Proteomic Analysis Of Plasma Samples Research Articles

Quantitative proteomics analysis of COVID-19 patients: Fetuin-A and tetranectin as potential modulators of innate immune responses

Treatment of severe cases of coronavirus disease 2019 (COVID-19) is extremely important to minimize death and end-organ damage. Here we performed a proteomic analysis of plasma samples from mild, moderate and severe COVID-19 patients. Analysis revealed differentially expressed proteins and different therapeutic potential targets related to innate immune responses such as fetuin-A, tetranectin (TN) and paraoxonase-1 (PON1). Furthermore, protein changes in plasma showed dysregulation of complement and coagulation cascades in COVID-19 patients compared to healthy controls. In conclusion, our proteomics data suggested fetuin-A and TN as potential targets that might be used for diagnosis as well as signatures for a better understanding of the pathogenesis of COVID-19 disease.

Protein Posttranslational Signatures Identified in COVID-19 Patient Plasma.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a highly contagious virus of the coronavirus family that causes coronavirus disease-19 (COVID-19) in humans and a number of animal species. COVID-19 has rapidly propagated in the world in the past 2 years, causing a global pandemic. Here, we performed proteomic analysis of plasma samples from COVID-19 patients compared to healthy control donors in an exploratory study to gain insights into protein-level changes in the patients caused by SARS-CoV-2 infection and to identify potential proteomic and posttranslational signatures of this disease. Our results suggest a global change in protein processing and regulation that occurs in response to SARS-CoV-2, and the existence of a posttranslational COVID-19 signature that includes an elevation in threonine phosphorylation, a change in glycosylation, and a decrease in arginylation, an emerging posttranslational modification not previously implicated in infectious disease. This study provides a resource for COVID-19 researchers and, longer term, and will inform our understanding of this disease and its treatment.

Proteomic Identification of Novel Plasma Biomarkers and Pathobiologic Pathways in Alcoholic Acute Pancreatitis.

Acute pancreatitis (AP) is a painful and potentially life-threatening disorder with the potential for therapeutic interventions. Biomarkers that characterize cases by severity and pathogenic mechanisms involved are not yet available but needed for the implementation of rational therapies. Here, we used shotgun proteomics to obtain information from plasma samples about local and systemic pathologies taking place during cases of alcoholic AP. Plasma was obtained at Kaunas University of Medicine Hospital (Lithuania) from 12 AP patients of alcohol related etiology (median age of 40) within 24 h of presentation, and 12 age-matched, healthy controls. Patients entered into the study had moderately severe AP with the following characteristics: mean blood lactate dehydrogenase level of 1127 mg/dl; median APACHEII score of 5.5 and mean IMRIE score of 3.5. For proteomic analysis, less-abundant proteins in plasma samples were enriched using Top 12 abundant protein depletion columns. Further processing was performed by a modified filter-assisted sample preparation combined with tandem mass tag labeling for quantitation. Samples were analyzed using an Orbitrap Elite mass spectrometer for high resolution liquid chromatography–tandem mass spectrometry (LC–MS/MS). Our analysis revealed 31 proteins that exhibited significant 1.5-fold or higher increases in the AP compared to control patients, and six that were significantly decreased. Gene ontology analysis indicated a strong correlation with exosomal origin in the elevated proteins, with 29/31 (93.5%) associated with this extracellularly-secreted compartment. Elevated proteins included established and proposed biomarkers of AP including C-reactive protein, LPS-binding protein, intercellular adhesion molecule-1, and von Willebrand factor, as well as several novel potential biomarkers. These results provide the methodology for proteomic analysis of plasma samples to discover novel biomarkers that characterize pancreatitis cases by pathogenic mechanism as well as disease activity at an early stage that is highly informative for routine clinical practice and clinical trials.

Plasma proteomics: a noninvasive window on pathology and pediatric cardiac surgery.

