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PubMed2006

Assessing the utility of SELDI-TOF and model averaging for serum proteomic biomarker discovery.

The SELDI-TOF technique was used to profile serum proteins from Type 1 diabetes (T1D) patients and healthy autoantibody-negative (AbN) controls. Univariate and multivariate analyses were performed to identify putative biomarkers for T1D and to assess the reproducibility of the SELDI technique. We found 146 protein/peptide peaks (581 total peaks discovered) in human serum showing statistical differences in expression levels between T1D patients and controls, with 84% of these peaks showing technical replication. Because individual proteins did not offer great power for disease prediction, we used our model averaging approach that combines the information from multiple multivariate models to accurately classify T1D and control subjects (88.9% specificity and 90.0% sensitivity). Analyses of a test subset of the data showed less accuracy (82.8% specificity and 76.2% sensitivity), although the results are still positive. Unfortunately, no multivariate model could be replicated using the same samples. This first attempt of high throughput analyses of the human serum proteome in T1D patients suggests that model averaging is a viable method for developing biomarkers; however, the reproducibility of SELDI-TOF is currently not sufficient to be used for classification of complex diseases like T1D.

Adolescent↗

PubMed2006

Proteomic-based discovery and characterization of glycosylated eosinophil-derived neurotoxin and COOH-terminal osteopontin fragments for ovarian cancer in urine.

PURPOSE: The objective was to identify and characterize low molecular weight proteins/peptides in urine and their posttranslational modifications that might be used as a screening tool for ovarian cancer. EXPERIMENTAL DESIGN: Urine samples collected preoperatively from postmenopausal women with ovarian cancer and benign conditions and from nonsurgical controls were analyzed by surface-enhanced laser desorption/ionization mass spectrometry and two-dimensional gel electrophoresis. Selected proteins from mass profiles were purified by chromatography and followed by liquid chromatography-tandem mass spectrometry sequence analysis. Specific antibodies were generated for further characterization, including immunoprecipitation and glycosylation. Quantitative and semiquantitative ELISAs were developed for preliminary validation in patients of 128 ovarian cancer, 52 benign conditions, 44 other cancers, and 188 healthy controls. RESULTS: A protein (m/z approximately 17,400) with higher peak intensities in cancer patients than in benign conditions and controls was identified and subsequently defined as eosinophil-derived neurotoxin (EDN). A glycosylated form of EDN was specifically elevated in ovarian cancer patients. A cluster of COOH-terminal osteopontin was identified from two-dimensional gels of urine from cancer patients. Modified forms EDN and osteopontin fragments were elevated in early-stage ovarian cancers and a combination of both resulted to 93% specificity and 72% sensitivity. CONCLUSIONS: Specific elevated posttranslationally modified urinary EDN and osteopontin COOH-terminal fragments in ovarian cancer might lead to potential noninvasive screening tests for early diagnosis. Urine with less complexity than serum and relatively high thermodynamic stability of peptides or metabolites is a promising study medium for discovery of the novel biomarkers which may present in many non-urinary tract neoplastic diseases.

Adenocarcinoma, Clear Cell↗

PubMed2002

Proteomics approaches to uncover the repertoire of circulating biomarkers for breast cancer.

There is substantial interest in applying proteomics to cancer biomarker discovery. Proteomics is particularly suited for profiling biological fluids and uncovering circulating markers and is currently being applied to the identification of novel biomarkers for breast cancer. Novel proteomic approaches include direct profiling of serum using mass spectrometry, application of a variety of strategies to harness immunity for cancer diagnosis, protein tagging to capture subproteomes rich in diagnostic markers, the use of protein microarrays, and the use of multi-dimensional liquid protein separation technologies for comprehensive profiling of serum and other biological fluids. It is likely that the application of a wide range of proteomics tools to breast cancer will yield a panel of markers that have utility for breast cancer diagnosis.

Autoantibodies↗

PubMed2006

Biomarker and drug-target discovery using proteomics in a new rat model of sepsis-induced acute renal failure.

