Label-free Quantitative Liquid Chromatography Research Articles

Proteomic Profiling in Patients With Peripartum Cardiomyopathy: A Biomarker Study of the ESC EORP PPCM Registry

BackgroundPeripartum cardiomyopathy (PPCM) remains an important cause of maternal morbidity and mortality globally. The pathophysiology remains incompletely understood, and the diagnosis is often missed or delayed. ObjectivesThis study explored the serum proteome profile of patients with newly diagnosed PPCM, as compared with matched healthy postpartum mothers, to unravel novel protein biomarkers that would further an understanding of the pathogenesis of PPCM and improve diagnostic precision. MethodsStudy investigators performed untargeted serum proteome profiling using data-independent acquisition–based label-free quantitative liquid chromatography–tandem mass spectrometry on 84 patients with PPCM, as compared with 29 postpartum healthy controls (HCs). Significant changes in protein intensities were determined with nonpaired Student’s t-tests and were further classified by using the Boruta algorithm. The proteins’ diagnostic performance was evaluated by area under the curve (AUC) and validated using the 10-fold cross-validation. ResultsPatients with PPCM presented with a mean left ventricular ejection fraction of 33.5% ± 9.3% vs 57.0% ± 8.8% in HCs (P < 0.001). Study investigators identified 15 differentially up-regulated and 14 down-regulated proteins in patients with PPCM compared with HCs. Seven of these proteins were recognized as significant by the Boruta algorithm. The combination of adiponectin, quiescin sulfhydryl oxidase 1, inter-α-trypsin inhibitor heavy chain, and N-terminal pro–B-type natriuretic peptide had the best diagnostic precision (AUC: 0.90; 95% CI: 0.84-0.96) to distinguish patients with PPCM from HCs. ConclusionsSalient biologic themes related to immune response proteins, inflammation, fibrosis, angiogenesis, apoptosis, and coagulation were predominant in patients with PPCM compared with HCs. These newly identified proteins warrant further evaluation to establish their role in the pathogenesis of PPCM and potential use as diagnostic markers.

Integrated proteomic and metabolomic profiling of urine of renal anemia patients uncovers the molecular mechanisms of roxadustat.

Roxadustat (FG-4592) is a hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) prescribed to patients with low hemoglobin associated with chronic kidney disease. Due to the various HIF-mediated adaptive responses, FG-4592 has attracted significant interest for therapeutic use against various diseases. However, the clinical application of Roxadustat remains limited due to a lack of understanding of its underlying mechanisms. Herein, we performed label-free quantitative liquid chromatography with tandem mass spectrometry (LC-MS-MS) proteomics and un-targeted metabolomics to study the protein and metabolite alterations in the urine of renal anemia patients before and after Roxadustat therapy. The results were validated by parallel reaction monitoring (PRM). A total of 46 proteins (including 15 upregulated and 31 downregulated proteins) and 207 metabolites were significantly altered after Roxadustat treatment in urine samples obtained from renal anemia patients. Then, the altered proteins were further validated by PRM. Finally, proteomics combined with metabolomics analysis revealed that the Ras signalling pathway, cysteine and methionine metabolism, arginine and proline metabolism, and cholesterol metabolism were the main pathways altered by Roxadustat treatment. The multi-omics analysis revealed that Roxadustat could alter the protein expression and reverse the potential metabolic changes to exert hypotensive, lipid metabolic regulation, and renoprotective effects in clinical practice.

Analysis of protein expression changes in patients with prediabetes using proteomics approaches.

