Next generation plasma proteome profiling to monitor health and disease

Correlation between Olink and SomaScan proteomics platforms in adults with a Fontan circulation

Background

High-throughput proteomics platforms using aptamers (SomaScan) or proximity extension assay (Olink) provide novel opportunities for improving diagnostic and risk stratification tools in cardiovascular diseases, including understudied congenital heart diseases. The correlation between these proteomics approaches has not yet been studied among individuals with a Fontan circulation.

Objective

The correlation of plasma protein measurements between SomaScan and Olink platforms was evaluated in adults with a Fontan circulation.

Methods

We measured 491 proteins in plasma of 71 adults with a Fontan circulation using Olink and SomaScan. Missing Olink measurements (0.13%, 47/34,861) were imputed using non-parametric imputation. Spearman's rank correlation coefficient for absolute values of protein expression between platforms was calculated. Protein correlation frequencies were compared to 3 cohorts reported in the literature using Pearson's Chi-squared test of independence.

Results

Overall, protein correlations between Olink and SomaScan measurements were moderately strong for most proteins, (rho > 0.4 for 57.2%), but with substantial variability (median correlation = 0.457, IQR = 0.538). The distribution of protein correlations was qualitatively similar to published literature in non-Fontan cohorts. Both Olink and SomaScan identified proteins with sex-based differences; both identified differences in myostatin and leptin, but each identified additional nonoverlapping sexually dimorphic proteins (n = 14 Olink, n = 5 SomaScan).

Conclusions

In adults with a Fontan circulation, correlations between plasma proteins measured by Olink and SomaScan varied widely, approximately in line with prior reports in other populations. While these tools may be uniquely useful to generate hypotheses, specifically regarding potential molecular mechanisms, more definitive inference requires independent validation.

Identification of growth differentiation factor 15 as an early predictive biomarker for metabolic dysfunction-associated steatohepatitis: A nested case-control study of UK Biobank proteomic data

AIMS

This study aims to determine the predictive capability for metabolic dysfunction-associated steatohepatitis (MASH) long before its diagnosis by using six previously identified diagnostic biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD) with proteomic data from the UK Biobank.

MATERIALS AND METHODS

A nested case-control study comprising a MASH group and three age- and sex-matched control groups (metabolic dysfunction-associated steatosis, viral hepatitis and normal liver controls) was conducted. Olink proteomics, anthropometric and biochemical data at baseline levels were obtained from the UK Biobank. The baseline levels of CDCP1, FABP4, FGF21, GDF15, IL-6 and THBS2 were analysed prospectively to determine their predictive accuracy for subsequent diagnosis with a mean lag time of over 10 years.

RESULTS

At baseline, GDF15 demonstrated the best performance for predicting MASH occurrence at 5 and 10 years later, with AUCs of 0.90 at 5 years and 0.86 at 10 years. A predictive model based on four biomarkers (GDF15, FGF21, IL-6 and THBS2) showed AUCs of 0.88 at both 5 and 10 years. Furthermore, a protein-clinical model that included these four circulating protein biomarkers along with three clinical factors (BMI, ALT and TC) yielded AUCs of 0.92 at 5 years and 0.89 at 10 years.

CONCLUSIONS

GDF15 at baseline levels outperformed other individual circulating protein biomarkers for the early prediction of MASH. Our data suggest that GDF15 and the GDF15-based model may be used as easy-to-implement tools to identify patients with high risks of developing MASH at a mean lag time of over 10 years.

The Identification of Biomarkers and Therapeutic Targets for Diabetic Kidney Disease by Integrating the Proteome with the Genome

Background: The blood proteome is a major source of biomarkers and therapeutic targets. We conducted a proteome-wide Mendelian randomization (MR) study to identify cardiometabolic protein markers for diabetic kidney disease (DKD). Methods: We measured all 369 proteins in the Olink Explore 384 Cardiometabolic and Cardiometabolic panel of 500 patients with type 2 diabetes from 11 communities in Shanghai. Protein quantitative trait loci (pQTLs) were derived by coupling genomic and proteomic data. Cis-pQTLs identified for proteins were used as instrumental variables in MR analyses of DKD risk, and the outcome data were obtained from 8401 Japanese individuals with type 2 diabetes (2809 cases and 5592 controls). Replication MR analysis was performed in the UK Biobank Pharma Proteomics Project (UKB-PPP). Colocalization analysis and the Heidi test were used to examine whether the identified proteins and DKD shared causal variants. Results: Among the 369 proteins, we identified 66 independent cis-pQTLs for 64 proteins. MR analysis suggested that two cardiometabolic proteins (UMOD and SIRPA) may play a causal role in increasing DKD risk, with UMOD showing replication in UKB-PPP. Bayesian colocalization further supported the causal effects of these proteins. Additional analyses indicated that UMOD is highly expressed in renal macrophages. Further downstream analyses suggested that UMOD could be a potential novel target and that SIRPA could be a potential repurposing target for DKD; however, further validation is needed. Conclusions: By integrating proteomic and genetic data from patients with type 2 diabetes, we identified two protein biomarkers potentially associated with DKD risk. These findings provide insights into DKD pathophysiology and therapeutic target development, but further replication and functional studies are needed to confirm these associations.

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

MOTIVATION

Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical outcomes. Current in silico approaches dovetail differential abundance, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.

RESULTS

We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically informed sparse deep learning model, to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests SJARACNe co-regulation and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.

AVAILABILITY AND IMPLEMENTATION

APNet's R, Python scripts, and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet.

Emerging protein sequencing technologies: proteomics without mass spectrometry?

INTRODUCTION

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry.

AREAS COVERED

We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture.