Sepsis is one of the common causes of acute renal failure (ARF). The objective of this study was to identify new biomarkers and therapeutic targets. We present a new rat model of sepsis-induced ARF based on cecal ligation and puncture (CLP). We used this model to find urinary proteins which may be potential biomarkers and/or drug targets. Aged rats were treated with fluids and antibiotics after CLP. Urinary proteins from septic rats without ARF and urinary proteins from septic rats with ARF were compared by difference in-gel electrophoresis (DIGE). CLP surgery elevated interleukin (IL)-6 and IL-10 serum cytokines and blood nitrite compared with sham-operated rats. However, there was a range of serum creatinine values at 24 h (0.4-2.3 mg/dl) and only 24% developed ARF. Histology confirmed renal injury in these rats. Forty-nine percent of rats did not develop ARF. Rats without ARF also had less liver injury. The mortality rate at 24 h was 27% but was increased by housing the post-surgery rats in metabolic cages. Creatinine clearance and urine output 2-8 h after CLP was significantly reduced in rats which died within 24 h. Using DIGE we identified changes in a number of urinary proteins including albumin, brush-border enzymes (e.g., meprin-1-alpha) and serine protease inhibitors. The meprin-1-alpha inhibitor actinonin prevented ARF in aged mice. In summary, we describe a new rat model of sepsis-induced ARF which has a heterogeneous response similar to humans. This model allowed us to use DIGE to find changes in urinary proteins and this approach identified a potential biomarker and drug target - meprin-1-alpha.

Acute Kidney Injury↗

PubMed2005

Targeted discovery tools: proteomics and chromatin immunoprecipitation-on-chip.

Despite the availability of several completely sequenced genomes, we are still, for the most part, ignorant about how genes interact and regulate each other within a given cell type to specify identity, function and cellular memory. A realistic model of cellular regulation based on current knowledge indicates that many interacting networks operate at the epigenetic, transcriptional, translational and post-translational levels, with feedback between the various levels. Protein-protein and protein-DNA interactions help to define which genes may be activated in a particular cell, and determine whether external cues cause activation or repression. New technologies, e.g. proteomics using mass spectrometry, high-density DNA or oligonucleotide microarrays (chips), and chromatin immunoprecipitation (ChIP), provide new and exciting tools for deciphering the pathways and proteins controlling gene expression. Analysis of these pathways offers new insight that aids targeted drug development.

Chromatin Immunoprecipitation↗

PubMed2003

Proteomics in biomarker discovery and drug development.

Proteomics is a research field aiming to characterize molecular and cellular dynamics in protein expression and function on a global level. The introduction of proteomics has been greatly broadening our view and accelerating our path in various medical researches. The most significant advantage of proteomics is its ability to examine a whole proteome or sub-proteome in a single experiment so that the protein alterations corresponding to a pathological or biochemical condition at a given time can be considered in an integrated way. Proteomic technology has been extensively used to tackle a wide variety of medical subjects including biomarker discovery and drug development. By complement with other new technique advances in genomics and bioinformatics, proteomics has a great potential to make considerable contribution to biomarker identification and to revolutionize drug development process. This article provides a brief overview of the proteomic technologies and their application in biomarker discovery and drug development.

Animals↗

PubMed2004

Cancer proteomics: Serum diagnostics for tumor marker discovery.

Cancer proteomics is an exciting field that is witnessing many new developments in recent years. It is hoped that these advances will result in decreased cancer death rates, which have not declined dramatically in the last several decades. Some of the problems with current tumor markers include the lack of sensitivity and specificity, factors that prevent their use in population-based screening of disease. Thus, there is an urgent need to identify novel biomarkers that can faithfully detect the disease state. As we are now in the post-genome era, many opportunities have been created. Genomic sequence data are available for human, as well as several other species. We are now poised to mine these data and to determine the functions of the encoded proteins constituting the human genome. Proteomics affords this opportunity by providing enhanced procedures and tools for discovery and also a framework for understanding these components in terms of pathogenesis. New technologies and improvements in existing methodologies will allow for the rapid growth in the identification and characterization of peptides and proteins that are unique to various clinical states. This technology can be successfully applied to clinical specimens for the identification of new tumor markers.

Algorithms↗

PubMed2006

Global and targeted quantitative proteomics for biomarker discovery.

The extraordinary developments made in proteomic technologies in the past decade have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples using unbiased methods. The major technology driving these studies is mass spectrometry (MS). The basic premises of most biomarker discovery studies is to use the high data-gathering capabilities of MS to compare biological samples obtained from healthy and disease-afflicted patients and identify proteins that are differentially abundant between the two specimen. To meet the need to compare the abundance of proteins in different samples, a number of quantitative approaches have been developed. In this article, many of these will be described with an emphasis on their advantageous and disadvantageous for the discovery of clinically useful biomarkers.

Biomarkers↗

PubMed2002

Oncoproteomics: state-of-the-art.

