Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry

Identification of candidate serum biomarkers of childhood-onset growth hormone deficiency using SWATH-MS and feature selection

A typical clinical manifestation of growth hormone deficiency (GHD) is a short stature resulting from delayed growth, but GHD affects bone health, cardiovascular function and metabolic profile and therefore quality of life. Although early GH treatment during childhood has been shown to improve outcomes, no single biochemical parameter is currently available for the accurate diagnosis of GHD in children. There is hence a need for non-invasive biomarkers. In this study, the relative abundance of serum proteins from GHD children and healthy controls was measured by next-generation proteomics SWATH-MS technology. The data generated was analysed by machine-learning feature-selection algorithms in order to discover the minimum number of protein biomarkers that best discriminate between both groups. The analysis of serum proteins by a SWATH-MS approach yielded a useful method for discovering potential biomarkers of GHD in children. A total of 263 proteins were confidently detected and quantified in each sample. Pathway analysis indicated an effect on tissue/organ structure and morphogenesis. The top ten serum protein biomarker candidates were identified after applying feature-selection data analysis. The combination of three proteins - apolipoprotein A-IV, complement factor H-related protein 4 and platelet basic protein - demonstrated the best classification performance for our data. In addition, the apolipoprotein group resulted in strong over-representation, thus highlighting these proteins as an additional promising biomarker panel.

SIGNIFICANCE

Currently there is no single biochemical parameter available for the accurate diagnosis of growth hormone (GH) deficiency (GHD) in children. Simple GH measurements are not an option: because GH is released in a pulsatile action, its blood levels fluctuate throughout the day and remain nearly undetectable for most of that time. This makes measurements of GH in a single blood sample useless for assessing GH deficiency. Actually, the diagnosis of GHD includes a combination of direct and indirect non-accurate measurements, such as taking several body measurements, testing GH levels in multiple blood samples after provocative tests (GH peak <7.3ng/mL, using radioimmunoassay), and conducting magnetic resonance imaging (MRI), among others. Therefore, there is a need for simple, non-invasive, accurate and cost-effective biomarkers. Here we report a case-control study, where relative abundance of serum proteins were measured by next-generation proteomics SWATH-MS technology in 15 GHD children and 15healthy controls matched by age, sex, and not receiving any treatment. Data generated was analysed by machine learning feature selection algorithms. 263 proteins could be confidently detected and quantified on each sample. The top 10 serum protein biomarker candidates could be identified after applying a feature selection data analysis. The combination of three proteins, apolipoprotein A-IV, complement factor H-related protein 4 and platelet basic protein, showed the best classification performance for our data. In addition, the fact that the pathway and GO analysis we performed pointed to the apolipoproteins as over-represented highlights this protein group as an additional promising biomarker panel for the diagnosis of GHD and for treatment evaluation.

Gender proteomics I. Which proteins in non-sexual organs

Differences related to gender have long been neglected but recent investigations show that they are widespread and may be recognized with all types of omics approaches, both in tissues and in biological fluids. Our review compiles evidence collected with proteomics techniques in our species, mainly focusing on baseline parameters in non-sexual organs in healthy men and women. Data from human specimens had to be replaced with information from other mammals every time invasive procedures of sample procurement were involved.

SIGNIFICANCE

As our knowledge, and the methods to build it, get refined, gender differences need to receive more and more attention, as they influence the outcome of all aspects in lifestyle, including diet, exercise and environmental factors. In turn this background modulates a differential susceptibility to some disease, or a different pathogenetic mechanism, depending on gender, and a different response to pharmacological therapy. Preparing this review we meant to raise awareness about the gender issue. We anticipate that more and more often, in the future, separate evaluations will be carried out on male and female subjects as an alternative - and an upgrade - to the current approach of reference and test groups being 'matched for age and sex'.

Omics and Exercise: Global Approaches for Mapping Exercise Biological Networks

The application of global "-omics" technologies to exercise has introduced new opportunities to map the complexity and interconnectedness of biological networks underlying the tissue-specific responses and systemic health benefits of exercise. This review will introduce major research tracks and recent advancements in this emerging field, as well as critical gaps in understanding the orchestration of molecular exercise dynamics that will benefit from unbiased omics investigations. Furthermore, significant research hurdles that need to be overcome to effectively fill these gaps related to data collection, computation, interpretation, and integration across omics applications will be discussed. Collectively, a cross-disciplinary physiological and omics-based systems approach will lead to discovery of a wealth of novel exercise-regulated targets for future mechanistic validation. This frontier in exercise biology will aid the development of personalized therapeutic strategies to improve athletic performance and human health through precision exercise medicine.

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction.

Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry

Proteomic characterization of blood plasma is of central importance to clinical proteomics and particularly to biomarker discovery studies. The vast dynamic range and high complexity of the plasma proteome have, however, proven to be serious challenges and have often led to unacceptable tradeoffs between depth of coverage and sample throughput. We present an optimized sample-processing pipeline for analysis of the human plasma proteome that provides greatly increased depth of detection, improved quantitative precision and much higher sample analysis throughput as compared with prior methods. The process includes abundant protein depletion, isobaric labeling at the peptide level for multiplexed relative quantification and ultra-high-performance liquid chromatography coupled to accurate-mass, high-resolution tandem mass spectrometry analysis of peptides fractionated off-line by basic pH reversed-phase (bRP) chromatography. The overall reproducibility of the process, including immunoaffinity depletion, is high, with a process replicate coefficient of variation (CV) of <12%. Using isobaric tags for relative and absolute quantitation (iTRAQ) 4-plex, >4,500 proteins are detected and quantified per patient sample on average, with two or more peptides per protein and starting from as little as 200 μl of plasma. The approach can be multiplexed up to 10-plex using tandem mass tags (TMT) reagents, further increasing throughput, albeit with some decrease in the number of proteins quantified. In addition, we provide a rapid protocol for analysis of nonfractionated depleted plasma samples analyzed in 10-plex. This provides ∼600 quantified proteins for each of the ten samples in ∼5 h of instrument time.

Quick 96FASP for high throughput quantitative proteome analysis

Filter aided sample preparation (FASP) is becoming a central method for proteomic sample cleanup and peptide generation prior to LC-MS analysis. We previously adapted this method to a 96-well filter plate, and applied to prepare protein digests from cell lysate and body fluid samples in a high throughput quantitative manner. While the 96FASP approach is scalable and can handle multiple samples simultaneously, two key advantages compared to single FASP, it is also time-consuming. The centrifugation-based liquid transfer on the filter plate takes 3-5 times longer than single filter. To address this limitation, we now present a quick 96FASP (named q96FASP) approach that, relying on the use of filter membranes with a large MWCO size (~30kDa), significantly reduces centrifugal times. We show that q96FASP allows the generation of protein digests derived from whole cell lysates and body fluids in a quality similar to that of the single FASP method. Processing a sample in multiple wells in parallel, we observed excellent experimental repeatability by label-free quantitation approach. We conclude that the q96FASP approach promises to be a promising cost- and time-effective method for shotgun proteomics and will be particularly useful in large scale biomarker discovery studies.

SIGNIFICANCE

High throughput sample processing is of particular interests for quantitative proteomics. The previously developed 96FASP is high throughput and appealing, however it is time-consuming in the context of centrifugation-based liquid transfer (~1.5h per spin). This study presents a truly high throughput sample preparation method based on large cut-off 96-well filter plate, which shortens the spin time to ~20min. To our knowledge, this is the first multi-well method that is entirely comparable with conventional FASP. This study thoroughly examined two types of filter plates and performed side-by-side comparisons with single FASP. Two types of samples, whole cell lysate of a UTI (urinary tract infection)-associated Klebsiella pneumoniae cell and human urine, were tested which demonstrated its capability for quantitative proteomics. The q96FSAP approach makes the filter plate-based approach more appealing for protein biomarker discovery projects, and could be broadly applied to large scale proteomics analysis.

The differential plasma proteome of obese and overweight individuals undergoing a nutritional weight loss and maintenance intervention

PURPOSE

The nutritional intervention program "DiOGenes" focuses on how obesity can be prevented and treated from a dietary perspective. We generated differential plasma proteome profiles in the DiOGenes cohort to identify proteins associated with weight loss and maintenance and explore their relation to body mass index, fat mass, insulin resistance, and sensitivity.

EXPERIMENTAL DESIGN

Relative protein quantification was obtained at baseline and after combined weight loss/maintenance phases using isobaric tagging and MS/MS. A Welch t-test determined proteins differentially present after intervention. Protein relationships with clinical variables were explored using univariate linear models, considering collection center, gender and age as confounding factors.

RESULTS

Four hundred and seventy three subjects were measured at baseline and end of the intervention; 39 proteins were longitudinally differential. Proteins with largest changes were sex hormone-binding globulin, adiponectin, C-reactive protein, calprotectin, serum amyloid A, and proteoglycan 4 (PRG4), whose association with obesity and weight loss is known. We identified new putative biomarkers for weight loss/maintenance. Correlation between PRG4 and proline-rich acidic protein 1 variation and Matsuda insulin sensitivity increment was showed.

