Challenges in translating plasma proteomics from bench to bedside: update from the NHLBI Clinical Proteomics Programs

Proteomic Signatures of Multisystem Inflammatory Syndrome in Children (MIS-C) Associated with COVID-19: A Narrative Review

BACKGROUND/OBJECTIVES

Multisystem Inflammatory Syndrome in Children (MIS-C) is a post-infectious complication of COVID-19. MIS-C has overlapping features with other pediatric inflammatory disorders including Kawasaki Disease (KD), Macrophage Activation Syndrome (MAS), Toxic Shock Syndrome and sepsis. The exact mechanisms responsible for the clinical overlap between MIS-C and these conditions remain unclear, and biomarkers that could distinguish MIS-C from its clinical mimics are lacking. This study aimed to provide an overview of how proteomic methods, like Mass Spectrometry (MS) and affinity-based proteomics, can offer a detailed understanding of pathophysiology and aid in the diagnosis and prognosis of MIS-C.

METHODS

A narrative review of relevant studies published up to July 2024 was conducted.

RESULTS

We identified 15 studies and summarized their key proteomic findings. These studies investigated the serum or plasma proteome of MIS-C patients using MS, Proximity Extension, or Aptamer-based assays. The studies associated the proteomic profile of MIS-C with laboratory and clinical parameters and/or compared it with that of other diseases including acute COVID-19, KD, MAS, pediatric rheumatic diseases, sepsis and myocarditis or pericarditis following COVID-19 mRNA immunization. Depending on the method and the control group, different proteins were increased or decreased in the MIS-C group. The limitations and challenges in MIS-C proteomic research are also discussed, and future research recommendations are provided.

CONCLUSIONS

Although proteomics appear to be a promising approach for understanding the pathogenesis and uncovering candidate biomarkers in MIS-C, proteomic studies are still needed to recognize and validate biomarkers that could accurately discriminate MIS-C from its clinical mimics.

Can we use biomarkers to identify those at risk of acute pain from sickle cell disease?

INTRODUCTION

Acute pain episodes, also known as vaso-occlusive crises (VOC), are a major symptom of sickle cell disease (SCD) and lead to frequent hospitalizations. The diagnosis of VOC can be challenging, particularly in adults with SCD, 50% of whom have chronic pain. Several potential biomarkers have been proposed for identifying individuals with VOC, including elevation above the baseline of various vascular growth factors, cytokines, and other markers of inflammation. However, none have been validated to date.

AREAS COVERED

We summarize prospective biomarkers for the diagnosis of acute pain in SCD, and how they may be involved in the pathophysiology of a VOC. Previous and current strategies for biomarker discovery, including the use of omics techniques, are discussed.

EXPERT OPINION

Implementing a multi-omics-based approach will facilitate the discovery of objective and validated biomarkers for acute pain.

Profound Properties of Protein-Rich, Platelet-Rich Plasma Matrices as Novel, Multi-Purpose Biological Platforms in Tissue Repair, Regeneration, and Wound Healing

Autologous platelet-rich plasma (PRP) preparations are prepared at the point of care. Centrifugation cellular density separation sequesters a fresh unit of blood into three main fractions: a platelet-poor plasma (PPP) fraction, a stratum rich in platelets (platelet concentrate), and variable leukocyte bioformulation and erythrocyte fractions. The employment of autologous platelet concentrates facilitates the biological potential to accelerate and support numerous cellular activities that can lead to tissue repair, tissue regeneration, wound healing, and, ultimately, functional and structural repair. Normally, after PRP preparation, the PPP fraction is discarded. One of the less well-known but equally important features of PPP is that particular growth factors (GFs) are not abundantly present in PRP, as they reside outside of the platelet alpha granules. Precisely, insulin-like growth factor-1 (IGF-1) and hepatocyte growth factor (HGF) are mainly present in the PPP fraction. In addition to their roles as angiogenesis activators, these plasma-based GFs are also known to inhibit inflammation and fibrosis, and they promote keratinocyte migration and support tissue repair and wound healing. Additionally, PPP is known for the presence of exosomes and other macrovesicles, exerting cell-cell communication and cell signaling. Newly developed ultrafiltration technologies incorporate PPP processing methods by eliminating, in a fast and efficient manner, plasma water, cytokines, molecules, and plasma proteins with a molecular mass (weight) less than the pore size of the fibers. Consequently, a viable and viscous protein concentrate of functional total proteins, like fibrinogen, albumin, and alpha-2-macroglobulin is created. Consolidating a small volume of high platelet concentrate with a small volume of highly concentrated protein-rich PPP creates a protein-rich, platelet-rich plasma (PR-PRP) biological preparation. After the activation of proteins, mainly fibrinogen, the PR-PRP matrix retains and facilitates interactions between invading resident cells, like macrophages, fibroblast, and mesenchymal stem cells (MSCs), as well as the embedded concentrated PRP cells and molecules. The administered PR-PRP biologic will ultimately undergo fibrinolysis, leading to a sustained release of concentrated cells and molecules that have been retained in the PR-PRP matrix until the matrix is dissolved. We will discuss the unique biological and tissue reparative and regenerative properties of the PR-PRP matrix.

Clustering Identifies Subtypes With Different Phenotypic Characteristics in Women With Polycystic Ovary Syndrome

CONTEXT

Hierarchical clustering (HC) identifies subtypes of polycystic ovary syndrome (PCOS).

OBJECTIVE

This work aimed to identify clinically significant subtypes in a PCOS cohort diagnosed with the Rotterdam criteria and to further characterize the distinct subtypes.

METHODS

Clustering was performed using the variables body mass index (BMI), luteinizing hormone (LH), follicle-stimulating hormone, dehydroepiandrosterone sulfate, sex hormone-binding globulin (SHBG), testosterone, insulin, and glucose. Subtype characterization was performed by analyzing the variables estradiol, androstenedione, dehydroepiandrosterone, cortisol, anti-Müllerian hormone (AMH), total follicle count (TFC), lipid profile, and blood pressure. Study participants were girls and women who attended our university hospital for reproductive endocrinology screening between February 1993 and February 2021. In total, 2502 female participants of European ancestry, aged 13 to 45 years with PCOS (according to the Rotterdam criteria), were included. A subset of these (n = 1067) fulfilled the National Institutes of Health criteria (ovulatory dysfunction and hyperandrogenism). Main outcome measures included the identification of distinct PCOS subtypes using cluster analysis. Additional clinical variables associated with these subtypes were assessed.

RESULTS

Metabolic, reproductive, and background PCOS subtypes were identified. In addition to high LH and SHBG levels, the reproductive subtype had the highest TFC and levels of AMH (all P < .001). In addition to high BMI and insulin levels, the metabolic subtype had higher low-density lipoprotein levels and higher systolic and diastolic blood pressure (all P < .001). The background subtype had lower androstenedione levels and features of the other 2 subtypes.

CONCLUSION

Reproductive and metabolic traits not used for subtyping differed significantly in the subtypes. These findings suggest that the subtypes capture distinct PCOS causal pathways.

Proteomic Analyses of Autologous Chondrocyte Implantation Plasma Highlight Cartilage Acidic Protein 1 as a Candidate for Preclinical Screening

BACKGROUND

Stratification is required to ensure that only patients likely to benefit receive autologous chondrocyte implantation (ACI). It would be advantageous to identify biomarkers to predict ACI outcome that are measurable in blood, avoiding the need for an invasive synovial fluid harvest.

