Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation

Label-free quantitative proteomics in serum reveals candidate biomarkers associated with low bone mineral density in Mexican postmenopausal women

Postmenopausal osteoporosis is a public health problem leading to an increased risk of fractures, negatively impacting women's health. The absence of sensitive and specific biomarkers for early detection of osteoporosis represents a substantial challenge for improving patient management. Herein, we aimed to identify potential candidate proteins associated with low bone mineral density (BMD) in postmenopausal women from the Mexican population. Serum samples from postmenopausal women (40 with normal BMD, 40 with osteopenia (OS), and 20 with osteoporosis (OP)) were analyzed by label-free LC-MS/MS quantitative proteomics. Proteome profiling revealed significant differences between the OS and OP groups compared to individuals with normal BMD. A quantitative comparison of proteins between groups indicated 454 differentially expressed proteins (DEPs). Compared to normal BMD, 14 and 214 DEPs were found in OS and OP groups, respectively, while 226 DEPs were identified between OS and OP groups. The protein-protein interaction and enrichment analysis of DEPs were closely linked to the bone mineral content, skeletal morphology, and immune response activation. Based on their role in bone metabolism, a panel of 12 candidate biomarkers was selected, of which 1 DEP (RYR1) was found upregulated in the OS and OP groups, 8 DEPs (APOA1, SHBG, FETB, MASP1, PTK2B, KNG1, GSN, and B2M) were upregulated in OP and 3 DEPs (APOA2, RYR3, and HBD) were downregulated in OS or OP. The proteomic analysis described here may help discover new and potentially non-invasive biomarkers for the early diagnosis of osteoporosis in postmenopausal women.

Proteomic analysis of the serum in dogs with pulmonary hypertension secondary to myxomatous mitral valve disease: the preliminary study

Background

Pulmonary hypertension (PH) is a common complication in dogs with myxomatous mitral valve disease (MMVD), characterized by elevated blood pressure in pulmonary artery. Echocardiography is a reliable technique for PH diagnosis in veterinary medicine. However, it is limited to use as an early detection method. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has found extensive application in the discovery of serum protein biomarkers for various diseases. The objective of this study was to identify serum proteins in healthy control dogs and MMVD dogs both with and without PH using LC-MS/MS.

Materials and methods

In this research, a total of 81 small-breed dogs participated, and they were categorized into three groups: the control (n = 28), MMVD (n = 24) and MMVD+PH (n = 29) groups. Serum samples were collected and analyzed by LC-MS/MS.

Results

Differentially expressed proteins were identified, and the upregulated and downregulated proteins in MMVD+PH group including Myomesin 1 (MYOM1) and Histone deacetylase 7 (HDAC7), Pleckstrin homology domain containing M3 (PLEKHM3), Diacylglycerol lipase alpha (DAGLA) and Tubulin tyrosine ligase like 6 (TTLL6) were selected as proteins of interest in MMVD dogs with PH.

Conclusion

Different types of proteins have been identified in healthy dogs and MMVD dogs with and without PH. Additional studies are needed to investigate the potential of these proteins as biomarkers for PH in dogs with MMVD.

Serum proteome analysis identifies a potential biomarker for axial psoriatic arthritis

BACKGROUND

To identify potential serum biomarkers for differentiating between axial psoriatic arthritis (axPsA) and peripheral psoriatic arthritis (pPsA).

METHODS

Serum samples were collected from patients with PsA to create a biomarker discovery cohort and a verification cohort. Patients with PsA were classified into axial or peripheral subtypes based on imaging criteria. Untargeted proteomics technology was used in the discovery phase to screen for biomarkers, and candidate biomarkers were evaluated using enzyme-linked immunosorbent assay (ELISA) in the verification phase.

