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Cerebrospinal fluid proteomics and human immunodeficiency virus dementia: preliminary observations.

Protein profiling using mass spectrometry may be useful in identifying previously unknown protein markers in human immunodeficiency virus (HIV) dementia and provide insight into disease pathogenesis. Six samples of matched cerebrospinal (CSF) and blood serum from patients with no, mild, and moderate dementia were prepped for biomarker screening by the Ciphergen system. Chips were analyzed in the matrix-assisted laser desorption/ionization (MALDI) mass spectrometer at low mass (700 to 20,000 Da) and at higher mass (5000 to 100,000 Da). In both serum and CSF samples, differences in protein intensity appeared to correlate with degree of dementia. This preliminary study suggests that protein markers of HIV dementia may be detected by MALDI mass spectrometry.

AIDS Dementia Complex↗

Integrative omics analysis identifies biomarkers of septic cardiomyopathy.

Septic Cardiomyopathy (SCM) is a syndrome of acute cardiac dysfunction in septic patients, unrelated to cardiac ischemia. Multiomics studies including transcriptomics and proteomics have provided new insights into the mechanisms of SCM. In here, a rat model of SCM was established by intraperitoneal injection of lipopolysaccharide (LPS). Biomarkers of SCM were characterized via a multi-omics analysis. The differentially expressed (DE) mRNAs predominantly appeared in pathways linked to the immune response, inflammatory response, and the complement and coagulation cascades, while DE proteins were mainly enriched in pathways associated with the complement and coagulation cascades. On this basis, the integrated analysis was performed between transcriptome and proteome. The potential biomarkers were further verified by RT-qPCR and WB. The current proteotranscriptomic research has furnished a valuable dataset and fresh perspectives that will enhance our comprehension of the development of SCM. This, in turn, is expected to expedite the formulation of novel approaches for the prevention and management of SCM in patients.

Cardiomyopathies↗

Functional genomic approaches using the nematode Caenorhabditis elegans as a model system.

Since the completion of the genome project of the nematode C. elegans in 1998, functional genomic approaches have been applied to elucidate the gene and protein networks in this model organism. The recent completion of the whole genome of C. briggsae, a close sister species of C. elegans, now makes it possible to employ the comparative genomic approaches for identifying regulatory mechanisms that are conserved in these species and to make more precise annotation of the predicted genes. RNA interference (RNAi) screenings in C. elegans have been performed to screen the whole genome for the genes whose mutations give rise to specific phenotypes of interest. RNAi screens can also be used to identify genes that act genetically together with a gene of interest. Microarray experiments have been very useful in identifying genes that exhibit co-regulated expression profiles in given genetic or environmental conditions. Proteomic approaches also can be applied to the nematode, just as in other species whose genomes are known. With all these functional genomic tools, genetics will still remain an important tool for gene function studies in the post genome era. New breakthroughs in C. elegans biology, such as establishing a feasible gene knockout method, immortalized cell lines, or identifying viruses that can be used as vectors for introducing exogenous gene constructs into the worms, will augment the usage of this small organism for genome-wide biology.

Animals↗

Quantitative proteomics by stable isotope labeling and mass spectrometry.

The goal of quantitative proteomics is to systematically study static state or perturbation-induced changes in protein profile. Most of the recently developed mass spectrometry (MS)-based quantitative proteomic methods employ stable isotope labeling to introduce signature mass tags to peptides/proteins that can be used by a mass spectrometer to quantify each analyte and to determine the sample from which it originates. In this chapter, we discuss several methods for the introduction of mass tags to proteins and peptides for MS-based quantitative proteomic analysis, including isotope-coded affinity tags, stable isotope labeling by amino acids in cell culture, global internal standard technology, and mass-coded abundance tagging.

Isotope Labeling↗

Processing methods for differential analysis of LC/MS profile data.

BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data. RESULTS: We present a software package MZmine that enables differential LC/MS analysis of metabolomics data. This software is a toolbox containing methods for all data processing stages preceding differential analysis: spectral filtering, peak detection, alignment and normalization. Specifically, we developed and implemented a new recursive peak search algorithm and a secondary peak picking method for improving already aligned results, as well as a normalization tool that uses multiple internal standards. Visualization tools enable comparative viewing of data across multiple samples. Peak lists can be exported into other data analysis programs. The toolbox has already been utilized in a wide range of applications. We demonstrate its utility on an example of metabolic profiling of Catharanthus roseus cell cultures. CONCLUSION: The software is freely available under the GNU General Public License and it can be obtained from the project web page at: http://mzmine.sourceforge.net/.

Algorithms↗

Proteomic analysis of integral plasma membrane proteins.

Efficient methods for profiling proteins integral to the plasma membrane are highly desirable for the identification of overexpressed proteins in disease cells. Such methods will aid in both understanding basic biological processes and discovering protein targets for the design of therapeutic monoclonal antibodies. Avoiding contamination by subcellular organelles and cytosolic proteins is crucial to the successful proteomic analysis of integral plasma membrane proteins. Here we report a biotin-directed affinity purification (BDAP) method for the preparation of integral plasma membrane proteins, which involves (1) biotinylation of cell surface membrane proteins in viable cells, (2) affinity enrichment using streptavidin beads, and (3) depletion of plasma membrane-associated cytosolic proteins by harsh washes with high-salt and high-pH buffers. The integral plasma membrane proteins are then extracted and subjected to SDS-PAGE separation and HPLC/MS/MS for protein identification. We used the BDAP method to prepare integral plasma membrane proteins from a human lung cancer cell line. Western blotting analysis showed that the preparation was almost completely devoid of actin, a major cytosolic protein. Nano-HPLC/MS/MS analysis of only 30 microg of protein extracted from the affinity-enriched integral plasma membrane preparation led to the identification of 898 unique proteins, of which 781 were annotated with regard to their plasma membrane localization. Among the annotated proteins, at least 526 (67.3%) were integral plasma membrane proteins. Notable among them were 62 prenylated proteins and 45 Ras family proteins. To our knowledge, this is the most comprehensive proteomic analysis of integral plasma membrane proteins in mammalian cells to date. Given the importance of integral membrane proteins for drug design, the described approach will expedite the characterization of plasma membrane subproteomes and the discovery of plasma membrane protein drug targets.

Cell Line, Tumor↗

A proteomic study on human osteoblastic cells proliferation and differentiation.

Changes in expression profiles for 17 proteins were ascertained in human mature osteoblasts compared to pre-osteoblasts (differentiation markers). A differential approach was used to highlight proteomic changes between human osteosarcoma cells and mature osteoblasts, showing a relative over-expression of 8 proteins (proliferation and tumor indicators), as well as under-expression of proteins also found down-regulated in pre-osteoblasts (specific markers of osteoblast differentiation). Our findings confirmed the differences between cell lines and primary human cell cultures and suggested caution on the use of osteosarcoma to study anti-osteoporotic drugs in humans.

Adult↗

Proteomic technologies and their application to pancreatic cancer.

Pancreatic ductal adenocarcinoma is a devastating disease that represents an important health problem. It spreads rapidly at a time when patients have relatively few symptoms and consequently is often only detected at an advanced stage when treatment options are limited. Rapid developments in technology and bioinformatics have recently led to a surge in proteomics-based cancer research. Comparative analysis of protein profiles from nonmalignant and malignant pancreas cells or tissue, or from different stages of pancreatic cancer, potentially offer unique insight into the biology of this tumor type. Furthermore, proteomic approaches may provide novel diagnostic or therapeutic markers for this disease. Although such analyses are still in their infancy, they show great potential in the ongoing battle against this dismal disease.

Electrophoresis, Gel, Two-Dimensional↗

Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry.

