Proteomics Analysis of the Cardiac Myofilament Subproteome Reveals Dynamic Alterations in Phosphatase Subunit Distribution

Myofilament proteins are responsible for cardiac contraction. The myofilament subproteome, however, has not been comprehensively analyzed thus far. In the present study, cardiomyocytes were isolated from rodent hearts and stimulated with endothelin-1 and isoproterenol, potent inducers of myofilament protein phosphorylation. Subsequently, cardiomyocytes were “skinned,” and the myofilament subproteome was analyzed using a high mass accuracy ion trap tandem mass spectrometer (LTQ Orbitrap XL) equipped with electron transfer dissociation. As expected, a small number of myofilament proteins constituted the majority of the total protein mass with several known phosphorylation sites confirmed by electron transfer dissociation. More than 600 additional proteins were identified in the cardiac myofilament subproteome, including kinases and phosphatase subunits. The proteomic comparison of myofilaments from control and treated cardiomyocytes suggested that isoproterenol treatment altered the subcellular localization of protein phosphatase 2A regulatory subunit B56α. Immunoblot analysis of myocyte fractions confirmed that β-adrenergic stimulation by isoproterenol decreased the B56α content of the myofilament fraction in the absence of significant changes for the myosin phosphatase target subunit isoforms 1 and 2 (MYPT1 and MYPT2). Furthermore, immunolabeling and confocal microscopy revealed the spatial redistribution of these proteins with a loss of B56α from Z-disc and M-band regions but increased association of MYPT1/2 with A-band regions of the sarcomere following β-adrenergic stimulation. In summary, we present the first comprehensive proteomics data set of skinned cardiomyocytes and demonstrate the potential of proteomics to unravel dynamic changes in protein composition that may contribute to the neurohormonal regulation of myofilament contraction.Myofilament proteins comprise the fundamental contractile apparatus of the heart, the cardiac sarcomere. They are subdivided into thin filament proteins, including actin, tropomyosin, the troponin complex (troponin C, troponin I, and troponin T), and thick filament proteins, including myosin heavy chains, myosin light chains, and myosin-binding protein C. Although calcium is the principal regulator of cardiac contraction through the excitation-contraction coupling process that culminates in calcium binding to troponin C, myofilament function is also significantly modulated by phosphorylation of constituent proteins, such as cardiac troponin I (cTnI),1 cardiac myosin-binding protein C (cMyBP-C), and myosin regulatory light chain (MLC-2). “Skinned” myocyte preparations from rodent hearts, in which the sarcolemmal envelope is disrupted through the use of detergents, have been invaluable in providing mechanistic information on the functional consequences of myofilament protein phosphorylation following exposure to neurohormonal stimuli that activate pertinent kinases prior to skinning or direct exposure to such kinases in active form after skinning (for recent examples, see studies on the phosphorylation of cTnI (1–3), cMyBP-C (4–6), and MLC-2 (7–9)). Nevertheless, to date, only a few myofilament proteins have been studied using proteomics (10–19), and a detailed proteomic characterization of the myofilament subproteome and its associated proteins from skinned myocytes has not been performed. In the present analysis, we used an LTQ Orbitrap XL equipped with ETD (20) to analyze the subproteome of skinned cardiomyocytes with or without prior stimulation. Endothelin-1 and isoproterenol were used to activate the endothelin receptor/protein kinase C and β-adrenoreceptor/protein kinase A pathway, respectively (21, 22). Importantly, the mass accuracy of the Orbitrap mass analyzer helped to distinguish true phosphorylation sites from false assignments, and the sensitivity of the ion trap provided novel insights into the translocation of phosphatase regulatory and targeting subunits following β-adrenergic stimulation.     