PURPOSE

To assess if proteomic analyses can be used to identify novel candidate blood biomarkers in individuals who respond well or poorly to ACI.

STUDY DESIGN

Controlled laboratory study.

METHODS

Isobaric tagging for relative and absolute quantitation (iTRAQ) mass spectrometry was used to assess the proteome in plasma pooled from ACI responders (mean Lysholm improvement after ACI, 33; n = 10) or nonresponders (mean, -13; n = 10), collected at the time of surgery for cartilage harvest (stage 1) or implantation of culture-expanded chondrocytes (stage 2). An alternative proteomic method, label-free quantitation liquid chromatography-tandem mass spectrometry, was used to analyze plasma samples (majority matched to iTRAQ) individually. Differentially abundant proteins (±2.0-fold) were analyzed from both proteomic data sets, and markers of interest identified via pooled iTRAQ were validated via immunoassay of individual samples.

RESULTS

Protein differences could be detected in the plasma preoperatively between ACI responders and nonresponders (16 proteins; ≥±2.0-fold change; P < .05) using iTRAQ proteomics. The most pronounced plasma proteome shift was evident in response to stage 1 surgery in ACI nonresponders, with 48 proteins being differentially abundant between the procedures. Label-free quantitation liquid chromatography-tandem mass spectrometry analysis of these same plasma samples (nonpooled) resulted in very few proteins being identified that were significantly differentially abundant. However, this work highlighted cartilage acidic protein 1 as being increased preoperatively in nonresponders as compared with responders.

CONCLUSIONS

This study is the first to use proteomic techniques to profile the plasma of individuals treated with ACI. Despite iTRAQ analysis of pooled plasmas indicating that there are differences in the plasma proteome between responders and nonresponders to ACI, these findings were not replicated when assessed using an alternative nonpooled technique. This study highlights some of the difficulties in profiling the plasma proteome in an attempt to identify novel biomarkers. Regardless, cartilage acidic protein 1 has been identified as a protein candidate, which is detectable in plasma and can predict outcome to ACI before treatment.

CLINICAL RELEVANCE

Candidate plasma protein biomarkers identified in this study have the potential to help determine which patients will be best suited to treatment with ACI.

Evaluating the Utility of Proteomics for the Identification of Circulating Pharmacodynamic Biomarkers of IFNβ-1a Biologics

Proteomics has the potential to identify pharmacodynamic (PD) biomarkers for similarity assessment of proposed biosimilars without relying on clinical efficacy end points. In this study, with 36 healthy participants randomized to therapeutic doses of interferon-beta 1a products (IFNβ-1a) or pegylated-IFNβ-1a (pegIFNβ-1a) approved to treat multiple sclerosis or placebo, we evaluated the utility of a proteomic assay that profiles > 7,000 plasma proteins. IFNβ-1a and pegIFNβ-1a resulted in 248 and 528 differentially expressed protein analytes, respectively, between treatment and placebo groups over the time course. Thirty-one proteins were prioritized based on a maximal fold change ≥ 2 from baseline, baseline adjusted area under the effect curve (AUEC) and overlap between the 2 products. Of these, the majority had a significant AUEC compared with placebo in response to either product; 8 proteins showed > 4-fold maximal change from baseline. We identified previously reported candidates, beta-2microglobulin and interferon-induced GTP-binding protein (Mx1) with ~ 50% coefficient of variation (CV) for AUEC, and many new candidates (including I-TAC, C1QC, and IP-10) with CVs ranging from 26%-129%. Upstream regulator analysis of differentially expressed proteins predicted activation of IFNβ1 signaling as well as other cytokine, enzyme, and transcription signaling networks by both products. Although independent replication is required to confirm present results, our study demonstrates the utility of proteomics for the identification of individual and composite candidate PD biomarkers that may be leveraged to support clinical pharmacology studies for biosimilar approvals, especially when biologics have complex mechanisms of action or do not have previously characterized PD biomarkers.

Aptamer-based applications for cardiovascular disease

Cardiovascular disease (especially atherosclerosis) is a major cause of death worldwide, and novel diagnostic tools and treatments for this disease are urgently needed. Aptamers are single-stranded oligonucleotides that specifically recognize and bind to the targets by forming unique structures in vivo, enabling them to rival antibodies in cardiac applications. Chemically synthesized aptamers can be readily modified in a site-specific way, so they have been engineered in the diagnosis of cardiac diseases and anti-thrombosis therapeutics. Von Willebrand Factor plays a unique role in the formation of thrombus, and as an aptamer targeting molecule, has shown initial success in antithrombotic treatment. A combination of von Willebrand Factor and nucleic acid aptamers can effectively inhibit the progression of blood clots, presenting a positive diagnosis and therapeutic effect, as well as laying a novel theory and strategy to improve biocompatibility paclitaxel drug balloon or implanted stent in the future. This review summarizes aptamer-based applications in cardiovascular disease, including biomarker discovery and future management strategy. Although relevant applications are relatively new, the significant advancements achieved have demonstrated that aptamers can be promising agents to realize the integration of diagnosis and therapy in cardiac research.

Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro

For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.

Biomarker Assay Validation by Mass Spectrometry

Decades of discussion and publication have gone into the guidance from the scientific community and the regulatory agencies on the use and validation of pharmacokinetic and toxicokinetic assays by chromatographic and ligand binding assays for the measurement of drugs and metabolites. These assay validations are well described in the FDA Guidance on Bioanalytical Methods Validation (BMV, 2018). While the BMV included biomarker assay validation, the focus was on understanding the challenges posed in validating biomarker assays and the importance of having reliable biomarker assays when used for regulatory submissions, rather than definition of the appropriate experiments to be performed. Different from PK bioanalysis, analysis of biomarkers can be challenging due to the presence of target analyte(s) in the control matrices used for calibrator and quality control sample preparation, and greater difficulty in procuring appropriate reference standards representative of the endogenous molecule. Several papers have been published offering recommendations for biomarker assay validation. The situational nature of biomarker applications necessitates fit-for-purpose (FFP) assay validation. A unifying theme for FFP analysis is that method validation requirements be consistent with the proposed context of use (COU) for any given biomarker. This communication provides specific recommendations for biomarker assay validation (BAV) by LC-MS, for both small and large molecule biomarkers. The consensus recommendations include creation of a validation plan that contains definition of the COU of the assay, use of the PK assay validation elements that support the COU, and definition of assay validation elements adapted to fit biomarker assays and the acceptance criteria for both.

Is proteomics of value in cardiovascular risk assessment?

PURPOSE OF REVIEW

To briefly summarize recently published evidence in the field of cardiovascular proteomics, focusing on its ability to improve cardiovascular risk stratification and critically discussing still open and burning issues and future perspectives of proteomics research.

RECENT FINDINGS

Several epidemiological studies have demonstrated an improvement in cardiovascular risk prediction beyond traditional risk factors by adding novel biomarkers, identified by both discovery and targeted proteomics. However, only a moderate improvement in risk discrimination over clinical variables was observed. Moreover, despite different outcomes there was also a strong overlap of identified candidates, with several of them being already well established cardiovascular risk markers such as growth differentiation factor 15, natriuretic peptides, C-reactive protein, interleukins, and metalloproteases.

SUMMARY

Although proteomics plays a crucial role in biomarker discovery, the modest discriminative ability of this technique raises the possibility that there are still hidden mechanisms in protein regulatory networks, which urgently need to be evaluated to improve a cardiovascular risk assessment to a clinically significant extent.

