Comparison of Proteomic Assessment Methods in Multiple Cohort Studies

Comparative Analysis of Protein Quantification by the SomaScan Assay versus Orthogonal Methods in Urine from People with Diabetic Kidney Disease

To our knowledge, calibration curves or other validations for thousands of SomaScan aptamers are not publicly available. Moreover, the abundance of urine proteins obtained from these assays is not routinely validated with orthogonal methods (OMs). We report an in-depth comparison of SomaScan readout for 23 proteins in urine samples from patients with diabetic kidney disease (n = 118) vs OMs, including liquid chromatography-targeted mass spectrometry (LC-MS), ELISA, and nephelometry. Pearson correlation between urine abundance of the 23 proteins from SomaScan 3.2 vs OMs ranged from -0.58 to 0.86, with a median (interquartile ratio, [IQR]) of 0.49 (0.18, 0.53). In multivariable linear regression, the SomaScan readout for 6 of the 23 examined proteins (26%) was most strongly associated with the OM-derived abundance of the same (target) protein. For 3 of 23 (13%), the SomaScan and OM-derived abundance of each protein were significantly associated, but the SomaScan readout was more strongly associated with OM-derived abundance of one or more "off-target" proteins. For the remaining 14 proteins (61%), the SomaScan readouts were not significantly associated with the OM-derived abundance of the targeted proteins. In 6 of the latest group, the SomaScan readout was not associated with urine abundance of any of the 23 quantified proteins. To sum, over half of the SomaScan results could not be confirmed by independent orthogonal methods.

A multidimensional classifier to support lung transplant referral in patients with pulmonary fibrosis

BACKGROUND

Lung transplantation remains the sole curative option for patients with idiopathic pulmonary fibrosis (IPF), but donor organs remain scarce, and many eligible patients die before transplant. Tools to optimize the timing of transplant referrals are urgently needed.

METHODS

Least absolute shrinkage and selection operator was applied to clinical and proteomic data generated as part of a prospective cohort study of interstitial lung disease (ILD) to derive clinical, proteomic, and multidimensional logit models of near-term death or lung transplant within 18 months of blood draw. Model-fitted values were dichotomized at the point of maximal sensitivity and specificity, and decision curve analysis was used to select the best-performing classifier. We then applied this classifier to independent IPF and non-IPF ILD cohorts to determine test performance characteristics. Cohorts were restricted to patients aged ≤72 years with body mass index 18 to 32 to increase the likelihood of transplant eligibility.

RESULTS

IPF derivation, IPF validation, and non-IPF ILD validation cohorts consisted of 314, 105, and 295 patients, respectively. A multidimensional model comprising 2 clinical variables and 20 proteins outperformed stand-alone clinical and proteomic models. Following dichotomization, the multidimensional classifier predicted near-term outcome with 70% sensitivity and 92% specificity in the IPF validation cohort and 70% sensitivity and 80% specificity in the non-IPF ILD validation cohort.

CONCLUSIONS

A multidimensional classifier of near-term outcomes accurately discriminated this end-point with good test performance across independent IPF and non-IPF ILD cohorts. These findings support refinement and prospective validation of this classifier in transplant-eligible individuals.

Epigenetic scores derived in saliva are associated with gestational age at birth

BACKGROUND

Epigenetic scores (EpiScores), reflecting DNA methylation (DNAm)-based surrogates for complex traits, have been developed for multiple circulating proteins. EpiScores for pro-inflammatory proteins, such as C-reactive protein (DNAm CRP), are associated with brain health and cognition in adults and with inflammatory comorbidities of preterm birth in neonates. Social disadvantage can become embedded in child development through inflammation, and deprivation is overrepresented in preterm infants. We tested the hypotheses that preterm birth and socioeconomic status (SES) are associated with alterations in a set of EpiScores enriched for inflammation-associated proteins.

RESULTS

In total, 104 protein EpiScores were derived from saliva samples of 332 neonates born at gestational age (GA) 22.14 to 42.14 weeks. Saliva sampling was between 36.57 and 47.14 weeks. Forty-three (41%) EpiScores were associated with low GA at birth (standardised estimates |0.14 to 0.88|, Bonferroni-adjusted p-value < 8.3 × 10-3). These included EpiScores for chemokines, growth factors, proteins involved in neurogenesis and vascular development, cell membrane proteins and receptors, and other immune proteins. Three EpiScores were associated with SES, or the interaction between birth GA and SES: afamin, intercellular adhesion molecule 5, and hepatocyte growth factor-like protein (standardised estimates |0.06 to 0.13|, Bonferroni-adjusted p-value < 8.3 × 10-3). In a preterm subgroup (n = 217, median [range] GA 29.29 weeks [22.14 to 33.0 weeks]), SES-EpiScore associations did not remain statistically significant after adjustment for sepsis, bronchopulmonary dysplasia, necrotising enterocolitis, and histological chorioamnionitis.

CONCLUSIONS

Low birth GA is substantially associated with a set of EpiScores. The set was enriched for inflammatory proteins, providing new insights into immune dysregulation in preterm infants. SES had fewer associations with EpiScores; these tended to have small effect sizes and were not statistically significant after adjusting for inflammatory comorbidities. This suggests that inflammation is unlikely to be the primary axis through which SES becomes embedded in the development of preterm infants in the neonatal period.

Plasma proteomics identify biomarkers predicting Parkinson's disease up to 7 years before symptom onset

Parkinson's disease is increasingly prevalent. It progresses from the pre-motor stage (characterised by non-motor symptoms like REM sleep behaviour disorder), to the disabling motor stage. We need objective biomarkers for early/pre-motor disease stages to be able to intervene and slow the underlying neurodegenerative process. Here, we validate a targeted multiplexed mass spectrometry assay for blood samples from recently diagnosed motor Parkinson's patients (n = 99), pre-motor individuals with isolated REM sleep behaviour disorder (two cohorts: n = 18 and n = 54 longitudinally), and healthy controls (n = 36). Our machine-learning model accurately identifies all Parkinson patients and classifies 79% of the pre-motor individuals up to 7 years before motor onset by analysing the expression of eight proteins-Granulin precursor, Mannan-binding-lectin-serine-peptidase-2, Endoplasmatic-reticulum-chaperone-BiP, Prostaglaindin-H2-D-isomaerase, Interceullular-adhesion-molecule-1, Complement C3, Dickkopf-WNT-signalling pathway-inhibitor-3, and Plasma-protease-C1-inhibitor. Many of these biomarkers correlate with symptom severity. This specific blood panel indicates molecular events in early stages and could help identify at-risk participants for clinical trials aimed at slowing/preventing motor Parkinson's disease.