Recently, the focus of cancer research has expanded from genetic information in the human genome to protein expression analyses. Because this 'proteome' reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumour markers for disease diagnosis and therapy monitoring. Some current proteomic technologies are particularly suitable for protein profiling in the search for new biomarkers. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry has been used frequently, highlighting many new proteins as biomarkers (e.g. for ovarian, breast, prostate and colorectal cancer). However, it is becoming increasingly recognized that reproducibility and validation of these biomarkers should be addressed carefully, as should their origin and identity. If these efforts are made, protein profiling can contribute to the better diagnosis of patients and the optimization of their treatment.

Biomarkers, Tumor↗

Concanavalin A-captured glycoproteins in healthy human urine.

Both the urinary proteome and its glycoproteome can reflect human health status, and more directly, functions of kidney and urinary tracts. Because the high abundance protein albumin is not N-glycosylated, the urine N-glycoprotein enrichment procedure could deplete it, and urine proteome could thus provide a more detailed protein profile in addition to glycosylation information especially when albuminuria occurs in some kidney diseases. In terms of describing the details of urinary proteins, the urine glycoproteome is even a better choice than the proteome itself. Pooled urine samples from healthy volunteers were collected and acetone-precipitated for proteins. N-Linked glycoproteins enriched with concanavalin A affinity purification were separated and analyzed by SDS-PAGE-reverse phase LC/MS/MS or two-dimensional LC/MS/MS. A total of 225 urinary proteins were identified based on two-hit criteria with reliability over 97% for each peptide. Among these proteins, 94 were identified in previous urine proteome works, 150 were annotated as glycoproteins in Swiss-Prot, and 43 were predicted as glycoproteins by NetNGlyc 1.0. A number of known biomarkers and disease-related glycoproteins were identified. Because changes in protein quantity or the glycosylation status can lead to changes in the concanavalin A-captured glycoprotein profile, specific urine glycoproteome patterns might be observed for specific pathological conditions as multiplex urinary biomarkers. Knowledge of the urine glycoproteome is important in understanding kidney and body function.

Concanavalin A↗

Chemical strategies for functional proteomics.

With complete genome sequences now available for several prokaryotic and eukaryotic organisms, biological researchers are charged with the task of assigning molecular and cellular functions to thousands of predicted gene products. To address this problem, the field of proteomics seeks to develop and apply methods for the global analysis of protein expression and protein function. Here we review a promising new class of proteomic strategies that utilizes synthetic chemistry to create tools and assays for the characterization of protein samples of high complexity. These approaches include the development of chemical affinity tags to measure the relative expression level and post-translational modification state of proteins in cell and tissue proteomes. Additionally, we discuss the emerging field of activity-based protein profiling, which aims to synthesize and apply small molecule probes that monitor dynamics in protein function in complex proteomes.

Affinity Labels↗

Systems level analysis of protein synthesis patterns associated with bacterial growth and metabolic transitions.

Gene expression databases, acquired by proteomics and transcriptomics, describe physiological and developmental programs at the systems level. Here we analyze proteosynthetic profiles in a bacterium undergoing defined metabolic changes. Streptomyces coelicolor cultured in a defined liquid medium displays four distinct patterns of gene expression associated with growth on glutamate, diauxic transition, and growth on maltose and ammonia that terminates by starvation for nitrogen and entry into stationary phase. Principal component and fuzzy cluster analyses of the proteome database of 935 protein spot profiles revealed principal kinetic patterns. Online linkage of the proteome database (SWICZ) to a protein-function database (KEGG) revealed limited correlations between expression profiles and metabolic pathway activities. Proteins belonging to principal metabolic pathways defined characteristic kinetic profiles correlated with the physiological state of the culture. These analyses supported the concept that metabolic flux was regulated not by individual enzymes but rather by groups of enzymes whose synthesis responded to changes in nutritional conditions. Higher-level regulation is reflected by the distribution of all kinetic profiles into only nine groups. The observation that enzymes representing principal metabolic pathways displayed their own distinctive average kinetic profiles suggested that expression of a "high-flux backbone" may dominate regulation of metabolic flux.