Optimizing a Proteomics Platform for Urine Biomarker Discovery

Biomarker discovery approaches in urine have been hindered by concerns for reproducibility and inadequate standardization of proteomics protocols. In this study, we describe an optimized quantitative proteomics strategy for urine biomarker discovery, which is applicable to fresh or long frozen samples. We used urine from healthy controls to standardize iTRAQ (isobaric tags for relative and absolute quantitation) for variation induced by protease inhibitors, starting protein and iTRAQ label quantities, protein extraction methods, and depletion of albumin and immunoglobulin G (IgG). We observed the following: (a) Absence of protease inhibitors did not affect the number or identity of the high confidence proteins. (b) Use of less than 20 μg of protein per sample led to a significant drop in the number of identified proteins. (c) Use of as little as a quarter unit of an iTRAQ label did not affect the number or identity of the identified proteins. (d) Protein extraction by methanol precipitation led to the highest protein yields and the most reproducible spectra. (e) Depletion of albumin and IgG did not increase the number of identified proteins or deepen the proteome coverage. Applying this optimized protocol to four pairs of long frozen urine samples from diabetic Pima Indians with or without nephropathy, we observed patterns suggesting segregation of cases and controls by iTRAQ spectra. We also identified several previously reported candidate biomarkers that showed trends toward differential expression, albeit not reaching statistical significance in this small sample set.With ongoing advances in mass spectrometry (MS) and proteomics technology, proteomics analysis is progressively occupying a central position in biomarker discovery platforms. Biofluids such as urine and blood are the preferred media for proteomics analysis because of their ease of collection and extensive history of use in clinical laboratory practice. Urine, in particular, is an information-rich fluid that can be collected non-invasively and in large quantities. Many urine proteins are produced or shed in the kidney and urogenital tract (1), making urine a promising proximal source of biomarkers for diseases affecting these structures.However, proteomics-based biomarker discovery in urine faces multiple challenges. Urine proteomics is complicated by low urine protein concentration, variations in pH, and high concentrations of salts and urea or other urine components that interfere with sample processing. The urine proteome can also change with individual variables such as hydration, diurnal change, diet, and physical activity as well as variation in sample collection, processing, and storage. In addition, urine proteomics shares the usual challenges of biomarker discovery in other biofluids such as throughput, cost, and the need for a reproducible and quantitative work flow.Isotopic or isobaric labeling methods to reduce variation, increase throughput, and enable quantitative analysis have been developed to address some of these challenges. One such method, isobaric tags for relative and absolute quantitation (iTRAQ)¹ (2), combines relative and absolute peptide quantification with multiplexing ability to enable an increased throughput as well as simultaneous comparison of up to eight samples within one experimental run. Variations induced by urine sample processing have been systematically evaluated for proteomics analyses using two-dimensional gel electrophoresis (3–6), differential gel electrophoresis (7), and liquid chromatography-coupled mass spectrometry (LC-MS) (5, 8, 9). However, no systematic analyses of urine sample collection and processing have been reported for iTRAQ.Before utilizing iTRAQ-based quantitative proteomics for urine biomarker discovery, we evaluated the impact of variation in several processing steps (addition of protease inhibitors, the starting protein quantities, quantity of the iTRAQ label, protein extraction methods, and depletion of abundant proteins) on iTRAQ protein identification and quantitation. Applying this optimized biomarker discovery protocol to small quantities of long frozen urine samples from the Pima longitudinal study of diabetic nephropathy, we observed patterns suggestive of segregation of cases and controls by iTRAQ spectra. We also observed trends toward differential expression in several proteins that had been identified as putative biomarkers in previous studies. However, given the small sample size, none of these proteins retained statistical significance after multiple testing correction.     

