Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods

Proteomic signatures for fibrosis in MASLD: a biopsy-proven dual-cohort study

OBJECTIVES

Predicting disease progression in metabolic dysfunction-associated steatotic liver disease (MASLD) is challenging, and current non-invasive tests (NITs) lack the precision to replace liver biopsy. This study aimed to identify plasma biomarkers for different stages of fibrosis using affinity-based proteomics in two biopsy-proven cohorts. The primary objective was to identify biomarkers capable of distinguishing between low-to-no fibrosis (F0-1) and significant fibrosis (F2-4) in MASLD.

MATERIALS AND METHODS

Participants in the discovery cohort were recruited from Uppsala University Hospital and Swedish CArdioPulmonary bioImage Study (SCAPIS), while the validation cohort was included from Linköping University Hospital. All participants diagnosed with MASLD underwent liver biopsy and were categorized by fibrosis stage (F0-1 or F2-4). A total of 276 plasma proteins were analyzed using Olink® panels, with biomarkers identified through ordinal logistic regression, random forest (RF) analysis and the Boruta algorithm.

RESULTS

The discovery cohort included 60 participants, with 60% having fibrosis stage F0-1 and 40% having F2-4. The validation cohort had 59 participants, of whom 35 had fibrosis stage F0-1 (59.3%) and 24 had stage F2-4 (40.7%). Five biomarkers were significantly associated with fibrosis stage in the discovery cohort, with four confirmed in the validation cohort. A model combining angiotensin converting enzyme-2 (ACE2), hepatocyte growth factor (HGF) and insulin-like growth factor-binding protein-7 (IGFBP-7) demonstrated strong predictive performance for significant fibrosis (c-statistics 0.82-0.83), outperforming fibrosis-4 (FIB-4) (c-statistics 0.61-0.72).

CONCLUSIONS

A biomarker model including ACE2, HGF and IGFBP7 shows promise in distinguishing between low-stage and significant fibrosis.

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.

Prolonged fasting promotes systemic inflammation and platelet activation in humans: A medically supervised, water-only fasting and refeeding study

OBJECTIVE

Prolonged fasting (PF), defined as abstaining from energy intake for ≥4 consecutive days, has gained interest as a potential health intervention. However, the biological effects of PF on the plasma proteome are not well understood.

METHODS

In this study, we investigated the effects of a medically supervised water-only fast (mean duration: 9.8 ± 3.1 days), followed by 5.3 ± 2.4 days of guided refeeding, in 20 middle-aged volunteers (mean age: 52.2 ± 11.8 years; BMI: 28.8 ± 6.4 kg/m2).

RESULTS

Fasting resulted in a 7.7% mean weight loss and significant increases in serum beta-hydroxybutyrate (BHB), confirming adherence. Untargeted high-dimensional plasma proteomics (SOMAScan, 1,317 proteins) revealed multiple adaptations to PF, including preservation of skeletal muscle and bone, enhanced lysosomal biogenesis, increased lipid metabolism via PPARα signaling, and reduced amyloid fiber formation. Notably, PF significantly reduced circulating amyloid beta proteins Aβ40 and Aβ42, key components of brain amyloid plaques. In addition, PF induced an acute inflammatory response, characterized by elevated plasma C-reactive protein (CRP), hepcidin, midkine, and interleukin 8 (IL-8), among others. A retrospective cohort analysis of 1,422 individuals undergoing modified fasting confirmed increased CRP levels (from 2.8 ± 0.1 to 4.3 ± 0.2 mg/L). The acute phase response, associated with transforming growth factor (TGF)-β signaling, was accompanied by increased platelet degranulation and upregulation of the complement and coagulation cascade, validated by ELISAs in blood and urine.

CONCLUSIONS

While the acute inflammatory response during PF may serve as a transient adaptive mechanism, it raises concerns regarding potential cardiometabolic effects that could persist after refeeding. Further investigation is warranted to elucidate the long-term molecular and clinical implications of PF across diverse populations.

Histo-blood group ABO system transferase plasma levels and risk of future venous thromboembolism: the HUNT study

ABSTRACT

The non-O blood group is a well-established risk factor for venous thromboembolism (VTE). However, the association between plasma levels of the histo-blood group ABO system transferase (BGAT), the gene product of the ABO locus, and VTE risk remains unclear. We aimed to investigate the association between plasma BGAT levels and risk of future VTE, and whether this relationship was mediated by plasma von Willebrand factor (VWF) or coagulation factor VIII (FVIII), as VWF is glycosylated by BGAT. Incident VTE-cases (n = 294) and a randomly sampled age- and-sex-weighted subcohort (n = 1066) were derived from the third survey of the Trøndelag Health Study. Baseline plasma samples (2006-2008) were subjected to the SomaScan aptamer-based-7K platform for protein measurements. Weighted Cox regression was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) across BGAT quartiles. We found that ABO haplotypes (A1/A2/B/O1/O2) explained ≈80% of the BGAT plasma variability. Participants with BGAT levels in the highest quartile had 2-fold higher VTE risk (HR, 2.12; 95% CI, 1.39-3.22) compared with those with BGAT in the lowest quartile in age-, sex-, and sample batch-adjusted models. The associations were particularly pronounced for unprovoked VTE (HR, 3.71; 95% CI, 1.79-7.67) and deep vein thrombosis (HR, 3.28; 95% CI, 1.63-6.59). The HRs were similar after further adjustment for body mass index, C-reactive protein, and estimated glomerular filtration rate, and moderately attenuated when adding VWF or FVIII plasma levels to the models. Our findings indicate that elevated BGAT plasma levels are associated with increased risk of future VTE beyond what is explained by VWF and FVIII.

Effects of In Vitro Hemolysis and Repeated Freeze-Thaw Cycles in Protein Abundance Quantification Using the SomaScan and Olink Assays

SomaScan and Olink are affinity-based platforms that aim to estimate the relative abundance of thousands of human proteins with a broad range of endogenous concentrations. In this study, we investigated the effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification across 10,776 (11 K SomaScan) and 1472 (Olink Explore 1536) analytes, respectively. Using SomaScan, we found two distinct groups, each one consisting of 4% of all aptamers, affected by either hemolysis or freeze-thaw cycles. Using Olink, we found 6% of analytes affected by freeze-thaw cycles and nearly half of all measured probes significantly impacted by hemolysis. Moreover, we observed that Olink probes affected by hemolysis target proteins with a larger number of annotated protein-protein interactions. We found that Olink probes affected by hemolysis were significantly associated with the erythrocyte proteome, whereas SomaScan probes were not. Given the extent of the observed nuisance effects, we propose that unbiased, quantitative methods of evaluating hemolysis, such as the hemolysis index successfully implemented in many clinical laboratories, should be adopted in proteomics studies. We provide detailed results for each SomaScan and Olink probe in the form of extensive Supporting Information files to be used as resources for the growing user communities of both platforms.

Effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification using the SomaScan and Olink assays

SomaScan and Olink are affinity-based platforms that aim to estimate the relative abundance of thousands of human proteins with a broad range of endogenous concentrations. In this study, we investigated the effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification across 10,776 (11K SomaScan) and 1472 (Olink Explore 1536) analytes, respectively. Using SomaScan, we found two distinct groups, each one consisting of 4% of all aptamers, affected by either hemolysis or freeze-thaw cycles. Using Olink, we found 6% of analytes affected by freeze-thaw cycles and nearly half of all measured probes significantly impacted by hemolysis. Moreover, we observed that Olink probes affected by hemolysis target proteins with a larger number of annotated protein-protein interactions. We found that Olink probes affected by hemolysis were significantly associated with the erythrocyte proteome, whereas SomaScan probes were not. Given the extent of the observed nuisance effects, we propose that unbiased, quantitative methods of evaluating hemolysis, such as the hemolysis index successfully implemented in many clinical laboratories, should be adopted in proteomics studies. We provide detailed results for each SomaScan and Olink probe in the form of extensive Supplementary Data files to be used as resources for the growing user communities of both platforms.

Genetic determinants and clinical significance of circulating and tumor-specific levels of insulin-like growth factor binding protein 7 (IGFBP7) in a Swedish breast cancer cohort

Previous research indicates that insulin-like growth factor binding protein 7 (IGFBP7) protein levels in breast cancer tissue and blood are prognostic. However, genetic determinants of IGFBP7 in breast cancer remain largely unexplored. We examined IGFBP7 in a cohort of 1701 patients with first breast cancer from Sweden, enrolled prior to surgery 2002-16 and followed for up to 15 years. Genotyping was performed on blood samples using OncoArray. Tumor-specific protein levels of IGFBP7, insulin receptor (InsR), and IGF-I receptor (IGFIR) were assessed on tumor tissue microarrays in 964 patients. Furthermore, 275 patients had plasma IGFBP7 levels measured. A genetic proxy marker for circulating IGFBP7 levels was constructed from five candidate single-nucleotide polymorphisms (SNPs) (rs6852762, rs1714014, rs9992658, rs10004910, and rs4865180) based on number of recessive genotypes. Age-adjusted linear regression was used to evaluate SNPs and tumor-specific IGFBP7 levels in relation to circulating IGFBP7 levels. Cox regression adjusted for age, tumor characteristics, and adjuvant treatments was used to assess associations with clinical outcomes. Circulating and tumor-specific IGFBP7 levels were significantly positively correlated. High circulating and genetically predicted IGFBP7 levels were associated with increased risk for distant metastasis and all-cause mortality. A significant interaction between high tumor-specific IGFBP7 levels and membrane-bound InsR resulted in a four-fold increased risk of breast cancer events and distant metastases. Both measured and genetically predicted IGFBP7 levels were independent prognostic biomarkers in breast cancer.

Correlations Within and Between Highly Multiplexed Proteomic Assays of Human Plasma

INTRODUCTION

The number of assays on proteomic platforms has grown rapidly. The leading platforms, SomaScan and Olink, have strengths and limitations. Comparisons of precision on the latest platforms-SomaScan 11k and Olink Explore HT-have not yet been established.

METHODS

Among 102 participants in the Atherosclerosis Risk in Communities Study (mean age 74 years, 53% women, 47% Black), we used split plasma samples to measure platform precision. CV and Spearman correlations were calculated for each assay. Cross-platform agreement was assessed for overlapping proteins.

RESULTS

SomaScan 11k demonstrated a median correlation of 0.85 for the 10 778 assays and a median CV of 6.8%, similar precision to earlier versions. The 5420 assays on Olink Explore HT exhibited a median correlation of 0.65 and median CV of 35.7%, which was higher than observed in its predecessors (e.g., 19.8% for Olink Explore 3072). Precision of Olink assays was inversely correlated with the percentage of samples above the limit of detection (LOD) (r = -0.77). Upon replacing Olink values below the LOD with values half the LOD, the median correlation for Olink assays measured in duplicate increased to 0.79; the median CV decreased to 13.3%. The distribution of between-platform correlations for the 4443 overlapping proteins had peaks at r approximately 0 and at r approximately 0.8. One-tenth of the protein pairs had cross-platform correlations r ≥ 0.8.

CONCLUSIONS

Precision of these 2 proteomics platforms in human plasma has diverged as the coverage has increased. These results highlight the need for careful consideration in platform selection based on specific research requirements.

Plasma proteomics-based brain aging signature and incident dementia risk

Investigating brain-enriched proteins with machine learning methods may enable a brain-specific understanding of brain aging and provide insights into the molecular mechanisms and pathological pathways of dementia. The study aims to analyze associations of brain-specific plasma proteomic aging signature with risks of incident dementia. In 45,429 dementia-free UK Biobank participants at baseline, we generated a brain-specific biological age using 63 brain-enriched plasma proteins with machine learning methods. The brain age gap was estimated, and Cox proportional hazards models were used to study the association with incident all-cause dementia, Alzheimer's disease (AD), and vascular dementia. Per-unit increment in the brain age gap z-score was associated with significantly higher risks of all-cause dementia (hazard ratio [95% confidence interval], 1.67 [1.56-1.79], P < 0.001), AD (1.85 [1.66-2.08], P < 0.001), and vascular dementia (1.86 [1.55-2.24], P < 0.001), respectively. Notably, 2.1% of the study population exhibited extreme old brain aging defined as brain age gap z-score > 2, correlating with over threefold increased risks of all-cause dementia and vascular dementia (3.42 [2.25-5.20], P < 0.001, and 3.41 [1.05-11.13], P = 0.042, respectively), and fourfold increased risk of AD (4.45 [2.32-8.54], P < 0.001). The associations were stronger among participants with healthier lifestyle factors (all P-interaction < 0.05). These findings were corroborated by magnetic resonance imaging assessments showing that a higher brain age gap aligns global pathophysiology of dementia, including global and regional atrophy in gray matter, and white matter lesions (P < 0.001). The brain-specific proteomic age gap is a powerful biomarker of brain aging, indicative of dementia risk and neurodegeneration.

Clinical Proteomics, Quo Vadis?

The field of clinical proteomics has seen enormous growth in the past 20 years, with over 40,000 scientific manuscripts published to date. At the same time, actual clinical application of the reported findings is obviously scarce. In this viewpoint article, we discuss the key issues that may be responsible for this apparent lack of success. We conclude that success must not be assessed based on the number of publications, but via the impact on patient management and treatment. We proceed with specific suggestions for potential solutions, which include keeping a strict focus on potential patient benefit. We hope this article can help shape the field, so it can in fact deliver on its realistic promise to bring significant improvement in management and care to patients.