Protein Corona over Mesoporous Silica Nanoparticles: Influence of the Pore Diameter on Competitive Adsorption and Application to Prostate Cancer Diagnostics

Diagnostic tests based on proteomics analysis can have significant advantages over more traditional biochemical tests. However, low molecular weight (MW) protein biomarkers are difficult to identify by standard mass spectrometric analysis, as they are usually present at low concentrations and are masked by more abundant resident proteins. We have previously shown that mesoporous silica nanoparticles are able to capture a predominantly low MW protein fraction from the serum, as compared to the protein corona (PC) adsorbed onto dense silica nanoparticles. In this study, we begin by further investigating this effect using liquid chromatography-mass spectrometry (LC-MS)/MS and thermogravimetric analysis (TGA) to compare the MW of the proteins in the coronas of mesoporous silica nanoparticles with the same particle size but different pore diameters. Next, we examine the process by which two proteins, one small and one large, adsorb onto these mesoporous silica nanoparticles to establish a theory of why the corona becomes enriched in low MW proteins. Finally, we use this information to develop a novel system for the diagnosis of prostate cancer. An elastic net statistical model was applied to LC-MS/MS protein coronas from the serum of 22 cancer patients, identifying proteins specific to each patient group. These studies help to explain why low MW proteins predominate in the coronas of mesoporous silica nanoparticles, and they illustrate the ability of this information to supplement more traditional diagnostic tests.

Protein Corona over Mesoporous Silica Nanoparticles: Influence of the Pore Diameter on Competitive Adsorption and Application to Prostate Cancer Diagnostics

Diagnostic tests based on proteomics analysis can have significant advantages over more traditional biochemical tests. However, low molecular weight (MW) protein biomarkers are difficult to identify by standard mass spectrometric analysis, as they are usually present at low concentrations and are masked by more abundant resident proteins. We have previously shown that mesoporous silica nanoparticles are able to capture a predominantly low MW protein fraction from the serum, as compared to the protein corona (PC) adsorbed onto dense silica nanoparticles. In this study, we begin by further investigating this effect using liquid chromatography-mass spectrometry (LC-MS)/MS and thermogravimetric analysis (TGA) to compare the MW of the proteins in the coronas of mesoporous silica nanoparticles with the same particle size but different pore diameters. Next, we examine the process by which two proteins, one small and one large, adsorb onto these mesoporous silica nanoparticles to establish a theory of why the corona becomes enriched in low MW proteins. Finally, we use this information to develop a novel system for the diagnosis of prostate cancer. An elastic net statistical model was applied to LC-MS/MS protein coronas from the serum of 22 cancer patients, identifying proteins specific to each patient group. These studies help to explain why low MW proteins predominate in the coronas of mesoporous silica nanoparticles, and they illustrate the ability of this information to supplement more traditional diagnostic tests.

Novel Application of Aptamer Proteomic Analysis in Cystic Fibrosis Bronchoalveolar Lavage Fluid

PURPOSE

Biomarkers are needed in cystic fibrosis (CF) to understand disease progression, assess response to therapy, and enrich enrollment for clinical trials. Aptamer-based proteomics have proven useful in blood samples. The aim is to evaluate proteins in bronchoalveolar lavage fluid (BALF) in CF children compared to controls and identify endotypes during CF exacerbations.

EXPERIMENTAL DESIGN

BALF is collected clinically from 50 patients with CF and nine disease controls, processed, and stored per protocol. BALF supernatants are analyzed for 1129 proteins by aptamer approach (SOMAscan proteomics platform). Proteins are compared across groups and used for pathway analysis. Endotypes are identified within the CF group.

RESULTS

CF BALF has increased concentrations of neutrophil elastase, myeloperoxidase, and decreased concentration of protein folding and host defense proteins. Pathways that distinguished CF subjects included interferon gamma signaling, membrane trafficking, and phospholipid metabolism. In the CF group, unbiased analysis of proteins identified two distinct endotypes that differed based on BALF white blood cell and neutrophil counts and detection of CF pathogens.

CONCLUSIONS AND CLINICAL RELEVANCE

Proteomic analysis of the CF airway demonstrates a complex environment of proteins and pathways. This work provides evidence that aptamer-based proteomics can differentiate between groups and can determine endotypes within CF.

Affinity enrichment for mass spectrometry: improving the yield of low abundance biomarkers

INTRODUCTION

Mass spectrometry (MS) is the premier tool for discovering novel disease-associated protein biomarkers. Unfortunately, when applied to complex body fluid samples, MS has poor sensitivity for the detection of low abundance biomarkers (≪10 ng/mL), derived directly from the diseased tissue cells or pathogens. Areas covered: Herein we discuss the strengths and drawbacks of technologies used to concentrate low abundance analytes in body fluids, with the aim to improve the effective sensitivity for MS discovery. Solvent removal by dry-down or dialysis, and immune-depletion of high abundance serum or plasma proteins, is shown to have disadvantages compared to positive selection of the candidate biomarkers by affinity enrichment. A theoretical analysis of affinity enrichment reveals that the yield for low abundance biomarkers is a direct function of the binding affinity (Association/Dissociation rates) used for biomarker capture. In addition, a high affinity capture pre processing step can effectively dissociate the candidate biomarker from partitioning with high abundance proteins such as albumin. Expert commentary: Properly designed high affinity capture materials can enrich the yield of low abundance (0.1-10 picograms/mL) candidate biomarkers for MS detection. Affinity capture and concentration, as an upfront step in sample preparation for MS, combined with MS advances in software and hardware that improve the resolution of the chromatographic separation can yield a transformative new class of low abundance biomarkers predicting disease risk or disease latency.

Application of Large-Scale Aptamer-Based Proteomic Profiling to Planned Myocardial Infarctions

BACKGROUND

Emerging proteomic technologies using novel affinity-based reagents allow for efficient multiplexing with high-sample throughput. To identify early biomarkers of myocardial injury, we recently applied an aptamer-based proteomic profiling platform that measures 1129 proteins to samples from patients undergoing septal alcohol ablation for hypertrophic cardiomyopathy, a human model of planned myocardial injury. Here, we examined the scalability of this approach using a markedly expanded platform to study a far broader range of human proteins in the context of myocardial injury.

METHODS

We applied a highly multiplexed, expanded proteomic technique that uses single-stranded DNA aptamers to assay 4783 human proteins (4137 distinct human gene targets) to derivation and validation cohorts of planned myocardial injury, individuals with spontaneous myocardial infarction, and at-risk controls.

RESULTS

We found 376 target proteins that significantly changed in the blood after planned myocardial injury in a derivation cohort (n=20; P<1.05E-05, 1-way repeated measures analysis of variance, Bonferroni threshold). Two hundred forty-seven of these proteins were validated in an independent planned myocardial injury cohort (n=15; P<1.33E-04, 1-way repeated measures analysis of variance); >90% were directionally consistent and reached nominal significance in the validation cohort. Among the validated proteins that were increased within 1 hour after planned myocardial injury, 29 were also elevated in patients with spontaneous myocardial infarction (n=63; P<6.17E-04). Many of the novel markers identified in our study are intracellular proteins not previously identified in the peripheral circulation or have functional roles relevant to myocardial injury. For example, the cardiac LIM protein, cysteine- and glycine-rich protein 3, is thought to mediate cardiac mechanotransduction and stress responses, whereas the mitochondrial ATP synthase F0 subunit component is a vasoactive peptide on its release from cells. Last, we performed aptamer-affinity enrichment coupled with mass spectrometry to technically verify aptamer specificity for a subset of the new biomarkers.