EXPERT OPINION

Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

Proximity extension assay revealed novel inflammatory biomarkers for follicular development and ovarian function: a prospective controlled study combining serum and follicular fluid

Background

Many components in follicular fluid (FF), such as peptide hormones, cytokines, and steroids, undergo dynamic changes during folliculogenesis and have important roles in follicular development. Because systemic inflammation has also been found to contribute to diminished ovarian reserve (DOR) in previous studies, do certain serum/FF inflammatory biomarkers affect both follicular development and ovarian function?

Methods

Serum samples from the menstruation phase (n=26), serum samples from the ovulation phase (n=26), FF samples of mature oocytes (n=26), and FF samples of immature oocytes (n=10) were collected. Olink proteomic proximity extension assay (PEA) technology was used to compare the differentially expressed proteins (DEPs), and patients were divided into two subgroups-the normal ovarian reserve (NOR) group and the DOR group-for further bioinformatics analysis and verification by enzyme-linked immunosorbent assay (ELISA).

Results

In total, 16 DEPs were detected between the mature group and the immature group (FF), and 11 DEPs were detected between the ovulation group and the menstruation group (serum). Further subdivision of the ovarian reserve subgroups revealed 22 DEPs in FF and 3 DEPs in serum. Among all four comparisons, only the expression of oncostatin M (OSM) significantly differed. The OSM signaling pathway, the IL-10 anti-inflammatory signaling pathway, and the PI3K-Akt signaling pathway are three notable pathways involved in affecting ovarian reserve capacity according to bioinformatics analysis. In addition, the concentration of estradiol on the hCG day was slightly but positively correlated with OSM (r=0.457, P=0.029). A significantly greater level of OSM (5.41 ± 2.65 vs. 3.94 ± 1.23 pg/mL, P=0.007) was detected in the serum of NOR patients via ELISA verification, and the sensitivity and specificity of ovarian reserve division were 50.00% and 83.33%, respectively.

Conclusion

This study proposed that immunological changes assessed by PEA technology affect ovarian function in humans and that OSM may serve as a potential inflammatory biomarker for ovarian function in serum, thus revealing alterations in FF.

Associations between plasma metabolism-associated proteins and future development of giant cell arteritis: results from a prospective study

OBJECTIVE

The aim of this study was to investigate the relationship between biomarkers associated with metabolism and subsequent development of GCA.

METHOD

Participants in the population-based Malmö Diet Cancer Study (MDCS; N = 30 447) who were subsequently diagnosed with GCA were identified in a structured process. Matched GCA-free controls were selected from the study cohort. Baseline plasma samples were analysed using the antibody-based OLINK proteomics metabolism panel (92 metabolic proteins). Analyses were pre-designated as hypothesis-driven or hypothesis-generating. In the latter, principal component analysis was used to identify groups of proteins that explained the variance in the proteome.

RESULTS

There were 95 cases with a confirmed incident diagnosis of GCA (median 12.0 years after inclusion). Among biomarkers with a priori hypotheses, adhesion G protein-coupled receptor E2 (ADGRE2) was positively associated [odds ratio (OR) per S.D. 1.67; 95% CI 1.08-2.57], and fructose-1,6-bisphosphatase 1 (FBP1) was negatively associated (OR per S.D. 0.59; 95% CI 0.35-0.99) with GCA. In particular, ADGRE2 levels were associated with subsequent GCA in the subset sampled <8.5 years before diagnosis. For meteorin-like protein (Metrnl), the highest impact on the risk of GCA was observed in those patients sampled closest to diagnosis, with a decreasing trend with longer time to GCA (P = 0.03). In the hypothesis-generating analyses, elevated levels of receptor tyrosine-like orphan receptor 1 (ROR1) were associated with subsequent GCA.

CONCLUSION

Biomarkers identified years before clinical diagnosis indicated a protective role of gluconeogenesis (FBP1) and an association with macrophage activation (ADGRE2 and Metrnl) and proinflammatory signals (ROR1) for development of GCA.

Network Mendelian randomisation analysis deciphers protein pathways linking type 2 diabetes and gastrointestinal disease

AIMS

The molecular mechanisms underlying the association between type 2 diabetes (T2D) and gastrointestinal (GI) disease are unclear. To identify protein pathways, we conducted a two-stage network Mendelian randomisation (MR) study.

MATERIALS AND METHODS

Genetic instruments for T2D were obtained from a large-scale summary-level genome-wide meta-analysis. Genetic associations with blood protein levels were obtained from three genome-wide association studies on plasma proteins (i.e. the deCODE study as the discovery and the UKB-PPP and Fenland studies as the replication). Summary-level data on 10 GI diseases were derived from genome-wide meta-analysis of the UK Biobank and FinnGen. MR and colocalisation analyses were performed. Pathways were constructed according to the directionality of total and indirect effects, and corresponding proportional mediation was estimated. Druggability assessments were conducted across four databases to prioritise protein mediators.

RESULTS

Genetic liability to T2D was associated with 69 proteins in the discovery protein dataset after multiple testing corrections. All associations were replicated at the nominal significance level. Among T2D-associated proteins, genetically predicted levels of nine proteins were associated with at least one of the GI diseases. Genetically predicted levels of SULT2A1 (odds ratio = 1.98, 95% CI 1.80-2.18), and ADH1B (odds ratio = 2.05, 95% CI 1.43-2.94) were associated with cholelithiasis and cirrhosis respectively. SULT2A1 and cholelithiasis (PH4 = 0.996) and ADH1B and cirrhosis (PH4 = 0.931) have strong colocalisation support, accounting for the mediation proportion of 72.8% (95% CI 45.7-99.9) and 42.9% (95% CI 15.5-70.4) respectively.

CONCLUSIONS

The study identified some proteins mediating T2D-GI disease associations, which provided biological insights into the underlying pathways.