CONCLUSION AND CLINICAL RELEVANCE

MS-based proteomic analysis of a large cohort of non-diabetic overweight and obese individuals concomitantly identified known and novel proteins associated with weight loss and maintenance.

ICP-MS/MS-Based Ionomics: A Validated Methodology to Investigate the Biological Variability of the Human Ionome

We here describe the development, validation and application of a quantitative methodology for the simultaneous determination of 29 elements in human serum using state-of-the-art inductively coupled plasma triple quadrupole mass spectrometry (ICP-MS/MS). This new methodology offers high-throughput elemental profiling using simple dilution of minimal quantity of serum samples. We report the outcomes of the validation procedure including limits of detection/quantification, linearity of calibration curves, precision, recovery and measurement uncertainty. ICP-MS/MS-based ionomics was used to analyze human serum of 120 older adults. Following a metabolomic data mining approach, the generated ionome profiles were subjected to principal component analysis revealing gender and age-specific differences. The ionome of female individuals was marked by higher levels of calcium, phosphorus, copper and copper to zinc ratio, while iron concentration was lower with respect to male subjects. Age was associated with lower concentrations of zinc. These findings were complemented with additional readouts to interpret micronutrient status including ceruloplasmin, ferritin and inorganic phosphate. Our data supports a gender-specific compartmentalization of the ionome that may reflect different bone remodelling in female individuals. Our ICP-MS/MS methodology enriches the panel of validated "Omics" approaches to study molecular relationships between the exposome and the ionome in relation with nutrition and health.

A Highly Automated Shotgun Proteomic Workflow: Clinical Scale and Robustness for Biomarker Discovery in Blood

With recent technological developments, protein biomarker discoveries directly from blood have regained interest due to elevated feasibility. Mass spectrometry (MS)-based proteomics can now characterize human plasma proteomes to a greater extent than has ever been possible before. Such deep proteome coverage comes, however, with important limitations in terms of analysis time which is a critical factor in the case of clinical studies. As a consequence, compromises still need to be made to balance the proteome coverage with realistic analysis time frame in clinical research. The analysis of a sufficient number of samples is compulsory to empower statistically robust candidate biomarker findings. We have, therefore, recently developed a scalable automated proteomic pipeline (ASAP²) to enable the proteomic analysis of large numbers of plasma and cerebrospinal fluid (CSF) samples, from dozens to a thousand of samples, with the latter number being currently processed in 15 weeks. A distinct characteristic of ASAP² relies on the possibility to prepare samples in a highly automated way, mostly using 96-well plates. We describe herein a sample preparation procedure for human plasma that includes internal standard spiking, abundant protein removal, buffer exchange, reduction, alkylation, tryptic digestion, isobaric labeling, pooling, and sample purification. Other key elements of the pipeline (i.e., study design, sample tracking, liquid chromatography (LC) tandem MS (MS/MS), data processing, and data analysis) are also highlighted.

Proteomics Analysis of Circulating Serum Exosomes

Proteomics characterization of biofluids, such as urine and plasma, has been explored for the discovery of predictive, prognostic, and mechanistic biomarkers of diseases and tissue injury. Here we describe comprehensive characterization of protein cargos from cell-derived secreted vesicles (extracellular vesicles or exosome) for biomarker discovery using the mass spectrometry-based technology.

Proteomics Research in Cardiovascular Medicine and Biomarker Discovery

Proteomics is a systems physiology discipline to address the large-scale characterization of protein species within a biological system, be it a cell, a tissue, a body biofluid, an organism, or a cohort population. Building on advances from chemical analytical platforms (e.g., mass spectrometry and other technologies), proteomics approaches have contributed powerful applications in cardiovascular biomedicine, most notably in: 1) the discovery of circulating protein biomarkers of heart diseases from plasma samples; and 2) the identification of disease mechanisms and potential therapeutic targets in cardiovascular tissues, in both preclinical models and translational studies. Contemporary proteomics investigations offer powerful means to simultaneously examine tens of thousands of proteins in various samples, and understand their molecular phenotypes in health and disease. This concise review introduces study design considerations, example applications and use cases, as well as interpretation and analysis of proteomics data in cardiovascular biomedicine.

Proteomics reveals the effects of sustained weight loss on the human plasma proteome

Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of −16% and +37%, respectively (P < 10⁻¹³), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten‐protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases.

Aptamer-Based Proteomic Profiling Reveals Novel Candidate Biomarkers and Pathways in Cardiovascular Disease

BACKGROUND

Single-stranded DNA aptamers are oligonucleotides of ≈50 base pairs in length selected for their ability to bind proteins with high specificity and affinity. Emerging DNA aptamer-based technologies may address limitations of existing proteomic techniques, including low sample throughput, which have hindered proteomic analyses of large cohorts.

