23rd Human Proteome Organization World Congress

Alcohol shapes multiple microenvironments in oral cancer initiation

Keynote Lecture

Head and neck cancer (HNC) is ranked the seventh leading cause of cancer worldwide, with a 5-year survival rate of less than 50% in advanced cases. Together with tobacco smoking, alcohol drinking is one of the major risk factors for HNC. Despite the advances in understanding HNC progression, the mechanism involved in HNC initiation is largely unknown. Through spatially resolved and bulk proteomics using holistic and reductionist approaches, we demonstrate in a 94 patient-cohort that alcohol modifies the proteome pattern of stromal and epithelial cells, circulating immune cells, primary fibroblasts and their extracellular vesicles (EVs). Besides tissue biopsy-derived EVs, alcohol-treated fibroblasts and their EVs shape the immune response. In addition, a selected protein, identified in both biopsy-derived EVs and those from alcohol-treated fibroblasts, modulates the phenotype of specific immune cells. These results improve our understanding of the molecular changes induced by alcohol in multiple microenvironments, and highlight the role of EVs in expanding its toxicity.

Keynote lecture

High-throughput proteomics in the analysses of human populations and drug discover

Professor Dr

Keynote speaker

Multi-omics analysis of human skeletal muscle in response to acute exercise: initial insights from MoTrPAC

Clinical Proteomics

The Molecular Transducers of Physical Exercise Consortium (MoTrPAC) was established to deeply and systematically characterize the molecular basis of exercise through integrative, multi-omics analysis of tissues obtained from rats and human participants1. For the clinical study, sedentary participants were recruited and randomized into endurance exercise (EE), resistance exercise (RE) and control (CON) groups. Vastus lateralis muscle biopsies were collected before a sub maximal acute bout of either EE or RE and at one of several time points after following the acute bout (30min, 3.5h, and 24h). Tissue collection was performed in the CON group at the same time points, but without exercise. Muscle tissue samples were analyzed by ATAC-seq, transcriptomics, proteomics, phosphoproteomics and metabolomics, with the goal of identifying dynamic molecular changes in response to an acute bout of EE or RE including divergent molecular responses between the two modes of exercise.Data QC and differential analysis for all omes were performed using limma and variancePartition packages in R2, respectively. For differential analysis, a linear mixed model was used with participant as the random effect, timepoint-exercise intervention as an interaction term and sex, age, clinical site, and BMI as covariates. Exercise intervention groups (EE and RE) were compared to matched timepoint CON values to identify significantly changed features.Initial multi-omics analysis of an acute bout of exercise showed divergent behavior of different omes throughout the time course. Maximal changes in ATACseq, phosphoproteome and metabolome occur earlier (30min) than changes in the transcriptome (3.5h) and proteome (3.5h and 24h). Furthermore, the proportion of genes with annotated differentially accessible regions (DARs) based on ATACseq that are also differentially regulated genes (DEGs) in the RNAseq data overlap significantly, especially at the later time points (3.5h and 24h), suggesting a time delay between chromatin remodeling and gene expression. Additionally, PTM signature enrichment analysis, using PTM-SEA3 of the phosphoproteome data, shows enrichment of many kinase signatures, more pronounced after RE than EE. These include several MAPK kinase signatures following an acute bout of either exercise mode, consistent with prior findings4. Some overlap of omic dynamics is observed between changes in EE and RE; however, there is a considerable difference in magnitude of changes between modes of exercise, with RE resulting in a greater number of changed features. These MoTrPAC data provide initial insights into the multi-omic exercise responses in human skeletal muscle highlighting a dynamic and varied time course across the omes with common and specific signatures between EE and RE.

Oral presentation

Prof.

The HDL proteome represents a novel, sensitive biomarker of metabolic inflammation and metabolic health in obesity that can track both disease progression and regression