Proteomics and metabolomics are widely used in the study of diabetes, but rarely in prediabetes research. This study aimed to explore the mechanisms of early-onset type 2 diabetes mellitus (T2DM) by analyzing proteomic changes at different stages of glucose metabolism. A total of 40 individuals undergoing routine physical health examinations between December 2016 and April 2017 were enrolled. Subjects were divided into four groups based on fasting blood glucose (FPG) levels: FPG < 5.6mmol/L (group A); FPG ≥ 5.6mmol/L and <6.1mmol/L (group B); FPG ≥ 6.1mmol/L and <7.0mmol/L (group C); and FPG ≥ 7.0mmol/L (group D). Each group had 10 cases. Sera from these 40 subjects were analyzed by label-free quantitative liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). LC/MS/MS with selected reaction monitoring mode was also performed for qualitative and quantitative metabolomics analysis. Differentially expressed proteins were identified. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the differentially expressed metabolites. A total of 202 differentially expressed proteins were screened and were identified as mainly secreted proteins. Comparing group A with group B, 32 proteins were up-regulated and 18 proteins were down-regulated. Comparing group A with group C, 24 proteins were up-regulated and 24 proteins were down-regulated. Comparing group A with group D, 19 proteins were up-regulated and 17 proteins were down-regulated. The fold change for up-regulated proteins was >1.2, p < 0.05, while the fold change for down-regulated proteins was <-1.2, p < 0.05. PLS-DA and OPLS-DA revealed 113 differentially expressed metabolites. Correlation analysis of differentially expressed metabolites of group A versus group B revealed that among the down-regulated differential proteins, transforming growth factor β-induced protein ig-h3 correlated negatively with metabolite L-saccharin, while among the up-regulated differential proteins, apolipoprotein C-IV correlated negatively with metabolite 3-methyloxindole. Among all differentially expressed proteins, 19 proteins were associated with early initiation of chronic inflammation, including CD14 and CSF-1R, which were newly identified in the early onset of T2DM. Many proteins are differentially expressed between prediabetes and after T2DM diagnosis, although the specific mechanism remains unclear. The expression level of CD14 was significantly up-regulated and that of CSF-1R was significantly down-regulated when FPG was ≥5.6mmol/L, suggesting that CD14 and CSF-1R may be important markers for early-onset T2DM and may serve as new targets for T2DM treatment.

Ubiquitin-modified proteome analysis of Eriocheir sinensis hemocytes during Spiroplasma eriocheiris infection

Spiroplasma eriocheiris, the pathogen of Eriocheir sinensis tremor disease (TD), has bring a huge economic loss to China aquaculture. The hemocytes of crab as the first target cells of S. eriocheiris, but the interactive relationship between the E. sinensis and this pathogen not particularly clear. The present study is the first time to analysis the role of protein ubiquitination in the process of E. sinensis hemocytes response S. eriocheiris infection. By applying label-free quantitative liquid chromatography with tandem mass spectrometry proteomics, 950 lysine ubiquitination sites and 803 ubiquitination peptides on 458 proteins were identified, of which 48 ubiquitination sites on 40 proteins were quantified as significantly changed after the S. eriocheiris infection. Bioinformatics analysis of ubiquitination different proteins suggested many biological process and pathways were participated in the interaction between S. eriocheiris and host cell, such as ubiquitin system, endocytosis, prophenoloxidase system (proPO system), cell apoptosis, glycolysis. Our study can enhance our understanding of interaction between the crab and S. eriocheiris, and also provides basis to study the role of protein ubiquitination in other crustacean innate immune system.