CONCLUSIONS

Our results demonstrate the feasibility of large-scale aptamer multiplexing at a level that has not previously been reported and with sample throughput that greatly exceeds other existing proteomic methods. The expanded aptamer-based proteomic platform provides a unique opportunity for biomarker and pathway discovery after myocardial injury.

Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease

Plasma biomarkers that reflect molecular states of the cardiovascular system are central for clinical decision making. Routinely used plasma biomarkers include troponins, natriuretic peptides, and lipoprotein particles, yet interrogate only a modest subset of pathways relevant to cardiovascular disease. Systematic profiling of a larger portion of circulating plasma proteins (the plasma proteome) will provide opportunities for unbiased discovery of novel markers to improve diagnostic or predictive accuracy. In addition, proteomic profiling may inform pathophysiological understanding and point to novel therapeutic targets. Obstacles for comprehensive proteomic profiling include the immense size and structural heterogeneity of the proteome, and the broad range of abundance levels, as well. Proteome-wide, untargeted profiling can be performed in tissues and cells with tandem mass spectrometry. However, applications to plasma are limited by the need for complex preanalytical sample preparation stages limiting sample throughput. Multiplexing of targeted methods based on capture and detection of specific proteins are therefore receiving increasing attention in plasma proteomics. Immunoaffinity assays are the workhorse for measuring individual proteins but have been limited for proteomic applications by long development times, cross-reactivity preventing multiplexing, specificity issues, and incomplete sensitivity to detect proteins in the lower range of the abundance spectrum (below picograms per milliliter). Emerging technologies to address these issues include nucleotide-labeled immunoassays and aptamer reagents that can be automated for efficient multiplexing of thousands of proteins at high sample throughput, coupling of affinity capture methods to mass spectrometry for improved specificity, and ultrasensitive detection systems to measure low-abundance proteins. In addition, proteomics can now be integrated with modern genomics tools to comprehensively relate proteomic profiles to genetic variants, which may both influence binding of affinity reagents and serve to validate the target specificity of affinity assays. The application of deep quantitative proteomic profiling to large cohorts has thus become increasingly feasible with emerging affinity methods. The aims of this article are to provide the broad readership of Circulation with a timely overview of emerging methods for affinity proteomics and recent progress in cardiovascular medicine based on such methods.

Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome

In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS.

Application of clinical proteomics in acute respiratory distress syndrome

Acute Respiratory Distress Syndrome (ARDS) is a devastating cause of hypoxic respiratory failure, which continues to have high mortality. It is expected that a comprehensive systems- level approach will identify global and complex changes that contribute to the development of ARDS and subsequent repair of the damaged lung. In the last decade, powerful genome-wide analytical and informatics tools have been developed, that have provided valuable insights into the mechanisms of complex diseases such as ARDS. These tools include the rapid and precise measure of gene expression at the proteomic level. This article reviews the contemporary proteomics platforms that are available for comprehensive studies in ARDS. The challenges of various biofluids that could be investigated and some of the studies performed are also discussed.

Hydrogel nanoparticle harvesting of plasma or urine for detecting low abundance proteins

Novel biomarker discovery plays a crucial role in providing more sensitive and specific disease detection. Unfortunately many low-abundance biomarkers that exist in biological fluids cannot be easily detected with mass spectrometry or immunoassays because they are present in very low concentration, are labile, and are often masked by high-abundance proteins such as albumin or immunoglobulin. Bait containing poly(N-isopropylacrylamide) (NIPAm) based nanoparticles are able to overcome these physiological barriers. In one step they are able to capture, concentrate and preserve biomarkers from body fluids. Low-molecular weight analytes enter the core of the nanoparticle and are captured by different organic chemical dyes, which act as high affinity protein baits. The nanoparticles are able to concentrate the proteins of interest by several orders of magnitude. This concentration factor is sufficient to increase the protein level such that the proteins are within the detection limit of current mass spectrometers, western blotting, and immunoassays. Nanoparticles can be incubated with a plethora of biological fluids and they are able to greatly enrich the concentration of low-molecular weight proteins and peptides while excluding albumin and other high-molecular weight proteins. Our data show that a 10,000 fold amplification in the concentration of a particular analyte can be achieved, enabling mass spectrometry and immunoassays to detect previously undetectable biomarkers.

Plasma protein analysis of patients with different B-cell lymphomas using high-content antibody microarrays

PURPOSE

In this study, plasma samples from a multicentric case-control study on lymphoma were analyzed for the identification of proteins useful for diagnosis.

EXPERIMENTAL DESIGN

The protein content in the plasma of 100 patients suffering from the three most common B-cell lymphomas and 100 control samples was studied with antibody microarrays composed of 810 antibodies that target cancer-associated proteins. Sample pools were screened for an identification of marker proteins. Then, the samples were analyzed individually to validate the usability of these markers.

RESULTS

More than 200 proteins with disease-associated abundance changes were found. The evaluation on individual patients confirmed some molecules as robust informative markers while others were inadequate for this purpose. In addition, the analysis revealed distinct subgroups for each of the three investigated B-cell lymphoma subtypes. With this information, we delineated a classifier that discriminates the different lymphoma entities.

CONCLUSIONS AND CLINICAL RELEVANCE

Variations in plasma protein abundance permit discrimination between different patient groups. After validation on a larger study cohort, the findings could have diagnostic as well as differential diagnostic potential. Beside this, methodological aspects were critically evaluated, such as the value of sample pooling for the identification of biomarkers that are useful for a diagnosis on individual patients.

Circulating proteolytic signatures of chemotherapy-induced cell death in humans discovered by N-terminal labeling

It is known that many chemotherapeutics induce cellular apoptosis over hours to days. During apoptosis, numerous cellular proteases are activated, most canonically the caspases. We speculated that detection of proteolytic fragments released from apoptotic cells into the peripheral blood may serve as a unique indicator of chemotherapy-induced cell death. Here we used an enzymatic labeling process to positively enrich free peptide α-amines in the plasma of hematologic malignancy patients soon after beginning treatment. This N-terminomic approach largely avoids interference by high-abundance proteins that complicate traditional plasma proteomic analyses. Significantly, by mass spectrometry methods, we found strong biological signatures of apoptosis directly in the postchemotherapy plasma, including numerous caspase-cleaved peptides as well as relevant peptides from apoptotic and cell-stress proteins second mitochondria-derived activator of caspases, HtrA serine peptidase 2, and activating transcription factor 6. We also treated hematologic cancer cell lines with clinically relevant chemotherapeutics and monitored proteolytic fragments released into the media. Remarkably, many of these peptides coincided with those found in patient samples. Overall, we identified 153 proteolytic peptides in postchemotherapy patient plasma as potential indicators of cellular apoptosis. Through targeted quantitative proteomics, we verified that many of these peptides were indeed increased post- vs. prechemotherapy in additional patients. Our findings reveal that numerous proteolytic fragments are released from dying tumor cells. Monitoring posttreatment proteolysis may lead to a novel class of inexpensive, rapid biomarkers of cell death.