RESULTS

We identified 45 significantly differentially expressed proteins (DEPs) between axPsA (n = 20) and pPsA (n = 20) with liquid chromatography-mass spectrometry. Among these DEPs, serum pigment epithelium-derived factor (PEDF) was identified as a candidate biomarker using the Boruta algorithm and lasso regression. Results of ELISA further confirmed that the level of serum PEDF expression was significantly higher in axPsA (n = 37) than in pPsA (n = 51) at the verification cohort (37.9 ± 10.1 vs. 30.5 ± 8.9 μg/mL, p < 0.001). Receiver operating characteristics analysis showed that PEDF had an area under the curve (AUC) of 0.72. Serum PEDF was positively correlated with body mass index and C-reactive protein. Additionally, there was a tendency towards a positive correlation between PEDF and the Bath Ankylosing Spondylitis Disease Activity Index.

CONCLUSIONS

This study provided a comprehensive characterization of the proteome in axPsA and pPsA and identified a candidate biomarker, PEDF, that may contribute to early diagnosis for axPsA.

From simple to complex: Protein-based biomarker discovery in tuberculosis

Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics

Diabetic kidney disease (DKD) affects an estimated 20-40% of type 2 diabetes patients and is among the most prevalent microvascular complications in this patient population, contributing to high morbidity and mortality rates. Currently, changes in albuminuria status are thought to be a primary indicator of the onset or progression of DKD, yet progressive nephropathy and renal impairment can occur in certain diabetic individuals who exhibit normal urinary albumin levels, emphasizing the lack of sensitivity and specificity associated with the use of albuminuria as a biomarker for detecting diabetic kidney disease and predicting DKD risk. According to the study, a non-invasive method for early detection or prediction of DKD may involve combining proteomic analytical techniques such second generation sequencing, mass spectrometry, two-dimensional gel electrophoresis, and other advanced system biology algorithms. Another category of proteins of relevance may now be provided by renal tissue biomarkers. The establishment of reliable proteomic biomarkers of DKD represents a novel approach to improving the diagnosis, prognostic evaluation, and treatment of affected patients. In the present review, a series of protein biomarkers that have been characterized to date are discussed, offering a theoretical foundation for future efforts to aid patients suffering from this debilitating microvascular complication.

Mapping of Urinary Volatile Organic Compounds by a Rapid Analytical Method Using Gas Chromatography Coupled to Ion Mobility Spectrometry (GC–IMS)

Volatile organic compounds (VOCs) are a differentiated class of molecules, continuously generated in the human body and released as products of metabolic pathways. Their concentrations vary depending on pathophysiological conditions. They are detectable in a wide variety of biological samples, such as exhaled breath, faeces, and urine. In particular, urine represents an easily accessible specimen widely used in clinics. The most used techniques for VOCs detections are expensive and time-consuming, thus not allowing for rapid clinical analysis. In this perspective, the aim of this study is a comprehensive characterisation of the urine volatilome by the development of an alternative rapid analytical method. Briefly, 115 urine samples are collected; sample treatment is not needed. VOCs are detected in the urine headspace using gas chromatography coupled to ion mobility spectrometry (GC–IMS) by an extremely fast analysis (10 min). The method is analytically validated; the analysis is sensitive and robust with results comparable to those reported with other techniques. Twenty-three molecules are identified, including ketones, aldehydes, alcohols, and sulphur compounds, whose concentration is altered in several pathological states such as cancer and metabolic disorders. Therefore, it opens new perspectives for fast diagnosis and screening, showing great potential for clinical applications.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

The Known Unknowns: An Overview of the State of Blood-Based Protein Biomarkers of Mild Traumatic Brain Injury