Large-scale proteomic analyses of incident Parkinson's disease reveal new pathophysiological insights and potential biomarkers

The early pathophysiology of Parkinson's disease (PD) is poorly understood. We analyzed 2,920 Olink-measured plasma proteins in 51,804 UK Biobank participants, identifying 859 incident PD cases after 14.45 years. We found 38 PD-related proteins, with six of the top ten validated in the Parkinson's Progression Markers Initiative (PPMI) cohort. ITGAV, HNMT and ITGAM showed consistent significant association (hazard ratio: 0.11-0.57, P = 6.90 × 10-24 to 2.10 × 10-11). Lipid metabolism dysfunction was evident 15 years before PD onset, and levels of BAG3, HPGDS, ITGAV and PEPD continuously decreased before diagnosis. These proteins were linked to prodromal symptoms and brain measures. Mendelian randomization suggested ITGAM and EGFR as potential causes of PD. A predictive model using machine learning combined the top 16 proteins and demographics, achieving high accuracy for 5-year (area under the curve (AUC) = 0.887) and over-5-year PD prediction (AUC = 0.816), outperforming demographic-only models. It was externally validated in PPMI (AUC = 0.802). Our findings reveal early peripheral pathophysiological changes in PD crucial for developing early biomarkers and precision therapies.

Protein Alterations in Patients with Delirium after Cardiac Surgery: An Exploratory Case-Control Substudy of the VISION Cardiac Surgery Biobank

BACKGROUND

Delirium is an acute state of confusion associated with adverse postoperative outcomes. Delirium is diagnosed clinically using screening tools; most cases go undetected. Identifying a delirium biomarker would allow for accurate diagnosis, application of therapies, and insight into causal pathways. To agnostically discover novel biomarkers of delirium, a case-control substudy was conducted using the Vascular Events in Surgery Patients Cohort Evaluation (VISION) Cardiac Surgery Biobank. The objective was to identify candidate biomarkers to investigate in future studies.

METHODS

The study gathered a convenience sample of 30 patients with delirium on postoperative day 1 matched to 30 controls by age, sex, ethnicity, center, and cardiopulmonary bypass time. The Olink Explore 3K platform was used to identify blood protein alterations on postoperative day 3. Protein concentrations were expressed as normalized protein expression units (log 2 fold scale). Protein expression was compared between cases and controls using a paired t test and identified significantly different biomarkers based on a false discovery rate-adjusted P value of less than 0.05.

RESULTS

Of 2,865 unique serum proteins, 26 (0.9%) were significantly associated with delirium status; all were elevated in cases versus controls at a false discovery rate of less than 0.05. Pathway analysis identified calcium-release channel activity ( Padj = 0.02) and GTP-binding ( Padj = 0.005) functions as characteristic of proteins associated with delirium. The top three differentially expressed biomarkers were FKBP1B ( Padj = 0.003), C2CD2L ( Padj = 0.004), and RAB6B ( Padj = 0.004). The inflammatory biomarker interleukin-8 (CXCL8; mean difference = 2.36; P = 3.6 × 10- 4 ) was also associated with delirium.

CONCLUSIONS

The study identified 26 biomarkers significantly associated with delirium; all are novel except for interleukin-8. An association between delirium and recognized neuroinflammatory proteins or markers of brain injury was not identifed, which supports using biomarkers to differentiate between delirium and other neurologic conditions. While exploratory, the study's findings support using biomarkers to diagnose postoperative delirium and validate using agnostic screens to identify potential delirium biomarkers.

Deep, Unbiased, and Quantitative Mass Spectrometry-Based Plasma Proteome Analysis of Individual Responses to mRNA COVID-19 Vaccine

Global campaign against COVID-19 have vaccinated a significant portion of the world population in recent years. Combating the COVID-19 pandemic with mRNA vaccines played a pivotal role in the global immunization effort. However, individual responses to a vaccine are diverse and lead to varying vaccination efficacy. Despite significant progress, a complete understanding of the molecular mechanisms driving the individual immune response to the COVID-19 vaccine remains elusive. To address this gap, we combined a novel nanoparticle-based proteomic workflow with tandem mass tag (TMT) labeling, to quantitatively assess the proteomic changes in a cohort of 12 volunteers following two doses of the Pfizer-BioNTech mRNA COVID-19 vaccine. This optimized protocol seamlessly integrates comprehensive proteome analysis with enhanced throughput by leveraging the enrichment of low-abundant plasma proteins by engineered nanoparticles. Our data demonstrate the ability of this workflow to quantify over 3,000 proteins, providing the deepest view into COVID-19 vaccine-related plasma proteome study. We identified 69 proteins with boosted responses post-second dose and 74 proteins differentially regulated between individuals who contracted COVID-19 despite vaccination and those who did not. These findings offer valuable insights into individual variability in response to vaccination, demonstrating the potential of personalized medicine approaches in vaccine development.

Biomarker identification for Alzheimer's disease through integration of comprehensive Mendelian randomization and proteomics data

BACKGROUND

Alzheimer's disease (AD) is the main cause of dementia with few effective therapies. We aimed to identify potential plasma biomarkers or drug targets for AD by investigating the causal association between plasma proteins and AD by integrating comprehensive Mendelian randomization (MR) and multi-omics data.

METHODS

Using two-sample MR, cis protein quantitative trait loci (cis-pQTLs) for 1,916 plasma proteins were used as an exposure to infer their causal effect on AD liability in individuals of European ancestry, with two large-scale AD genome-wide association study (GWAS) datasets as the outcome for discovery and replication. Significant causal relationships were validated by sensitivity analyses, reverse MR analysis, and Bayesian colocalization analysis. Additionally, we investigated the causal associations at the transcriptional level with cis gene expression quantitative trait loci (cis-eQTLs) data across brain tissues and blood in European ancestry populations, as well as causal plasma proteins in African ancestry populations.

RESULTS

In those of European ancestry, the genetically predicted levels of five plasma proteins (BLNK, CD2AP, GRN, PILRA, and PILRB) were causally associated with AD. Among these five proteins, GRN was protective against AD, while the rest were risk factors. Consistent causal effects were found in the brain for cis-eQTLs of GRN, BLNK, and CD2AP, while the same was true for PILRA in the blood. None of the plasma proteins were significantly associated with AD in persons of African ancestry.

CONCLUSIONS

Comprehensive MR analyses with multi-omics data identified five plasma proteins that had causal effects on AD, highlighting potential biomarkers or drug targets for better diagnosis and treatment for AD.

Plasma proteome variation and its genetic determinants in children and adolescents

Our current understanding of the determinants of plasma proteome variation during pediatric development remains incomplete. Here, we show that genetic variants, age, sex and body mass index significantly influence this variation. Using a streamlined and highly quantitative mass spectrometry-based proteomics workflow, we analyzed plasma from 2,147 children and adolescents, identifying 1,216 proteins after quality control. Notably, the levels of 70% of these were associated with at least one of the aforementioned factors, with protein levels also being predictive. Quantitative trait loci (QTLs) regulated at least one-third of the proteins; between a few percent and up to 30-fold. Together with excellent replication in an additional 1,000 children and 558 adults, this reveals substantial genetic effects on plasma protein levels, persisting from childhood into adulthood. Through Mendelian randomization and colocalization analyses, we identified 41 causal genes for 33 cardiometabolic traits, emphasizing the value of protein QTLs in drug target identification and disease understanding.