METHODS

To identify early biomarkers of myocardial injury, we applied an aptamer-based proteomic platform that measures 1129 proteins to a clinically relevant perturbational model of planned myocardial infarction (PMI), patients undergoing septal ablation for hypertrophic cardiomyopathy. Blood samples were obtained before and at 10 and 60 minutes after PMI, and protein changes were assessed by repeated-measures analysis of variance. The generalizability of our PMI findings was evaluated in a spontaneous myocardial infarction cohort (Wilcoxon rank-sum). We then tested the platform's ability to detect associations between proteins and Framingham Risk Score components in the Framingham Heart Study, performing regression analyses for each protein versus each clinical trait.

RESULTS

We found 217 proteins that significantly changed in the peripheral vein blood after PMI in a derivation cohort (n=15; P<5.70E-5). Seventy-nine of these proteins were validated in an independent PMI cohort (n=15; P<2.30E-4); >85% were directionally consistent and reached nominal significance. We detected many protein changes that are novel in the context of myocardial injury, including Dickkopf-related protein 4, a WNT pathway inhibitor (peak increase 124%, P=1.29E-15) and cripto, a growth factor important in cardiac development (peak increase 64%, P=1.74E-4). Among the 40 validated proteins that increased within 1 hour after PMI, 23 were also elevated in patients with spontaneous myocardial infarction (n=46; P<0.05). Framingham Heart Study analyses revealed 156 significant protein associations with the Framingham Risk Score (n=899), including aminoacylase 1 (β=0.3386, P=2.54E-22) and trigger factor 2 (β=0.2846, P=5.71E-17). Furthermore, we developed a novel workflow integrating DNA-based immunoaffinity with mass spectrometry to analytically validate aptamer specificity.

CONCLUSIONS

Our results highlight an emerging proteomics tool capable of profiling >1000 low-abundance analytes with high sensitivity and high precision, applicable both to well-phenotyped perturbational studies and large human cohorts, as well.

Temporal profiles of plasma proteome during childhood development

Human blood plasma proteome reflects physiological changes associated with a child's development as well as development of disease states. While age-specific normative values are available for proteins routinely measured in clinical practice, there is paucity of comprehensive longitudinal data regarding changes in human plasma proteome during childhood. We applied TMT-10plex isobaric labeling-based quantitative proteomics to longitudinally profile the plasma proteome in 10 healthy children during their development, each with 9 serial time points from 9months to 15years of age. In total, 1828 protein groups were identified at peptide and protein level false discovery rate of 1% and with at least two razor and unique peptides. The longitudinal expression profiles of 1747 protein groups were statistically modeled and their temporal changes were categorized into 7 different patterns. The patterns and relative abundance of proteins obtained by LC-MS were also verified with ELISA. To our knowledge, this study represents the most comprehensive longitudinal profiling of human plasma proteome to date. The temporal profiles of plasma proteome obtained in this study provide a comprehensive resource and reference for biomarker studies in childhood diseases. Biological significance: A pediatric plasma proteome database with longitudinal expression patterns of 1747 proteins from neonate to adolescence was provided to the research community. 970 plasma proteins had age-dependent expression trends, which demonstrated the importance of longitudinal profiling study to identify the potential biomarkers specific to childhood diseases, and the requirement of strictly age-matched clinical samples in a cross-sectional study in pediatric population.

Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science

Environmental health science aims to link environmental pollution sources to adverse health outcomes to develop effective exposure intervention strategies that reduce long-term disease risks. Over the past few decades, the public health community recognized that health risk is driven by interaction between the human genome and external environment. Now that the human genetic code has been sequenced, establishing this "G × E" (gene-environment) interaction requires a similar effort to decode the human exposome, which is the accumulation of an individual's environmental exposures and metabolic responses throughout the person's lifetime. The exposome is composed of endogenous and exogenous chemicals, many of which are measurable as biomarkers in blood, breath, and urine. Exposure to pollutants is assessed by analyzing biofluids for the pollutant itself or its metabolic products. New methods are being developed to use a subset of biomarkers, termed bioindicators, to demonstrate biological changes indicative of future adverse health effects. Typically, environmental biomarkers are assessed using noninvasive (excreted) media, such as breath and urine. Blood is often avoided for biomonitoring due to practical reasons such as medical personnel, infectious waste, or clinical setting, despite the fact that blood represents the central compartment that interacts with every living cell and is the most relevant biofluid for certain applications and analyses. The aims of this study were to (1) review the current use of blood samples in environmental health research, (2) briefly contrast blood with other biological media, and (3) propose additional applications for blood analysis in human exposure research.