Proteomics is a promising approach in the identification of proteins and biochemical pathways involved in carcinogenesis. Proteomic technologies are now being incorporated in oncology in the post-genomic era. Cancer involves alterations in protein expression and provides a good model not only for detection of biomarkers but also their use in drug discovery. Proteomics has an impact on diagnostics as well as drug discovery. Genomics still remains an important approach but the value of proteomics lies in the fact that most of the diagnostics and drugs target proteins. The importance of application of proteomics in oncology is recognized by the publication of this special issue of TRCT.

Genome↗

PubMed2025

Pitavastatin, Procollagen Pathways, and Plaque Stabilization in Patients With HIV: A Secondary Analysis of the REPRIEVE Randomized Clinical Trial.

IMPORTANCE: In a mechanistic substudy of the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) randomized clinical trial, pitavastatin reduced noncalcified plaque (NCP) volume, but specific protein and gene pathways contributing to changes in coronary plaque remain unknown. OBJECTIVE: To use targeted discovery proteomics and transcriptomics approaches to interrogate biological pathways beyond low-density lipoprotein cholesterol (LDL-C), relating statin outcomes to reduce NCP volume and promote plaque stabilization among people with HIV (PWH). DESIGN, SETTING, AND PARTICIPANTS: This was a post hoc analysis of the double-blind, placebo-controlled, REPRIEVE randomized clinical trial. Participants underwent coronary computed tomography angiography (CTA), plasma protein analysis, and transcriptomic analysis at baseline and 2-year follow-up. The trial enrolled PWH from April 2015 to February 2018 at 31 US research sites. PWH without known cardiovascular diseases taking antiretroviral therapy and with low to moderate 10-year cardiovascular risk were eligible. Data analyses were conducted from October 2023 to February 2024. INTERVENTION: Oral pitavastatin calcium, 4 mg per day. MAIN OUTCOMES AND MEASURES: Relative change in plasma proteomics, transcriptomics, and noncalcified plaque volume among those receiving treatment vs placebo. RESULTS: Among 558 individuals (mean [SD] age, 51 [6] years; 455 male [82%]) included in the proteomics assessment, 272 (48.7%) received pitavastatin and 286 (51.3%) received placebo. After adjusting for false discovery rates, pitavastatin increased abundance of procollagen C-endopeptidase enhancer 1 (PCOLCE), neuropilin 1 (NRP-1), major histocompatibility complex class I polypeptide-related sequence A (MIC-A) and B (MIC-B), and decreased abundance of tissue factor pathway inhibitor (TFPI), tumor necrosis factor ligand superfamily member 10 (TRAIL), angiopoietin-related protein 3 (ANGPTL3), and mannose-binding protein C (MBL2). Among these proteins, the association of pitavastatin with PCOLCE (a rate-limiting enzyme of collagen deposition) was greatest, with an effect size of 24.3% (95% CI, 18.0%-30.8%; P < .001). In a transcriptomic analysis, individual collagen genes and collagen gene sets showed increased expression. Among the 195 individuals with plaque at baseline (88 [45.1%] taking pitavastatin, 107 [54.9%] taking placebo), changes in NCP volume were most strongly associated with changes in PCOLCE (%change NCP volume/log2-fold change = -31.9%; 95% CI, -42.9% to -18.7%; P < .001), independent of changes in LDL-C level. Increases in PCOLCE related most strongly to change in the fibro-fatty (<130 Hounsfield units) component of NCP (%change fibro-fatty volume/log2-fold change = -38.5%; 95% CI, -58.1% to -9.7%; P = .01) with a directionally opposite, although nonsignificant, increase in calcified plaque (%change calcified volume/log2-fold change = 34.4%; 95% CI, -7.9% to 96.2%; P = .12). CONCLUSIONS AND RELEVANCE: Results of this secondary analysis of the REPRIEVE randomized clinical trial suggest that PCOLCE may be associated with the atherosclerotic plaque stabilization effects of statins by promoting collagen deposition in the extracellular matrix transforming vulnerable plaque phenotypes to more stable coronary lesions. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02344290.

Humans↗

PubMedDate not supplied

Studies of quantitative analysis of protein expression in Saccharomyces cerevisiae.

In the present study amino acid analysis is applied to quantitation of Saccharomyces cerevisiae proteome expression. The quantitation levels obtained are compared to data using densitometric analysis of silver or amido black staining and to the theoretical expression level (codon bias) of the identified proteins determined from their amino acid analysis (AAA). The results show that relative volume ratio (%vol) using Melanie II is a better parameter for spot quantitation than relative optical density ratio (%OD), and amino black staining provides good linearity within the range 1-100 pmol protein. However, AAA shows that theoretical expression levels are not well correlated with actual protein expression level, although there is better correlation when isoforms of the expressed protein are identified and included. It is concluded that amino acid analysis provides accurate protein quantitation and has a continuing role in proteome studies in terms of the rapid and inexpensive quantitation of proteins displayed on proteome maps. We do however recognize that in the context of future clinical applications and large-scale proteome discovery projects, quantitation and post-translational modification need to be analyzed by 'proteomatic' (i.e., proteome automatic bioinformatic analysis directly from the gel) techniques.