Self-assembled aptamer nanoparticles for enhanced recognition and anticancer therapy through a lysosome-independent pathway

Aptamers and aptamer-drug conjugates (ApDCs) have shown some success as targeted therapies in cancer theranostics. However, their stability in complex media and their capacity to evade lysosomal breakdown still need improvement. To address these challenges, we herein developed a one-step self-assembly strategy to improve the stability of aptamers or ApDCs, while simultaneously enhancing their delivery performance and therapeutic efficiency through a lysosome-independent pathway. This strategy involves the formation of stable complexes between disulfide monomer and aptamers (Sgc8) or ApDCs (Gem-Sgc8). Self-assembled Sgc8 NPs resisted nuclease degradation for up to 24 h, whereas the aptamer alone degraded within just 3 h. These self-assembled Sgc8 NPs, as well as Gem-Sgc8 NPs, demonstrated enhanced binding capabilities compared to Sgc8 aptamers or Gem-Sgc8 alone. Furthermore, lysosome-independent cellular uptake was significantly improved, which in turn increased the therapeutic efficacy of Gem-Sgc8 NPs by 2.5 times compared to Gem-Sgc8 alone. In vivo results demonstrated that Gem-Sgc8 NPs can effectively suppress the growth of tumors. The same self-assembly strategy was successfully applied to other aptamers, such as MJ5C and cMET, showing the generalizability of our method, Overall, this aptamer self-assembly strategy not only overcomes the limitations associated with instability and lysosomal degradation but also demonstrates its broad applicability, highlighting its potential as a promising avenue for advancing targeted cancer theranostics. STATEMENT OF SIGNIFICANCE: We developed a one-step self-assembly strategy to improve the stability of aptamers or ApDCs and enhance their drug therapeutic efficiency through a lysosome-independent pathway. The stability of self-assembled Sgc8 nanoparticles (NPs) was significantly improved. The resulting Sgc8 NPs or GEM-Sgc8 NPs exhibited enhanced binding ability compared to Sgc8 aptamers or GEM-Sgc8 alone, and they also facilitated lysosome-independent cellular uptake, resulting in a 2.5-fold increase in therapeutic efficacy of GEM-Sgc8-NPs. The same self-assembly strategy was successfully applied to other aptamers, such as MJ5C and cMET, showing the generalizability of our method.

Cardio-metabolic-related plasma proteins reveal biological links between cardiovascular diseases and fragility fractures: a cohort and Mendelian randomisation investigation

BACKGROUND

How cardiovascular diseases (CVD) predispose to a higher risk of fragility fractures is not well understood. Both contribute to significant components of disease burden and health expenditure. Poor bone quality, central obesity, sarcopenia, falls, and low grip strength are independent risk factors for hip and other fragility fractures and also for CVD and early death.

METHODS

We used proteomics and a cohort design combined with Mendelian randomisation analysis to understand shared mechanisms for developing CVD and fragility fractures, two significant sources of disease burden and health expenditure. We primarily aimed to discover and replicate the association of 274 cardio-metabolic-related proteins with future rates of hip and any fracture in two separate population-based cohorts, with a total of 12,314 women and men.

FINDINGS

The average age at baseline was 68 years in the discovery cohort of women and 74 years in the mixed-sex replication cohort. During 100,619 person-years of follow-up, 2168 had any fracture, and 538 had a hip fracture. Our analysis resulted in 24 cardiometabolic proteins associated with fracture risk: 20 with hip fracture, 9 with any fracture, and 5 with both. The associations remained even if protein concentrations were measured from specimens taken during preclinical stages of cardio-metabolic diseases, and 19 associations remained after adjustment for bone mineral density. Twenty-two of the proteins were associated with total body fat mass or lean body mass. Mendelian randomisation (MR) analysis supported causality since genetically predicted levels of SOST (Sclerostin), CCDC80 (Coiled-coil domain-containing protein 80), NT-proBNP (N-terminal prohormone brain natriuretic peptide), and BNP (Brain natriuretic peptide) were associated with risk of hip fracture. MR analysis also revealed a possible negative impact on bone mineral density (BMD) by genetically predicted higher levels of SOST, CCDC80, and TIMP4 (Metalloproteinase inhibitor 4). The MR association with BMD was positive for PTX3 (Pentraxin-related protein) and SPP1 (Osteopontin). Genetically predicted higher concentrations of SOST and lower concentrations of SPP1 also conferred a higher risk of falls and lowered grip strength. The genetically determined concentration of nine proteins influenced fat mass, and one influenced lean body mass.

INTERPRETATION

These data reveal biological links between cardiovascular diseases and fragility fractures. The proteins should be further evaluated as shared targets for developing pharmacological interventions to prevent fractures and cardiovascular disease.

FUNDING

The study was supported by funding from the Swedish Research Council (https://www.vr.se; grants No. 2015-03257, 2017-00644, 2017-06100, and 2019-01291 to Karl Michaëlsson) and funding from Olle Engkvist Byggmästares stiftelse (SOEB).

Comparative studies of 2168 plasma proteins measured by two affinity-based platforms in 4000 Chinese adults

Proteomics offers unique insights into human biology and drug development, but few studies have directly compared the utility of different proteomics platforms. We measured plasma levels of 2168 proteins in 3976 Chinese adults using both Olink Explore and SomaScan platforms. The correlation of protein levels between platforms was modest (median rho = 0.29), with protein abundance and data quality parameters being key factors influencing correlation. For 1694 proteins with one-to-one matched reagents, 765 Olink and 513 SomaScan proteins had cis-pQTLs, including 400 with colocalising cis-pQTLs. Moreover, 1096 Olink and 1429 SomaScan proteins were associated with BMI, while 279 and 154 proteins were associated with risk of ischaemic heart disease, respectively. Addition of Olink and SomaScan proteins to conventional risk factors for ischaemic heart disease improved C-statistics from 0.845 to 0.862 (NRI: 12.2%) and 0.863 (NRI: 16.4%), respectively. These results demonstrate the utility of these platforms and could inform the design and interpretation of future studies.

Cross-Ancestry Comparison of Aptamer and Antibody Proteomics Measures

Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants (pQTL) and phenotypes. Here, we examined 2,157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1,930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2,157 proteins followed a bimodal distribution (median r=0.30). Protein measures from each platform were associated with genetic variants (pQTLs), and one-third of the pQTL signals were driven by protein-altering variants (PAVs). We highlight 80 proteins that correlate differently across ancestry groups likely due to differing PAV frequencies by ancestry. Furthermore, adjustment for PAVs with opposite directions of effect by platform improved inter-platform protein measure correlation and resulted in more concordant genetic and phenotypic associations. Hence, PAVs need to be accounted for across ancestries to facilitate platform-concordant and accurate protein measurement.

