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23rd Human Proteome Organization World Congress

Challenges and opportunities of genome-wide association studies with proteomics

Keynote Lecture

Protein quantitative trait loci (pQTLs) are important tools in the drug discovery process as they provide supporting evidence for drug target identification and hypothesis generation regarding their modes of action. Most genome-wide association study studies conducted so far have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of pQTLs. Mass spectrometry-based (MS) proteomics can overcome some of these limitations and provide insights regarding the role that specific protein isoforms may play. However, MS proteomics also have their own challenges, such protein quantification in the presence of peptides containing protein altering variants. Here we report insights from a recent GWAS conducted using MS-based proteomics and how the results relate to pQTLs discovered using affinity platforms.

Keynote lecture

A plasma protein atlas of the UK biobank

Population Proteomics in Health and Disease

Affinity-based proteomic techniques now measure thousands of proteins circulating in blood at population scale, but only ~10% of which have established roles in blood leaving the sources and relevance of variation for most proteins poorly understood. Here, we used machine learning to systematically identify and quantify major predictors of plasma levels of ~3,000 protein targets among 43,240 participants of the UK Biobank. We identify 427 diverse, modifiable and non-modifiable, factors that cumulatively explained on average 23.7% (range: 0.001%-79.7%) of variation in plasma levels of 2,846 protein targets (97.4% of all protein targets), selected from over 1,800 participant and technical characteristics. We demonstrate that protein targets segregate into 11 clusters, of which all but one, are predominantly explained by a single predictor of plasma protein variances, including platelet count (n=635), germline genetic variation (n=372), renal (n=199) and liver function (n=22), low-grade inflammation (n=208), ancestry (n=150), and sex (n=10). With few exceptions (2.7%), we obtain largely similar explained variances in plasma protein levels across the sexes and ancestries (British Central South Asians and Africans), but with evidence that selected participant characteristics differed by sex or ancestry. Our results highlight new tissue and cell-type disease links, as well as potentially (adverse) effects of medications. For example, intake of warfarin or clopidogrel explains variance in plasma levels of proteins specifically expressed in fibroblasts. In summary, we provide a data-driven approach to better understand the factors that shape the human plasma proteome and help guide blood-based protein biomarker discovery.

Oral presentation

Prof.

A comprehensive human pan-disease blood atlas based on next-generation protein profiling

In this study, we present the creation of an open-access resource designed to explore the protein profiles in blood across a wide range of diseases. Using a highly sensitive and specific multiplex assay, we conducted parallel analysis of 1,463 proteins in blood from patients with 57 diseases. This approach allowed us to identify several proteins correlating with age and BMI, as well as sex-related proteins showing significant differences in males and females.Notably, we identified proteins significantly altered in various cancers, including acute myeloid leukemia, glioma, and ovarian cancer, whereas fewer proteins were observed for some of the common cancers, such as breast and prostate cancer. Our findings also revealed that proteins elevated in diseases like pancreatic cancer and autoimmune disorders were similarly elevated in patients with infectious diseases, indicating these proteins are markers for inflammation rather than disease-specific markers. Furthermore, proteins elevated in hepatocellular cancer were also elevated in other liver diseases, showcasing the power of our pan-disease approach in identifying proteins with cross-disease patterns. For cardiovascular disease, several proteins were identified, including proteins elevated in stenosis of the carotid artery. In pediatric patients, we observed protein elevation patterns similar to those in corresponding adult diseases, along with unique proteins elevated across all pediatric samples.The analyzed protein levels for each disease are visualized and compared against (i) a cohort of healthy individuals, (ii) all other diseases, and (iii) all other diseases within the same class, providing comprehensive differential expression results. This resource offers valuable insights for biomarker discovery and enhances our understanding of protein behavior across various diseases.

Professor

Mass spectrometry-based proteomics at extreme scale reveals biomarkers for adverse pregnancy outcomes

Objective Proteomic technology has progressed tremendously in the past years fueling a revival of mass spectrometry (MS)-based plasma proteomics (Bader, PMID 37209816). We have recently developed a highly robust, deep and economic workflow that should enable the analysis of very large cohorts. We are currently applying this to study adverse pregnancy outcomes (APOs) in low- and middle-income countries and identify biomarkers for improved prediction aiding clinical decision-making.Methods The Multi-Omics for Mothers and Infants (MOMI) consortium funded by the Bill &amp; Melinda Gates Foundation has collected more than 40,000 longitudinal plasma samples from women in Africa and South Asia, taken at up to three different time points during pregnancy. Using a semi-automated liquid handling robot, we subjected these samples to our plasma profiling workflow, which features an adaption of the recently reported perchloric-acid enrichment technique (Viode, PMID 36989362). Here, high abundant proteins partition more to the precipitate effectively enriching other plasma proteins in the supernatant. Subsequently we heat-denature and digest them using LysC/trypsin. Peptides are measured on an Orbitrap Astral mass spectrometer interfaced with the Evosep One system using IonOpticks columns.Here, we report on results of three sub-cohorts accounting in total for more than 10,000 samples provided by the Pakistani AMANHI (Alliance-for-Maternal-and-Newborn-Health-Improvement) and the Zambian IPOP (Improving Pregnancy Outcomes with Progesterone) and ZAPPS (Zambian Preterm Birth Prevention Study) cohorts of MOMI.Results and Conclusion The AMANHI cohort with 5,500 plasma samples was measured with a throughput of 60 samples-per-day, yielding on average 2,000 protein groups. Our preliminary data suggest that this in-depth dataset reveals statistically significant changes in more than 1,500 proteins between the first and second trimesters. We identified both known and novel pregnancy-related markers, while corroborating the presence of known markers for APOs such as PAPP-A. Potential novel markers of APOs are currently being validated across the different MOMI subcohort in a "rectangular study approach" (Geyer, PMID 28951502). Initial results from the two Zambian cohorts with more than 5,000 plasma samples are being measured with a throughput of 100 samples-per-day yielding on average 2,400 protein groups. Results appear to support findings from the AMANHI cohort, promising novel biomarkers that would allow for improved early detection of APOs in routine clinical diagnostics.

Conference room 1-2

Session

Population proteomics: genotype to phenotype association

Session
Population proteomics: genotype to phenotype association

Appointment

Chairs

Program

SessionPopulation proteomics: genotype to phenotype association

Appointment

Chairs

Program

Session
Population proteomics: genotype to phenotype association