Developing the biomarker panels and drugs by proteomic analysis for autoimmune uveitis and posterior scleritis

Autoimmune uveitis and posterior scleritis are ocular diseases caused by immune dysregulation. Their pathogenesis remains elusive, and delayed diagnosis can exacerbate vision loss. Our study analyzed proteomic profiles of 190 patients with Behcet's disease uveitis, posterior scleritis, and Vogt-Koyanagi-Harada syndrome. Bioinformatics methods revealed potential pathogenesis and biomarkers for the diseases, which were verified by enzyme-linked immunosorbent assay. The diagnostic accuracy was improved by constructing a biomarker combination. In addition, we used the Connectivity Map tool to analyze the differentially expressed proteins and identified small molecules with potential clinical applications. In this study, EMINIL1 and LYZ were identified as biomarkers for Behcet's uveitis, GSTP1 and PGLYRP1 for posterior scleritis, and APOH and STXBP1 for Vogt-Koyanagi-Harada syndrome. This study mapped the plasma proteins of these diseases, revealing potential pathogenesis and clinical applications of these biomarkers.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Integrative proteomic analyses across common cardiac diseases yield mechanistic insights and enhanced prediction

Cardiac diseases represent common highly morbid conditions for which molecular mechanisms remain incompletely understood. Here we report the analysis of 1,459 protein measurements in 44,313 UK Biobank participants to characterize the circulating proteome associated with incident coronary artery disease, heart failure, atrial fibrillation and aortic stenosis. Multivariable-adjusted Cox regression identified 820 protein-disease associations-including 441 proteins-at Bonferroni-adjusted P < 8.6 × 10-6. Cis-Mendelian randomization suggested causal roles aligning with epidemiological findings for 4% of proteins identified in primary analyses, prioritizing therapeutic targets across cardiac diseases (for example, spondin-1 for atrial fibrillation and the Kunitz-type protease inhibitor 1 for coronary artery disease). Interaction analyses identified seven protein-disease associations that differed Bonferroni-significantly by sex. Models incorporating proteomic data (versus clinical risk factors alone) improved prediction for coronary artery disease, heart failure and atrial fibrillation. These results lay a foundation for future investigations to uncover disease mechanisms and assess the utility of protein-based prevention strategies for cardiac diseases.

Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach

Objectives

The burgeoning prevalence of cardiometabolic disorders, including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) within Africa is concerning. Machine learning (ML) techniques offer a unique opportunity to leverage data-driven insights and construct predictive models for MetS risk, thereby enhancing the implementation of personalised prevention strategies. In this work, we employed ML techniques to develop predictive models for pre-MetS and MetS among diabetic patients.

Methods

This multi-centre cross-sectional study comprised of 919 T2DM patients. Age, gender, novel anthropometric indices along with biochemical measures were analysed using BORUTA feature selection and an ensemble majority voting classification model, which included logistic regression, k-nearest neighbour, Gaussian Naive Bayes, Gradient boosting classification, and support vector machine.

Results

Distinct metabolic profiles and phenotype clusters were associated with MetS progression. The BORUTA algorithm identified 10 and 16 significant features for pre-MetS and MetS prediction, respectively. For pre-MetS, the top-ranked features were lipid accumulation product (LAP), triglyceride-glucose index adjusted for waist-to-height ratio (TyG-WHtR), coronary risk (CR), visceral adiposity index (VAI) and abdominal volume index (AVI). For MetS prediction, the most influential features were VAI, LAP, waist triglyceride index (WTI), Very low-density cholesterol (VLDLC) and TyG-WHtR. Majority voting ensemble classifier demonstrated superior performance in predicting pre-MetS (AUC = 0.79) and MetS (AUC = 0.87).

Conclusion

Identifying these risk factors reveals the complex interplay between visceral adiposity and metabolic dysregulation in African populations, enabling early detection and treatment. Ethical integration of ML algorithms in clinical decision-making can streamline identification of high-risk individuals, optimize resource allocation, and enable precise, tailored interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01491-7.

Rationale and design of a randomized placebo-controlled nutritional trial embracing a citizen science approach

Modulation of the gut microbiota through specific dietary interventions shows potential for maintenance and optimization of health. A dietary fiber diet and fermented foods diet appear to alter the gut microbiota, but evidence is limited. Therefore, we designed the Gut Health Enhancement by Eating Favorable Food study, a 21-week randomized controlled trial studying effects of dietary fibers and fermented foods on gut microbiota diversity and composition, while also stimulating dietary behavior changes through a citizen science (CS) approach. We hypothesized that a high-fermented food diet would increase microbial diversity, whereas a high-dietary fiber diet would stimulate the growth of specific fiber-degrading bacteria. The following elements of CS were adopted: education on the gut microbiota, tailored dietary intervention, remote data collection by participants, sharing of personal gut microbiota outcomes with participants, and vlogs by participants for dissemination of results. Here we describe the study protocol and report the flow of participants, baseline characteristics, and compliance rates. Completed in March 2024, the trial included 147 healthy adults randomized to a high-dietary fiber intervention, high-fermented food intervention, or control group. Each group received an additional study product after 2 weeks: dried chicory root, a fermented beverage, or maltodextrin (placebo). A 3-month follow-up assessed the participants' ability to sustain dietary changes. The recruitment of participants was successful, reflected by 1448 applications. The compliance with the dietary guidelines and study products was >90%. This study shows that including elements of CS in an randomized controlled trial is feasible and may help recruitment and compliance.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BACKGROUND

Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.

METHODS

Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.

RESULTS

Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.

CONCLUSIONS

Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.

TRIAL REGISTRATION

This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.

CLINICALTRIALS

gov/ct2/show/NCT05432128 .