Introduction: Low-grade metabolic inflammation is causally implicated in the development of multiple obesity-related complications, such as cardiovascular disease, chronic kidney disease (CKD) and liver disease. Our previous preclinical studies in mice have shown that changes in the proteome of high-density lipoprotein (HDL) particles in obesity occur independently of changes in HDL-cholesterol. The HDL proteome in obesity has increased pro-inflammatory acute phase proteins and reduced antioxidant/anti-inflammatory proteins. In the current study we hypothesized that the HDL proteome may serve as a novel biomarker of metabolic inflammation in human obesity, and that a reversal in 'adverse' particle protein composition would be evident after weight-loss.Methods: HDL particles were isolated from serum using a novel two-step extraction and purification method followed by tryptic digestion for LC-MS/MS. Proteins associated with HDL were identified by data-dependent acquisition (DDA) mass spectrometry using a timsTOF Pro (Bruker Daltonics) coupled with an EvoSep One system (EvoSep BioSystems). MS data was analysed using MaxQuant (v2.0.3.0) and Perseus (v2.0.3.1) software. HDL proteome composition was determined in pair-matched individuals ± obesity and in a separate cohort of patients with obesity and CKD at baseline and 1 year post gastric bypass (n=20), sleeve gastrectomy (n=19), or liraglutide±SGLT2 inhibitors treatment (n=18). An algorithm to determine inflammatory profile of the HDL particle (HDL inflammatory index score) was generated, using 20 proteins identified in obesity setting.Results: LC-MS/MS analysis of HDL protein composition identified a total 194 proteins in ≥70% of the HDL samples. There was a significant (p≤0.05) change in 64 proteins with increase in inflammatory-related proteins and loss of antioxidant/anti-inflammatory proteins in HDL isolated from obese individuals. Surgically induced weight-loss reversed the pro-inflammatory phenotype of HDL particles with changes in 55 proteins, including reductions in Complement factor I (CFI), Complement factor B (CFB), Vitronectin (VTN) and increases in Serum paraoxonase 3 (PON3), Apolipoprotein D (APOD). In contrast, intervention with liraglutide had little impact on the HDL proteome. An HDL-inflammatory index score significantly (p≤0.0001) delineated the non-obese and obese groups. Furthermore, significant (p≤0.0001) recovery of the HDL inflammatory index was evident upon surgically induced weight-loss.Conclusions: The HDL proteome exhibits a pro-inflammatory phenotype with deteriorating metabolic health independent of static measurements of HDL-C. The adverse effects of obesity on the HDL proteome are reversible with surgical-induced weight-loss. The data presented here suggest that the HDL proteome composition and an associated inflammatory index score can potentially be used to monitor metabolic health and identify patients at highest risk of cardiometabolic complications

High-throughput and deep plasma proteomics allows the classification of cancer types

Plasma proteomics is challenging with the 22 most abundant proteins constituting more than 99% of the total protein content, limiting the detection of less abundant proteins using discovery mass spectrometry–based proteomics. To address this limitation, workflows based on protein corona formation have emerged as a strong method to dig deeper into the proteome. Plasma proteins upon exposure to particles forms a layer around it, known as protein corona, leading to a reduction in dynamic range and enabling of detection of lesser abundant proteins. Here, we introduce an enrichment workflow that allows for robust, quantitative and deep enrichment of low-abundant plasma proteins, termed P2 Plasma Enrichment System. We applied this workflow to a cohort of 62 plasma samples from 4 cancers and 36 healthy patients to discover classification biomarkers.We developed and optimized the P2 Plasma Enrichment System starting from 100µL of plasma followed by tryptic digestion. All samples were analyzed using a 17min analytical gradient and data independent acquisition on a timsTOF HT (Bruker). Data analysis was performed using Spectronaut 19 (Biognosys) using directDIA. From a reference pool plasma, we achieved a proteome depth of 6324 proteins. The enrichment factor is up to 10x the one of neat plasma and 2x compared to an optimized enrichment based on MagReSyn SAX-beads (Mag-Net protocol). Our experimental conditions allow for robust protein corona formation leading to high workflow reproducibility with coefficient of variation on protein level of 6.7% in median, 5498 proteins with a CV &lt;20%. To evaluate quantitative performance of our workflow we performed a controlled quantitative experiment (CQE) based on human plasma spiked with known amounts of chicken plasma (1:9). The mixture of plasma of different species shows the conservation of relative protein quantities, demonstrates its suitable for quantitative plasma proteomics.We applied the enrichment workflow to 62 plasma samples from 4 cancers type (Breast n= 15; Colon Cancer n=19, Lung n=13 and Prostate n=15) and 36 healthy patients. We identified cumulatively 6979 protein groups in the study and 5498 protein groups on average per sample with a gradient of 17min.We probed the 4 cancer cohort for classification of the plasma specimen into the cancer types and healthy. Therefore, an analysis pipeline was developed consisting of an ANOVA analysis with filtering by adjusted p-value &lt;0.05. Next, correlating features were removed (R2 &lt; 0.7), finally a s-PLSDA model was trained on 70% of the data (with 10-fold cross validation). The model was tested for classification accuracy on the 30% hold out data and was found to be 92% on average for 10 repeats of the analysis pipeline.Taken together, the optimized plasma proteomic workflow showcased a powerful way to profile plasma biomarkers. The ease of automating and scaling up such an approach could enable a broader application to other indications and biofluids.

Conference room 3-4

Session

Proteomics in medicine and clinical research

Session
Proteomics in medicine and clinical research

Appointment

Chairs

Program

SessionProteomics in medicine and clinical research

Appointment

Chairs

Program

Session
Proteomics in medicine and clinical research