Blood-based protein biomarkers have revolutionized several fields of medicine by enabling molecular level diagnosis, as well as monitoring disease progression and treatment efficacy. Traumatic brain injury (TBI) so far has benefitted only moderately from using protein biomarkers to improve injury outcome. Because of its complexity and dynamic nature, TBI, especially its most prevalent mild form (mild TBI; mTBI), presents unique challenges toward protein biomarker discovery and validation given that blood is frequently obtained and processed outside of the clinical laboratory (e.g., athletic fields, battlefield) under variable conditions. As it stands, the field of mTBI blood biomarkers faces a number of outstanding questions. Do elevated blood levels of currently used biomarkers-ubiquitin carboxy-terminal hydrolase L1, glial fibrillary acidic protein, neurofilament light chain, and tau/p-tau-truly mirror the extent of parenchymal damage? Do these different proteins represent distinct injury mechanisms? Is the blood-brain barrier a "brick wall"? What is the relationship between intra- versus extracranial values? Does prolonged elevation of blood levels reflect de novo release or extended protein half-lives? Does biological sex affect the pathobiological responses after mTBI and thus blood levels of protein biomarkers? At the practical level, it is unknown how pre-analytical variables-sample collection, preparation, handling, and stability-affect the quality and reliability of biomarker data. The ever-increasing sensitivity of assay systems and lack of quality control of samples, combined with the almost complete reliance on antibody-based assay platforms, represent important unsolved issues given that false-negative results can lead to false clinical decision making and adverse outcomes. This article serves as a commentary on the state of mTBI biomarkers and the landscape of significant challenges. We highlight and discusses several biological and methodological "known unknowns" and close with some practical recommendations.

Investigating Different Forms of Hydrogen Sulfide in Cerebrospinal Fluid of Various Neurological Disorders

Over the past 30 years a considerable amount of data has accumulated on the multifaceted role of hydrogen sulfide (H₂S) in the central nervous system. Depending on its concentrations, H₂S has opposite actions, ranging from neuromodulator to neurotoxic. Nowadays, accurate determination of H₂S is still an important challenge to understand its biochemistry and functions. In this perspective, this study aims to explore H₂S levels in cerebrospinal fluid (CSF), key biofluid for neurological studies, and to assess alleged correlations with neuroinflammatory and neurodegenerative mechanisms. A validated analytical determination combining selective electrochemical detection with ion chromatography was developed to measure free and bound sulfur forms of H₂S. A first cohort of CSF samples (n = 134) was analyzed from patients with inflammatory and demyelinating disorders (acute disseminated encephalomyelitis; multiple sclerosis), chronic neurodegenerative diseases (Alzheimer disease; Parkinson disease), and motor neuron disease (Amyotrophic lateral sclerosis). Given its analytical features, the chromatographic method resulted sensitive, reproducible and robust. We also explored low molecular weight-proteome linked to sulphydration by proteomics analysis on matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). This study is a first clinical report on CSF H₂S concentrations from neurological diseases and opens up new perspectives on the potential clinical relevance of H₂S and its potential therapeutic application.

Leukotriene A4 Hydrolase Is a Candidate Predictive Biomarker for Successful Allergen Immunotherapy

Background

Allergic rhinitis is a common disorder that affects 10% to 40% of the population worldwide. Allergen immunotherapy (AIT) represents the only therapy that has the potential to resolve clinical symptoms of allergic rhinitis. However, up to 30% of patients do not respond to AIT. Biomarkers predicting the clinical efficacy of AIT as early as possible would significantly improve the patient selection and reduce unnecessary societal costs.

Methods

Artemisia pollen allergic patients who received at least 1-year AIT were enrolled. Clinical responses before and after 1-year AIT were evaluated to determine AIT responders. Artemisia specific IgE and IgG4 levels were measured by using ImmunoCAP and enzyme-linked immunosorbent assay (ELISA) separately. Stepwise regression analysis was performed to identify which rhinitis-relevant parameters explained the most variability in AIT results. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics was applied to identify the potential candidate biomarkers in the sera of responders and non-responders collected before and after 1-year therapy. The diagnostic performance of the potential biomarkers was then assessed using enzyme-linked immunosorbent assay (ELISA) in 30 responders and 15 non-responders.

Results

Artemisia specific IgE and IgG4 levels were elevated only in the responders. Regression analysis of allergic rhinitis-relevant parameters provided a robust model that included two most significant variables (sneeze and nasal congestion). Thirteen candidate biomarkers were identified for predicting AIT outcomes. Based on their association with allergy and protein fold change (more than 1.1 or less than 0.9), four proteins were identified to be potential biomarkers for predicting effective AIT. However, further ELISA revealed that only leukotriene A₄ hydrolase (LTA₄H) was consistent with the proteomics data. The LTA₄H level in responders increased significantly (P < 0.001) after 1-year therapy, while that of non-responders remained unchanged. Assessment of LTA₄H generated area under curve (AUC) value of 0.844 (95% confidence interval: 0.727 to 0.962; P < 0.05) in distinguishing responders from the non-responders, suggesting that serum LTA₄H might be a potential biomarker for predicting the efficiency of AIT.