Development of Unique Molecular Signatures for Bone Marrow Failure Syndromes

Background/Purpose There is inherent difficulty with accurate diagnosis of bone marrow failure syndromes including Severe Aplastic Anemia (SAA), Paroxysmal Nocturnal Hemoglobinuria (PNH) and Hypoplastic Myelodysplastic Syndrome (MDS). We have attempted to identify and validate panels of disease-specific /disease-associated proteins biomarkers for their potential clinical value for accurate definition of patients presenting with features of bone marrow failure syndromes.Methodology: Bone marrow plasma (MBP) and peripheral blood plasma (PBP) samples from 24 subjects with bone marrow failure; including AA/MDS/PNH/Fanconi Anemia (FA) as well as normal BMP and PBP were subjected to expression proteome analysis using label-free quantitative liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS).Results: Over 900 unique protein species were identified of which approximately 124 and 174 were significantly differentially expressed (> 1.5- ∞- fold change &p < 0.05) in BMP and PBP respectively. These protein signatures independently discriminate patients into four distinct clusters; AA/MDS/PNH/FA using principal component analysis (PCA) (Figure 1). Approximately 25% of the identified proteins showed similar expression patterns across the two datasets from BMP and PBP. Ingenuity Pathway Analysis showed some of our identified proteins as potential biomarkers were implicated in hematological diseases and multiple signaling networks associated with known bone marrow failure syndrome genes.In addition, six (6) of the identified proteins (THBS1, S100A9, GSTP1, GPX1, SOD2 and LMNB1) were independently confirmed as bone marrow failure associated genes using the 65 bone marrow failure syndromes gene panel from the curated CTD Gene-Disease Associations dataset (1). Interestingly, this differentially expressed protein panel were uniquely expressed across all disease sample groups as well as normal control sample group, thus making them potential biomarkers.Conclusions: Our data indicates the usefulness of multivariate analysis of quantitative proteome data as a means of discovery of disease related or disease specific biomarkers for bone marrow syndromes. These proteins once validated, on a larger cohort of patients, might be valuable to complement the currently existing parameters for reliable and objective disease diagnosis, monitoring treatment response and clinical outcome of bone marrow failure syndrome patients.Reference: 1. Andrew D. Rouillard Gregory W. Gundersen Nicolas F. Fernandez Zichen Wang Caroline D. Monteiro Michael G. McDermott Avi Ma'ayan, The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins ; Database (Oxford) (2016) 2016: baw100. DOI: https://doi.org/10.1093/database/baw100 [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Towards identification of novel putative biomarkers for infective endocarditis by serum proteomic analysis

IntroductionInfective endocarditis (IE) has high mortality, partly due to delayed diagnosis. No biomarker can identify IE in patients with fever and clinical picture of infection. To find putative biomarkers we analyzed serum levels of two proteins found in cardiac valves, fibulin-1 (n=696) and osteoprotegerin (n=689) among patients on clinical suspicion of IE. Proteomic analyses were performed in 24 patients with bacteremia, 12 patients with definite IE and 12 patients with excluded IE. MethodsFibulin-1 and osteoprotegerin were studied by enzyme linked immunosorbent assay (ELISA). Proteomic analyses were conducted by 2-dimensional polyacrylamid gel electrophoresis (2D-PAGE) and label-free quantitative liquid chromatography - tandem mass spectrometry (LFQ LC-MS/MS). Controls for 2D 2D-PAGE and LFQ LC-MS/MS had bacteremia and excluded IE. ResultsOsteoprotegerin levels were significantly increased in IE patients compared with non-IE patients. Fibulin-1 showed no difference. 2D-PAGE showed significant differences of 6 proteoforms: haptoglobin, haptoglobin-related protein, α-2-macroglobulin, apolipoprotein A-I and ficolin-3. LFQ LC-MS/MS analysis revealed significant level changes of 7 proteins: apolipoprotein L1, complement C1q subcomponent B and C, leukocyte immunoglobulin-like receptor subfamily A member 3, neuropilin-2, multimerin-1 and adiponectin. ConclusionsThe concentration changes in a set of proteoforms/proteins suggest that stress and inflammation responses are perturbed in patients with IE compared to patients with bacteremia without IE.