Proteomics Characterization of Extracellular Space Components in the Human Aorta

The vascular extracellular matrix (ECM) is essential for the structural integrity of the vessel wall and also serves as a substrate for the binding and retention of secreted products of vascular cells as well as molecules coming from the circulation. Although proteomics has been previously applied to vascular tissues, few studies have specifically targeted the vascular ECM and its associated proteins. Thus, its detailed composition remains to be characterized. In this study, we describe a methodology for the extraction of extracellular proteins from human aortas and their identification by proteomics. The approach is based on (a) effective decellularization to enrich for scarce extracellular proteins, (b) successful solubilization and deglycosylation of ECM proteins, and (c) relative estimation of protein abundance using spectral counting. Our three-step extraction approach resulted in the identification of 103 extracellular proteins of which one-third have never been reported in the proteomics literature of vascular tissues. In particular, three glycoproteins (podocan, sclerostin, and agrin) were identified for the first time in human aortas at the protein level. We also identified extracellular adipocyte enhancer-binding protein 1, the cartilage glycoprotein asporin, and a previously hypothetical protein, retinal pigment epithelium (RPE) spondin. Moreover, our methodology allowed us to screen for proteolysis in the aortic samples based on the identification of proteolytic enzymes and their corresponding degradation products. For instance, we were able to detect matrix metalloproteinase-9 by mass spectrometry and relate its presence to degradation of fibronectin in a clinical specimen. We expect this proteomics methodology to further our understanding of the composition of the vascular extracellular environment, shed light on ECM remodeling and degradation, and provide insights into important pathological processes, such as plaque rupture, aneurysm formation, and restenosis.Vascular cells, in particular vascular smooth muscle cells, produce and maintain a complex meshwork of ECM.¹ The ECM is not only the scaffold for the anchorage and mobility of residing cells but also absorbs and transduces the shear and strain forces of the blood flow. It is primarily composed of elastin, collagen, proteoglycans, and glycoproteins. The elastin fibers and type I and III fibrillar collagens form a rigid network of highly cross-linked interstitial matrix. They offer elasticity (elastin) and tensile strength (collagens). Proteoglycans, because of their negative charge, attract water and confer resistance to compression. Finally, glycoproteins participate in matrix organization and are essential for cell attachment.The vascular ECM also serves as a substrate for the binding and retention of secreted, soluble proteins of vascular cells as well as molecules coming from the circulation, including lipoproteins, growth factors, cytokines, proteases, and protease inhibitors. These components are invariably associated with ECM proteins, especially proteoglycans. Together they comprise the vascular extracellular environment and are pivotal for disease processes, such as atherosclerosis and aneurysm formation (1).Although proteomics has been previously applied to vascular tissues, only one study has specifically targeted the extracellular vascular environment (2). This study was focused on the isolation of intimal proteoglycans from human carotid arteries. Moreover, most proteomics studies use whole tissue lysates, which are rich in cellular proteins that inevitably mask the identification of the less abundant proteins of the vascular extracellular environment (3–5). Thus, the composition of the vascular ECM and its associated proteins remains poorly defined. In the present study, we used morphologically normal human aortic samples to develop a method for the extraction of proteins present in the extracellular environment, including ECM proteins and proteins attached to the ECM. We had three specific aims: first, to reduce the contamination with cellular proteins, thereby increasing the chance of identifying scarce extracellular proteins; second, to efficiently solubilize and deglycosylate ECM proteins to improve their analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS); and third, to interface the nanoflow LC system to a recently developed injection device, which splits the flow from the analytical column, to allow the reanalysis of the same sample during a single LC-MS/MS run (RePlay, Advion).Our methodology provides a detailed overview of the aortic ECM and its associated proteins, many reported for the first time in proteomics analysis of the vasculature. Most importantly, this method could be adapted for use with other tissues to further our understanding of the composition of extracellular environment and ECM turnover under various disease conditions.     

Targeting A20 Decreases Glioma Stem Cell Survival and Tumor Growth

Glioblastomas are deadly cancers that display a functional cellular hierarchy maintained by self-renewing glioblastoma stem cells (GSCs). GSCs are regulated by molecular pathways distinct from the bulk tumor that may be useful therapeutic targets. We determined that A20 (TNFAIP3), a regulator of cell survival and the NF-κB pathway, is overexpressed in GSCs relative to non-stem glioblastoma cells at both the mRNA and protein levels. To determine the functional significance of A20 in GSCs, we targeted A20 expression with lentiviral-mediated delivery of short hairpin RNA (shRNA). Inhibiting A20 expression decreased GSC growth and survival through mechanisms associated with decreased cell-cycle progression and decreased phosphorylation of p65/RelA. Elevated levels of A20 in GSCs contributed to apoptotic resistance: GSCs were less susceptible to TNFα-induced cell death than matched non-stem glioma cells, but A20 knockdown sensitized GSCs to TNFα-mediated apoptosis. The decreased survival of GSCs upon A20 knockdown contributed to the reduced ability of these cells to self-renew in primary and secondary neurosphere formation assays. The tumorigenic potential of GSCs was decreased with A20 targeting, resulting in increased survival of mice bearing human glioma xenografts. In silico analysis of a glioma patient genomic database indicates that A20 overexpression and amplification is inversely correlated with survival. Together these data indicate that A20 contributes to glioma maintenance through effects on the glioma stem cell subpopulation. Although inactivating mutations in A20 in lymphoma suggest A20 can act as a tumor suppressor, similar point mutations have not been identified through glioma genomic sequencing: in fact, our data suggest A20 may function as a tumor enhancer in glioma through promotion of GSC survival. A20 anticancer therapies should therefore be viewed with caution as effects will likely differ depending on the tumor type.     