A challenge of pediatric research is the limited ability to obtain tissue samples from small patients. To confront this problem, blood biomarkers can be used as surrogate markers of disease processes and aid in patient monitoring and disease detection. Furthermore, proteomic analysis of plasma samples is one approach for large-scale discovery of disease biomarkers. This study examined the use of plasma for disease process biomarkers in pediatric patients undergoing cardiopulmonary bypass (CPB) surgery. Proteomic studies of plasma are limited by the presence of a few high abundance proteins that mask the presence of lower abundance proteins of interest. Plasma immunoaffinity depletion (removing 6 of the highest abundance proteins of little pathological importance) increases sensitivity of detection for proteins such as those related to inflammation, remodeling, and damage. Using two-dimensional in-gel fluorescence electrophoresis, changes in the expression levels of proteins that occur as a result of CPB can be identified. In the present study, plasma depletion removed 83% of the plasma protein mass, allowing approximately 1400 spots to be observed by two-dimensional in-gel fluorescence electrophoresis. Of the detected spots, 79 (5.7%) were altered by CPB. These data illuminate the strength of plasma proteomics in identification of candidate biomarkers of CPB-associated disease processes.

Tissue-enhanced proteomic analysis of plasma samples reveals new mechanistic biomarker candidates for the stratification of Amyotrophic lateral sclerosis patients

Tissue-enhanced proteomic analysis of plasma samples reveals new mechanistic biomarker candidates for the stratification of Amyotrophic lateral sclerosis patients

Proteomic identification of putative biomarkers for early detection of sudden cardiac death in a family with a LMNA gene mutation causing dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a severe heart disease characterized by progressive ventricular dilation and impaired systolic function of the left ventricle. We recently identified a novel pathogenic mutation in the LMNA gene in a family affected by DCM showing sudden death background. We now aimed to identify potential biomarkers of disease status, as well as sudden death predictors, in members of this family. We analysed plasma samples from 14 family members carrying the mutation, four of which (with relevant clinical symptoms) were chosen for the proteomic analysis. Plasma samples from these four patients and from four sex- and age-matched healthy controls were processed for their enrichment in low- and medium-abundance proteins (ProteoMiner™) prior to proteomic analysis by 2D-DIGE and MS. 111 spots were found to be differentially regulated between mutation carriers and control groups, 83 of which were successfully identified by MS, corresponding to 41 different ORFs. Some proteins of interest were validated either by turbidimetry or western blot in family members and healthy controls. Actin, alpha-1-antytripsin, clusterin, vitamin-D binding protein and antithrombin-III showed increased levels in plasma from the diseased group. We suggest following these proteins as putative biomarkers for the evaluation of DCM status in LMNA mutation carriers. Biological significanceWe developed a proteomic analysis of plasma samples from a family showing history of dilated cardiomyopathy caused by a LMNA mutation, which may lead to premature death or cardiac transplant. We identified a number of proteins augmented in mutation carriers that could be followed as potential biomarkers for dilated cardiomyopathy on these patients.

Proteomics of Microparticles with SILAC Quantification (PROMIS-Quan): A Novel Proteomic Method for Plasma Biomarker Quantification*[S

Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However, the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan-PROteomics of MIcroparticles with Super-Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification. PROMIS-Quan enables a two-step relative and absolute SILAC quantification. First, plasma microparticle proteomes are quantified relative to a super-SILAC mix composed of cell lines from distinct origins. Next, the absolute amounts of selected proteins of interest are quantified relative to the super-SILAC mix. We applied PROMIS-Quan to prostate cancer and compared plasma microparticle samples of healthy individuals and prostate cancer patients. We identified in total 5374 plasma-microparticle proteins, and revealed a predictive signature of three proteins that were elevated in the patient-derived plasma microparticles. Finally, PROMIS-Quan enabled determination of the absolute quantitative changes in prostate specific antigen (PSA) upon treatment. We propose PROMIS-Quan as an innovative platform for biomarker discovery, validation, and quantification in both the biomedical research and in the clinical worlds.