Post-GWAS multiomic functional investigation of the TNIP1 locus in Alzheimer's disease highlights a potential role for GPX3

INTRODUCTION

Recent genome-wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear.

METHODS

We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS-implicated variants (rs34294852 and rs871269).

RESULTS

CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10-5) and higher CSF phosphorylated tau (p-tau) levels (P = 9.28 × 10-7). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10-6) and CSF p-tau levels (P = 4.38 × 10-9).

DISCUSSION

These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels.

HIGHLIGHTS

Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau. The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category. GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls. rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia.

Identifying novel biomarkers using proteomics to predict cancer-associated thrombosis

Comprehensive protein analyses of plasma are made possible by high-throughput proteomic screens, which may help find new therapeutic targets and diagnostic biomarkers. Patients with cancer are frequently affected by venous thromboembolism (VTE). The limited predictive accuracy of current VTE risk assessment tools highlights the need for new, more targeted biomarkers. Although coagulation biomarkers for the diagnosis, prognosis, and treatment of VTE have been investigated, none of them have the necessary clinical validation or diagnostic accuracy. Proteomics holds the potential to uncover new biomarkers and thrombotic pathways that impact the risk of thrombosis. This review explores the fundamental methods used in proteomics and focuses on particular biomarkers found in VTE and cancer-associated thrombosis.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Protein-Protein interactive networks identified in bronchoalveolar lavage of severe compared to nonsevere asthma

INTRODUCTION

Previous bronchoalveolar lavage fluid (BALF) proteomic analysis has evaluated limited numbers of subjects for only a few proteins of interest, which may differ between asthma and normal controls. Our objective was to examine a more comprehensive inflammatory biomarker panel in quantitative proteomic analysis for a large asthma cohort to identify molecular phenotypes distinguishing severe from nonsevere asthma.

METHODS

Bronchoalveolar lavage fluid from 48 severe and 77 nonsevere adult asthma subjects were assessed for 75 inflammatory proteins, normalized to BALF total protein concentration. Validation of BALF differences was sought through equivalent protein analysis of autologous sputum. Subjects' data, stratified by asthma severity, were analysed by standard statistical tests, principal component analysis and 5 machine learning algorithms.

RESULTS

The severe group had lower lung function and greater health care utilization. Significantly increased BALF proteins for severe asthma compared to nonsevere asthma were fibroblast growth factor 2 (FGF2), TGFα, IL1Ra, IL2, IL4, CCL8, CCL13 and CXCL7 and significantly decreased were platelet-derived growth factor a-a dimer (PDGFaa), vascular endothelial growth factor (VEGF), interleukin 5 (IL5), CCL17, CCL22, CXCL9 and CXCL10. Four protein differences were replicated in sputum. FGF2, PDGFaa and CXCL7 were independently identified by 5 machine learning algorithms as the most important variables for discriminating severe and nonsevere asthma. Increased and decreased proteins identified for the severe cluster showed significant protein-protein interactions for chemokine and cytokine signalling, growth factor activity, and eosinophil and neutrophil chemotaxis differing between subjects with severe and nonsevere asthma.

CONCLUSION

These inflammatory protein results confirm altered airway remodelling and cytokine/chemokine activity recruiting leukocytes into the airways of severe compared to nonsevere asthma as important processes even in stable status.

Cross-Platform Comparison of Highly Sensitive Immunoassays for Inflammatory Markers in a COVID-19 Cohort

A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing Ab pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on human serum samples from the National Institutes of Health IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. First, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Second, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Third, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.

Angiogenic and vasoactive proteins in the maternal-fetal interface in healthy pregnancies and preeclampsia

BACKGROUND

Preeclampsia is characterized by maternal endothelial activation and placental dysfunction. Imbalance in maternal angiogenic and vasoactive factors has been linked to the pathophysiology. The contribution of the placenta as a source of these factors remains unclear. Furthermore, little is known about fetal angiogenic and vasoactive proteins and the relation between maternal and fetal levels.

OBJECTIVE

We describe placental growth factor, soluble Fms-like tyrosine kinase 1, soluble endoglin, and endothelin 1-3 in 5 vessels in healthy pregnancies, early- and late-onset preeclampsia. Specifically, we aimed to (1) compare protein abundance in vessels at the maternal-fetal interface between early- and late-onset preeclampsia, and healthy pregnancies, (2) describe placental uptake and release of proteins, and (3) describe protein abundance in the maternal vs fetal circulations.

STUDY DESIGN

Samples were collected from the maternal radial artery, uterine vein and antecubital vein, and fetal umbilical vein and artery in 75 healthy and 37 preeclamptic mother-fetus pairs (including 19 early-onset preeclampsia and 18 late-onset preeclampsia), during scheduled cesarean delivery. This method allows estimation of placental release and uptake of proteins by calculation of venoarterial differences on each side of the placenta. The microarray-based SomaScan assay quantified the proteins.