Quantitative Proteomics Analysis Reveals Unique But Overlapping Protein Signatures in HIV Infection

Human immunodeficiency virus-1 (HIV-1) exploits human host factors to complete its life cycle. Hence, it is very important to identify HIV-regulated host proteins for better understanding of the virus life cycle and its contribution to pathogenesis. This can potentially lead to identifying core target molecules that can be used as diagnostic and prognostic markers and/or as targets for potential therapeutic intervention. We present here a first holistic attempt to apply global plasma proteomics analysis of three closely related study populations including: 1) patients with HIV type 1 (HIV-1), 2) HIV-1 elite controllers (HIV-1 EC), and 3) patients infected with HIV type 2 (HIV-2). Peripheral blood plasma (PBP) samples from these groups of patients, together with healthy donors, were subjected to expression proteomics using label-free quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS). Over 314 unique PBP protein species were identified of which 100 (approx. 32%) were significantly differentially expressed (≥ 2 to ∞ - fold change; p < 0.05) between HIV-1, HIV-2 and HIV-1 EC control subjects. A total of 91 of the 100 proteins were significantly differentially expressed between pairs of HIV-1 versus HIV-1 EC, while 83 of the 100 proteins differed significantly between HIV-2 and HIV-1 EC. Interestingly, 76 proteins (87.5%) overlap between the two data sets indicating that majority of these proteins share similar expression changes between the three sample groups. Two proteins, XRCC5 and PSME1, were implicated in the early phase of the pathway network for HIV life cycle. Other identified proteins were involved in infectious disease and disease of signal transduction. Among them were MAP2K1, RPL23A, RPS3, RPS8, CALR, PRDX1, SOD2, LMNB1, LMNA, PHB, FGA, and FGB. Interestingly, despite high degree of similarity in protein profiles of HIV-1 and HIV-2, we identified only six proteins with significant expression changes (P<0.05). These include ETFB, PHB2, S100A9, LMO2, PPP3R1 and Vif, a fragment of virion infectivity factor of HIV-1. In conclusion, we have identified HIV-related protein expression changes and these proteins once validated in large sample cohort might potentially be capable of early diagnosis and prognosis of HIV diseases and other related infectious diseases. Funding: We would like to acknowledge King Abdulaziz City of Science and Technology for funding this project, advanced Strategic Program, National plan for Science, Technology and Innovation (10-BIO 1340-46). Declaration of Interest: The authors declare no competing interests. Ethical Approval: This study was conducted in accordance with the Helsinki Declaration and approved by the Research Ethics Committee and Research Advisory Council (ORA # 2110 001). All participants provided written and signed informed consents that were obtained by the institutional (KFSHRC) IRB approval.

Proteomics Analysis Reveals Protein Panels That Are Associated with Prediction to Tyrosine Kinase Inhibitors Response, Bone Marrow Transplant, Survival and Disease Outcome of Chronic Myeloid Leukemia Patients

Clinical and molecular diagnosis of most hematological malignancies including Chronic Myeloid Leukemia (CML) can be accurately made. However, prediction of treatment response and estimation of disease survival period eludes the currently available tools for patient care. Quantitative expression proteomics can potentially be developed as effective tool to monitor therapy response towards achieving personalized medicine for CML patients.We have over 10 years follow up period for some of the CML patients, and the majorities of them are alive and doing well on Imatinib. On the other hand, a small fraction of the patients were switched to alternative Tyrosine Kinase Inhibitors (TKI) and some underwent bone marrow transplants (BMT).Our follow up results stratified all 37 analyzed newly diagnosed CP-CML patients into 5 distinct cohorts including 52% on Imatinib, 12% on Nilotinib, 9% on Dasatinib, and 15% BMT, while 12% of the patients had disease-related mortality. Kaplan-Meier survival curve showed no significant difference between all patients on TKI and BMT that were alive under a follow up period of 4-11 years compared. On the other hand 3 of 4 patients had disease related deaths less than 2 years of diagnosis. Only one patient survived for almost 10 years (Figure 1). Besides survival analysis, peripheral blood samples obtained from the patients at time of diagnosis were subjected to expression proteome analysis using label-free quantitative liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). A subset of significantly differentially expressed proteins was able to distinctively discriminate samples into their respective treatment response and disease outcome groups based on unique protein expression signatures (P<0.05 and > 2- ∞- fold change, (Figure 2). Some of the identified proteins were implicated in hematological diseases including CML as potential biomarkers using Ingenuity Pathway Analysis.These protein signatures once validated in larger sample cohorts might be capable of prediction of molecular response, choice of therapy and disease outcome for CML patients. Therefore; allowing for identification of would be high risk patients that might potentially benefit from aggressive treatment at point of diagnosis pre initiation of conventional therapy. Altogether our findings indicate that analysis of panel of protein markers have the potential of clinical utility for prediction of response to therapy, disease survival and objective prognostication of disease outcome, thus bringing personalized medicine closer to CML patients. DisclosuresNo relevant conflicts of interest to declare.