Adaptation, Physiological↗

Rationale and study design of the CardioGene Study: genomics of in-stent restenosis.

BACKGROUND AND AIMS: in-stent restenosis is a major limitation of stent therapy for atherosclerosis coronary artery disease. The CardioGene Study is an ongoing study of restenosis in bare mental stents (BMS) for the treatment of coronary artery disease. The overall goal is to understand the genetic determinants of the responses to vascular injury that result in the development of restenosis in some patients but not in others. Gene expression profiling at transcriptional and translational levels provides global assessment of gene activity after vascular injury and mechanistic insight. Furthermore, the delineation of genetic biomarkers would be of value in the clinical setting of risk-stratify patients prior to stent therapy. Prospective risk stratification would allow for the rational selection of specialized treatments against the development of in-stent restenosis (ISR), such as drug-eluting stents. SETTING: Patients are enrolled at two sites in the US with high-volume cardiac catheterization facilities: the William Beaumont Hospital in Royal Oak, MI, USA, and the Mayo Clinic in Rochester, MN, USA. STUDY DESIGN: Two complementary study designs are used to understand the molecular mechanisms of restenosis and the genetic biomarkers predictive of restenosis. First, 350 patients are enrolled prospectively at the time of stent implantation. Blood is sampled prior to stent placement and afterwards at 2 weeks and 6 months. The clinical outcome of restenosis is determined 6 and 12 months after stent placement. The primary outcome is clinical restenosis at 6 months. The major secondary outcome is clinical restenosis at 12 months. Second, a corollary case-control analysis will be carried out with the enrollment of an additional 250 cases with a history of recurrent restenosis after treatment with BMS. Controls for this analysis are derived from the prospective cohort. PATIENTS AND METHODS: Consecutive patients presenting to the cardiac catheterization laboratory are screened, informed about the study and enrolled after signing the consent form. Enrollment has been completed for the prospective cohort, and enrollment of the additional group is ongoing. A standardized questionnaire is used to collect clinical data primarily through direct patient interview to assess medical history, medication use, functional status, family history, environmental factors, and social history. Further data are abstracted from the medical charts and catheterization reports. A total of 276 clinical variables are collected per individual at baseline, and 49 variables are collected at each of the 6- and 12-month follow-up visits. A Clinical Events Committee adjudicates clinical outcomes. Blood samples are processed at each clinical enrollment site using standardized operating procedures. From each blood sample, several aliquots are prepared and stored of peripheral blood mononuclear cells, granulocytes, platelets, serum, and plasma. Additionally, a portion of each patient's leukocytes is cryopreserved for future cell-line creation. Samples are frozen and shipped to the National Heart, Lung and Blood Institute (NHLBI). Additional materials generated in the analysis of the samples at the NHLBI are frozen and stored, including isolated genomic DNA, total RNA, reverse transcribed cDNA libraries and labeled RNA hybridization mixtures used in microarray analysis. Per individual in the prospective cohort, high-quality transcript profiles of peripheral blood mononuclear cells at each time of blood sampling are obtained using Affymetrix U133A microarrays (Affymetrix, Santa Clara, CA, USA). Per chip, this yields 495,930 features per individual per time of sampling. This represents expression levels for 22,283 genes per patients oer time of blood sampling, including 14,500 well-characterized human genes. Proteomics of plasma is performed with multidimensional liquid chromatography and tandem mass spectrometry. Protein expression is examined similarly to mRNA expression as a measure of gene expression. Genotyping is performed in two manners. First, those genes showing differential expression at the levels of mRNA and protein are investigated using a candidate gene approach. Specific variants in known gene regulatory regions, such as promoters, are sought initially, as those variants may explain differences in expression level. Second, a genome-wide scan is used to identify genetic loci that are associated with ISR. Those regions identified are further examined for genes that show differential expression in the mRNA microarray profiling or proteomics investigations. These genes are finely investigated for candidate SNPs and other gene variants. Complementary genomic and proteomic approaches are expected to be robust. Integration of data sets is accomplished using a variety of informatics tools, organization of gene expression into functional pathways, and investigation of physical maps of up- and downregulated sets of genes. CONCLUSIONS: The CardioGene Study is designed to understand ISR. Global gene and protein expression profiling define molecular phenotypes of patients. Well-defined clinical phenotypes will be paired with genomic data to define analyses aimed to achieve several goals. These include determining blood gene and protein expression in patients with ISR, investigating the genetic basis of ISR, developing predictive gene and protein biomarkers, and the identification of new targets for treatment.