RESULTS

The abundance of soluble Fms-like tyrosine kinase 1 and endothelin 1 was higher in the maternal vessels in preeclampsia than in healthy pregnancies, with the highest abundance in early-onset preeclampsia. Placental growth factor was lower in the maternal vessels in early-onset preeclampsia than in both healthy and late-onset preeclampsia. Maternal endothelin 2 was higher in preeclampsia, with late-onset preeclampsia having the highest abundance. Our model confirmed placental release of placental growth factor and soluble Fms-like tyrosine kinase 1 to the maternal circulation in all groups. The placenta released soluble Fms-like tyrosine kinase 1 into the fetal circulation in healthy and late-onset preeclampsia pregnancies. Fetal endothelin 1 and soluble Fms-like tyrosine kinase 1 were higher in early-onset preeclampsia, whereas soluble endoglin and endothelin 3 were lower in both preeclampsia groups than healthy controls. Across groups, abundances of placental growth factor, soluble Fms-like tyrosine kinase 1, and endothelin 3 were higher in the maternal artery than the fetal umbilical vein, whereas endothelin 2 was lower.

CONCLUSION

An increasing abundance of maternal soluble Fms-like tyrosine kinase 1 and endothelin 1 across the groups healthy, late-onset preeclampsia and early-onset combined with a positive correlation may suggest that these proteins are associated with the pathophysiology and severity of the disease. Elevated endothelin 1 in the fetal circulation in early-onset preeclampsia represents a novel finding. The long-term effects of altered protein abundance in preeclampsia on fetal development and health remain unknown. Further investigation of these proteins' involvement in the pathophysiology and as treatment targets is warranted.

Proteomic Analyses of Plasma From Patients With Fracture-Related Infection Reveals Systemic Activation of the Complement and Coagulation Cascades

OBJECTIVES

The objective of this study was to compare plasma proteomes of patients with confirmed fracture-related infections (FRIs) matched to noninfected controls using liquid chromatography-mass spectrometry.

METHODS

DESIGN

This was a prospective case-control study.

SETTING

The study was conducted at a single, academic, Level 1 trauma center.

PATIENT SELECTION CRITERIA

Patients meeting confirmatory FRI criteria were matched to controls without infection based on fracture region, age, and time after surgery from June 2019 to January 2022. Tandem mass tag liquid chromatography-mass spectrometry analysis of patient plasma samples was performed.

OUTCOME MEASURES AND COMPARISONS

Protein abundance ratios in plasma for patients with FRI compared with those for matched controls without infection were calculated.

RESULTS

Twenty-seven patients meeting confirmatory FRI criteria were matched to 27 controls. Abundance ratios for more than 1000 proteins were measured in the 54 plasma samples. Seventy-three proteins were found to be increased or decreased in patients with FRI compared with those in matched controls (unadjusted t test P < 0.05). Thirty-two of these proteins were found in all 54 patient samples and underwent subsequent principal component analysis to reduce the dimensionality of the large proteomics dataset. A 3-component principal component analysis accounted for 45.7% of the variation in the dataset and had 88.9% specificity for the diagnosis of FRI. STRING protein-protein interaction network analysis of these 3 PCs revealed activation of the complement and coagulation cascades through the Reactome pathway database (false discovery rates <0.05).

CONCLUSIONS

Proteomic analyses of plasma from patients with FRI demonstrate systemic activation of the complement and coagulation cascades. Further investigation along these lines may help to better understand the systemic response to FRI and improve diagnostic strategies using proteomics.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Proteomic workflows for deep phenotypic profiling of 3D organotypic liver models

Organotypic human tissue models constitute promising systems to facilitate drug discovery and development. They allow to maintain native cellular phenotypes and functions, which enables long-term pharmacokinetic and toxicity studies, as well as phenotypic screening. To trace relevant phenotypic changes back to specific targets or signaling pathways, comprehensive proteomic profiling is the gold-standard. A multitude of proteomic workflows have been applied on 3D tissue models to quantify their molecular phenotypes; however, their impact on analytical results and biological conclusions in this context has not been evaluated. The performance of twelve mass spectrometry-based global proteomic workflows that differed in the amount of cellular input, lysis protocols and quantification methods was compared for the analysis of primary human liver spheroids. Results differed majorly between protocols in the total number and subcellular compartment bias of identified proteins, which is particularly relevant for the reliable quantification of transporters and drug metabolizing enzymes. Using a model of metabolic dysfunction-associated steatotic liver disease, we furthermore show that critical disease pathways are robustly identified using a standardized high throughput-compatible workflow based on thermal lysis, even using only individual spheroids (1500 cells) as input. The results increase the applicability of proteomic profiling to phenotypic screens in organotypic microtissues and provide a scalable platform for deep phenotyping from limited biological material.

Serum/Plasma Proteome in Non-Malignant Liver Disease

The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.

Plasma Neuronal Growth Regulator 1 May Link Physical Activity to Reduced Risk of Type 2 Diabetes: A Proteome-Wide Study of ARIC Participants

Habitual physical activity (PA) impacts the plasma proteome and reduces the risk of developing type 2 diabetes (T2D). Using a large-scale proteome-wide approach in Atherosclerosis Risk in Communities study participants, we aimed to identify plasma proteins associated with PA and determine which of these may be causally related to lower T2D risk. PA was associated with 92 plasma proteins in discovery (P < 1.01 × 10-5), and 40 remained significant in replication (P < 5.43 × 10-4). Eighteen of these proteins were independently associated with incident T2D (P < 1.25 × 10-3), including neuronal growth regulator 1 (NeGR1; hazard ratio per SD 0.85; P = 7.5 × 10-11). Two-sample Mendelian randomization (MR) inverse variance weighted analysis indicated that higher NeGR1 reduces T2D risk (odds ratio [OR] per SD 0.92; P = 0.03) and was consistent with MR-Egger, weighted median, and weighted mode sensitivity analyses. A stronger association was observed for the single cis-acting NeGR1 genetic variant (OR per SD 0.80; P = 6.3 × 10-5). Coupled with previous evidence that low circulating NeGR1 levels promote adiposity, its association with PA and potential causal role in T2D shown here suggest that NeGR1 may link PA exposure with metabolic outcomes. Further research is warranted to confirm our findings and examine the interplay of PA, NeGR1, adiposity, and metabolic health.

ARTICLE HIGHLIGHTS

Circadian protein expression patterns in healthy young adults

OBJECTIVES

To explore how the blood plasma proteome fluctuates across the 24-hour day and identify a subset of proteins that show endogenous circadian rhythmicity.