Distinct injury markers for the early detection and prognosis of incident acute kidney injury in critically ill adults with preserved kidney function

The use of novel biomarkers to detect incident acute kidney injury (AKI) in the critically ill is hindered by heterogeneity of injury and the potentially confounding effects of prevalent AKI. Here we examined the ability of urine NGAL (NGAL), L-type Fatty Acid Binding Protein (L-FABP), and Cystatin C to predict AKI development, death, and dialysis in a nested case-control study of 380 critically ill adults with an eGFR over 60 ml/min/1.73 m². One-hundred thirty AKI cases were identified following biomarker measurement and were compared to 250 controls without AKI. Areas under the receiver-operator characteristic curves (AUC-ROCs) for discriminating incident AKI from non-AKI were 0.58(95%CI: 0.52-0.64), 0.59(0.52-0.65), and 0.50(0.48-0.57) for urine NGAL, L-FABP, and Cystatin C, respectively. The combined AUC-ROC for NGAL and L-FABP was 0.59(56-0.69). Both urine NGAL and L-FABP independently predicted AKI during multivariate regression; however, risk reclassification indices were mixed. Neither urine biomarker was independently associated with death or acute dialysis [NGAL hazard ratio 1.35(95%CI: 0.93-1.96), L-FABP 1.15(0.82-1.61)] though both independently predicted the need for acute dialysis [NGAL 3.44(1.73-6.83), L-FABP 2.36(1.30-4.25)]. Thus, urine NGAL and L-FABP independently associated with the development of incident AKI and receipt of dialysis but exhibited poor discrimination for incident AKI using conventional definitions.

The spectrum of circulating RNA: a window into systems toxicology

Adverse effects caused by therapeutic drugs are a serious and costly health concern. Despite the body's systemic responses to therapeutics, the liver is often the focus of damage and is usually the focus of studies of toxic effects due to its active roles in the metabolism of xenobiotics. It is extremely difficult, however, to assess systemic responses with currently available methods. Comprehensive cataloging of cell-free circulating RNAs using next-generation sequencing technology may open a window to assess drug-associated adverse effects at the systems level. To explore this potential, we conducted an RNA profiling study using the well-characterized acetaminophen overdose mouse model on liver and plasma with microarray and next-generation sequencing platforms, respectively. After drug treatment, the levels of a number of transcripts, both endogenous and exogenous RNAs, showed significant changes in plasma, reflecting not only the classical liver injury induced by acetaminophen overdose but also damage in tissues other than the liver. The changes in exogenous RNAs also reflect alteration on dieting behavior after acetaminophen overdose. Besides reporting an extensive list of circulating RNA-based biomarker candidates, this study illustrates the possibility of using circulating RNAs to assess global effects of therapeutics. This could also lead to a new approach for a more comprehensive assessment of the efficacy and safety of therapeutics.

Plasma Shh levels reduced in pancreatic cancer patients

OBJECTIVES

Normally, sonic hedgehog (Shh) is expressed in the pancreas during fetal development and transiently after tissue injury. Although pancreatic cancers express Shh, it is not known if the protein is secreted into the blood and whether its plasma levels change with pancreatic transformation. The goal of this study was to develop an enzyme-linked immunosorbent assay to detect human Shh in blood and determine its levels in subjects with and without pancreatic cancer.

METHODS

A human Shh enzyme-linked immunosorbent assay was developed, and plasma Shh levels were measured in blood samples from healthy subjects and patients with pancreatitis or pancreatic cancer. The biological activity of plasma Shh was tested using NIH-3T3 cells.

RESULTS

The mean levels of Shh in human blood were lower in patients with pancreatitis and pancreatic cancer than in healthy subjects. Hematopoietic cells did not express Shh, suggesting that Shh is secreted into the bloodstream. Plasma fractions enriched with Shh did not induce Gli-1 messenger RNA, suggesting that the protein was not biologically active.

CONCLUSIONS

Shh is secreted from tissues and organs into the circulation, but its activity is blocked by plasma proteins. Reduced plasma levels were found in pancreatic cancer patients, but alone were not sufficient to predict pancreatic cancer.

Novel effects of hormonal contraceptive use on the plasma proteome

Background

Hormonal contraceptive (HC) use may increase cardiometabolic risk; however, the effect of HC on emerging cardiometabolic and other disease risk factors is not clear.

Objectives

To determine the association between HC use and plasma proteins involved in established and emerging disease risk pathways.

Method

Concentrations of 54 high-abundance plasma proteins were measured simultaneously by LC-MRM/MS in 783 women from the Toronto Nutrigenomics and Health Study. C-reactive protein (CRP) was measured separately. ANCOVA was used to test differences in protein concentrations between users and non-users, and among HC users depending on total hormone dose. Linear regression was used to test the association between duration (years) of HC use and plasma protein concentrations. Principal components analysis (PCA) was used to identify plasma proteomic profiles in users and non-users.

Results

After Bonferroni correction, 19 proteins involved in inflammation, innate immunity, coagulation and blood pressure regulation were significantly different between users and non-users (P<0.0009). These differences were replicated across three distinct ethnocultural groups. Traditional markers of glucose and lipid metabolism were also significantly higher among HC users. Neither hormone dose nor duration of use affected protein concentrations. PCA identified 4 distinct proteomic profiles in users and 3 in non-users.

Conclusion

HC use was associated with different concentrations of plasma proteins along various disease-related pathways, and these differences were present across different ethnicities. Aside from the known effect of HC on traditional biomarkers of cardiometabolic risk, HC use also affects numerous proteins that may be biomarkers of dysregulation in inflammation, coagulation and blood pressure.

Urinary desmosine: a biomarker of structural lung injury during CF pulmonary exacerbation

RATIONALE

Cystic fibrosis (CF) lung disease is characterized by structural changes and remodeling in airway architecture and lung parenchyma. Neutrophilic inflammation and infection lead to injury and breakdown of airway matrix constituents, including elastin. The non-invasive measurement of urinary desmosine (UDes), a breakdown product of elastin, may be reflective of ongoing lung injury and may serve as a biomarker of active short-term damage during pulmonary exacerbation. Our objectives were to measure desmosine in the urine of CF patients hospitalized for treatment of a pulmonary exacerbation and to explore the correlation between desmosine concentration and other markers of clinical improvement, including lung function and inflammatory mediators.

METHODS

Urine and blood samples plus lung function measurements were collected at up to three points during hospitalization for treatment of a CF pulmonary exacerbation. We used a repeated measures model, adjusted for age and time between measurements, to compare log transformed urine desmosine concentrations across multiple time points and to correlate those concentrations with related clinical variables. Change in UDes concentration was investigated using a statistical model that incorporated normalization factors to account for variations in urinary concentration.

RESULTS

Desmosine was measured by radioimmunoassay (RIA) in 155 spot urine samples from 53 CF patients hospitalized for 63 pulmonary exacerbations (range of results: 0-235 pmol Des/ml). Specific gravity (SG) adjusted UDes concentration decreased significantly during admission for CF pulmonary exacerbation, P < 0.01 (average length of stay = 11 days). No correlation was observed between UDes concentration and lung function or inflammatory markers.

CONCLUSIONS

UDes decreased significantly following treatment for an acute pulmonary exacerbation and may be a useful biomarker of short-term injury to the CF lung. Further investigation is needed to evaluate the utility of UDes concentration in the long-term progression of CF lung disease.

β-actin as a loading control for plasma-based Western blot analysis of major depressive disorder patients

Western blot analysis is a commonly used technique for determining specific protein levels in clinical samples. For normalization of protein levels in Western blot, a suitable loading control is required. On account of its relatively high and constant expression, β-actin has been widely employed in Western blot of cell cultures and tissue extracts. However, β-actin's presence in human plasma and this protein's putative role as a plasma-based loading control for Western blot analysis remain unknown. In this study, an enzyme-linked immunosorbent assay was used to determine the concentration of β-actin in human plasma, which is 6.29±0.54 ng/ml. In addition, the linearity of β-actin immunostaining and loaded protein amount was evaluated by Western blot, and a fine linearity (R²=0.974±0.012) was observed. Furthermore, the expression of plasma β-actin in major depressive disorder subjects and healthy controls was compared. The data revealed no statistically significant difference between these two groups. Moreover, the total coefficient of variation for β-actin expression in the two groups was 9.2±1.2%. These findings demonstrate that β-actin is present in human plasma and may possibly be used as a suitable loading control for plasma-based Western blot analysis in major depressive disorder.