Back pain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: an exploratory study in young male participants

Background

With its high and increasing lifetime prevalence, back pain represents a contemporary challenge for patients and healthcare providers. Monitored exercise therapy is a commonly prescribed treatment to relieve pain and functional limitations. However, the benefits of exercise are often gradual, subtle, and evaluated by subjective self-reported scores. Back pain pathogenesis is interlinked with epigenetically mediated processes that modify gene expression without altering the DNA sequence. Therefore, we hypothesize that therapy effects can be objectively evaluated by measurable epigenetic histone posttranslational modifications and proteome expression. Because epigenetic modifications are dynamic and responsive to environmental exposure, lifestyle choices-such as physical activity-can alter epigenetic profiles, subsequent gene expression, and health traits. Instead of invasive sampling (e.g., muscle biopsy), we collect easily accessible buccal swabs and plasma. The plasma proteome provides a systemic understanding of a person's current health state and is an ideal snapshot of downstream, epigenetically regulated, changes upon therapy. This study investigates how molecular profiles evolve in response to standardized sport therapy and non-controlled lifestyle choices.

Results

We report that the therapy improves agility, attenuates back pain, and triggers healthier habits. We find that a subset of participants' histone methylation and acetylation profiles cluster samples according to their therapy status, before or after therapy. Integrating epigenetic reprogramming of both buccal cells and peripheral blood mononuclear cells (PBMCs) reveals that these concomitant changes are concordant with higher levels of self-rated back pain improvement and agility gain. Additionally, epigenetic changes correlate with changes in immune response plasma factors, reflecting their comparable ability to rate therapy effects at the molecular level. We also performed an exploratory analysis to confirm the usability of molecular profiles in (1) mapping lifestyle choices and (2) evaluating the distance of a given participant to an optimal health state.

Conclusion

This pre-post cohort study highlights the potential of integrated molecular profiles to score therapy efficiency. Our findings reflect the complex interplay of an individual's background and lifestyle upon therapeutic exposure. Future studies are needed to provide mechanistic insights into back pain pathogenesis and lifestyle-based epigenetic reprogramming upon sport therapy intervention to maintain therapeutic effects in the long run.

Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations. A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant. Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31-7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429-0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls. CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.

Plasma Profiling of Acute Myeloid Leukemia With Fever- and Infection-Related Complications During Chemotherapy-Induced Neutropenia

BACKGROUND

Acute myeloid leukemia (AML) is a heterogenous and complex blood cancer requiring aggressive treatment. Early identification and prediction of the complications following treatment is vital for effective disease management.

AIMS

We explored associations between plasma protein levels and fever- and infection-related complications in 26 AML patients during chemotherapy-induced neutropenia.

MATERIAL AND METHODS

Longitudinal plasma profiling was conducted using data-dependent mass spectrometry analysis.

RESULTS

Mass spectrometry-based plasma profiling data correlated well with laboratory parameters, including C-reactive protein, and revealed a broader inflammation protein network associated with fever- and infection-related complications.

DISCUSSION AND CONCLUSION

These data indicate the potential of longitudinal plasma profiling in AML patients for identifying and predicting complications that may aid in improved disease monitoring and treatment.

Hybrid immunity augments cross-variant protection against COVID-19 among immunocompromised individuals

BACKGROUND

Immunity to SARS-CoV-2 vaccination and infection differs considerably among individuals. We investigate the critical pathways that influence vaccine-induced cross-variant serological immunity among individuals at high-risk of COVID-19 complications.

METHODS

Neutralizing antibodies to the wild-type SARS-CoV-2 virus and its variants (Beta, Gamma, Delta and Omicron) were analyzed in patients with autoimmune diseases, chronic comorbidities (multimorbidity), and healthy controls. Antibody levels were assessed at baseline and at different intervals up to 12 months following primary and booster vaccination with either BNT162b2 or mRNA-1273. Immunity induced by vaccination with and without infection (hybrid immunity) was compared with that of unvaccinated individuals with recent SARS-CoV-2 infection. Plasma cytokines were analyzed to investigate variations in antibody production following vaccination.

RESULTS

Patients with autoimmune diseases (n = 137) produced lesser antibodies to the wild-type SARS-CoV-2 virus and its variants compared with those in the multimorbidity (n = 153) and healthy groups (n = 229); antibody levels were significantly lower in patients with neuromyelitis optica and those on prednisolone, mycophenolate or rituximab treatment. Multivariate logistic regression analysis identified neuromyelitis optica (odds ratio 8.20, 95% CI 1.68-39.9) and mycophenolate (13.69, 3.78-49.5) as significant predictors of a poorer antibody response to vaccination (i.e, neutralizing antibody <40%). Infected participants exhibited antibody levels that were 28.7% higher (95% CI 24.7-32.7) compared to non-infected participants six months after receiving a booster vaccination. Individuals infected during the Delta outbreak generated cross-protective neutralizing antibodies against the Omicron variant in quantities comparable to those observed after infection with the Omicron variant itself. In contrast, unvaccinated individuals recently infected with the wild-type (n = 2390) consistently displayed lower levels of neutralizing antibodies against both the wild-type virus and other variants. Pathway analyses suggested an inverse relationship between baseline T cell subsets and antibody production following vaccination.

CONCLUSION

Hybrid immunity confers a robust protection against COVID-19 among immunocompromised individuals.