A differential protein solubility approach for the depletion of highly abundant proteins in plasma using ammonium sulfate.

Depletion of highly abundant proteins is an approved step in blood plasma analysis by mass spectrometry (MS). In this study, we explored a precipitation and differential protein solubility approach as a fractionation strategy for abundant protein removal from plasma. Total proteins from plasma were precipitated with 90% saturated ammonium sulfate, followed by differential solubilization in 55% and 35% saturated ammonium sulfate solutions. Using a four hour liquid chromatography (LC) gradient and an LTQ-Orbitrap XL mass spectrometer, a total of 167 and 224 proteins were identified from the 55% and 35% ammonium sulfate fractions, whereas 235 proteins were found in the remaining protein fractions with at least two unique peptides. SDS-PAGE and exclusive total spectrum counts from LC-MS/MS analyses clearly showed that majority of the abundant plasma proteins were solubilized in 55% and 35% ammonium sulfate solutions, indicating that the remaining protein fraction is of potential interest for identification of less abundant plasma proteins. Serum albumin, serotransferrin, alpha-1-antitrypsin and transthyretin were the abundant proteins that were highly enriched in 55% ammonium sulfate fractions. Immunoglobulins, complement system proteins, and apolipoproteins were among other abundant plasma proteins that were enriched in 35% ammonium sulfate fractions. In the remaining protein fractions a total of 40 unique proteins were identified of which, 32 proteins were identified with at least 10 exclusive spectrum counts. According to PeptideAtlas, 9 of these 32 proteins were estimated to be present at low μg ml(-1) (0.12-1.9 μg ml(-1)) concentrations in the plasma, and 17 at low ng ml(-1) (0.1-55 ng ml(-1)) range.

Knockdown of paraoxonase 1 expression influences the ageing of human dermal microvascular endothelial cells

Skin is one of the most commonly studied tissues for microcirculation research owing to its close correlation of cutaneous vascular function, ageing and age-related cardiovascular events. To elucidate proteins that determine this correlation between endothelial cell function and ageing in the vascular environment of the skin, we performed a proteomic analysis of plasma samples from six donors in their 20s (young) and six donors in their 60s (old). Among identified proteins, paraoxonase 1 (PON1) was selected in this study. To elucidate the role of PON1 on skin ageing and determine how it controls cellular senescence, the characteristics of PON1 in human dermal microvascular endothelial cells (HDMECs) were determined. When the expression of endogenous PON1 was knocked-down by small interfering RNA (siRNA) targeting PON1, HDMECs showed characteristic features of cellular senescence such as increases in senescence-associated β-galactosidase stained cells and enlarged and flattened cell morphology. At 48h post-transfection, the protein expression of p16 in PON1 siRNA-treated HDMECs was higher than that in scrambled siRNA-treated HDMECs. In addition, the expressions of moesin and rho GTP dissociation inhibitor, additional age-related candidate biomarkers, were decreased by PON1 knock-down in HDMECs. In conclusion, these results suggest that PON1 functions as an ageing-related protein and plays an important role in the cellular senescence of HDMECs.

Proteomic analysis of plasma samples from acute coronary syndrome patients — The pilot study

Molecular profiling technologies to assess the occurrence, appearance and amount of DNA, RNA, protein and metabolites provide the potential to the concept of “personalised medicine” [1]. Proteomic analysis, as one of the “omics” disciplines, is one of the most promising new methods for evaluating the translation of human genetic information into the “real time” synthesis of proteins and their posttranslational modifications from individual patient clinical conditions [2].