METHODS

Plasma samples from 17 healthy adults were collected hourly under controlled conditions designed to unmask endogenous circadian rhythmicity; in a subset of 8 participants, we also collected samples across a day on a typical sleep-wake schedule. A total of 6916 proteins were analyzed from each sample using the SomaScan aptamer-based multiplexed platform. We used differential rhythmicity analysis based on a cosinor model with mixed effects to identify a subset of proteins that showed circadian rhythmicity in their abundance.

RESULTS

One thousand and sixty-three (15%) proteins exhibited significant daily rhythmicity. Of those, 431 (6.2%) proteins displayed consistent endogenous circadian rhythms on both a sleep-wake schedule and under controlled conditions: it included both known and novel proteins. When models were fitted with two harmonics, an additional 259 (3.7%) proteins exhibited significant endogenous circadian rhythmicity, indicating that some rhythmic proteins cannot be solely captured by a simple sinusoidal model. Overall, we found that the largest number of proteins had their peak levels in the late afternoon/evening, with another smaller group peaking in the early morning.

CONCLUSIONS

This study reveals that hundreds of plasma proteins exhibit endogenous circadian rhythmicity in humans. Future analyses will likely reveal novel physiological pathways regulated by circadian clocks and pave the way for improved diagnosis and treatment for patients with circadian disorders and other pathologies. It will also advance efforts to include knowledge about time-of-day, thereby incorporating circadian medicine into personalized medicine.

Proteomic insights into the pathophysiology of periodic limb movements and restless legs syndrome

OBJECTIVES

We used a high-throughput assay of 5000 plasma proteins to identify biomarkers associated with periodic limb movements (PLM) and restless legs syndrome (RLS) in adults.

METHODS

Participants (n = 1410) of the Stanford Technology Analytics and Genomics in Sleep (STAGES) study had blood collected, completed a sleep questionnaire, and underwent overnight polysomnography with the scoring of PLMs. An aptamer-based array (SomaScan) was used to quantify 5000 proteins in plasma. A second cohort (n = 697) that had serum assayed using a previous iteration of SomaScan (1300 proteins) was used for replication and in a combined analysis (n = 2107). A 5% false discovery rate was used to assess significance.

RESULTS

Multivariate analyses in STAGES identified 68 proteins associated with the PLM index after correction for multiple testing (ie, base model). Most significantly decreased proteins were iron-related and included Hepcidin (LEAP-1), Ferritin, and Ferritin light chain. Most significantly increased proteins included RANTES, Cathepsin A, and SULT 1A3. Of 68 proteins significant in the base model, 17 were present in the 1300 panel, and 15 of 17 were replicated. The most significant proteins in the combined model were Hepcidin (LEAP-1), Cathepsin A, Ferritin, and RANTES. Exploration of proteins in RLS versus non-RLS identified Cathepsin Z, Heme oxygenase 2 (HO-2), Interleukin-17A (upregulated in the combined cohort), and Megalin (upregulated in STAGES only) although results were less significant than for proteins associated with PLM index.

CONCLUSIONS

These results confirm the association of PLM with low iron status and suggest the involvement of catabolic enzymes in PLM/RLS.

Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients

The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between viral cytopathic effects and a dysregulated host immune response. In critically ill patients, imatinib treatment demonstrated potential for reducing invasive ventilation duration and mortality. Here, we perform longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predict critical illness development. Next to dysregulation of inflammation, critical illness is characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuates protein perturbations associated with inflammation and extracellular matrix turnover. These proteomic alterations are contextualised using external pulmonary RNA-sequencing data of deceased COVID-19 patients and imatinib-treated Syrian hamsters. Together, we show that alveolar capillary barrier disruption in critical COVID-19 is reflected in the plasma proteome, and is attenuated with imatinib treatment. This study comprises a secondary analysis of both clinical data and plasma samples derived from a clinical trial that was registered with the EU Clinical Trials Register (EudraCT 2020-001236-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001236-10/NL ) and Netherlands Trial Register (NL8491, https://www.trialregister.nl/trial/8491 ).

Analytical validation of the PROphet test for treatment decision-making guidance in metastatic non-small cell lung cancer

The blood proteome, consisting of thousands of proteins engaged in various biological processes, acts as a valuable source of potential biomarkers for various medical applications. PROphet is a plasma proteomics-based test that serves as a decision-support tool for non-small cell lung cancer (NSCLC) patients, combining proteomic profiling using SomaScan technology and subsequent computational algorithm. PROphet was implemented as a laboratory developed test (LDT). Under the Clinical Laboratory Improvement Amendments (CLIA) and Commission on Office Laboratory Accreditation (COLA) regulations, prior to releasing patient test results, a clinical laboratory located in the United States employing an LDT must examine its performance characteristics with regard to analytical validity. This study describes the experimental and computational analytical validity of the PROphet test, as required by CLIA/COLA regulations. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9 % and 4.7 % for intra-plate and inter-plate examination, respectively, and the median accuracy rate between sites was 88 %. Computational precision exhibited a high accuracy rate, with 93 % of samples displaying complete concordance in results. A cross-platform comparison between SomaScan and other proteomics platforms yielded a median Spearman's rank correlation coefficient of 0.51, affirming the consistency and reliability of the SomaScan platform as used under the PROphet test. Our study presents a robust framework for evaluating the analytical validity of a platform that combines an experimental assay with subsequent computational algorithms. When applied to the PROphet test, strong analytical performance of the test was demonstrated.

Multi-omics in stress and health research: study designs that will drive the field forward

Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals' stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.

Large-scale proteome and metabolome analysis of CSF implicates altered glucose and carbon metabolism and succinylcarnitine in Alzheimer's disease

INTRODUCTION

A hallmark of Alzheimer's disease (AD) is the aggregation of proteins (amyloid beta [A] and hyperphosphorylated tau [T]) in the brain, making cerebrospinal fluid (CSF) proteins of particular interest.

METHODS

We conducted a CSF proteome-wide analysis among participants of varying AT pathology (n = 137 participants; 915 proteins) with nine CSF biomarkers of neurodegeneration and neuroinflammation.