Redox proteomics identification of oxidatively modified myocardial proteins in human heart failure: implications for protein function

Increased oxidative stress in a failing heart may contribute to the pathogenesis of heart failure (HF). The aim of this study was to identify the oxidised proteins in the myocardium of HF patients and analyse the consequences of oxidation on protein function. The carbonylated proteins in left ventricular tissue from failing (n = 14) and non-failing human hearts (n = 13) were measured by immunoassay and identified by proteomics. HL-1 cardiomyocytes were incubated in the presence of stimuli relevant for HF in order to assess the generation of reactive oxygen species (ROS), the induction of protein carbonylation, and its consequences on protein function. The levels of carbonylated proteins were significantly higher in the HF patients than in the controls (p<0.01). We identified two proteins that mainly underwent carbonylation: M-type creatine kinase (M-CK), whose activity is impaired, and, to a lesser extent, α-cardiac actin. Exposure of cardiomyocytes to angiotensin II and norepinephrine led to ROS generation and M-CK carbonylation with loss of its enzymatic activity. Our findings indicate that protein carbonylation is increased in the myocardium during HF and that these oxidative changes may help to explain the decreased CK activity and consequent defects in energy metabolism observed in HF.

Candidate serum biomarkers for prostate adenocarcinoma identified by mRNA differences in prostate tissue and verified with protein measurements in tissue and blood

BACKGROUND

Improved tests are needed for detection and management of prostate cancer. We hypothesized that differential gene expression in prostate tissue could help identify candidate blood biomarkers for prostate cancer and that blood from men with advanced prostate disease could be used to verify the biomarkers presence in circulation.

METHODS

We identified candidate markers using mRNA expression patterns from laser-capture microdissected prostate tissue and confirmed tissue expression using immunohistochemistry (IHC) for the subset of candidates having commercial antisera. We analyzed tissue extracts with tandem mass spectrometry (MS/MS) and measured blood concentrations using immunoassays and MS/MS of trypsin-digested, immunoextracted peptides.

RESULTS

We selected 35 novel candidate prostate adenocarcinoma biomarkers. For all 13 markers having commercial antisera for IHC, tissue expression was confirmed; 6 showed statistical discrimination between nondiseased and malignant tissue, and only 5 were detected in tissue extracts by MS/MS. Sixteen of the 35 candidate markers were successfully assayed in blood. Four of 8 biomarkers measured by ELISA and 3 of 10 measured by targeted MS showed statistically significant increases in blood concentrations of advanced prostate cancer cases, compared with controls.

CONCLUSIONS

Seven novel biomarkers identified by gene expression profiles in prostate tissue were shown to have statistically significant increased concentrations in blood from men with advanced prostate adenocarcinoma compared with controls: apolipoprotein C1, asporin, cartilage oligomeric matrix protein, chemokine (C-X-C motif) ligand 11 (CXCL11), CXCL9, coagulation factor V, and proprotein convertase subtilisin/kexin 6.

Pre-analytical effects of blood sampling and handling in quantitative immunoassays for rheumatoid arthritis

Variability in pre-analytical blood sampling and handling can significantly impact results obtained in quantitative immunoassays. Understanding the impact of these variables is critical for accurate quantification and validation of biomarker measurements. Particularly, in the design and execution of large clinical trials, even small differences in sample processing and handling can have dramatic effects in analytical reliability, results interpretation, trial management and outcome. The effects of two common blood sampling methods (serum vs. plasma) and two widely-used serum handling methods (on the clot with ambient temperature shipping, "traditional", vs. centrifuged with cold chain shipping, "protocol") on protein and autoantibody concentrations were examined. Matched serum and plasma samples were collected from 32 rheumatoid arthritis (RA) patients representing a wide range of disease activity status. Additionally, a set of matched serum samples with two sample handling methods was collected. One tube was processed per manufacturer's instructions and shipped overnight on cold packs (protocol). The matched tube, without prior centrifugation, was simultaneously shipped overnight at ambient temperatures (traditional). Upon delivery, the traditional tube was centrifuged. All samples were subsequently aliquoted and frozen prior to analysis of protein and autoantibody biomarkers. Median correlation between paired serum and plasma across all autoantibody assays was 0.99 (0.98-1.00) with a median % difference of -3.3 (-7.5 to 6.0). In contrast, observed protein biomarker concentrations were significantly affected by sample types, with median correlation of 0.99 (0.33-1.00) and a median % difference of -10 (-55 to 23). When the two serum collection/handling methods were compared, the median correlation between paired samples for autoantibodies was 0.99 (0.91-1.00) with a median difference of 4%. In contrast, significant increases were observed in protein biomarker concentrations among certain biomarkers in samples processed with the 'traditional' method. Autoantibody quantification appears robust to both sample type (plasma vs. serum) and pre-analytical sample collection/handling methods (protocol vs. traditional). In contrast, for non-antibody protein biomarker concentrations, sample type had a significant impact; plasma samples generally exhibit decreased protein biomarker concentrations relative to serum. Similarly, sample handling significantly impacted the variability of protein biomarker concentrations. When biomarker concentrations are combined algorithmically into a single test score such as a multi-biomarker disease activity test for rheumatoid arthritis (MBDA), changes in protein biomarker concentrations may result in a bias of the score. These results illustrate the importance of characterizing pre-analytical methodology, sample type, sample processing and handling procedures for clinical testing in order to ensure test accuracy.

Biomarkers in acute lung injury--marking forward progress

This article reviews the state of the art regarding biomarkers for prediction, diagnosis, and prognosis in acute lung injury. Biomarkers and the goals of biomarker research are defined. Progress along 4 general routes is examined. First, the results of wide-ranging existing protein biomarkers are reported. Second, newer biomarkers awaiting or with strong potential for validation are described. Third, progress in the fields of genomics and proteomics is reported. Finally, given the complexity and number of potential biomarkers, the results of combining clinical predictors with protein and other biomarkers to produce better prognostic and diagnostic indices are examined.

Multifunctional core-shell nanoparticles: discovery of previously invisible biomarkers

Many low-abundance biomarkers for early detection of cancer and other diseases are invisible to mass spectrometry because they exist in body fluids in very low concentrations, are masked by high-abundance proteins such as albumin and immunoglobulins, and are very labile. To overcome these barriers, we created porous, buoyant, core-shell hydrogel nanoparticles containing novel high affinity reactive chemical baits for protein and peptide harvesting, concentration, and preservation in body fluids. Poly(N-isopropylacrylamide-co-acrylic acid) nanoparticles were functionalized with amino-containing dyes via zero-length cross-linking amidation reactions. Nanoparticles functionalized in the core with 17 different (12 chemically novel) molecular baits showed preferential high affinities (K(D) < 10(-11) M) for specific low-abundance protein analytes. A poly(N-isopropylacrylamide-co-vinylsulfonic acid) shell was added to the core particles. This shell chemistry selectively prevented unwanted entry of all size peptides derived from albumin without hindering the penetration of non-albumin small proteins and peptides. Proteins and peptides entered the core to be captured with high affinity by baits immobilized in the core. Nanoparticles effectively protected interleukin-6 from enzymatic degradation in sweat and increased the effective detection sensitivity of human growth hormone in human urine using multiple reaction monitoring analysis. Used in whole blood as a one-step, in-solution preprocessing step, the nanoparticles greatly enriched the concentration of low-molecular weight proteins and peptides while excluding albumin and other proteins above 30 kDa; this achieved a 10,000-fold effective amplification of the analyte concentration, enabling mass spectrometry (MS) discovery of candidate biomarkers that were previously undetectable.