Proteomics Analysis of Plasma for Early Diagnosis of Endometriosis

To test the hypothesis that differential surface-enhanced laser desorption/ionization time-of-flight mass spectrometry protein or peptide expression in plasma can be used in infertile women with or without pelvic pain to predict the presence of laparoscopically and histologically confirmed endometriosis, especially in the subpopulation with a normal preoperative gynecologic ultrasound examination. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry analysis was performed on 254 plasma samples obtained from 89 women without endometriosis and 165 women with endometriosis (histologically confirmed) undergoing laparoscopies for infertility with or without pelvic pain. Data were analyzed using least squares support vector machines and were divided randomly (100 times) into a training data set (70%) and a test data set (30%). Minimal-to-mild endometriosis was best predicted (sensitivity 75%, 95% confidence interval [CI] 63-89; specificity 86%, 95% CI 71-94; positive predictive value 83.6%, negative predictive value 78.3%) using a model based on five peptide and protein peaks (range 4.898-14.698 m/z) in menstrual phase samples. Moderate-to-severe endometriosis was best predicted (sensitivity 98%, 95% CI 84-100; specificity 81%, 95% CI 67-92; positive predictive value 74.4%, negative predictive value 98.6%) using a model based on five other peptide and protein peaks (range 2.189-7.457 m/z) in luteal phase samples. The peak with the highest intensity (2.189 m/z) was identified as a fibrinogen β-chain peptide. Ultrasonography-negative endometriosis was best predicted (sensitivity 88%, 95% CI 73-100; specificity 84%, 95% CI 71-96) using a model based on five peptide peaks (range 2.058-42.065 m/z) in menstrual phase samples. A noninvasive test using proteomic analysis of plasma samples obtained during the menstrual phase enabled the diagnosis of endometriosis undetectable by ultrasonography with high sensitivity and specificity. II.

Proteomic analysis of the plasma samples of patients with stable angina pectoris

ObjectiveIn this study, the results of the proteomic analysis of plasma samples of stable angina patients are presented. BackgroundPlasma samples from 44 patients were analysed in two groups: (A) a control group of 22 patients without significant coronary artery disease and (B) 22 patients admitted with stable angina pectoris. MethodsDuring coronary angiography, plasma samples were collected and further analysed using two-dimensional gel electrophoresis, image analysis and mass spectrometry. ResultsFive spots were found with significantly increased or decreased normalised volumes resulting from either a change in the amount of protein or changes in posttranslational modifications. Spots containing serum amyloid A protein and fibrinogen gamma chains displayed increased normalised volumes, and spots containing apoliprotein A-1 and pigment epithelium derived factor showed decreased normalised volumes. ConclusionsAlmost identical proteomic profiles of the stable angina pectoris group compared with the control group corresponded well with clinical results comparing stable patients with the general population.

Proteomic analysis of plasma samples from patients with acute myocardial infarction identifies haptoglobin as a potential prognostic biomarker

Prognosis of clinical outcome following myocardial infarction is variable and difficult to predict. We have analyzed the plasma proteome of thirty patients with acute myocardial infarction to search for new prognostic biomarkers. Proteomic analyses of blood samples were performed by 2-D-DiGE after plasma depletion of albumin and immunoglobulins G. New York Heart Association (NYHA) class determined at 1-year follow-up was used to identify patients with heart failure. Principal component analysis and hierarchical clustering of proteomic data revealed that patients could be separated into 3 groups. The 22 differentially expressed proteins involved in this grouping were identified as haptoglobin (Hp) and respective isoforms. The 3 groups of patients had distinct Hp isoforms: patients from group 1 had the α1-α1, patients from group 2 the α2-α1, and patients from group 3 the α2-α2 genotype. This classification was also associated with different total plasma levels of Hp. The presence of the α2 genotype and low plasma levels of Hp was associated with a higher NYHA class and therefore with a detrimental functional outcome after myocardial infarction. A plasma level of Hp below 1.4g/L predicted the occurrence of heart failure (NYHA 2, 3, 4) at 1-year with 100% sensitivity.