Circulating RKIP and pRKIP in Early-Stage Lung Cancer: Results from a Pilot Study

Background: Lung cancer (LC) is the leading cause of cancer-related deaths. Although low-dose computed tomography (LD-CT) reduces mortality, its clinical use is limited by cost, radiation, and false positives. Therefore, there is an urgent need for non-invasive and cost-effective biomarkers. The Raf Kinase Inhibitor Protein (RKIP) plays a crucial role in cancer development and progression and may also contribute to regulating the tumor-immune system axis. This protein has recently been described in biological fluids. Therefore, we conducted a pilot case-control study to assess RKIP and phosphorylated RKIP (pRKIP) levels in the urine and blood of LC patients. Methods: A novel enzyme linked immunosorbent assay (ELISA) assay was used to measure RKIP and pRKIP levels in urine and blood samples of two cohorts of LC patients and healthy controls (HSs). Furthermore, the biomarkers levels were correlated with tumor characteristics. Results: Serum, but not urine, levels of RKIP were significantly elevated in LC patients, distinguishing them from low- and high-risk healthy subjects with 93% and 74% accuracy, respectively. The RKIP/pRKIP ratio (RpR score) showed an accuracy of 90% and 79% in distinguishing LC patients from HS and HR-HS, respectively. Additionally, the RpR score correlated better with dimension, stage, and lymph node involvement in the tumor group. Conclusions: The serum RKIP and pRKIP profile may be a promising novel biomarker for early-stage LC.

Integration of proteomics with prospective birth cohort to elucidate early life origins of cardiometabolic diseases: rationale, study design, lab assay, and quality control

There is growing evidence that the plasma proteome provides insights into personal health status at different stages of life. However, limited data are available on high-throughput proteomic studies in pediatric populations, especially, using prospective birth cohorts. We launched a proteomics study in 990 children from a US predominantly urban, low-income, multi-ethnic prospective Boston Birth Cohort (BBC, referred as "BBC proteomics study"), which aimed to leverage proteomics to investigate the biological pathways underlying the link between preterm birth and child long-term cardiometabolic health. The objective of this paper is to describe the rationale, study design, proteomic assay and quality control steps for the BBC proteomics study in a subset of children with available proteomic profiling. Using the OLINK® Explore 3072 platform, proteomic profiling was performed in cord plasma at birth and in postnatal plasma collected during early childhood. Quality control (QC) steps were performed, including calculation of coefficient of variation (CV), missingness rates per sample or per protein, principal component analyses to identify clustering and outliers, and correlation analyses among the duplicates to indicate reproducibility. A total of 2,941 proteins from eight OLINK panels were successfully measured at both time points. Almost 100% of samples passed lab-prespecified QC. Approximately 89% of proteins were detected in > 50% samples; 79.6% had intra-CV < 15% and 79.9% of had inter-CV < 30%. Four samples were identified as outliers due to high missingness rates. Our data also demonstrated that this assay had a good reproducibility with correlation coefficient (r) > 0.65 in most of the duplicates, although we also identified potential batch effects. In conclusion, our data suggests that this high-throughput proteomic profiling is feasible and reproducible in archived plasma samples, including cord blood. We anticipated that successful completion of this proteomics study will help identify novel predictive biomarkers and therapeutic targets so that high-risk newborns can be identified, and effective interventions can be initiated during the earliest developmental window when they may have the greatest life-long benefit.

Proteomic signatures improve risk prediction for common and rare diseases

For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81-6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02-0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases.

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

A plasma protein-based risk score to predict hip fractures

As there are effective treatments to reduce hip fractures, identification of patients at high risk of hip fracture is important to inform efficient intervention strategies. To obtain a new tool for hip fracture prediction, we developed a protein-based risk score in the Cardiovascular Health Study using an aptamer-based proteomic platform. The proteomic risk score predicted incident hip fractures and improved hip fracture discrimination in two Trøndelag Health Study validation cohorts using the same aptamer-based platform. When transferred to an antibody-based proteomic platform in a UK Biobank validation cohort, the proteomic risk score was strongly associated with hip fractures (hazard ratio per s.d. increase, 1.64; 95% confidence interval 1.53-1.77). The proteomic risk score, but not available polygenic risk scores for fractures or bone mineral density, improved the C-index beyond the fracture risk assessment tool (FRAX), which integrates information from clinical risk factors (C-index, FRAX 0.735 versus FRAX + proteomic risk score 0.776). The developed proteomic risk score constitutes a new tool for stratifying patients according to hip fracture risk; however, its improvement in hip fracture discrimination is modest and its clinical utility beyond FRAX with information on femoral neck bone mineral density remains to be determined.

Large-scale proteomics reveals precise biomarkers for detection of ovarian cancer in symptomatic women

Ovarian cancer is the 8th most common cancer among women and has a 5-year survival of only 30-50%. While the survival is close to 90% for stage I tumours it is only 20% for stage IV. Current biomarkers are not sensitive nor specific enough, and novel biomarkers are urgently needed. We used the Explore PEA technology for large-scale analysis of 2943 plasma proteins to search for new biomarkers using two independent clinical cohorts. The discovery analysis using the first cohort identified 296 proteins that had significantly different levels in malign tumours as compared to benign and for 269 (91%) of these, the association was replicated in the second cohort. Multivariate modelling, including all proteins independent of their association in the univariate analysis, identified a model for separating benign conditions from malign tumours (stage I-IV) consisting of three proteins; WFDC2, KRT19 and RBFOX3. This model achieved an AUC of 0.92 in the replication cohort and a sensitivity and specificity of 0.93 and 0.77 at a cut-off developed in the discovery cohort. There was no statistical difference of the performance in the replication cohort compared to the discovery cohort. WFDC2 and KRT19 have previously been associated with ovarian cancer but RBFOX3 has not previously been identified as a potential biomarker. Our results demonstrate the ability of using high-throughput precision proteomics for identification of novel plasma protein biomarker for ovarian cancer detection.

Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study

BACKGROUND

Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes.

METHODS

We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins.

FINDINGS

Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures.

INTERPRETATION

We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings.

FUNDING

Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.