Functional analysis of synovial fluid from osteoarthritic knee and carpometacarpal joints unravels different molecular profiles.

In this work, we aimed to elucidate the molecular mechanisms driving primary OA. By studying the dynamics of protein expression in two different types of OA joints we searched for similarities and disparities to identify key molecular mechanisms driving OA. For this purpose, human SF samples were obtained from CMC-I OA and knee joint of OA patients. SF samples were analysed by label-free quantitative liquid chromatography mass spectrometry. Disease-relevant proteins identified in proteomics studies, such as clusterin, paraoxonase/arylesterase 1 (PON1) and transthyretin were validated by enzyme-linked immunosorbent assays, and on the mRNA level by droplet digital PCR. Functional studies were performed in vitro using primary chondrocytes. Differential proteomic changes were observed in the concentration of 40 proteins including clusterin, PON1 and transthyretin. Immunoassay analyses of clusterin, PON1, transthyretin and other inflammatory cytokines confirmed significant differences in protein concentration in SF of CMC-I and knee OA patients, with primarily lower protein expression levels in CMC-I. Functional studies on chondrocytes unequivocally demonstrated that stimulation with SF obtained from knee OA, in contrast to CMC-I OA joint, caused a significant upregulation in pro-inflammatory response, cell death and hypertrophy. This study demonstrates that differential expression of molecular players in SF from different OA joints evokes diverse effects on primary chondrocytes. The pathomolecular mechanisms of OA may significantly differ in various joints, a finding that brings a new dimension into the pathogenesis of primary OA.

Mining the acidic serum proteome utilizing off-gel isoelectric focusing and label free quantitative liquid chromatography mass spectrometry

Serum remains an attractive source for the discovery of disease related biomarkers due to its intimate interaction with the majority of tissues within the body. The dynamic range of proteins present within serum has long complicated the ability to detect low level tissue leakage proteins that offer more promise as potential biomarkers due to their higher degree of specificity. Depletion strategies, using affinity based sorbents, to remove the most abundant serum proteins are routinely used for matrix simplification during discovery strategies focused on the serum proteome or glycoproteome. Glycoproteins bearing glycans with terminal sialic acid residues have been shown to be differentially expressed in a variety of cancers and are of interest due to the varied involvement of sialic acid in biological functions related to cancer development and metastasis. Herein, we describe the use of 14P serum depletion with subsequent off-gel isoelectric focusing using narrow pH range acidic strips, to facilitate simplification and enrichment of the acidic proteome and glycoproteome prior to label free liquid chromatography mass spectrometry (LC–MS) based proteomic analysis. The developed platform enabled the detection of proteins present within serum at sub-nanomolar concentrations while data analysis using a variety of bioinformatics resources suggested that many of the detected proteins were tissue leakage proteins or proteins associated with specific cellular compartments, rather than traditional secreted proteins. Comparison with theoretical isoelectric point (pI) values also revealed that some detected proteins had higher pI values than the separation range applied during the IEF step, suggesting that their presence may be due to glycans with high degrees of sialylation being attached to the protein backbone, as well as the presence of other post translational modifications. Such findings agree with offline glycomic profiling of the IEF fractions wherein increasing degrees of sialylation were detected across the pH strip from the basic towards the acidic end. Application of the platform using pooled serum from patients bearing gastric cancer, both before and after surgical intervention for tumour removal, revealed a number of differentially expressed proteins associated with cell recognition and cell signalling. The developed platform enabled excellent sensitivity and offers strong potential for application within biomarker discovery studies focused on the acidic proteome.