RESULTS

We identified 61 proteins significantly associated with the AT category (P < 5.46 × 10-5 ) and 636 significant protein-biomarker associations (P < 6.07 × 10-6 ). Proteins from glucose and carbon metabolism pathways were enriched among amyloid- and tau-associated proteins, including malate dehydrogenase and aldolase A, whose associations with tau were replicated in an independent cohort (n = 717). CSF metabolomics identified and replicated an association of succinylcarnitine with phosphorylated tau and other biomarkers.

DISCUSSION

These results implicate glucose and carbon metabolic dysregulation and increased CSF succinylcarnitine levels with amyloid and tau pathology in AD.

HIGHLIGHTS

Cerebrospinal fluid (CSF) proteome enriched for extracellular, neuronal, immune, and protein processing. Glucose/carbon metabolic pathways enriched among amyloid/tau-associated proteins. Key glucose/carbon metabolism protein associations independently replicated. CSF proteome outperformed other omics data in predicting amyloid/tau positivity. CSF metabolomics identified and replicated a succinylcarnitine-phosphorylated tau association.

Evaluation of a New Aptamer-Based Array for Soluble Suppressor of Tumorgenicity (ST2) and N-terminal Pro-B-Type Natriuretic Peptide (NTproBNP) in Heart Failure Patients

BACKGROUND

Recent advances in multi-marker platforms offer faster data generation, but the fidelity of these methods compared to the ELISA is not established. We tested the correlation and predictive performance of SOMAscan vs. ELISA methods for NTproBNP and ST2.

METHODS

Patients ≥ 18 years with heart failure and ejection fraction < 50% were enrolled. We tested the correlation between SOMA and ELISA for each biomarker and their association with outcomes.

RESULTS

There was good correlation of SOMA vs. ELISA for ST2 (ρ = 0.71) and excellent correlation for NTproBNP (ρ = 0.94). The two versions of both markers were not significantly different regarding survival association. The two ST2 assays and NTproBNP assays were similarly associated with all-cause mortality and cardiovascular mortality. These associations remained statistically significant when adjusted for MAGGIC risk score (all p < 0.05).

CONCLUSION

SOMAscan quantifications of ST2 and NTproBNP correlate to ELISA versions and carry similar prognosis.

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.

Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations

The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.

Cross-platform comparison of highly-sensitive immunoassays for inflammatory markers in a COVID-19 cohort 1

A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing antibody pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on serum samples from the NIH IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. Firstly, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Secondly, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Thirdly, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Large-scale plasma proteomics comparisons through genetics and disease associations

High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.

Plasma proteomic associations with genetics and health in the UK Biobank

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Aptamer-Based Proteomics Measuring Preoperative Cerebrospinal Fluid Protein Alterations Associated with Postoperative Delirium

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case-control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort, and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p < 0.05). Due to the limited sample size, these proteins did not remain significant by multiple hypothesis testing using the Benjamini-Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case-control samples correctly, and principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80-0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identified 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

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.

Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics

Background

The wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics.

Methods

Investigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph™ Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments.

Results

We show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts.

Conclusions

The automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Plasma proteomic signatures of enteric permeability among hospitalized and community children in Kenya and Pakistan

We aimed to establish if enteric permeability was associated with similar biological processes in children recovering from hospitalization and relatively healthy children in the community. Extreme gradient boosted models predicting the lactulose-rhamnose ratio (LRR), a biomarker of enteric permeability, using 7,500 plasma proteins and 34 fecal biomarkers of enteric infection among 89 hospitalized and 60 community children aged 2-23 months were built. The R2 values were calculated in test sets. The models performed better among community children (R2: 0.27 [min-max: 0.19, 0.53]) than hospitalized children (R2: 0.07 [min-max: 0.03, 0.11]). In the community, LRR was associated with biomarkers of humoral antimicrobial and cellular lipopolysaccharide responses and inversely associated with anti-inflammatory and innate immunological responses. Among hospitalized children, the selected biomarkers had few shared functions. This suggests enteric permeability among community children was associated with a host response to pathogens, but this association was not observed among hospitalized children.

Protein biomarkers for radiation injury and testing of medical countermeasure efficacy: promises, pitfalls, and future directions

INTRODUCTION

Radiological/nuclear accidents, hostile military activity, or terrorist strikes have the potential to expose a large number of civilians and military personnel to high doses of radiation resulting in the development of acute radiation syndrome and delayed effects of exposure. Thus, there is an urgent need for sensitive and specific assays to assess the levels of radiation exposure to individuals. Such radiation exposures are expected to alter primary cellular proteomic processes, resulting in multifaceted biological responses.

AREAS COVERED

This article covers the application of proteomics, a promising and fast developing technology based on quantitative and qualitative measurements of protein molecules for possible rapid measurement of radiation exposure levels. Recent advancements in high-resolution chromatography, mass spectrometry, high-throughput, and bioinformatics have resulted in comprehensive (relative quantitation) and precise (absolute quantitation) approaches for the discovery and accuracy of key protein biomarkers of radiation exposure. Such proteome biomarkers might prove useful for assessing radiation exposure levels as well as for extrapolating the pharmaceutical dose of countermeasures for humans based on efficacy data generated using animal models.

EXPERT OPINION

The field of proteomics promises to be a valuable asset in evaluating levels of radiation exposure and characterizing radiation injury biomarkers.

Comparison of multiplexed protein analysis platforms for the detection of biomarkers in the nasal epithelial lining fluid of healthy subjects

BACKGROUND

Multiplexed protein analysis platforms are a novel and efficient way to characterize biomarkers in a variety of biological samples. Few studies have compared protein quantitation and reproducibility of results across platforms. We utilize a novel nasosorption technique to collect nasal epithelial lining fluid (NELF) from healthy subjects, and compare the detection of proteins in NELF across three commonly used platforms.