Proteomic profiling for biomarker discovery in heparin-induced thrombocytopenia

ABSTRACT

New analytical techniques can assess hundreds of proteins simultaneously with high sensitivity, facilitating the observation of their complex interplay and role in disease mechanisms. We hypothesized that proteomic profiling targeting proteins involved in thrombus formation, inflammation, and the immune response would identify potentially new biomarkers for heparin-induced thrombocytopenia (HIT). Four existing panels of the Olink proximity extension assay covering 356 proteins involved in thrombus formation, inflammation, and immune response were applied to randomly selected patients with suspected HIT (confirmed HIT, n = 32; HIT ruled out, n = 38; and positive heparin/platelet factor 4 [H/PF4] antibodies, n = 28). The relative difference in protein concentration was analyzed using a linear regression model adjusted for sex and age. To confirm the test results, soluble P-selectin was determined using enzyme-linked immunosorbent assay (ELISA) in above mentioned patients and an additional second data set (n = 49). HIT was defined as a positive heparin-induced platelet activation assay (washed platelet assay). Among 98 patients of the primary data set, the median 4Ts score was 5 in patients with HIT, 4 in patients with positive H/PF4 antibodies, and 3 in patients without HIT. The median optical density of a polyspecific H/PF4 ELISA were 3.0, 0.9, and 0.3. Soluble P-selectin remained statistically significant after multiple test adjustments. The area under the receiver operating characteristic curve was 0.81 for Olink and 0.8 for ELISA. Future studies shall assess the diagnostic and prognostic value of soluble P-selectin in the management of HIT.

Systemic proteome adaptions to 7-day complete caloric restriction in humans

Surviving long periods without food has shaped human evolution. In ancient and modern societies, prolonged fasting was/is practiced by billions of people globally for religious purposes, used to treat diseases such as epilepsy, and recently gained popularity as weight loss intervention, but we still have a very limited understanding of the systemic adaptions in humans to extreme caloric restriction of different durations. Here we show that a 7-day water-only fast leads to an average weight loss of 5.7 kg (±0.8 kg) among 12 volunteers (5 women, 7 men). We demonstrate nine distinct proteomic response profiles, with systemic changes evident only after 3 days of complete calorie restriction based on in-depth characterization of the temporal trajectories of ~3,000 plasma proteins measured before, daily during, and after fasting. The multi-organ response to complete caloric restriction shows distinct effects of fasting duration and weight loss and is remarkably conserved across volunteers with >1,000 significantly responding proteins. The fasting signature is strongly enriched for extracellular matrix proteins from various body sites, demonstrating profound non-metabolic adaptions, including extreme changes in the brain-specific extracellular matrix protein tenascin-R. Using proteogenomic approaches, we estimate the health consequences for 212 proteins that change during fasting across ~500 outcomes and identified putative beneficial (SWAP70 and rheumatoid arthritis or HYOU1 and heart disease), as well as adverse effects. Our results advance our understanding of prolonged fasting in humans beyond a merely energy-centric adaptions towards a systemic response that can inform targeted therapeutic modulation.

Isolation and Enrichment of Extracellular Vesicles with Double-Positive Membrane Protein for Subsequent Biological Studies

The isolation and enrichment of specific extracellular vesicle (EV) subpopulations are essential in the context of precision medicine. However, the current methods predominantly rely on a single-positive marker and are susceptible to interference from soluble proteins or impurities. This limitation represents a significant obstacle to the widespread application of EVs in biological research. Herein, a novel approach that utilizes proximity ligation assay (PLA) and DNA-RNA hybridization are proposed to facilitate the binding of two proteins on the EV membrane in advance enabling the isolation and enrichment of intact EVs with double-positive membrane proteins followed by using functionalized magnetic beads for capture and enzymatic cleavage for isolated EVs release. The isolated subpopulations of EVs can be further utilized for cellular uptake studies, high-throughput small RNA sequencing, and breast cancer diagnosis. Hence, developing and implementing a specialized system for isolating and enriching a specific subpopulation of EVs can enhance basic and clinical research in this field.

A type I interferon regulatory network for human plasmacytoid dendritic cells based on heparin, membrane-bound and soluble BDCA-2

Plasmacytoid dendritic cells (pDCs) produce type I interferons (IFNs) after sensing viral/bacterial RNA or DNA by toll-like receptor (TLR) 7 or TLR9, respectively. However, aberrant pDCs activation can cause adverse effects on the host and contributes to the pathogenesis of type I IFN-related autoimmune diseases. Here, we show that heparin interacts with the human pDCs-specific blood dendritic cell antigen 2 (BDCA-2) but not with related lectins such as DCIR or dectin-2. Importantly, BDCA-2-heparin interaction depends on heparin sulfation and receptor glycosylation and results in inhibition of TLR9-driven type I IFN production in primary human pDCs and the pDC-like cell line CAL-1. This inhibition is mediated by unfractionated and low-molecular-weight heparin, as well as endogenous heparin from plasma, suggesting that the local blood environment controls the production of IFN-α in pDCs. Additionally, we identified an activation-dependent soluble form of BDCA-2 (solBDCA-2) in human plasma that functions as heparin antagonist and thereby increases TLR9-driven IFN-α production in pDCs. Of importance, solBDCA-2 levels in the serum were increased in patients with scrub typhus (an acute infectious disease caused by Orientia tsutsugamushi) compared to healthy control subjects and correlated with anti-dsDNA antibodies titers. In contrast, solBDCA-2 levels in plasma from patients with bullous pemphigoid or psoriasis were reduced. In summary, this work identifies a regulatory network consisting of heparin, membrane-bound and solBDCA-2 modulating TLR9-driven IFN-α production in pDCs. This insight into pDCs function and regulation may have implications for the treatment of pDCs-related autoimmune diseases.

Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry

The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.

Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins

Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.

Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues

Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.

