichev V dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts Nat. Commun. 2022 13 3944 10.1038/s41467-022-31492-0 35803928 PMC9270362 · DOI
  13. [16]Peckner R Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics Nat. Methods 2018 15 371 378 10.1038/nmeth.4643 29608554 PMC5924490 · DOI
  14. [17]Liu Y Nano-Bio interactions in cancer: from therapeutics delivery to early detection Acc. Chem. Res. 2021 54 291 301 10.1021/acs.accounts.0c00413 33180454 · DOI
  15. [18]Ferdosi S Enhanced competition at the nano-bio interface enables comprehensive characterization of protein corona dynamics and deep coverage of proteomes Adv. Mater. (Deerfield Beach, Fla) 2022 34 e2206008 10.1002/adma.202206008 35986672 · DOI
  16. [19]Blume JE Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona Nat. Commun. 2020 11 3662 10.1038/s41467-020-17033-7 32699280 PMC7376165 · DOI
  17. [20]Mook-Kanamori DO 1,5-Anhydroglucitol in saliva is a noninvasive marker of short-term glycemic control J. Clin. Endocrinol. Metab. 2014 99 E479 E483 10.1210/jc.2013-3596 24423354 · DOI
  18. [21]Yousri NA A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood, and urine at different timescales of glycaemic control Diabetologia 2015 58 1855 1867 10.1007/s00125-015-3636-2 26049400 PMC4499109 · DOI
  19. [22]Sharapov SZ Defining the genetic control of human blood plasma N-glycome using genome-wide association study Hum. Mol. Genet. 2019 28 2062 2077 31163085 PMC6664388
  20. [23]Kamat MA PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations Bioinforma. (Oxf., Engl.) 2019 35 4851 4853 PMC6853652 31233103
  21. [24]Hayes MR Borner T De Jonghe BC The Role of GIP in the Regulation of GLP-1 Satiety and Nausea Diabetes 2021 70 1956 1961 10.2337/dbi21-0004 34176783 PMC8576421 · DOI
  22. [25]Sinnott-Armstrong N Genetics of 35 blood and urine biomarkers in the UK Biobank Nat. Genet. 2021 53 185 194 10.1038/s41588-020-00757-z 33462484 PMC7867639 · DOI · Paper page
  23. [26]Suhre, K. et al. Lipoprotein profile and metabolic fine-mapping of genetic lipid risk loci. medRxiv, 2022.2006.2012.22276286 (2022).
  24. [27]McLaren W The Ensembl Variant Effect Predictor Genome Biol. 2016 17 122 10.1186/s13059-016-0974-4 27268795 PMC4893825 · DOI · Paper page
  25. [28]Arnold M Raffler J Pfeufer A Suhre K Kastenmüller G SNiPA: an interactive, genetic variant-centered annotation browser Bioinforma. (Oxf., Engl.) 2015 31 1334 1336 PMC4393511 25431330
  26. [29]Boughton AP LocusZoom.js: interactive and embeddable visualization of genetic association study results Bioinforma. (Oxf., Engl.) 2021 37 3017 3018 PMC8479674 33734315

Authors
Suhre, K., Venkataraman, G. R., Guturu, H., Halama, A., Stephan, N., Thareja, G., Sarwath, H., Motamedchaboki, K., Donovan, M. K. R., Siddiqui, A., Batzoglou, S., & Schmidt, F.
Affiliations
Weill Cornell Medical College in Qatar (QA)
Corresponding author
Suhre, Karsten
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-02-02
Volume
15
Issue
1
Pages
989-989
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-024-45233-y
PMC
PMC10837160
OpenAlex
W4391463996
Cited by
57
References
32
Citations by year
2026: 14 · 2025: 32 · 2024: 11
FWCI
7.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
Funders
  • Qatar National Research Fund (NPRP11C-0115-180010)
  • Qatar Foundation (Biomedical Research Program at Weill Cornell Medicine in Qatar)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
proteogenomics, protein quantitative trait loci, protein-altering variants, peptide variants, bottom-up mass spectrometry, drug target prioritization, plasma proteins, multi-ethnic cohort
MeSH terms
Proteogenomics, Humans, Peptides, Proteins, Mass Spectrometry, Proteomics, Mutant Proteins

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