Conclusion

Serum LTA₄H may be a potential biomarker for early prediction of an effective AIT.

Data independent acquisition of plasma biomarkers of response to neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma

The detection of disease-related plasma biomarkers has challenged the proteomic community for years. Attractive features for plasma proteomics includes the ease of collection and small volume needed for analysis, but on the other hand, the presence of highly abundant proteins complicates sample preparation procedures and reduces dynamic range. Data independent acquisition label free quantitation (DIA-LFQ) by mass spectrometry partly overcomes the dynamic range issue; however, generating the peptide spectral reference libraries that allow extensive analysis of the plasma proteome can be a slow and expensive task which is unattainable for many laboratories. We investigated the re-purposing of publically available plasma proteome datasets and the impact on peptide/protein detection for DIA-LFQ. We carried out these studies in the context of identifying putative biomarkers of response to neoadjuvant chemotherapy (NAC) for pancreatic ductal adenocarcinoma, as no useful plasma biomarkers have been clinically adopted. We demonstrated the benefit in searching DIA data against multiple spectral libraries to show that complement proteins were linked to NAC response in PDAC patients, confirming previous observations of the prognostic utility of complement following adjuvant chemotherapy. Our workflow demonstrates that DIA-LFQ can be readily applied in the oncology setting for the putative assignment of clinically relevant plasma biomarkers. STATEMENT OF SIGNIFICANCE: The proteomic mass spectrometry analysis of undepleted, unfractionated human plasma has benefits for sample throughput but remains challenging to obtain deep coverage. This work evaluated the re-purposing of open source peptide mass spectrometry data from human plasma to create spectral reference libraries for use in Data independent acquisition (DIA). We showed how seeding in locally acquired data to integrate iRT peptides into spectral libraries increased identification confidence by facilitating querying of multiple libraries. This workflow was applied to the discovery of putative plasma biomarkers for response to neoadjuvant chemotherapy (NAC) in pancreatic ductal adenocarcinoma patients. There is a paucity of prior information in the literature on this topic and we show that good responder patients have reduced levels of complement proteins.

Comparing Glial Fibrillary Acidic Protein (GFAP) in Serum and Plasma Following Mild Traumatic Brain Injury in Older Adults

Objective: Identification and validation of blood-based biomarkers for the diagnosis and prognosis of mild traumatic brain injury (mTBI) is of critical importance. There have been calls for more research on mTBI in older adults. We compared blood-based protein marker glial fibrillary acidic protein (GFAP) concentrations in serum and in plasma within the same cohort of older adults and assessed their ability to discriminate between individuals based on intracranial abnormalities and functional outcome following mTBI.

Methods: A sample of 121 older adults [≥50 years old with head computed tomography (CT), n = 92] seeking medical care for a head injury [Glasgow Coma Scale scores of 14 (n = 6; 5.0%) or 15 (n = 115; 95.0%)] were enrolled from the emergency department (ED). The mean time between injury and blood sampling was 3.4 h (SD = 2.1; range = 0.5–11.7). Serum GFAP concentration was measured first using the Human Neurology 4-Plex Assay, while plasma GFAP concentration was later measured using the GFAP Discovery Kit, both on an HD-1 Single molecule array (Simoa) instrument. Glasgow Outcome Scale-Extended was assessed 1 week after injury.

Results: Both serum and plasma GFAP levels were significantly higher in those with abnormal CT scans compared to those with normal head CT scans (plasma: U = 1,198, p < 0.001; serum: U = 1,253, p < 0.001). The ability to discriminate those with and without intracranial abnormalities was comparable between serum (AUC = 0.814) and plasma (AUC = 0.778). In the total sample, GFAP concentrations were considerably higher in plasma than in serum (Wilcoxon signed-rank test z = 0.42, p < 0.001, r = 0.42). Serum and plasma GFAP levels were highly correlated in the total sample and within all subgroups (Spearman's rho range: 0.826–0.907). Both serum and plasma GFAP levels were significantly higher in those with poor compared to good functional outcome (serum: U = 1,625, p = 0.002; plasma: U = 1,539, p = 0.013). Neither plasma (AUC = 0.653) nor serum (AUC = 0.690) GFAP were adequate predictors of functional outcome 1 week after injury.