β-catenin downregulates Dicer to promote ovarian cancer metastasis.

Ovarian cancer is a nearly uniform lethal disease and its highly aggressive metastatic phenotype portends a poor prognosis. Lack of a well-controlled, relevant experimental model has been a major obstacle to identifying key molecules causing metastasis. Here we describe the creation of a new isogenic model of spontaneous human ovarian cancer metastasis exhibiting opposite phenotypes-highly metastatic (HM) and non-metastatic (NM)-both in vitro and in vivo. HM was unique in its ability to metastasize consistently to the peritoneum, mimicking the major dissemination route of human ovarian cancer. In contrast, NM failed to form detectable metastases, although it was equally tumorigenic. Using comparative label-free quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS), we identified β-catenin, which we demonstrated for the first time as having a direct role in the pathogenesis of ovarian cancer metastasis. Our studies also revealed a previously unrecognized role of β-catenin in the downregulation of multiple microRNAs (miRNAs) through attenuating miRNA biogenesis by targeting Dicer, a key component of the miRNA-processing machinery. One such downregulated miRNAs was miR-29s involved in epithelial-to-mesenchymal transition and subsequent stem cell traits. Silencing β-catenin or overexpressing Dicer or miR-29 mimics in HM significantly reduced the ability of these cells to migrate. β-catenin-knockdown cells also failed to metastasize in an orthotopic model of ovarian cancer. Meta-analysis revealed an increase in CTNNB1 and a decrease in DICER1 expression levels in the high-risk group. These results uncover β-catenin as a critical factor in promoting ovarian cancer aggressiveness and a new mechanism linking between β-catenin and miRNA downregulation underlying this process.

Molecular Classification of Bone Marrow Failure Syndromes: Protein Signatures As Surrogate Biomarker for Accurate Diagnosis of Severe Aplastic Anemia, Hypoplastic Myelodysplastic Syndrome and Paroxysmal Nocturnal Hemoglobinuria Patients

Background/Purpose: Bone marrow failure syndrome is an example of disease entity where accurate diagnosis of Severe Aplastic Anemia (SAA), Paroxysmal Nocturnal Hemoglobinuria (PNH) and Hypoplastic Myelodysplastic Syndrome (MDS) is very challenging. The aim of this study was to identify panels of disease-specific /disease-associated proteins biomarkers to be used for more objective diagnosis and better prediction of disease prognosis of patients presenting with features of bone marrow failure syndromes.Methodology:Bone marrow plasma (MBP) and peripheral blood plasma (PBP) samples from 20 patients with bone marrow hypoplasia; including AA/MDS/PNH were subjected to expression proteome analysis using label-free quantitative liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS).Results: Approximately 300 unique protein species were identified of which 107 and 218 were significantly differentially expressed (> 2- ∞- fold change &p < 0.05) in BMP and PBP respectively. These protein fingerprints independently discriminates patients into three distinct clusters; AA/MDS/PNH. Furthermore, only approx. 25% of the proteins were common between the two datasets from BMP and PBP. Some of the identified proteins were filtered and mapped using Ingenuity Pathway Analysis, and were associated with five different networks. The top two of these networks involved cell-to-cell signaling interaction, hematological system development and function, and immune cell trafficking. Only three of the differentially expressed proteins were uniquely expressed in SAA and MDS but absent in PNH, thus making these proteins potential biomarkers.The probable diagnostic utility of these proteins would be validated in large archival clinical samples. Our data indicates the utility of multivariate analysis of quantitative proteome data as a means of discovery of disease related or disease specific biomarkers for bone marrow syndromes.Conclusions:We have identified protein signatures capable of objective classification of bone marrow failure syndromes patients. Our expression proteomics strategy is very promising for identification of clinically useful biomarkers. These proteins once validated, on a larger cohort of patients, might be valuable to complement the currently existing parameters for reliable and objective disease diagnosis, monitoring treatment response and clinical outcome of bone marrow failure syndrome patients. DisclosuresOwaidah:King abdulaziz city for science, Novo Nordisk, Bayer: Honoraria, Research Funding.