High-affinity ssDNA aptamer and chemiluminescent aptasensor for TIMP-1 detection in human serum

TIMP-1 (Tissue Inhibitor of Metalloproteinases-1) is a protein involved in regulating extracellular matrix (ECM) degradation. It is recognized as a significant biomarker for cancer diagnosis. This study aimed to develop and characterize a single-stranded DNA (ssDNA) aptamer targeting human TIMP-1 protein with high affinity and specificity. A magnetic beads-based SELEX process combined with qPCR was used to select aptamers over seven rounds. The enriched ssDNA library was analyzed using high-throughput sequencing to identify candidate sequences, and these sequences were characterized using surface plasmon resonance (SPR) and binding assays to evaluate their affinity and specificity. The selected ssDNA aptamer demonstrated a dissociation equilibrium constant (KD) of 0.41 nM and a very slow off-rate, enabling effective capture of TIMP-1 in serum samples. Furthermore, a chemiluminescent aptasensor was developed for TIMP-1 detection, which exhibited high specificity and a broad linear detection range from 1 to 500 ng/mL in human serum. The developed ssDNA aptamer targeting TIMP-1 shows high affinity and specificity, and the chemiluminescent aptasensor demonstrates promising potential for clinical diagnosis of TIMP-1 levels in human serum.

Cross-platform proteomics signatures of extreme old age

In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.

Plasma proteomics for cognitive decline and dementia-A Southeast Asian cohort study

INTRODUCTION

The prognostic utility of plasma proteomics for cognitive decline and dementia in a Southeast Asian population characterized by high cerebrovascular disease (CeVD) burden is underexplored.

METHODS

We examined this in a Singaporean memory clinic cohort of 528 subjects (n = 300, CeVD; n = 167, incident cognitive decline) followed-up for 4 years.

RESULTS

Of 1441 plasma proteins surveyed, a 12-protein signature significantly predicted cognitive decline (q-value < .05). Sixteen diverse biological processes were implicated in cognitive decline. Ten proteins independently predicted incident dementia (q-value < .05). A unified prediction model combining plasma proteins with clinical risk factors increased the area under the curve for outcome prediction from 0.62 to 0.85. External validation in the cerebrospinal fluid proteome of an independent Caucasian cohort replicated four of the significantly predictive plasma markers for cognitive decline namely: GFAP, NEFL, AREG, and PPY.

DISCUSSION

The prognostic proteins prioritized in our study provide robust signals in two different biological matrices, representing potential mechanistic targets for dementia and cognitive decline.

HIGHLIGHTS

A total of 1441 plasma proteins were profiled in a Singaporean memory clinic cohort. We report prognostic plasma protein signatures for cognitive decline and dementia. External validation was performed in the cerebrospinal fluid proteome of a Caucasian cohort. A concordant proteomic signature was identified across both biofluids and cohorts. Further studies are needed to explore the therapeutic implications of these proteins for dementia.

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

The Proteomics of MASLD Progression: Insights From Functional Analysis to Drive the Development of New Therapeutic Solutions

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading chronic liver disease worldwide, with alarming prevalence reaching epidemic proportions.

AIMS AND METHODS

The objective of this study is to provide a comprehensive review of the latest blood proteomics studies on MASLD and metabolic dysfunction-associated steatohepatitis (MASH), with emphasis on fibrosis. Furthermore, our objective is to conduct an analysis of protein pathways and interactions by integrating proteomics data using functional enrichment analysis of the deregulated proteins.

RESULTS

Notwithstanding the considerable discrepancies in the methodology and the number of proteins examined in the circulation, the analysis reveals a consistent pattern among the list of proteins that are decreased or increased in the blood of the affected patients. The relevant biological processes (BP) associated with down- and upregulated proteins are high-density lipoprotein remodelling and complement activation, respectively. The protein families identified include not only those expected to be involved in the immune system and cell adhesion and migration but also ligands of glycoproteins expressed in cells that have been subjected to stress and proteins containing the Sushi domain.

CONCLUSIONS

The application of cutting-edge methodologies to investigate the blood proteome in MASH is yielding insights that facilitate the elucidation of disease mechanisms and the identification of optimal noninvasive biomarkers. However, several challenges remain to be addressed in future research, including the generalisation of results on a global scale, the optimisation of analytical technologies and the implementation of large longitudinal studies to gain insights into the molecular mechanisms that underpin the development of advanced disease.

Proteome-wide mendelian randomization identifies causal plasma proteins in interstitial lung disease

Interstitial lung disease (ILD) has shown limited treatment advancements, with minimal exploration of circulating protein biomarkers causally linked to ILD and its subtypes beyond idiopathic pulmonary fibrosis (IPF). In this study, we aimed to identify potential drug targets and circulating protein biomarkers for ILD and its subtypes. We utilized the most recent large-scale plasma protein quantitative trait loci (pQTL) data detected from the antibody-based method and ILD and its subtypes' GWAS data from the updated FinnGen database for Mendelian randomization analysis. To enhance the reliability of causal associations, we conducted external validation and sensitivity analyses, including Bayesian colocalization and bidirectional Mendelian randomization analysis. Our study identified eight plasma proteins genetically associated with ILD or its subtypes. Among these, three proteins-CDH15 (Cadherin-15), LTBR (Lymphotoxin-beta receptor), and ADAM15 (A disintegrin and metalloproteinase 15)-emerged as priority biomarkers and potential therapeutic targets, demonstrating more reliable associations by passing a series of sensitivity analyses compared to the others. Based on these findings, we propose for the first time that CDH15, ADAM15, and LTBR hold promise as novel potential circulating protein biomarkers and therapeutic targets for the diagnosis and treatment of ILD, IPF, and sarcoidosis, respectively, especially ADAM15, and these findings have the potential to provide new perspectives for advancing the research on the heterogeneity of ILD.

Geroscience in heart failure: the search for therapeutic targets in the shared pathobiology of human aging and heart failure

Age is a major risk factor for heart failure, but one that has been historically viewed as non-modifiable. Emerging evidence suggests that the biology of aging is malleable, and can potentially be intervened upon to treat age-associated chronic diseases, such as heart failure. While aging biology represents a new frontier for therapeutic target discovery in heart failure, the challenges of translating Geroscience research to the clinic are multifold. In this review, we propose a strategy that prioritizes initial target discovery in human biology. We review the rationale for starting with human omics, which has generated important insights into the shared (patho)biology of human aging and heart failure. We then discuss how this knowledge can be leveraged to identify the mechanisms of aging biology most relevant to heart failure. Lastly, we provide examples of how this human-first Geroscience approach, when paired with rigorous functional assessments in preclinical models, is leading to early-stage clinical development of gerotherapeutic approaches for heart failure.