Conclusions: Despite differences in concentration, serum and plasma GFAP levels were highly correlated and had similar discriminability between those with and without intracranial abnormalities on head CT following an mTBI. Neither serum nor plasma GFAP had adequate discriminability to identify patients who would have poor functional outcome.

Metalloproteins and apolipoprotein C: candidate plasma biomarkers of T2DM screened by comparative proteomics and lipidomics in ZDF rats

Background

Early diagnosis of type 2 diabetes mellitus (T2DM) is still difficult. Screening of plasma biomarkers has great significance of optimizing diagnosis and predicting the complications of T2DM.

Methods

We used a special diet, Purina #5008, to induce diabetes in Zucker leptin receptor gene-deficient rats (fa/fa) to establish Zucker diabetic fatty (ZDF) rats, simulating the early stage of T2DM. The differentially expressed proteins (DEP) and lipids (DEL), as potential biomarkers, were screened to compare the plasma expression levels in ZDF rats and their basic diet-fed wild-type controls (fa/+) by Tandem Mass Tags (TMT) and liquid chromatography-tandem mass spectrometry.

Results

These two groups had different plasma proteins and lipids profiles consisting of 84 DEPs and, 179 DELs identified in the positive ion mode and 178 DELs in the negative ion mode, respectively. Enrichment analysis of these different indicators showed that oxidative stress, insulin resistance and metabolic disorders of glycan and lipid played an important role in generating the difference. Some markers can be used as candidate biomarkers in prediction and treatments of T2DM, such as ceruloplasmin, apolipoprotein C-I, apolipoprotein C-II and apolipoprotein C-IV.

Conclusion

These plasma differences help to optimize the diagnosis and predict the complications of T2DM, although this remains to be verified in the crowd. Trace elements related-metalloproteins, such as ceruloplasmin, and lipid metabolism and transport-related apolipoprotein C are expected to be candidate biomarkers of T2DM and should be given more attention.

Duchenne Muscular Dystrophy: recent advances in protein biomarkers and the clinical application

INTRODUCTION

Early biomarker discovery studies have praised the value of their emerging results, predicting an unprecedented impact on health care. Biomarkers are expected to provide tests with increased specificity and sensitivity compared to existing measures, improve the decision-making process, and accelerate the development of therapies. For rare disorders, like Duchenne Muscular Dystrophy (DMD) such biomarkers can assist the development of therapies, therefore also helping to find a cure for the disease.

AREA COVERED

State-of-the-art technologies have been used to identify blood biomarkers for DMD and efforts have been coordinated to develop and promote translation of biomarkers for clinical practice. Biomarker translation to clinical practice is however, adjoined by challenges related to the complexity of the disease, involving numerous biological processes, and the limited sample resources. This review highlights the current progress on the development of biomarkers, describing the proteomics technologies used, the most promising findings and the challenges encountered.

EXPERT OPINION

Strategies for effective use of samples combined with orthogonal proteomics methods for protein quantification are essential for translating biomarkers to the patient's bed side. Progress is achieved only if strong evidence is provided that the biomarker constitutes a reliable indicator of the patient's health status for a specific context of use.