Multimarker panels in sepsis

Biomarkers differentiate between 2 or more biologic states. The complexity of diseases like sepsis makes it unlikely that any single marker will allow for precise disease specification. Combining several biomarkers into a single classification rule should help to improve their accuracy and, therefore, their usefulness. This article reviews several studies using multimarker panels, and highlights the potential of more sophisticated diagnostic and prognostic techniques in future multimarker panels. More complex algorithms should accelerate the adoption of multimarker panels into the routine management of patients with sepsis, provided that clinicians understand the multimarker approach.

A roadmap for successful applications of clinical proteomics

Despite over 30,000 publications on proteomics in the last decade, and the accumulation of extensive interesting information on the human proteome in diverse observations, the clinical translation of proteomics to-date has had major setbacks. I review here a roadmap for improving the success rate of clinical proteomics. The roadmap includes steps for improvements that need to be made in analytical tools, discovery, validation, clinical application, and post-clinical application appraisal. It is likely that most if not all of the components that are necessary for clinical success are either readily available, or should be possible to put in place with more rigorous research standards and concerted efforts of the research community, clinicians, and health agencies. Enthusiasm for the clinical impact of proteomics may need to be tempered currently until robust evidence can be obtained, but some clinical successes should eventually be feasible.

Biological markers of acute kidney injury

An abrupt change in serum creatinine, the most common indicator of acute kidney injury (AKI), is strongly linked to poor outcomes across multiple clinical settings. Despite endless attempts to distill the magnitude and timing of a changing serum creatinine into a standardized metric, singular focus on this traditional functional marker obligates the characterization of AKI to remain, at best, retrospective and causally noninformative. The resultant inability to meaningfully segregate critical aspects of injury such as type, onset, propagation, and recovery from ongoing decrements in renal function has hindered successful translation of promising therapeutics. Over the past decade, however, the emerging field of clinical proteomics reinvigorates hope of identifying novel plasma and urine biomarkers to characterize cause and course of kidney injury. Efforts to validate these markers for use in clinical studies now show early promise but face important obstacles including interpretive difficulties inherent in using serum creatinine as a sole comparator for diagnostic performance, a need to better evaluate the incremental performance of new markers above established clinical and biochemical predictors, a relative lack of power to sufficiently examine hard clinical end points, and a potential over-reliance on use alone of receiver operating curves for assessing biomarker utility. Here, we discuss efforts to address these barriers and further ascertain the clinical value of new markers.

Serum protein profiling of systemic lupus erythematosus and systemic sclerosis using recombinant antibody microarrays

Systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) are two severe autoimmune connective tissue diseases. The fundamental knowledge about their etiology is limited and the conditions display complex pathogenesis, multifaceted presentations, and unpredictable courses. Despite significant efforts, the lack of fully validated biomarkers enabling diagnosis, classification, and monitoring of disease activity represents significant unmet clinical needs. In this discovery study, we have for the first time used recombinant antibody microarrays for miniaturized, multiplexed serum protein profiling of SLE and SSc, targeting mainly immunoregulatory proteins. The data showed that several candidate SLE-associated multiplexed serum biomarker signatures were delineated, reflecting disease (diagnosis), disease severity (phenotypic subsets), and disease activity. Selected differentially expressed markers were validated using orthogonal assays and a second, independent patient cohort. Further, biomarker signatures differentiating SLE versus SSc were demonstrated, and the observed differences increased with severity of SLE. In contrast, the data showed that the serum profiles of SSc versus healthy controls were more similar. Hence, we have shown that affinity proteomics could be used to de-convolute crude, nonfractionated serum proteomes, extracting molecular portraits of SLE and SSc, further enhancing our fundamental understanding of these complex autoimmune conditions.

Age-related differences in plasma proteins: how plasma proteins change from neonates to adults

The incidence of major diseases such as cardiovascular disease, thrombosis and cancer increases with age and is the major cause of mortality world-wide, with neonates and children somehow protected from such diseases of ageing. We hypothesized that there are major developmental differences in plasma proteins and that these contribute to age-related changes in the incidence of major diseases. We evaluated the human plasma proteome in healthy neonates, children and adults using the 2D-DIGE approach. We demonstrate significant changes in number and abundance of up to 100 protein spots that have marked differences in during the transition of the plasma proteome from neonate and child through to adult. These proteins are known to be involved in numerous physiological processes such as iron transport and homeostasis, immune response, haemostasis and apoptosis, amongst others. Importantly, we determined that the proteins that are differentially expressed with age are not the same proteins that are differentially expressed with gender and that the degree of phosphorylation of plasma proteins also changes with age. Given the multi-functionality of these proteins in human physiology, understanding the differences in the plasma proteome in neonates and children compared to adults will make a major contribution to our understanding of developmental biology in humans.

Proteomic profiling of cancer--opportunities, challenges, and context

The article by Roesch-Ely and colleagues in a recent issue of The Journal of Pathology describes the use of proteomic techniques to examine mucosal biopsies in patients with head and neck squamous cell cancer (HNSCC) and in corresponding control samples. The authors were able to determine the anatomical site of origin of the biopsies based on modelling of multiplex protein datasets, and to use the information to analyse field cancerization as a means of predicting tumour recurrence. Although the study included only a relatively small number of cases, and will require future validation in a larger patient cohort, the results point to the potential of proteomics to increase our understanding of cancer biology, and in this instance to offer clinical value.

The genetics of congestive heart failure

The heart failure syndrome is known to represent a final common pathway for a broad range of etiologies, but there is tremendous variation in the propensity to develop congestive heart failure after a given insult. This variation is thought to result in part from inherited differences in myocardial, vascular or systemic responses, but the nature of the underlying traits responsible ultimately for the development of heart failure has remained elusive. There has been limited progress in the genetic exploration of the key clinical phenotype itself: heart failure. In this article, the author attempts to place the results of genetic studies of cardiomyopathy in the broader context of the clinical syndrome of heart failure, highlighting some of the key questions for future study.

Association of serum osteoprotegerin with left ventricular mass in African American adults with hypertension

BACKGROUND

African-Americans with hypertension are susceptible to left ventricular hypertrophy (LVH). Serum osteoprotegerin level has been reported to be associated with LVH. We investigated the association of osteoprotegerin with LV mass (LVM) in 898 African-Americans with hypertension (mean age 65 years, 71% women).

METHODS

Osteoprotegerin levels were measured in serum by an immunoassay and log-transformed for analyses. LVM index (LVMi; LVM/height(2.7)) was estimated using M-mode echocardiography. Linear regression analyses using generalized estimating equations were used to assess the association of osteoprotegerin with LVMi.