Sequential sequencing by synthesis and the next-generation sequencing revolution

The impact of next-generation sequencing (NGS) cannot be overestimated. The technology has transformed the field of life science, contributing to a dramatic expansion in our understanding of human health and disease and our understanding of biology and ecology. The vast majority of the major NGS systems today are based on the concept of 'sequencing by synthesis' (SBS) with sequential detection of nucleotide incorporation using an engineered DNA polymerase. Based on this strategy, various alternative platforms have been developed, including the use of either native nucleotides or reversible terminators and different strategies for the attachment of DNA to a solid support. In this review, some of the key concepts leading to this remarkable development are discussed.

Proteomics provides insights into the theranostic potential of extracellular vesicles

Extracellular vesicles (EVs) encompass a diverse range of membranous structures derived from cells, including exosomes and microvesicles. These vesicles are present in biological fluids and play vital roles in various physiological and pathological processes. They facilitate intercellular communication by enabling the exchange of proteins, lipids, and genetic material between cells. Understanding the cellular processes that govern EV biology is essential for unraveling their physiological and pathological functions and their potential clinical applications. Despite significant advancements in EV research in recent years, there is still much to learn about these vesicles. The advent of improved mass spectrometry (MS)-based techniques has allowed for a deeper characterization of EV protein composition, providing valuable insights into their roles in different physiological and pathological conditions. In this chapter, we provide an overview of proteomics studies conducted to identify the protein contents of EVs, which contribute to their therapeutic and pathological features. We also provided evidence on the potential of EV proteome contents as biomarkers for early disease diagnosis, progression, and treatment response, as well as factors that influence their composition. Additionally, we discuss the available databases containing information on EV proteome contents, and finally, we highlight the need for further research to pave the way toward their utilization in clinical settings.

Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis

Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.

Transcriptomic analysis of diabetic kidney disease and neuropathy in mouse models of type 1 and type 2 diabetes

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are common complications of type 1 (T1D) and type 2 (T2D) diabetes. However, the mechanisms underlying pathogenesis of these complications are unclear. In this study, we optimized a streptozotocin-induced db/+ murine model of T1D and compared it to our established db/db T2D mouse model of the same C57BLKS/J background. Glomeruli and sciatic nerve transcriptomic data from T1D and T2D mice were analyzed by self-organizing map and differential gene expression analysis. Consistent with prior literature, pathways related to immune function and inflammation were dysregulated in both complications in T1D and T2D mice. Gene-level analysis identified a high degree of concordance in shared differentially expressed genes (DEGs) in both complications and across diabetes type when using mice from the same cohort and genetic background. As we have previously shown a low concordance of shared DEGs in DPN when using mice from different cohorts and genetic backgrounds, this suggests that genetic background may influence diabetic complications. Collectively, these findings support the role of inflammation and indicate that genetic background is important in complications of both T1D and T2D.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Proteomics Identifies Circulating TIMP-1 as a Prognostic Biomarker for Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, although disease stratification using in-depth plasma proteomics has not been performed to date. By measuring more than 1000 proteins in the plasma of 147 DLBCL patients using data-independent acquisition mass spectrometry and antibody array, DLBCL patients were classified into four proteomic subtypes (PS-I-IV). Patients with the PS-IV subtype and worst prognosis had increased levels of proteins involved in inflammation, including a high expression of metalloproteinase inhibitor-1 (TIMP-1) that was associated with poor survival across two validation cohorts (n = 180). Notably, the combination of TIMP-1 with the international prognostic index (IPI) identified 64.00% to 88.24% of relapsed and 65.00% to 80.49% of deceased patients in the discovery and two validation cohorts, which represents a 24.00% to 41.67% and 20.00% to 31.70% improvement compared to the IPI score alone, respectively. Taken together, we demonstrate that DLBCL heterogeneity is reflected in the plasma proteome and that TIMP-1, together with the IPI, could improve the prognostic stratification of patients.

The development of blood protein profiles in extremely preterm infants follows a stereotypic evolution pattern

BACKGROUND

Preterm birth is the leading cause of neonatal mortality and morbidity. Early diagnosis and interventions are critical to improving the clinical outcomes of extremely premature infants. Blood protein profiling during the first months of life in preterm infants can shed light on the role of early extrauterine development and provide an increased understanding of maturation after extremely preterm birth and the underlying mechanisms of prematurity-related disorders.

METHODS

We have investigated the blood protein profiles during the first months of life in preterm infants on the role of early extrauterine development. The blood protein levels were analyzed using next generation blood profiling on 1335 serum samples, collected longitudinally at nine time points from birth to full-term from 182 extremely preterm infants.

RESULTS

The protein analysis reveals evident predestined serum evolution patterns common for all included infants. The majority of the variations in blood protein expression are associated with the postnatal age of the preterm infants rather than any other factors. There is a uniform protein pattern on postnatal day 1 and after 30 weeks postmenstrual age (PMA), independent of gestational age (GA). However, during the first month of life, GA had a significant impact on protein variability.

CONCLUSIONS

The unified pattern of protein development for all included infants suggests an age-dependent stereotypic development of blood proteins after birth. This knowledge should be considered in neonatal settings and might alter the clinical approach within neonatology, where PMA is today the most dominant age variable.

Next generation pan-cancer blood proteome profiling using proximity extension assay

A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.

Recent advances in proteomic-based diagnostics of cystic fibrosis

INTRODUCTION

Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF.

AREAS COVERED

We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers.

EXPERT OPINION

Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.