Electrophoresis, Gel, Two-Dimensional↗

PubMed1999

Proteomics: a major new technology for the drug discovery process.

Proteomics is a new enabling technology that is being integrated into the drug discovery process. This will facilitate the systematic analysis of proteins across any biological system or disease, forwarding new targets and information on mode of action, toxicology and surrogate markers. Proteomics is highly complementary to genomic approaches in the drug discovery process and, for the first time, offers scientists the ability to integrate information from the genome, expressed mRNAs, their respective proteins and subcellular localization. It is expected that this will lead to important new insights into disease mechanisms and improved drug discovery strategies to produce novel therapeutics.

Journal Article↗

PubMed2005

Proteomics for biodefense applications: progress and opportunities.

The increasing threat of bioterrorism and continued emergence of new infectious diseases has driven a major resurgence in biomedical research efforts to develop improved treatments, diagnostics and vaccines, as well as increase the fundamental understanding of the host immune response to infectious agents. The availability of multiple mass spectrometry platforms combined with multidimensional separation technologies and microbial genomic databases provides an unprecedented opportunity to develop these much needed resources. An overview of current proteomic strategies applied to microbes and viruses considered potential bioterrorism agents is presented. The emerging area of immunoproteomics as applied to the development of new vaccine targets is also summarized. These powerful research approaches can generate a multitude of potential new protein targets; however, translating these proteomic discoveries to useful counter-bioterrorism products will require large collaborative research efforts across multiple basic science and clinical disciplines. A translational proteomic research paradigm illustrating this approach using influenza virus as an example is discussed.

Adult↗

PubMed2026

Cross-Platform Proteomics and Machine Learning Algorithms Nominate Plasma Biomarkers of Stroke Diagnosis.

BACKGROUND: Blood-based biomarkers for stroke subtyping could improve triage in emergency settings. We used cross-platform proteomics to identify plasma biomarkers differentiating major stroke diagnostic groups. METHODS: We conducted a case-control study using 2 biorepositories. Plasma was collected in the emergency department from adults with suspected stroke before therapeutic intervention. Differentially enriched proteins were identified across acute ischemic stroke, intracerebral hemorrhage, transient ischemic attack, and stroke mimics using SomaScan discovery proteomics (Grady). Differentially enriched proteins were nominated using pairwise and multigroup comparisons and adjusted for clinical covariates. Protein panels were created using least absolute shrinkage and selection operator logistic regression. Internal validation used repeated nested cross-validation (rCV) and targeted mass spectrometry (MS), while external validation used data-independent acquisition  mass spectrometry in an independent cohort (Yale). RESULTS: We included 100 subjects (40 with acute ischemic stroke, 20 with intracerebral hemorrhage, 20 with transient ischemic attack, 20 with stroke mimics) in discovery and 80 subjects (20 per group) in external validation cohorts. SomaScan quantified 7307 proteins, of which 61 differentiated stroke subtypes. We identified 7 protein classifiers for acute ischemic stroke (rCV-area under the curve, 0.82 [95% CI, 0.78-0.86]), 6 for intracerebral hemorrhage (rCV-area under the curve, 0.70 [95% CI, 0.64-0.76]), 8 for transient ischemic attack (rCV-area under the curve, 0.78 [95% CI, 0.73-0.84]), and 7 for stroke mimics (rCV-area under the curve, 0.81 [95% CI, 0.77-0.86]). Targeted proteomics internally validated 11 proteins, and data-independent acquisition-mass spectrometry externally validated 32 proteins, including VTN (vitronectin), PLG (plasminogen), and S100A9 as top stroke mimics, transient ischemic attack, and intracerebral hemorrhage classifiers. CONCLUSIONS: This study highlights plasma proteomics as a valuable tool for discovering protein biomarkers of stroke diagnosis. These findings support further validation in larger, multicenter cohorts to facilitate biomarker-guided stroke diagnosis in acute care.

Humans↗

PubMed2026

Takotsubo Syndrome: The First Non-Acute Proteomic Analysis by Remote Dried Blood Microsampling.