The BAMBOO method for correcting batch effects in high throughput proximity extension assays for proteomic studies

The proximity extension assay (PEA) enables large-scale proteomic investigations across numerous proteins and samples. However, discrepancies between measurements, known as batch-effects, potentially skew downstream statistical analyses and increase the risks of false discoveries. While implementing bridging controls (BCs) on each plate has been proposed to mitigate these effects, a clear method for utilizing this strategy remains elusive. Here, we characterized batch effects in PEA proteomics and identified three types: protein-specific, sample-specific, and plate-wide. We developed a robust regression-based method called BAMBOO (Batch Adjustments using Bridging cOntrOls) to correct them. Simulations comparing BAMBOO with established correction techniques (median centering, median of the difference (MOD), and ComBat) revealed that median centering and ComBat were significantly impacted by outliers within the BCs, whereas BAMBOO and MOD were more robust when no plate-wide effects were introduced. Optimal batch correction was achieved with 10-12 BCs. We validated the simulation results using experimental data and found that BAMBOO and MOD had a reduced incidence of false discoveries compared to alternative methods. Our findings emphasize the prevalence of batch effects in PEA proteomic studies and advocate for BAMBOO as a robust and effective tool to enhance the reliability of large-scale analyses in the proteomic field.

Proteomic analysis identifies novel biological pathways that may link dietary quality to type 2 diabetes risk: evidence from African American and Asian cohorts

BACKGROUND

Diet affects the development of chronic diseases such as type 2 diabetes, but the underlying biological mechanisms are only partly understood.

OBJECTIVES

This study aimed to identify proteomic markers of the Alternative Healthy Eating Index (AHEI) and the Dietary Approaches to Stop Hypertension (DASH) diet and their association with type 2 diabetes risk.

METHODS

We examined the associations between the AHEI and DASH diet quality scores and 1317 plasma proteins in African American participants of the Jackson Heart Study (JHS, n = 1878). These findings were validated in a Singapore Multi-Ethnic Cohort (n = 2395) and examined in relation to type 2 diabetes incidence (n = 539 cases). We adjusted for multiple testing by using false discovery rate-adjusted q values.

RESULTS

We identified 13 proteins consistently associated with the AHEI or DASH scores with the strongest associations for the AHEI score and epidermal growth factor receptor (β:0.089; SE: 0.017; q < 0.001) and for the DASH score and tissue factor (β: -0.114; SE: 0.022; q < 0.001). Most of these proteins were related to inflammation, thrombosis, adipogenesis, and glucose metabolism. Concentrations of myeloperoxidase, epidermal growth factor receptor, hepatocyte growth factor receptor, coagulation factor Xa, contactin 4, kynureninase, neurogenic locus notch homolog protein 1, and vesicular integral-membrane protein VIP36 were associated with the risk of type 2 diabetes in the Asian cohort. The diabetes odds ratio for a 2-fold higher protein abundance concentration ranged from 0.03 (95% CI: 0.01, 0.08) for neurogenic locus notch homolog protein 1 to 3.04 (95% CI: 2.13, 4.33) for kynureninase. Furthermore, genetic markers for myeloperoxidase and hepatocyte growth factor receptor were significantly associated with diabetes risk.

CONCLUSIONS

Our study across geographically and ethnically diverse populations identified robust protein biomarkers for healthy dietary patterns. Furthermore, our findings suggest novel biological mechanisms linking dietary patterns with type 2 diabetes development.

Blood plasma proteomic markers of Alzheimer's disease, current status and application prospects

INTRODUCTION

Identifying early risks of developing Alzheimer's disease (AD) is a major challenge as the number of patients with AD steadily increases and requires innovative solutions. Current molecular diagnostic modalities, such as cerebrospinal fluid (CSF) testing and positron emission tomography (PET) imaging, exhibit limitations in their applicability for large-scale screening. In recent years, there has been a marked shift toward the development of blood plasma-based diagnostic tests, which offer a more accessible and clinically viable alternative for widespread use. Furthermore, advances in large-scale proteomics technologies have boosted an interest in identifying novel biomarkers and developing panels of AD-associated proteins.

AREAS COVERED

This review mainly examines the results of recent searches for proteomic markers of AD in blood plasma (from 2022-2024 PubMed), focuses on some aspects for special attention in further studies, and discusses the prospects for their further application.

EXPERT OPINION

Recent advances in AD plasma/serum proteomic studies are largely driven using novel Olink/PEA and SomaScan/aptamer technologies, which complement the 'gold standard' of MS-based quantitative proteomics (MRM/SRM), and particularly expand the capabilities for studying low-abundant proteins.

Head-to-Head Comparison of Aptamer- and Antibody-Based Proteomic Platforms in Human Cerebrospinal Fluid Samples from a Real-World Memory Clinic Cohort

High-throughput proteomic platforms are crucial to identify novel Alzheimer's disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan® 7k) and antibody-based (Olink® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and inter-platform reproducibility were evaluated through correlations between two independent SomaScan® assays analyzing the same samples, and between SomaScan® and Olink® results. Association analyses were performed between proteomic measures, CSF biological traits, sample demographics, and AD endophenotypes. Our 12-category metric of reproducibility combining correlation analyses identified 2428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.

Assessment of Inflammatory Biomarkers and Incident Atrial Fibrillation in Older Adults

BACKGROUND

Available evidence supports the importance of inflammation in atrial fibrillation (AF) pathogenesis, yet general anti-inflammatory therapies have failed to show benefit for prevention of the arrhythmia. Better understanding of the specific inflammatory pathways involved is necessary to advance therapeutics.

METHODS AND RESULTS

We evaluated 9 circulating markers of inflammation measured by immunoassays and incidence of AF in a population-based older cohort. Biomarkers included measures of general inflammation and the NLR (nucleotide-binding oligomerization domain-like receptor) family pyrin domain containing 3 inflammasome, TNF-α (tumor necrosis factor α), monocyte activation markers, and sIL-2 (soluble interleukin-2). Among 5726 participants (median age 72 years), 1836 developed AF over median follow-up of 11.5 years. After adjustment for conventional risk factors, 5 biomarkers were positively associated with incident AF: IL-6 (interleukin-6), hazard ratio (HR), 1.14 (95% CI, 1.07-1.21); hs-CRP (high-sensitivity C-reactive protein), HR, 1.05 (95% CI, 1.01-1.09); white blood cell count, HR, 1.18 (95% CI, 1.04-1.35); sTNFR1 (soluble TNF receptor 1), HR, 1.21 (95% CI, 1.05-1.39); and sIL-2Rα (sIL-2 receptor α), HR, 1.16 (95% CI, 1.05-1.29) (all per doubling of biomarker). sCD14, sCD163, IL-18, and IL-1 receptor antagonist showed no association with AF. Upon concurrent adjustment for all biomarkers, only IL-6 remained significantly associated with the arrhythmia, HR, 1.17 (95% CI, 1.07-1.26).

CONCLUSIONS

Among older adults, IL-6, hs-CRP, white blood cell count, sTNFR1, and sIL-2Rα were positively associated with incident AF, but only IL-6 retained significance on concurrent adjustment. These findings newly document associations for sTNFR1 and sIL-2Rα and lend support to a preeminent role for IL-6 in development of this arrhythmia. The efficacy of IL-6 blockade for AF prevention awaits completion of appropriate clinical trials.