Proteomics–Based Approach Predicts Molecular Response and Stratifies Responders to Tyrosine Kinase Inhibitors (TKI) in Chronic Myeloid Leukemia (CML) Patients

Purpose: Chronic Myeloid Leukemia (CML) among other hematological malignancies has witnessed development of advanced molecular diagnostics. However, there is an unmet need for efficient objective markers for selection of available therapeutic agents and accurate prediction of patient’s response. Clinical proteomics can potentially be complementary to currently existing proven tools to monitor therapy response towards achieving personalized medicine for CML patients.Experimental Design: Thirty four bone marrow and serum, samples from patients with newly diagnosed chronic–phase CML were subjected to expression proteome analysis using combined gel-based 2-DE and label-free in-solution quantitative liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The study aims to identify disease-specific/disease-associated protein biomarkers demanding less invasive procedures for objective prediction of individual’s best treatment options and prognostic monitoring of CML patients (Overview in figure 1).Results: Quantitative protein fingerprints from 2-DE analysis accurately predicts 13 individuals that achieved Major Molecular Response (MMR) at 6 months from 12 subjects without Major Molecular Response (NoMMR) using unsupervised principal component analysis (Figure 1 left lower panel). Some of the results were independently validated using label free quantitative liquid chromatography tandem mass spectrometry. Sixty three (63) differentially expressed proteins were identified (> 2- ∞- fold change, p < 0.05).The panel of proteins also discriminates accurately patients that stay on TKI after 1year of Imatinib Rx from patients ultimately requiring alternative treatment (Second Generation TKI/others). We have had more than 2 years follow up of these patients and the same dataset of potential protein biomarkers could still accurately separates all four sample groups into their respective molecular response and treatment sub groups (Figure 1middle lower panel). Some of the identified proteins were implicated in hematological diseases as potential biomarkers using Ingenuity Pathway Analysis (Figure 1right lower panel).Conclusion: Our results highlight the power of Proteomics as a molecular scanner for objective stratification of CML patients for treatment options. We have identified protein signatures capable of prediction of molecular response and choice of therapy for CML patients at 6 months and beyond. Our expression proteomics strategy is very promising for identification of clinically useful biomarkers. These proteins might be valuable once validated, to complement the currently existing parameters for reliable and objective prediction of disease progression, monitoring treatment response and clinical outcome of CML patients as a model of personalized medicine. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Characterization of the Ubiquitin-Modified Proteome Regulated by Transient Forebrain Ischemia

Ubiquitylation is a posttranslational protein modification that modulates various cellular processes of key significance, including protein degradation and DNA damage repair. In animals subjected to transient cerebral ischemia, ubiquitin-conjugated proteins accumulate in Triton-insoluble aggregates. Although this process is widely considered to modulate the fate of postischemic neurons, few attempts have been made to characterize the ubiquitin-modified proteome in these aggregates. We performed proteomics analyses to identify ubiquitylated proteins in postischemic aggregates. Mice were subjected to 10 minutes of forebrain ischemia and 4 hours of reperfusion. The hippocampi were dissected, aggregates were isolated, and trypsin-digested after spiking with GG-BSA as internal standard. K-ɛ-GG-containing peptides were immunoprecipitated and analyzed by label-free quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. We identified 1,664 peptides to 520 proteins containing at least one K-ɛ-GG. Sixty-six proteins were highly ubiquitylated, with 10 or more K-ɛ-GG peptides. Based on selection criteria of greater than fivefold increase and P<0.001, 763 peptides to 272 proteins were highly enriched in postischemic aggregates. These included proteins involved in important neuronal functions and signaling pathways that are impaired after ischemia. Results of this study could serve as an important platform to uncover the mechanisms linking insoluble ubiquitin aggregates to the functions of postischemic neurons.