METHODS

NELF was collected from both nares of twenty healthy subjects using an absorbent fibrous matrix and analyzed using three different protein analysis platforms: Luminex, Meso Scale Discovery (MSD), and Olink. Twenty-three protein analytes were shared across two or more platforms, and correlations across platforms were assessed using Spearman correlations.

RESULTS

Among the twelve proteins represented on all three platforms, IL1⍺ and IL6 were very highly correlated (Spearman correlation coefficient [r] ≥ 0.9); CCL3, CCL4, and MCP1 were highly correlated (r ≥ 0.7); and IFNɣ, IL8, and TNF⍺ were moderately correlated (r ≥ 0.5). Four proteins (IL2, IL4, IL10, IL13) were poorly correlated across at least two platform comparisons (r < 0.5); for two of these proteins (IL10 and IL13), the majority of observations were below the limits of detection for Olink and Luminex.

DISCUSSION

Multiplexed protein analysis platforms are a promising method for analyzing nasal samples for biomarkers of interest in respiratory health research. For most proteins evaluated, there was good correlation across platforms, although results were less consistent for low abundance proteins. Of the three platforms tested, MSD had the highest sensitivity for analyte detection.

Plasma Proteomic Associations With Incident Ischemic Stroke in Older Adults: The Cardiovascular Health Study

BACKGROUND AND OBJECTIVES

Plasma proteomics may elucidate novel insights into the pathophysiology of ischemic stroke (IS), identify biomarkers of IS risk, and guide development of nascent prevention strategies. We evaluated the relationship between the plasma proteome and IS risk in the population-based Cardiovascular Health Study (CHS).

METHODS

Eligible CHS participants were free of prevalent stroke and underwent quantification of 1,298 plasma proteins using the aptamer-based SOMAScan assay platform from the 1992-1993 study visit. Multivariable Cox proportional hazards regression was used to evaluate associations between a 1-SD increase in the log2-transformed estimated plasma protein concentrations and incident IS, adjusting for demographics, IS risk factors, and estimated glomerular filtration rate. For proteins independently associated with incident IS, a secondary stratified analysis evaluated associations in subgroups defined by sex and race. Exploratory analyses evaluated plasma proteomic associations with cardioembolic and noncardioembolic IS and proteins associated with IS risk in participants with left atrial dysfunction but without atrial fibrillation.

RESULTS

Of 2,983 eligible participants, the mean age was 74.3 (±4.8) years, 61.2% were women, and 15.4% were Black. Over a median follow-up of 12.6 years, 450 participants experienced an incident IS. N-terminal probrain natriuretic peptide (NTproBNP, adjusted HR 1.37, 95% CI 1.23-1.53, p = 2.08 × 10-08) and macrophage metalloelastase (MMP12, adjusted HR 1.30, 95% CI 1.16-1.45, p = 4.55 × 10-06) were independently associated with IS risk. These 2 associations were similar in men and women and in Black and non-Black participants. In exploratory analyses, NTproBNP was independently associated with incident cardioembolic IS, E-selectin with incident noncardioembolic IS, and secreted frizzled-related protein 1 with IS risk in participants with left atrial dysfunction.

DISCUSSION

In a cohort of older adults, NTproBNP and MMP12 were independently associated with IS risk. We identified plasma proteomic determinants of incident cardioembolic and noncardioembolic IS and found a novel protein associated with IS risk in those with left atrial dysfunction.

The Human Gastric Juice: A Promising Source for Gastric Cancer Biomarkers

Gastric cancer (GC) is a major public health problem worldwide, with high mortality rates due to late diagnosis and limited treatment options. Biomarker research is essential to improve the early detection of GC. Technological advances and research methodologies have improved diagnostic tools, identifying several potential biomarkers for GC, including microRNA, DNA methylation markers, and protein-based biomarkers. Although most studies have focused on identifying biomarkers in biofluids, the low specificity of these markers has limited their use in clinical practice. This is because many cancers share similar alterations and biomarkers, so obtaining them from the site of disease origin could yield more specific results. As a result, recent research efforts have shifted towards exploring gastric juice (GJ) as an alternative source for biomarker identification. Since GJ is a waste product during a gastroscopic examination, it could provide a "liquid biopsy" enriched with disease-specific biomarkers generated directly at the damaged site. Furthermore, as it contains secretions from the stomach lining, it could reflect changes associated with the developmental stage of GC. This narrative review describes some potential biomarkers for gastric cancer screening identified in gastric juice.

A blood and bronchoalveolar lavage protein signature of rapid FEV1 decline in smoking-associated COPD

Accelerated progression of chronic obstructive pulmonary disease (COPD) is associated with increased risks of hospitalization and death. Prognostic insights into mechanisms and markers of progression could facilitate development of disease-modifying therapies. Although individual biomarkers exhibit some predictive value, performance is modest and their univariate nature limits network-level insights. To overcome these limitations and gain insights into early pathways associated with rapid progression, we measured 1305 peripheral blood and 48 bronchoalveolar lavage proteins in individuals with COPD [n = 45, mean initial forced expiratory volume in one second (FEV1) 75.6 ± 17.4% predicted]. We applied a data-driven analysis pipeline, which enabled identification of protein signatures that predicted individuals at-risk for accelerated lung function decline (FEV1 decline ≥ 70 mL/year) ~ 6 years later, with high accuracy. Progression signatures suggested that early dysregulation in elements of the complement cascade is associated with accelerated decline. Our results propose potential biomarkers and early aberrant signaling mechanisms driving rapid progression in COPD.

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.

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Genetic evidence implicating natriuretic peptide receptor-3 in cardiovascular disease risk: a Mendelian randomization study

BACKGROUND

C-type natriuretic peptide (CNP) is a known target for promoting growth and has been implicated as a therapeutic opportunity for the prevention and treatment of cardiovascular disease (CVD). This study aimed to explore the effect of CNP on CVD risk using the Mendelian randomization (MR) framework.

METHODS

Instrumental variables mimicking the effects of pharmacological intervention on CNP were identified as uncorrelated genetic variants located in the genes coding for its primary receptors, natriuretic peptide receptors-2 and 3 (NPR2 and NPR3), that associated with height. We performed MR and colocalization analyses to investigate the effects of NPR2 signalling and NPR3 function on CVD outcomes and risk factors. MR estimates were compared to those obtained when considering height variants from throughout the genome.