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.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

Proteomic analysis of APOEε4 carriers implicates lipid metabolism, complement and lymphocyte signaling in cognitive resilience

BACKGROUND

Apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late onset Alzheimer's disease (AD). This case-cohort study used targeted plasma biomarkers and large-scale proteomics to examine the biological mechanisms that allow some APOEε4 carriers to maintain normal cognitive functioning in older adulthood.

METHODS

APOEε4 carriers and APOEε3 homozygotes enrolled in the Women's Health Initiative Memory Study (WHIMS) from 1996 to 1999 were classified as resilient if they remained cognitively unimpaired beyond age 80, and as non-resilient if they developed cognitive impairment before or at age 80. AD pathology (Aß42/40) and neurodegeneration (NfL, tau) biomarkers, as well as 1007 proteins (Olink) were quantified in blood collected at study enrollment (on average 14 years prior) when participants were cognitively normal. We identified plasma proteins that distinguished between resilient and non-resilient APOEε4 carriers, examined whether these associations generalized to APOEε3 homozygotes, and replicated these findings in the UK Biobank.

RESULTS

A total of 1610 participants were included (baseline age: 71.3 [3.8 SD] years; all White; 42% APOEε4 carriers). Compared to resilient APOEε4 carriers, non-resilient APOEε4 carriers had lower Aß42/40/tau ratio and greater NfL at baseline. Proteomic analyses identified four proteins differentially expressed between resilient and non-resilient APOEε4 carriers at an FDR-corrected P < 0.05. While one of the candidate proteins, a marker of neuronal injury (NfL), also distinguished resilient from non-resilient APOEε3 homozygotes, the other three proteins, known to be involved in lipid metabolism (ANGPTL4) and immune signaling (PTX3, NCR1), only predicted resilient vs. non-resilient status among APOEε4 carriers (protein*genotype interaction-P < 0.05). Three of these four proteins also predicted 14-year dementia risk among APOEε4 carriers in the UK Biobank validation sample (N = 9420). While the candidate proteins showed little to no association with targeted biomarkers of AD pathology, protein network and enrichment analyses suggested that natural killer (NK) cell and T lymphocyte signaling (via PKC-θ) distinguished resilient from non-resilient APOEε4 carriers.

CONCLUSIONS

We identified and replicated a plasma proteomic signature associated with cognitive resilience among APOEε4 carriers. These proteins implicate specific immune processes in the preservation of cognitive status despite elevated genetic risk for AD. Future studies in diverse cohorts will be needed to assess the generalizability of these results.

Integrating metabolomics and proteomics to identify novel drug targets for heart failure and atrial fibrillation

BACKGROUND

Altered metabolism plays a role in the pathophysiology of cardiac diseases, such as atrial fibrillation (AF) and heart failure (HF). We aimed to identify novel plasma metabolites and proteins associating with cardiac disease.

METHODS

Mendelian randomisation (MR) was used to assess the association of 174 metabolites measured in up to 86,507 participants with AF, HF, dilated cardiomyopathy (DCM), and non-ischemic cardiomyopathy (NICM). Subsequently, we sourced data on 1567 plasma proteins and performed cis MR to identify proteins affecting the identified metabolites as well as the cardiac diseases. Proteins were prioritised on cardiac expression and druggability, and mapped to biological pathways.

RESULTS

We identified 35 metabolites associating with cardiac disease. AF was affected by seventeen metabolites, HF by nineteen, DCM by four, and NCIM by taurine. HF was particularly enriched for phosphatidylcholines (p = 0.029) and DCM for acylcarnitines (p = 0.001). Metabolite involvement with AF was more uniform, spanning for example phosphatidylcholines, amino acids, and acylcarnitines. We identified 38 druggable proteins expressed in cardiac tissue, with a directionally concordant effect on metabolites and cardiac disease. We recapitulated known associations, for example between the drug target of digoxin (AT1B2), taurine and NICM risk. Additionally, we identified numerous novel findings, such as higher RET values associating with phosphatidylcholines and decreasing AF and HF. RET is targeted by drugs such as regorafenib which has known cardiotoxic side-effects. Pathway analysis implicated involvement of GDF15 signalling through RET, and ghrelin regulation of energy homeostasis in cardiac pathogenesis.

CONCLUSIONS

This study identified 35 plasma metabolites involved with cardiac diseases and linked these to 38 druggable proteins, providing actionable leads for drug development.

Large-scale plasma proteomics in the UK Biobank modestly improves prediction of major cardiovascular events in a population without previous cardiovascular disease

AIMS

Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs).

METHODS AND RESULTS

Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACEs: fatal and non-fatal myocardial infarction, stroke, and coronary artery revascularization) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. Data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC = 0.771, P < 001, NRI = 0.140, and AUC = 0.767, P = 0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not.

CONCLUSION

Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs.

Plasma proteomics and carotid intima-media thickness in the UK biobank cohort

Background and aims

Ultrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later.

Method

We examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained (R 2).

Result

The mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest R 2 (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower R 2 (0.261, 0.228-0.294 and 0.260, 0.226-0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models.

Conclusion

Plasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology.

Proteome-Wide Genetic Investigation of Large Artery Stiffness

The molecular mechanisms contributing to large artery stiffness (LAS) are not fully understood. The aim of this study was to investigate the association between circulating plasma proteins and LAS using complementary proteomic and genomic analyses. A total of 106 proteins associated with carotid-femoral pulse-wave velocity, a noninvasive measure of LAS, were identified in 1,178 individuals from the Asklepios study cohort. Mendelian randomization analyses revealed causal effects of 13 genetically predicted plasma proteins on pulse pressure, including cartilage intermediate layer protein-2, high-temperature requirement A serine peptidase-1, and neuronal growth factor-1. These findings suggest potential novel therapeutic targets to reduce LAS and its related diseases.

Mapping biological influences on the human plasma proteome beyond the genome

Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10-77% of the variation in plasma (median 19.88%, interquartile range 14.01-31.09%), independent of technical factors (median 2.48%, interquartile range 0.78-6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein-disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.

Plasma proteomics profile-based comparison of torso versus brain injury: A prospective cohort study

BACKGROUND

Trauma-related deaths and posttraumatic sequelae are a global health concern, necessitating a deeper understanding of the pathophysiology to advance trauma therapy. Proteomics offers insights into identifying and analyzing plasma proteins associated with trauma and inflammatory conditions; however, current proteomic methods have limitations in accurately measuring low-abundance plasma proteins. This study compared plasma proteomics profiles of patients from different acute trauma subgroups to identify new therapeutic targets and devise better strategies for personalized medicine.