L001 Abdominal aortic aneurysm: searching for plasma biomarkers

Aortic Abdominal Aneurysm (AAA) is characterized by an increase of diameter (>1.5 times to reference diameter) and loss of parallelism of the vessel wall. This disease is more often asymptomatic and its rupture is responsible for 1-4 % of mortality in males older than 65 years. Surgery treatment is possible in the case of detection by echography but no specific biological markers are available. In order to identify markers of AAA, we have established a clinical protocol, named CORONA, in which 265 patients undergoing coronary bypass grafting were recruited from 2002 to 2006 in CHRU of Lille. For 6.4 % of them (n=17), an AAA was detected by abdominal ultrasound after the bypass. From the 265 patients included, 98 % of plasma samples, 89 % of macrophages cultures and 40 % of smooth muscle cells cultures were obtained to compare proteomes between aneurismal and non-aneurismal patients. First, every AAA patient was matched to three non-AAA, according to their age, tobacco consumption, arterial hypertension, diabetes and dyslipidemia. Then, according to this classification, the biological and clinical data of the CORONA sample set, composed of 68 plasma samples from 17 AAA and 51 non-AAA bypass patients, were analyzed by exact nonparametric inference tests using StatXact.8 software. Next, proteomic analysis of plasma samples by a powerful data independent mass spectrometry method was performed in order to obtain qualitative as well as quantitative information. The results obtained in the statistical analysis showed that there are not important significant differences in the biological and clinical characteristics between both groups of patients, suggesting that the potential biomarkers found in the proteomic analysis will be more specific for AAA than in the case that control samples were from completely healthy patients. Regarding plasma proteome analysis, around 1000 plasma proteins were identified with false positive discovery rate < 1 %. From identified proteins, one third are multiple hits (at least 2 peptides per protein were identified), and from those around 75 proteins might be involved in AAA pathology (matrix proteins, proteases and inflammation proteins). The quantification of these proteins is still under work.

SELDI-TOF-Based Proteomic Analysis of Plasma Samples as a Diagnostic Tool to Predict Acute Graft-Versus-Host Disease after Allogeneic Stem Cell Transplantation

Allogeneic hematopoietic stem cell transplantation (SCT) offers a curative treatment option for high-risk hematological tumors. Graft-versus-host disease (GvHD) is a severe and potentially life-threatening complication of allogeneic SCT. GvHD is caused by the activation of transplanted alloreactive T-cells and results in severe inflammation of host tissues in skin, liver, and gastrointestinal tract. Manifest GvHD can be diagnosed by clinical criteria and histology only. So far, no diagnostic blood test is able to detect imminent GvHD. GvHD is prevented and treated by systemic immunosuppression which is associated with opportunistic infections and higher relapse rates. Therefore, reliable detection of incipient GvHD would allow to investigate preemptive immunosuppressive therapy tailored to individual patients at appropriate time points. Proteomic profiling is based on the premise that alterations related to pathological states (e.g., GvHD) are accompanied by quantitative and qualitative alterations in plasma protein profiles. The surface-enhanced laser desorption/ionization (SELDI) technology relies on time-of-flight mass spectrometry for accurate measurement of the mass-to-charge ratio (m/z) of proteins that have been preselected on appropriate functional surfaces. We conducted a prospective case-control study based on >3000 serial plasma samples of 236 consecutive patients undergoing allogeneic SCT. 43 patients developed acute GvHD grade I-IV during their sampling period and were matched to control patients remaining GvHD-free. Matching criteria were prior antithymocyte globulin treatment, donor type (related versus unrelated), conditioning protocol, remission status at SCT, diagnosis, age, and sex. These factors were ranked in the listed order. Plasma protein patterns were obtained by SELDI-TOF on day -2 prior to the onset of clinical GvHD and compared to 43 plasma samples obtained on the corresponding day after SCT from the matched GvHD-free patients. In order to enhance the number of analyzable proteins, plasma samples were fractionated to yield 5 different sets of SELDI spectra. Protein peak positions and calibrated peak heights were determined according to Yasui et al (J Biomed Biotechnol, 2003). To identify a limited number of predictive peaks, a sparse logistic regression model was fitted by a boosting approach (Tutz & Binder, Computational Statistics & Data Analysis, 2007), resulting in 4 peaks at 11963, 22363, 22479, and 30977 Da. Prediction performance of a signature derived from this approach on new data was evaluated using bootstrap samples, resulting in an estimated misclassification rate of 38.5%, an estimated sensitivity of 61.0%, and specificity of 60.5% In addition, the identified predictive peaks could be recovered in most bootstrap samples, indicating sufficient stability of the signature. While the performance of this assay strategy is insufficient for use in clinical practice at present, our data nevertheless demonstrate the potential of proteomic plasma analysis in combination with a tailored statistical analysis to identify GvHD prior to its clinically recognizable onset. In contrast to the clinical routine of immunosuppressive GvHD prophylaxis for all patients after allogeneic SCT with treatment escalation in the case of clinically relevant GvHD, this diagnostic tool therefore offers the perspective for preemptive GvHD therapy administered only to patients with an imminent GvH reaction. Such a tailored GvHD strategy may represent a cornerstone to reduce the risk of infectious complications and tumor relapse while maintaining effective GvHD control in patients at risk. Current work addresses the identity of the index peaks, the temporal evolution of the signature, and refinement of the proteomic analysis.