RESULTS

Genetically-proxied reduced NPR3 function was associated with a lower risk of CVD, with odds ratio (OR) 0.74 per standard deviation (SD) higher NPR3-predicted height, and 95% confidence interval (95% CI) 0.64-0.86. This effect was greater in magnitude than observed when considering height variants from throughout the genome. For CVD subtypes, similar MR associations for NPR3-predicted height were observed when considering the outcomes of coronary artery disease (0.75, 95% CI 0.60-0.92), stroke (0.69, 95% CI 0.50-0.95) and heart failure (0.77, 95% CI 0.58-1.02). Consideration of CVD risk factors identified systolic blood pressure (SBP) as a potential mediator of the NPR3-related CVD risk lowering. For stroke, we found that the MR estimate for NPR3 was greater in magnitude than could be explained by a genetically predicted SBP effect alone. Colocalization results largely supported the MR findings, with no evidence of results being driven by effects due to variants in linkage disequilibrium. There was no MR evidence supporting effects of NPR2 on CVD risk, although this null finding could be attributable to fewer genetic variants being identified to instrument this target.

CONCLUSIONS

This genetic analysis supports the cardioprotective effects of pharmacologically inhibiting NPR3 receptor function, which is only partly mediated by an effect on blood pressure. There was unlikely sufficient statistical power to investigate the cardioprotective effects of NPR2 signalling.

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".

High-Multiplex Aptamer-Based Serum Proteomics to Identify Candidate Serum Biomarkers of Oral Squamous Cell Carcinoma

Improved serological biomarkers are needed for the early detection, risk stratification and treatment surveillance of patients with oral squamous cell carcinoma (OSCC). We performed an exploratory study using advanced, highly specific, DNA-aptamer-based serum proteomics (SOMAscan, 1305-plex) to identify distinct proteomic changes in patients with OSCC pre- vs. post-resection and compared to healthy controls. A total of 63 significantly differentially expressed serum proteins (each p < 0.05) were found that could discriminate between OSCC and healthy controls with 100% accuracy. Furthermore, 121 proteins were detected that were significantly altered between pre- and post-resection sera, and 12 OSCC-associated proteins reversed to levels equivalent to healthy controls after resection. Of these, 6 were increased and 6 were decreased relative to healthy controls, highlighting the potential relevance of these proteins as OSCC tumor markers. Pathway analyses revealed potential pathophysiological mechanisms associated with OSCC. Hence, quantitative proteome analysis using SOMAscan technology is promising and may aid in the development of defined serum marker assays to predict tumor occurrence, progression and recurrence in OSCC, and to guide personalized therapies.

Comparison of Proteomic Measurements Across Platforms in the Atherosclerosis Risk in Communities (ARIC) Study

BACKGROUND

The plasma proteome can be quantified using different types of highly multiplexed technologies, including aptamer-based and proximity-extension immunoassay methods. There has been limited characterization of how these protein measurements correlate across platforms and with absolute measures from targeted immunoassays.

METHODS

We assessed the comparability of (a) highly multiplexed aptamer-based (SomaScan v4; Somalogic) and proximity-extension immunoassay (OLINK Proseek® v5003; Olink) methods in 427 Atherosclerosis Risk in Communities (ARIC) Study participants (Visit 5, 2011-2013), and (b) 18 of the SomaScan protein measurements against targeted immunoassays in 110 participants (55 cardiovascular disease cases, 55 controls). We calculated Spearman correlations (r) between the different measurements and compared associations with case-control status.

RESULTS

There were 417 protein comparisons (366 unique proteins) between the SomaScan and Olink platforms. The average correlation was r = 0.46 (range: -0.21 to 0.97; 79 [19%] with r ≥ 0.8). For the comparison of SomaScan and targeted immunoassays, 6 of 18 assays (growth differentiation factor 15 [GDF15], interleukin-1 receptor-like 1 [ST2], interstitial collagenase [MMP1], adiponectin, leptin, and resistin) had good correlations (r ≥ 0.8), 2 had modest correlations (0.5 ≤ r < 0.8; osteopontin and interleukin-6 [IL6]), and 10 were poorly correlated (r < 0.5; metalloproteinase inhibitor 1 [TIMP1], stromelysin-1 [MMP3], matrilysin [MMP7], C-C motif chemokine 2 [MCP1], interleukin-10 [IL10], vascular cell adhesion protein 1 [VCAM1], intercellular adhesion molecule 1 [ICAM1], interleukin-18 [IL18], tumor necrosis factor [TNFα], and visfatin) overall. Correlations for SomaScan and targeted immunoassays were similar according to case status.

CONCLUSIONS

There is variation in the quantitative measurements for many proteins across aptamer-based and proximity-extension immunoassays (approximately 1/2 showing good or modest correlation and approximately 1/2 poor correlation) and also for correlations of these highly multiplexed technologies with targeted immunoassays. Design and interpretation of protein quantification studies should be informed by the variation across measurement techniques for each protein.

An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes

Background

Patients suffering from acute myocardial infarction (AMI) are at risk of secondary outcomes including major adverse cardiovascular events (MACE) and heart failure (HF). Comprehensive molecular phenotyping and cardiac imaging during the post-discharge time window may provide cues for risk stratification for the outcomes.

Materials and methods

In a prospective AMI cohort in New Zealand (N = 464), we measured plasma proteins and lipids 30 days after hospital discharge and inferred a unified partial correlation network with echocardiographic variables and established clinical biomarkers (creatinine, c-reactive protein, cardiac troponin I and natriuretic peptides). Using a network-based data integration approach (iOmicsPASS+), we identified predictive signatures of long-term secondary outcomes based on plasma protein, lipid, imaging markers and clinical biomarkers and assessed the prognostic potential in an independent cohort from Singapore (N = 190).