Expression Proteomics As Surrogate Biomarkers For Early Treatment Response In Chronic Myeloid Leukemia (CML) Patients

PurposeDespite advancements in diagnosis and treatment modalities for hematological malignancies, presently, there are no reliable blood tests for disease prognosis or prediction of patient's response to particular treatments. Identification of such biomarkers for diagnostic or therapeutic response in clinical monitoring of hematologic diseases such as Chronic Myeloid Leukemia (CML) would be very useful. Experimental DesignThe ability to identify biomarkers in body fluids (i.e., plasma or serum) will not only involve minimally invasive procedures, but also more importantly, the potential for monitoring disease progression and effective response to therapy in relatively early stages. In this study, global proteome analysis of serum, plasma, and bone marrow samples from 28 patients with newly diagnosed chronic–phase CML were analyzed by using expression proteomics technology (label free quantitative liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The goal was to evaluate for early treatment response and discovery disease–specific /disease-associated proteins for prognostic monitoring and patient's response to therapy at protein level. ResultsWe identified a panel of 15 differentially expressed proteins (> 2- °- fold change, p< 0.001) to accurately discriminate between CML patients that achieved Major Molecular Response (MMR) vs. No-Major Molecular Response (NoMMR) at 6 months (Figure 1 & Table 1). Six (6) of the 15 identified proteins were filtered and mapped as potential biomarkers using Ingenuity Pathway Analysis. Among the identified proteins implicated in hematological diseases include Group-specific component (vitamin D binding protein), haptoglobin and vitronectin. Others were inter-alpha-trypsin inhibitor heavy chain 1, leucine-rich alpha-2-glycoprotein 1 and metallophosphoesterase 1. We also observed most of these proteins to be located in extracellular space, acts as transporters and have been implicated as markers of hematological disease and inflammatory response. Our data indicates that multivariate analysis of quantitative proteome data can potentially be useful as a means of unsupervised artificial intelligence algorithm for classification and stratification of clinical CML patients. [Display omitted] [Display omitted] ConclusionOur results highlight the power of proteomics in the discovery of biomarkers for more accurate prediction of prognosis and monitoring treatment response in CML patients. These proteins might be valuable, once validated, to complement the currently existing parameters for reliable and objective prediction of disease progression, monitoring treatment response and clinical outcome of CML patients. Disclosures:No relevant conflicts of interest to declare.

Comparative Proteomic Analysis of Non-small-cell Lung Cancer and Normal Controls Using Serum Label-Free Quantitative Shotgun Technology

The aim of this study was to distinguish non-small-cell lung cancer from normal controls and explore the new potential biomarkers of lung cancer by serum proteomics technology. Label-free quantitative liquid chromatography tandem mass spectrometry (1D-LC/MS/MS) analysis was performed on eight non-small-cell lung cancer (NSCLC) serum samples and eight normal controls. The proteomic data we obtained was analyzed by normalized, randomly paired t test and integrated bioinformatic methods, including hierarchical clustering analysis, principal-component analysis, and support vector machine. We obtained 931 proteins with at least two peptides identified from the 16 serum samples, and 62 proteins were differentially expressed between non-small-cell lung cancer patients and normal controls. There were 16 proteins expressed much higher in the lung cancer group than in the controls. Two hundred eight proteins were shared in all 16 serum samples. Through hierarchical clustering analysis and principal-component analysis based on the 62 differentially expressed proteins, we could distinguish non-small-cell lung cancer from the normal controls. The prediction accuracy of non-small-cell lung cancer analyzed by the support vector machine algorithm based on 208 proteins which were shared in all serum samples is 93.75%. Protein expression patterns have changed in the serum of non-small-cell lung cancer patients. Label-free quantitative LC/MS/MS may be a good method to improve the diagnostic accuracy for lung cancer and it can help in discovering the new biomarkers.