Magnetically Detected Protein Binding Using Spin-Labeled Slow Off-Rate Modified Aptamers

Recent developments in aptamer chemistry open up opportunities for new tools for protein biosensing. In this work, we present an approach to use immobilized slow off-rate modified aptamers (SOMAmers) site-specifically labeled with a nitroxide radical via azide-alkyne click chemistry as a means for detecting protein binding. Protein binding induces a change in rotational mobility of the spin label, which is detected via solution-state electron paramagnetic resonance (EPR) spectroscopy. We demonstrate the workflow and test the protocol using the SOMAmer SL5 and its protein target, platelet-derived growth factor B (PDGF-BB). In a complete site scan of the nitroxide over the SOMAmer, we determine the rotational mobility of the spin label in the absence and presence of target protein. Several sites with sufficiently tight affinity and large rotational mobility change upon protein binding are identified. We then model a system where the spin-labeled SOMAmer assay is combined with fluorescence detection via diamond nitrogen-vacancy (NV) center relaxometry. The NV center spin-lattice relaxation time is modulated by the rotational mobility of a proximal spin label and thus responsive to SOMAmer-protein binding. The spin label-mediated assay provides a general approach for transducing protein binding events into magnetically detectable signals.

A comprehensive mechanistic model of adipocyte signaling with layers of confidence

Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70-90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.

MS-based proteomics of body fluids: The end of the beginning

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 a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). 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. They are also required for machine learning or deep learning. 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 multi-protein 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 into 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.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

Plasma protein changes reflect colorectal cancer development and associated inflammation

Introduction

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive blood-based biomarkers for CRC early detection and prognosis are urgently needed.

Methods

To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few μL of plasma sample.

Results

Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon γ (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC.

Discussion

Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

Physiological and Functional Effects of Dominant Active TCRα Expression in Transgenic Mice

A T cell receptor (TCR) consists of α- and β-chains. Accumulating evidence suggests that some TCRs possess chain centricity, i.e., either of the hemi-chains can dominate in antigen recognition and dictate the TCR’s specificity. The introduction of TCRα/β into naive lymphocytes generates antigen-specific T cells that are ready to perform their functions. Transgenesis of the dominant active TCRα creates transgenic animals with improved anti-tumor immune control, and adoptive immunotherapy with TCRα-transduced T cells provides resistance to infections. However, the potential detrimental effects of the dominant hemi-chain TCR’s expression in transgenic animals have not been well investigated. Here, we analyzed, in detail, the functional status of the immune system of recently generated 1D1a transgenic mice expressing the dominant active TCRα specific to the H2-Kb molecule. In their age dynamics, neither autoimmunity due to the random pairing of transgenic TCRα with endogenous TCRβ variants nor significant disturbances in systemic homeostasis were detected in these mice. Although the specific immune response was considerably enhanced in 1D1a mice, responses to third-party alloantigens were not compromised, indicating that the expression of dominant active TCRα did not limit immune reactivity in transgenic mice. Our data suggest that TCRα transgene expression could delay thymic involution and maintain TCRβ repertoire diversity in old transgenic mice. The detected changes in the systemic homeostasis in 1D1a transgenic mice, which are minor and primarily transient, may indicate variations in the ontogeny of wild-type and transgenic mouse lines.

Plasma Proteomics Unveil Novel Immune Signatures and Biomarkers upon SARS-CoV-2 Infection

Several elements have an impact on COVID-19, including comorbidities, age and sex. To determine the protein profile changes in peripheral blood caused by a SARS-CoV-2 infection, a proximity extension assay was used to quantify 1387 proteins in plasma samples among 28 Finnish patients with COVID-19 with and without comorbidities and their controls. Key immune signatures, including CD4 and CD28, were changed in patients with comorbidities. Importantly, several unreported elevated proteins in patients with COVID-19, such as RBP2 and BST2, which show anti-microbial activity, along with proteins involved in extracellular matrix remodeling, including MATN2 and COL6A3, were identified. RNF41 was downregulated in patients compared to healthy controls. Our study demonstrates that SARS-CoV-2 infection causes distinct plasma protein changes in the presence of comorbidities despite the interpatient heterogeneity, and several novel potential biomarkers associated with a SARS-CoV-2 infection alone and in the presence of comorbidities were identified. Protein changes linked to the generation of SARS-CoV-2-specific antibodies, long-term effects and potential association with post-COVID-19 condition were revealed. Further study to characterize the identified plasma protein changes from larger cohorts with more diverse ethnicities of patients with COVID-19 combined with functional studies will facilitate the identification of novel diagnostic, prognostic biomarkers and potential therapeutic targets for patients with COVID-19.

LC-MS/MS: A sensitive and selective analytical technique to detect COVID-19 protein biomarkers in the early disease stage

INTRODUCTION

The COVID-19 outbreak has put enormous pressure on the scientific community to detect infection rapidly, identify the status of disease severity, and provide an immediate vaccine/drug for the treatment. Relying on immunoassay and a real-time reverse transcription polymerase chain reaction (rRT-PCR) led to many false-negative and false-positive reports. Therefore, detecting biomarkers is an alternative and reliable approach for determining the infection, its severity, and disease progression. Recent advances in liquid chromatography and mass spectrometry (LC-MS/MS) enable the protein biomarkers even at low concentrations, thus facilitating clinicians to monitor the treatment in hospitals.

AREAS COVERED

This review highlights the role of LC-MS/MS in identifying protein biomarkers and discusses the clinically significant protein biomarkers such as Serum amyloid A, Interleukin-6, C-Reactive Protein, Lactate dehydrogenase, D-dimer, cardiac troponin, ferritin, Alanine transaminase, Aspartate transaminase, gelsolin and galectin-3-binding protein in COVID-19, and their analysis by LC-MS/MS in the early stage.

EXPERT OPINION

Clinical doctors monitor significant biomarkers to understand, stratify, and treat patients according to disease severity. Knowledge of clinically significant COVID-19 protein biomarkers is critical not only for COVID-19 caused by the coronavirus but also to prepare us for future pandemics of other diseases in detecting by LC-MS/MS at the early stages.