RESULTS

Serum osteoprotegerin was correlated with LVMi (r = 0.21; P < 0.0001), an estimated increase in LVMi of 5.05 (95% confidence interval 2.93, 7.17) g/m(2.7) in the highest compared to the lowest osteoprotegerin quartile. This association remained statistically significant after adjustment for conventional cardiovascular risk factors (age, sex, body mass index (BMI), history of smoking, diabetes, systolic blood pressure (BP), total and high-density lipoprotein cholesterol), estimated renal function, history of myocardial infarction and stroke, lifestyle factors (physical activity score, years of education, amount of alcohol consumption), medications (aspirin, antihypertensives, statins, estrogens), and C-reactive protein (CRP) (P = 0.02). Additionally, osteoprotegerin was correlated with early/atrial (E/A) ratio (r = -0.16; P < 0.0001), LV mean wall thickness (r = 0.17; P < 0.0001) and relative wall thickness (r = 0.14; P < 0.0001) but not ejection fraction (r = 0.04; P = 0.24) or internal end-diastolic dimension (r = 0.02; P = 0.60).

CONCLUSION

In African-Americans with hypertension, a higher serum osteoprotegerin level is weakly but independently associated with a higher LVM.

Elevated urinary IL-18 levels at the time of ICU admission predict adverse clinical outcomes

BACKGROUND AND OBJECTIVES

Urine IL-18 (uIL-18) has demonstrated moderate capacity to predict acute kidney injury (AKI) and adverse outcomes in defined settings. Its ability to predict AKI and provide prognostic information in broadly selected, critically ill adults remains unknown.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

The study prospectively evaluated the capacity of uIL-18 measured within 24 hours of intensive care unit (ICU) admission to predict AKI, death, and receipt of acute dialysis in a large mixed-adult ICU population.

RESULTS

Of 451 patients, 86 developed AKI within 48 hours of enrollment and had higher median uIL-18 levels [426 (interquartile range [IQR]: 152 to 1183) pg/mg creatinine] compared with those without AKI [248 (IQR: 120 to 559) pg/mg]. The area under the receiver operating characteristic curve for uIL-18 predicting subsequent AKI within 24 hours was 0.62 (95% CI: 0.54 to 0.69) and improved modestly to 0.67 (95% CI: 0.53 to 0.81) in patients whose enrollment eGFR was >or=75 ml/min per 1.73 m(2). The highest median uIL-18 levels were observed in patients with sepsis at enrollment [508 (IQR: 230 to 1281) pg/mg], those receiving acute dialysis [571 (IQR: 161 to 1614) pg/mg] or dying [532 (IQR: 210 to 1614) pg/mg] within 28 days of ascertainment. After adjustment for a priori selected clinical predictors, uIL-18 remained independently predictive of composite outcome of death or acute dialysis within 28 days of ascertainment (odds ratio, 1.86 [95% CI: 1.31 to 2.64]).

CONCLUSIONS

uIL-18 did not reliably predict AKI development, but did predict poor clinical outcomes in a broadly selected, critically ill adult population.

Peptide and protein imaging mass spectrometry in cancer research

MALDI mass spectrometry is able to acquire protein profiles directly from tissue that can describe the levels of hundreds of distinct proteins. MALDI imaging MS can simultaneously reveal how each of these proteins varies in heterogeneous tissues. Numerous studies have now demonstrated how MALDI imaging MS can generate different protein profiles from the different cell types in a tumor, which can act as biomarker profiles or enable specific candidate protein biomarkers to be identified. MALDI imaging MS can be directly applied to patient samples where its utility is to accomplish untargeted multiplex analysis of the tissue's protein content, enabling the different regions of the tissue to be differentiated on the basis of previously unknown protein profiles/biomarkers. The technique continues to rapidly develop and is now approaching the cusp whereby its potential to provide new diagnostic/prognostic tools for cancer patients can be routinely investigated. Here the latest methodological developments are summarized and its application to a range of tumors is reported in detail. The prospects of MALDI imaging MS are then described from the perspectives of modern pathological practice and MS-based proteomics, to ensure the outlook addresses real clinical needs and reflects the real capabilities of MS-based proteomics of complex tissue samples.

Proteomics and mass spectrometry: what have we learned about the heart?

The emergence of new platforms for the discovery of innovative therapeutics has provided a means for diagnosing cardiac disease in its early stages. Taking into consideration the global health burden of cardiac disease, clinicians require innovations in medical diagnostics that can be used for risk stratification. Proteomic based studies offer an avenue for the discovery of proteins that are differentially regulated during disease; such proteins could serve as novel biomarkers of the disease state. For instance, in clinical practice, the abundance of such biomarkers in blood could be correlated with the severity of the disease state. As such, early detection of biomarkers would enable an improvement in patient prognosis. In this review, we outline advancements in various proteomic platforms used to study the disease proteome and their applications to the field of clinical medicine. Specifically, we highlight the contributions of proteomic-based profiling experiments to the analysis of cardiovascular diseases.

Beyond mortality: future clinical research in acute lung injury

Mortality in National Heart, Lung and Blood Institute-sponsored clinical trials of treatments for acute lung injury (ALI) has decreased dramatically during the past two decades. As a consequence, design of such trials based on a mortality outcome requires ever-increasing numbers of patients. Recognizing that advances in clinical trial design might be applicable to these trials and might allow trials with fewer patients, the National Heart, Lung and Blood Institute convened a workshop of extramural experts from several disciplines. The workshop assessed the current state of clinical research addressing ALI, identified research needs, and recommended: (1) continued performance of trials evaluating treatments of patients with ALI; (2) development of strategies to perform ALI prevention trials; (3) observational studies of patients without ALI undergoing prolonged mechanical ventilation; and (4) development of a standardized format for reporting methods, endpoints, and results of ALI trials.

Sex and ethnic differences in 47 candidate proteomic markers of cardiovascular disease: the Mayo Clinic proteomic markers of arteriosclerosis study

BACKGROUND

Cardiovascular disease (CVD) susceptibility differs between men and women and varies with ethnicity. This variability is not entirely explained by conventional CVD risk factors. We examined differences in circulating levels of 47 novel protein markers of CVD in 2561 men and women of African-American (AA) and non-Hispanic White (NHW) ethnicity, enrolled at geographically distinct sites.

METHODOLOGY/PRINCIPAL FINDINGS

Participants (1,324 AAs, mean age 63.5 y, 71% women; 1,237 NHWs, mean age 58.9 y, 57% women) belonged to sibships ascertained on the basis of hypertension. Solid-phase immunoassays and immunoturbidometric, clot-based, chromogenic, and electrophoretic assays were used to measure the 47 protein markers in plasma or serum. Marker levels were log transformed and outliers were adjusted to within 4 SD. To identify markers independently associated with sex or ethnicity, we employed multivariable regression analyses that adjusted for conventional risk factors, prior history of CVD, medication use and lifestyle factors (physical activity, alcohol consumption and education). Generalized estimating equations were used to correct for intrafamilial correlations. After adjustment for the above covariates, female sex was associated with higher levels of 29 markers and lower levels of 6 markers. Female sex was independently associated with higher levels of several inflammatory markers as well as lipoproteins, adipokines, natriuretic peptides, vasoconstrictor peptides and markers of calcification and thrombosis. AA ethnicity was associated with higher levels of 19 markers and lower levels of 6 markers, including higher levels of several inflammatory makers, higher leptin and lower adiponectin levels, lower levels of vasodilator-natriuretic peptides, higher levels of vasoconstrictor-antidiuretic peptides and markers of calcification and thrombosis.

CONCLUSIONS/SIGNIFICANCE

Plasma levels of several novel protein markers of CVD differ significantly in the context of sex and ethnicity. These results have implications for individualized CVD risk assessment.