Characterization of Fibrinogen as a Key Modulator in Patients with Wilson's Diseases with Functional Proteomic Tools

Wilson’s disease (WD) is an autosomal recessive disorder of copper metabolism caused by defects in the ATPase gene (ATP7B). The various clinical features result from the massive accumulation of copper in the liver, cornea and basal ganglia. Although WD can be effectively treated with proper medicine, this disease is difficult to clearly diagnose due to its indefinite symptoms. In the current study, we achieved a positive correlation between clinical symptoms and the enzymatic activity of ceruloplasmin in WD patients. Furthermore, proteome profiles of plasma as well as network analysis demonstrated that fibrinogen is a critical indicator which is significantly unregulated in WD subjects in comparison to healthy donors and closely linked to pathogenesis of WD. Here, we applied 2DE-immunoblots and immunohistochemistry to verify the protein level and localization in situ. The enhanced expression of fibrinogen in the plasma of WD subjects with respect to that of healthy controls and patients with distinct disorders was also confirmed by utilizing clinical samples. As expected, application of high dose of copper induced expression of fibrinogen, while knockdown of ceruloplasmin also resulted in upregulation of fibrinogen as well as elimination of superoxide dismutase (SOD), leading to increased oxidative stress in cells. In summary, the liver injury or oxidative stress induced by the progression of WD may account for the obvious increase of fibrinogen, which in turn triggers inflammatory responses and interferes coagulation cascades; this finding sheds light on the early detection and diagnosis of WD.

Mining cancer biology through bioinformatic analysis of proteomic data

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.

Diagnostic accuracy of serum dickkopf-1 protein in diagnosis hepatocellular carcinoma: An updated meta-analysis

BACKGROUND

To verify the accuracy of serum dickkopf-1 protein (DKK-1) in the diagnosis of hepatocellular carcinoma (HCC) by an updated meta-analysis.

METHODS

We searched potential eligible studies in PubMed and Embase before July 8, 2018. Sensitivity (SN), specificity (SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), summary receiver operating characteristics curve (sROC), and diagnostic odds ratio (DOR) were pooled with their 95% confidence intervals CIs) using a bivariate random-effects model.

RESULTS

A total of 8 articles contained 10 studies on diagnosis of HCC with DKK-1 alone,7 articles contained 9 studies on diagnosis of HCC with a-fetoprotein (AFP) alone and 5 articles contained 7 studies on diagnosis of HCC with DKK-1 + AFP were identified. The pooled SN, SP, PLR, NLR, and DOR of DKK-1 alone, AFP alone and DKK-1 + AFP were 0.72 (95% CI: 0.70-0.75), 0.62 (95% CI:0.59-0.64) and 0.80 (95% CI:0.78-0.83), 0.86 (95% CI: 0.84-0.87), 0.82 (95% CI:0.80-0.84) and 0.87 (95% CI: 0.85-0.88), 4.91 (95% CI: 2.73-8.83), 3.60 (95% CI:2.01-6.44) and 6.18 (95% CI: 4.68-8.16), 0.32 (95% CI: 0.22-0.47), 0.49 (95% CI:0.40-0.60) and 0.20 (95% CI: 0.15-0.26), and 17.21 (95% CI: 9.10-32.57), 7.45 (95% CI:3.69-15.01) and 31.39 (95% CI: 23.59-43.20), respectively. The area under the sROC was 0.88, 0.70, and 0.92 for the 3 diagnostic methods.

CONCLUSIONS

Serum DKK-1 + AFP showed a high accuracy for diagnosis of HCC, and serum DKK-1 alone had moderate accuracy as compared to a previous meta-analysis, while AFP alone owned an unsatisfied diagnostic behavior for HCC. Due to the limitations of the current analysis, further well-designed studies are needed to confirm the diagnostic value of DKK-1 and DKK-1 + AFP in HCC diagnosis.

iTRAQ plasma proteomics analysis for candidate biomarkers of type 2 incipient diabetic nephropathy

Background

Diabetic nephropathy is the most frequent cause of end-stage renal disease worldwide. Identification of biomarkers for diabetic nephropathy for early diagnosis may be the key to avoiding damage from this condition.

Methods

Proteomic iTRAQ technology was first used to identify differentially expressed plasma proteins in type 2 incipient diabetic nephropathy (IDN) using a Q-Exactive mass spectrometer.

Results

Compared with controls, 57 proteins (32 upregulated and 25 downregulated proteins) were identified. Furthermore, the gelsolin, collectin-11, PTPRJ, and AKAP-7 proteins were confirmed by Western blots as candidate biomarkers for type 2 IDN through ROC analysis.

Conclusions

These findings offer a theoretical basis for the early treatment of diabetic nephropathy.