Clinical Trials as Topic↗

Expression profiles of proteins in fetal brain with Down syndrome.

Proteomics is a powerful tool for evaluating differential protein expression comparing hundreds of proteins simultaneously. In the current study we performed "gene hunting" at the protein level and identified and quantified 10 protein spots in control and Down syndrome (DS) fetal brains. Using two-dimensional (2-D) electrophoresis of fetal brain proteins with subsequent MALDI-identification and quantification with specific software, we identified a series of poorly known proteins, in part hypothetical and orphans or poorly documented proteins. Hypothetical protein DKFZp564D177.1-human (fragment), one of these proteins was identified in fetal brain and was significantly decreased in DS (0.61+/-0.44, n = 7) compared to controls (3.43+/-1.83, n = 7). Septin 6, previously shown to be associated with synaptic vesicles, was present in all of 7 controls, but only in 1 out of 6 DS brains. We suggest that decreased protein levels of hypothetical protein DKFZp564D177.1-human (fragment) and lower prevalence of septin 6 could be involved in the maldevelopment of fetal DS brains. The other 8 proteins (WD repeat protein 1, novel protein highly similar to septin 2 homolog, septin 5, septin 2, DJ37E16.5 (novel protein similar to nitrophenylphosphatases from various organism), hypothetical 30.2 kDa protein, neuronal protein NP25, and DC7 protein (vacuolar sorting protein 29)) were comparable between controls and DS but could be identified in fetal and DS cortex, thus proposing them as tentative brain proteins.

Brain↗

Proteomic studies on the white matter of human brain.

Limited information on the protein expression profiles of the different components of mammalian brain is available to date. In the present study, proteomic analysis was performed on 32 white matter samples obtained from 8 different regions of brains of four post mortem cases. Proteins were separated by 2D gel electrophoresis and identified by mass spectrometry. Most of the protein spots (98%) are reproducibly present in all the samples analyzed. A total of 64 different proteins were identified and divided into seven functional groups. These include metabolic proteins (33%), structural proteins (9%), proteins involved in signal transduction (9%), blood proteins (8%), stress related proteins (23%), and proteins involved in the ubiquitin mediated proteolysis (6%). This protein database obtained from the white matter of human brain contributes to deepen our knowledge on the molecular mechanisms that control several pathologies affecting this key component of the brain.

Brain↗

Phage display and PEGylation of therapeutic proteins.

With the success of the human genome project, the focus of life science research has shifted to the functional and structural analyses of proteins, such as disease proteomics and structural genomics. These novel approaches to the analyses of proteins, including newly identified ones, are expected to help in the identification and development of protein therapies for various diseases. Thus, disease proteomic-based drug discovery has a very high profile. Nevertheless, the use of bioactive proteins in the clinical setting is not straightforward because, in vivo, these proteins have a low stability and a pleiotropic action. To promote disease proteomic-based drug discovery and development, we have attempted to establish a system for creating functional mutant proteins (muteins) with the desired properties, and also to develop a site-specific polymer-conjugation system for further improving their therapeutic potency. These innovative protein-drug systems are discussed in this review.

Animals↗