Takotsubo syndrome (TTS) is an under-recognized form of acute-onset heart failure typically precipitated by stress. While recovery of cardiac function is described over the course of weeks, adverse outcomes after apparent recovery are increasingly recognized. However, the pathophysiology of non-acute manifestations remains poorly understood. We used mass-spectrometry-based discovery proteomics from remotely collected non-acute dried blood microsamples to perform a case-control study in 62 participants with a prior TTS episode (median of 2.24 years prior to sample collection) and 47 reference controls. We quantified 398 unique proteins, and found that agnostic clustering techniques showed separation between TTS and reference control samples. This represents the first proteomic characterization of non-acute TTS. Pathway analysis of the 52 differentially regulated proteins demonstrated enrichment of proteins involved in complement activation, nitric oxide signaling, and with antioxidant activity. These enriched pathways may be suggestive of a persistent cardiomyopathy resulting from or predisposing to TTS.

Humans↗

PubMed2026

A validated, modifiable proteomic score from the EXSCEL trial predicts cardiovascular events in diabetes.

BACKGROUNDAdults with type 2 diabetes mellitus (T2DM) are at increased risk for stroke, myocardial infarction, and cardiovascular death, yet individual risk is heterogeneous and incompletely captured by clinical models.METHODSIn the Exenatide Study of Cardiovascular Event Lowering (EXSCEL), adults with T2DM were randomized to a GLP-1 RA (exenatide) or a placebo and followed longitudinally for major adverse cardiovascular events (MACE). High-throughput discovery proteomics was done in plasma collected at baseline and 12 months. Proteins associated with time to MACE were identified using multivariable regression and incorporated into supervised machine learning models. A multi-protein score was developed and externally validated in 2 independent population-based and trial cohorts.RESULTSThe proteomic score showed incremental improvement in cardiovascular risk discrimination beyond clinical factors alone, and several proteins were consistently prioritized across modeling approaches. The protein score and a top-ranked protein, tetranectin, were modified by GLP-1 RA treatment, and a decrease in protein score was associated with improved outcomes, supporting modifiability of MACE risk.CONCLUSIONExternal validation confirmed generalizability across cohorts with and without diabetes. Together, these findings demonstrate that plasma proteomic signatures can enhance cardiovascular risk stratification and identify treatment-responsive biomarkers in T2DM, supporting their potential role in precision prevention strategiesFUNDINGThe EXSCEL study was funded by Amylin Pharmaceuticals. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006, and grants R01HL146145, U01HL080295, U01HL130114, R01HL172803, and R01HL144483 from the National Heart, Lung, and Blood Institute, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by R01AG023629 from the National Institute on Aging.

Aged↗

PubMed2002

Optical barcoding of colloidal suspensions: applications in genomics, proteomics and drug discovery.

The enormous amount of information generated through sequencing of the human genome has increased demands for more economical and flexible alternatives in genomics, proteomics and drug discovery. Many companies and institutions have recognised the potential of increasing the size and complexity of chemical libraries by producing large chemical libraries on colloidal support beads. Since colloid-based compounds in a suspension are randomly located, an encoding system such as optical barcoding is required to permit rapid elucidation of the compound structures. We describe in this article innovative methods for optical barcoding of colloids for use as support beads in both combinatorial and non-combinatorial libraries. We focus in particular on the difficult problem of barcoding extremely large libraries, which if solved, will transform the manner in which genomics, proteomics and drug discovery research is currently performed.

Colloids↗

PubMed2006

Capillary separations enabling tissue proteomics-based biomarker discovery.

Development of the capability to enable large-scale proteome studies, analogous to comprehensive gene expression analysis, will clearly have far-reaching impacts on protein biomarker investigations of human diseases such as cancer through interrogation of the archived fresh frozen and formalin-fixed and paraffin-embedded tissue collections. This review therefore focuses on the most recent advances in microdissection techniques and proteome platforms for procuring homogeneous subpopulations of tumor cells or structures and performing comprehensive analysis of protein profiles within tissue specimens, respectively. Developments in capillary separations capable of providing extremely high resolving power and selective analyte enrichment are particularly highlighted for their roles within the broader context of a state-of-the-art integrated tissue proteome effort. The capabilities of CIEF-based multidimensional separations for performing proteome analysis from minute samples create new opportunities in the pursuit of biomarker discovery using enriched and selected cell populations procured from tissue specimens. These proteome technological advances combined with recently developed tissue microdissection techniques provide powerful tools for those seeking to gain a greater understanding at the global level of the cellular machinery associated with human diseases such as cancer.

Biomarkers↗