METHODS

This prospective observational single-center cohort study was conducted between August 2020 and September 2021 in the intensive care unit of Osaka University Hospital in Japan. Enrolling 59 consecutive patients with blunt trauma, we meticulously analyzed plasma proteomics profiles in participants with torso or head trauma, comparing them with those of controls (mild trauma). Using the Olink Explore 3072 instrument (Olink Proteomics AB, Uppsala, Sweden), we identified five endotypes (α-ε) via unsupervised hierarchical clustering.

RESULTS

The median time from injury to blood collection was 47 minutes [interquartile range, 36-64 minutes]. The torso trauma subgroup exhibited 26 unique proteins with significantly altered expression, while the head trauma subgroup showed 68 unique proteins with no overlap between the two. The identified endotypes included α (torso trauma, n = 8), β (young patients with brain injury, n = 5), γ (severe brain injury postsurgery, n = 8), δ (torso or brain trauma with mild hyperfibrinolysis, n = 18), and ε (minor trauma, n = 20). Patients with torso trauma showed changes in blood pressure, smooth muscle adaptation, hypermetabolism, and hypoxemia. Patients with traumatic brain injury had dysregulated blood coagulation and altered nerves regeneration and differentiation.

CONCLUSION

This study identified unique plasma protein expression patterns in patients with torso trauma and traumatic brain injury, helping categorize five distinct endotypes. Our findings may offer new insights for clinicians, highlighting potential strategies for personalized medicine and improved trauma-related care.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance

Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs.

Proteome- and Transcriptome-Wide Genetic Analysis Identifies Biological Pathways and Candidate Drug Targets for Preeclampsia

BACKGROUND

Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality. However, the current understanding of its underlying biological pathways remains limited.

METHODS

In this study, we performed a cross-platform proteome- and transcriptome-wide genetic analysis aimed at evaluating the causal relevance of >2000 circulating proteins with preeclampsia, supported by data on the expression of over 15 000 genes across 36 tissues leveraging large-scale preeclampsia genetic association data from women of European ancestry.

RESULTS

We demonstrate genetic associations of 18 circulating proteins with preeclampsia (SULT1A1 [sulfotransferase 1A1], SH2B3 [SH2B adapter protein 3], SERPINE2 [serpin family E member 2], RGS18 [regulator of G-protein signaling 18], PZP [pregnancy zone protein], NOTUM [notum, palmitoleoyl-protein carboxylesterase], METAP1 [methionyl aminopeptidase 1], MANEA [mannosidase endo-alpha], jun-D [JunD proto-oncogene], GDF15 [growth differentiation factor 15], FGL1 [fibrinogen like 1], FGF5 [fibroblast growth factor 5], FES [FES proto-oncogene], APOBR [apolipoprotein B receptor], ANP [natriuretic peptide A], ALDH-E2 [aldehyde dehydrogenase 2 family member], ADAMTS13 [ADAM metallopeptidase with thrombospondin type 1 motif 13], and 3MG [N-methylpurine DNA glycosylase]), among which 11 were either directly or indirectly supported by gene expression data, 9 were supported by Bayesian colocalization analyses, and 5 (SERPINE2, PZP, FGF5, FES, and ANP) were supported by all lines of evidence examined. Protein interaction mapping identified potential shared biological pathways through natriuretic peptide signaling, blood pressure regulation, immune tolerance, and thrombin activity regulation.

CONCLUSIONS

This investigation identified multiple targetable proteins linked to cardiovascular, inflammatory, and coagulation pathways, with SERPINE2, PZP, FGF5, FES, and ANP identified as pivotal proteins with likely causal roles in the development of preeclampsia. The identification of these potential targets may guide the development of targeted therapies for preeclampsia.

Proteogenomic analysis integrated with electronic health records data reveals disease-associated variants in Black Americans

BACKGROUNDMost GWAS of plasma proteomics have focused on White individuals of European ancestry, limiting biological insight from other ancestry-enriched protein quantitative loci (pQTL).METHODSWe conducted a discovery GWAS of approximately 3,000 plasma proteins measured by the antibody-based Olink platform in 1,054 Black adults from the Jackson Heart Study (JHS) and validated our findings in the Multi-Ethnic Study of Atherosclerosis (MESA). The genetic architecture of identified pQTLs was further explored through fine mapping and admixture association analysis. Finally, using our pQTL findings, we performed a phenome-wide association study (PheWAS) across 2 large multiethnic electronic health record (EHR) systems in All of Us and BioMe.RESULTSWe identified 1,002 pQTLs for 925 protein assays. Fine mapping and admixture analyses suggested allelic heterogeneity of the plasma proteome across diverse populations. We identified associations for variants enriched in African ancestry, many in diseases that lack precise biomarkers, including cis-pQTLs for cathepsin L (CTSL) and Siglec-9, which were linked with sarcoidosis and non-Hodgkin's lymphoma, respectively. We found concordant associations across clinical diagnoses and laboratory measurements, elucidating disease pathways, including a cis-pQTL associated with circulating CD58, WBC count, and multiple sclerosis.CONCLUSIONSOur findings emphasize the value of leveraging diverse populations to enhance biological insights from proteomics GWAS, and we have made this resource readily available as an interactive web portal.FUNDINGNIH K08 HL161445-01A1; 5T32HL160522-03; HHSN268201600034I; HL133870.

Proteomic Risk Score of Increased Respiratory Susceptibility: A Multi-Cohort Study

RATIONALE

Accelerated decline in lung function is associated with incident COPD, hospitalizations and death. However, identifying this trajectory with longitudinal spirometry measurements is challenging in clinical practice.

OBJECTIVE

To determine whether a proteomic risk score trained on accelerated decline in lung function can assess risk of future respiratory disease and mortality.

METHODS

In CARDIA, a population-based cohort starting in young adulthood, longitudinal measurements of FEV1 percent predicted (up to six timepoints over 30 years) were used to identify accelerated and normal decline trajectories. Protein aptamers associated with an accelerated decline trajectory were identified with multivariable logistic regression followed by LASSO regression. The proteomic respiratory susceptibility score was derived based on these circulating proteins and applied to the UK Biobank and COPDGene studies to examine associations with future respiratory morbidity and mortality.

MEASUREMENTS AND RESULTS

Higher susceptibility score was independently associated with all-cause mortality (UKBB: HR 1.56, 95%CI 1.50-1.61; COPDGene: HR 1.75, 95%CI 1.63-1.88), respiratory mortality (UKBB: HR 2.39, 95% CI 2.16-2.64; COPDGene: HR 1.83, 95%CI 1.33-2.51), incident COPD (UKBB: HR 1.84, 95%CI 1.71-1.98), incident respiratory exacerbation (COPDGene: OR 1.11, 95%CI 1.03-1.20), and incident exacerbation requiring hospitalization (COPDGene: OR 1.18, 95%CI 1.08-1.28).

CONCLUSIONS

A proteomic signature of increased respiratory susceptibility identifies people at risk of respiratory death, incident COPD, and respiratory exacerbations. This susceptibility score is comprised of proteins with well-known and novel associations with lung health and holds promise for the early detection of lung disease without requiring years of spirometry measurements.