Results

The post-discharge levels of plasma proteins and lipids showed strong correlations within each molecular type, reflecting concerted homeostatic regulation after primary MI events. However, the two molecular types were largely independent with distinct correlation structures with established prognostic imaging parameters and clinical biomarkers. To deal with massively correlated predictive features, we used iOmicsPASS + to identify subnetwork signatures of 211 and 189 data features (nodes) predictive of MACE and HF events, respectively (160 overlapping). The predictive features were primarily imaging parameters, including left ventricular and atrial parameters, tissue Doppler parameters, and proteins involved in extracellular matrix (ECM) organization, cell differentiation, chemotaxis, and inflammation. The network signatures contained plasma protein pairs with area-under-the-curve (AUC) values up to 0.74 for HF prediction in the validation cohort, but the pair of NT-proBNP and fibulin-3 (EFEMP1) was the best predictor (AUC = 0.80). This suggests that there were a handful of plasma proteins with mechanistic and functional roles in predisposing patients to the secondary outcomes, although they may be weaker prognostic markers than natriuretic peptides individually. Among those, the diastolic function parameter (E/e' - an indicator of left ventricular filling pressure) and two ECM proteins, EFEMP1 and follistatin-like 3 (FSTL3) showed comparable performance to NT-proBNP and outperformed left ventricular measures as benchmark prognostic factors for post-MI HF.

Conclusion

Post-discharge levels of E/e', EFEMP1 and FSTL3 are promising complementary markers of secondary adverse outcomes in AMI patients.

Blood Plasma Proteome: A Meta-Analysis of the Results of Protein Quantification in Human Blood by Targeted Mass Spectrometry

A meta-analysis of the results of targeted quantitative screening of human blood plasma was performed to generate a reference standard kit that can be used for health analytics. The panel included 53 of the 296 proteins that form a “stable” part of the proteome of a healthy individual; these proteins were found in at least 70% of samples and were characterized by an interindividual coefficient of variation <40%. The concentration range of the selected proteins was 10−10−10−3 M and enrichment analysis revealed their association with rare familial diseases. The concentration of ceruloplasmin was reduced by approximately three orders of magnitude in patients with neurological disorders compared to healthy volunteers, and those of gelsolin isoform 1 and complement factor H were abruptly reduced in patients with lung adenocarcinoma. Absolute quantitative data of the individual proteome of a healthy and diseased individual can be used as the basis for personalized medicine and health monitoring. Storage over time allows us to identify individual biomarkers in the molecular landscape and prevent pathological conditions.

Increased soluble urokinase plasminogen activator levels modulate monocyte function to promote atherosclerosis

People with kidney disease are disproportionately affected by atherosclerosis for unclear reasons. Soluble urokinase plasminogen activator receptor (suPAR) is an immune-derived mediator of kidney disease, levels of which are strongly associated with cardiovascular outcomes. We assessed suPAR's pathogenic involvement in atherosclerosis using epidemiologic, genetic, and experimental approaches. We found serum suPAR levels to be predictive of coronary artery calcification and cardiovascular events in 5,406 participants without known coronary disease. In a genome-wide association meta-analysis including over 25,000 individuals, we identified a missense variant in the plasminogen activator, urokinase receptor (PLAUR) gene (rs4760), confirmed experimentally to lead to higher suPAR levels. Mendelian randomization analysis in the UK Biobank using rs4760 indicated a causal association between genetically predicted suPAR levels and atherosclerotic phenotypes. In an experimental model of atherosclerosis, proprotein convertase subtilisin/kexin-9 (Pcsk9) transfection in mice overexpressing suPAR (suPARTg) led to substantially increased atherosclerotic plaques with necrotic cores and macrophage infiltration compared with those in WT mice, despite similar cholesterol levels. Prior to induction of atherosclerosis, aortas of suPARTg mice excreted higher levels of CCL2 and had higher monocyte counts compared with WT aortas. Aortic and circulating suPARTg monocytes exhibited a proinflammatory profile and enhanced chemotaxis. These findings characterize suPAR as a pathogenic factor for atherosclerosis acting at least partially through modulation of monocyte function.

Enteric Permeability, Systemic Inflammation, and Post-Discharge Growth Among a Cohort of Hospitalized Children in Kenya and Pakistan

OBJECTIVES

To determine whether gut permeability is associated with post-discharge growth and systemic inflammation among hospitalized children in low- and middle-income countries.

METHODS

Children aged 2-23 months being discharged from Civil Hospital Karachi (Pakistan) and Migori County Referral Hospital (Kenya) underwent lactulose-rhamnose ratio (LRR) permeability testing and were compared to age-matched children from their home communities. Linear mixed effect models estimated the associations between LRR among discharged children with change in length-for-age (LAZ) and weight-for-age z score (WAZ) at 45, 90, and 180 days after discharge. Linear regression tested if relationships between LRR, systemic inflammation [C-reative protein (CRP), Cluster of Differentiation 14 (CD14), Tumour Necrosis Factor Alpha (TNFα), Interleukin-6 (IL-6)], and enterocyte damage [Intestinal Fatty-Acid Binding protein (I-FABP)] differed between the hospitalized and community groups.

RESULTS

One hundred thirty-seven hospitalized and 84 community participants were included. The hospitalized group had higher log-LRR [0.43, 95% confidence interval (CI): 0.15-0.71, P = 0.003] than the community children. Adjustment for weight-for-length z score at discharge attenuated this association (0.31, 95% CI: 0.00-0.62, P = 0.049). LRR was not associated with changes in WAZ or LAZ in the post-discharge period. Associations between LRR and CRP (interaction P = 0.036), TNFα ( P = 0.017), CD14 ( P = 0.078), and IL-6 ( P = 0.243) differed between community and hospitalized groups. LRR was associated with TNFα ( P = 0.004) and approached significance with CD14 ( P = 0.078) and IL-6 ( P = 0.062) in community children, but there was no evidence of these associations among hospitalized children.

CONCLUSIONS

Although increased enteric permeability is more prevalent among children being discharged from hospital compared to children in the community, it does not appear to be an important determinant of systemic inflammation or post-discharge growth among hospitalized children.