Integrated analytical approach in veal calves administered the anabolic androgenic steroids boldenone and boldione: urine and plasma kinetic profile and changes in plasma protein expression

Surveillance of illegal use of steroids hormones in cattle breeding is a key issue to preserve human health. To this purpose, an integrated approach has been developed for the analysis of plasma and urine from calves treated orally with a single dose of a combination of the androgenic steroids boldenone and boldione. A quantitative estimation of steroid hormones was obtained by LC-APCI-Q-MS/MS analysis of plasma and urine samples obtained at various times up to 36 and 24 h after treatment, respectively. These experiments demonstrated that boldione was never found, while boldenone alpha- and beta-epimers were detected in plasma and urine only within 2 and 24 h after drug administration, respectively. Parallel proteomic analysis of plasma samples was obtained by combined 2-DE, MALDI-TOF-MS and muLC-ESI-IT-MS/MS procedures. A specific protein, poorly represented in normal plasma samples collected before treatment, was found upregulated even 36 h after hormone treatment. Extensive mass mapping experiments proved this component as an N-terminal truncated form of apolipoprotein A1 (ApoA1), a protein involved in cholesterol transport. The expression profile of ApoA1 analysed by Western blot analysis confirmed a significant and time dependent increase of this ApoA1 fragment. Then, provided that further experiments performed with a growth-promoting schedule will confirm these preliminary findings, truncated ApoA1 may be proposed as a candidate biomarker for steroid boldenone and possibly other anabolic androgens misuse in cattle veal calves, when no traces of hormones are detectable in plasma or urine.

Peptide Shifter: Enhancing Separation Reproducibility Using Correlated Expression Profiles

Chromatographic protein and peptide separation technologies enable comprehensive proteomic analysis of plasma and other complex biological samples by mass spectrometry. However, as the number of separations and/or fractions increases, so does the number of peptides split across fraction boundaries. Irreproducibility of peptide chromatographic separation results in peptides on or near the boundary moving partially or entirely into adjacent fractions. Peptide shifting across fraction boundaries increases the variability of measured peptide abundance, and so there is a trade-off between proteomic comprehensiveness using separation technologies and accurate quantitative proteomic measurements. In this paper, a method for detecting and correcting split peptides, called Peptide Shifter, is introduced and evaluated. An essential component of Peptide Shifter is a global peptide expression profile analysis that allows the inference of the underlying peptide shift pattern without the use of peptide labeling or internal standards. A controlled proteomic analysis of plasma samples demonstrates a 34% decrease in peptide intensity variability after the application of Peptide Shifter.