Computational Prediction and Experimental Assessment of Secreted/Surface Proteins from Mycobacterium tuberculosis H37Rv

The mycobacterial cell envelope has been implicated in the pathogenicity of tuberculosis and therefore has been a prime target for the identification and characterization of surface proteins with potential application in drug and vaccine development. In this study, the genome of Mycobacterium tuberculosis H37Rv was screened using Machine Learning tools that included feature-based predictors, general localizers and transmembrane topology predictors to identify proteins that are potentially secreted to the surface of M. tuberculosis, or to the extracellular milieu through different secretory pathways. The subcellular localization of a set of 8 hypothetically secreted/surface candidate proteins was experimentally assessed by cellular fractionation and immunoelectron microscopy (IEM) to determine the reliability of the computational methodology proposed here, using 4 secreted/surface proteins with experimental confirmation as positive controls and 2 cytoplasmic proteins as negative controls. Subcellular fractionation and IEM studies provided evidence that the candidate proteins Rv0403c, Rv3630, Rv1022, Rv0835, Rv0361 and Rv0178 are secreted either to the mycobacterial surface or to the extracellular milieu. Surface localization was also confirmed for the positive controls, whereas negative controls were located on the cytoplasm. Based on statistical learning methods, we obtained computational subcellular localization predictions that were experimentally assessed and allowed us to construct a computational protocol with experimental support that allowed us to identify a new set of secreted/surface proteins as potential vaccine candidates.     

Phylogenetic approach reveals that virus genotype largely determines HIV set-point viral load

HIV virulence, i.e. the time of progression to AIDS, varies greatly among patients. As for other rapidly evolving pathogens of humans, it is difficult to know if this variance is controlled by the genotype of the host or that of the virus because the transmission chain is usually unknown. We apply the phylogenetic comparative approach (PCA) to estimate the heritability of a trait from one infection to the next, which indicates the control of the virus genotype over this trait. The idea is to use viral RNA sequences obtained from patients infected by HIV-1 subtype B to build a phylogeny, which approximately reflects the transmission chain. Heritability is measured statistically as the propensity for patients close in the phylogeny to exhibit similar infection trait values. The approach reveals that up to half of the variance in set-point viral load, a trait associated with virulence, can be heritable. Our estimate is significant and robust to noise in the phylogeny. We also check for the consistency of our approach by showing that a trait related to drug resistance is almost entirely heritable. Finally, we show the importance of taking into account the transmission chain when estimating correlations between infection traits. The fact that HIV virulence is, at least partially, heritable from one infection to the next has clinical and epidemiological implications. The difference between earlier studies and ours comes from the quality of our dataset and from the power of the PCA, which can be applied to large datasets and accounts for within-host evolution. The PCA opens new perspectives for approaches linking clinical data and evolutionary biology because it can be extended to study other traits or other infectious diseases.     

The p53-target gene puma drives neutrophil-mediated protection against lethal bacterial sepsis

Disruption of p53/Puma-mediated apoptosis protects against lethality due to DNA damage. Here we demonstrate the unexpected requirement of the pro-apoptotic p53-target gene Puma to mount a successful innate immune response to bacterial sepsis. Puma^−/− mice rapidly died when challenged with bacteria. While the immune response in Puma^−/− mice was unchanged in cell migration, phagocytosis and bacterial killing, sites of infection accumulated large abscesses and sepsis was progressive. Blocking p53/Puma-induced apoptosis during infection caused resistance to ROS-induced cell death in the CD49d+ neutrophil subpopulation, resulting in insufficient immune resolution. This study identifies a biological role for p53/Puma apoptosis in optimizing neutrophil lifespan so as to ensure the proper clearance of bacteria and exposes a counter-balance between the innate immune response to infection and survival from DNA damage.     

Urate oxidase of Chlamydomonas reinhardii

Urate oxidase (EC 1.7.3.3) of Chlamydomonas reinhardii cells grown on purines and purine derivatives has been partially characterized. Crude enzyme preparations have a pH optimum of 9.0, require O₂ for activity, have an apparent K_m of 12 μ M for urate, and are inhibited by high concentrations of this substrate. Enzyme activity was particularly sensitive to metal ion chelating agents like cyanide, cupferron, diethyldithiocarbamate and o -phenanthroline, and to structural analogues of urate like hypoxanthine and xanthine. Chlamydomonas cells grow phototrophically on adenine, guanine, hypoxanthine, xanthine, urate, allantoin or allantoate as sole nitrogen source, indicating that in this alga the standard pathway of aerobic degradation of purines of higher plants, animals and many microorganisms operates. As deduced from experiments in vivo , urate oxidase from Chlamydomonas is repressed in the presence of ammonia or nitrate.