Proteomics and Lipidomics in Inflammatory Bowel Disease Research: From Mechanistic Insights to Biomarker Identification

Inflammatory bowel disease (IBD) represents a group of progressive disorders characterized by recurrent chronic inflammation of the gut. Ulcerative colitis and Crohn's disease are the major manifestations of IBD. While our understanding of IBD has progressed in recent years, its etiology is far from being fully understood, resulting in suboptimal treatment options. Complementing other biological endpoints, bioanalytical "omics" methods that quantify many biomolecules simultaneously have great potential in the dissection of the complex pathogenesis of IBD. In this review, we focus on the rapidly evolving proteomics and lipidomics technologies and their broad applicability to IBD studies; these range from investigations of immune-regulatory mechanisms and biomarker discovery to studies dissecting host⁻microbiome interactions and the role of intestinal epithelial cells. Future studies can leverage recent advances, including improved analytical methodologies, additional relevant sample types, and integrative multi-omics analyses. Proteomics and lipidomics could effectively accelerate the development of novel targeted treatments and the discovery of complementary biomarkers, enabling continuous monitoring of the treatment response of individual patients; this may allow further refinement of treatment and, ultimately, facilitate a personalized medicine approach to IBD.

Applications of MALDI-TOF mass spectrometry in clinical proteomics

INTRODUCTION

The development of precision medicine requires advanced technologies to address the multifactorial disease stratification and to support personalized treatments. Among omics techniques, proteomics based on Mass Spectrometry (MS) is becoming increasingly relevant in clinical practice allowing a phenotypic characterization of the dynamic functional status of the organism. From this perspective, Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) MS is a suitable platform for providing a high-throughput support to clinics. Areas covered: This review aims to provide an updated overview of MALDI-TOF MS applications in clinical proteomics. The most relevant features of this analysis have been discussed, highlighting both pre-analytical and analytical factors that are crucial in proteomics studies. Particular emphasis is placed on biofluids proteomics for biomarkers discovery and on recent progresses in clinical microbiology, drug monitoring, and minimal residual disease (MRD). Expert commentary: Despite some analytical limitations, the latest technological advances together with the easiness of use, the low time and low cost consuming and the high throughput are making MALDI-TOF MS instruments very attractive for the clinical practice. These features offer a significant potential for the routine of the clinical laboratory and ultimately for personalized medicine.

Ensuring sample quality for blood biomarker studies in clinical trials: a multicenter international study for plasma and serum sample preparation

Background

Sample quality is critical for biomarker detection in oncology, and platelet degradation and contamination in plasma have a remarkable impact on the ability to accurately quantify many blood-based biomarkers. Platelet factor 4 (PF4) can be used as an indicator to monitor sample quality. This multicenter study aimed to determine the impact of critical components of the blood sample handling process on platelet degradation/contamination and to establish an optimal method for collecting platelet-poor plasma samples.

Methods

At each of six participating centers, blood samples were drawn from 12-13 healthy volunteers. Serum and plasma samples were prepared from whole blood samples using nine different methods that have been commonly used in ongoing multicenter trials. PF4 levels in the prepared samples were measured by enzyme-linked immunosorbent assay (ELISA). Paired t-tests were used for statistical analysis.

Results

Blood samples were collected from 74 subjects enrolled in six centers. PF4 levels were significantly higher in serum samples than in plasma samples (P<0.001), in plasma samples from blood that sat at room temperature for 5 minutes (P=0.021), in plasma samples prepared at an insufficient centrifugal force (P<0.001), and in plasma samples prepared from blood that sat for longer than 4 hours on ice (P=0.001). For each method, the PF4 levels did not differ significantly among the centers or between Chinese and American subjects. The methods that resulted in normal levels of PF4 involved keeping blood samples on ice for 30 minutes to <4 hours and centrifugation at 2,500-3,000 ×g for 30 min.

Conclusions

This multicenter study evaluated multiple blood sample handling conditions for minimizing platelet degradation during plasma serum preparation and determined an optimal method for preparing platelet-poor plasma. The findings of this study can be applied in future blood biomarker studies.