982 resultados para Alpha-Macroglobulins
Resumo:
Objective and Design: To determine the alpha-2-macroglobulin (alpha2M) levels in mice during acute and chronic inflammatory responses. Materials and Methods: Inflammation was induced by one of the following stimuli: carrageenin, zymosan, lipopolysacharide, thioglycollate, bacilli Calmette Guerin, PPD (in pre-immunized and non-immunized animals) and tumor cells. The concentration of alpha2M was determined in plasma or peritoneal liquid by electroimmunoassay. Results: In all the treatments employed, the plasma levels of alpha2M were higher than in untreated animals. This increase varied from 9%, 24 h after injection up a maximum of 66% 72 h post-injection. When compared to animals injected only with saline, the increases were significant 48 h after treatment with either zymosan or LPS, and 72 h after treatment with either thioglycollate or carrageenin. Treatment with BCG triggers an increase in alpha2M levels after 24 h (18.60%) and 48 h (27.90%). Immunized mice presented higher levels of this protein than non-immunized animals after challenge with PPD. The growth of Ehrlich tumor cells in the peritoneal cavity was directly correlated with the local levels of alpha2M which increased 3.5 fold, 10 days after injection. Conclusions: These results strongly indicate that in mice, the concentration of alpha2M can increase during acute and chronic inflammatory reactions with kinetics dependent on the particular kind of inflammatory agent.
Resumo:
Thrombocytopenia and platelet dysfunction occur in patients bitten by Bothrops sp snakes in Latin America. An experimental model was developed in mice to study the effects of B. asper venom in platelet numbers and function. Intravenous administration of this venom induces rapid and prominent thrombocytopenia and ex vivo platelet hypoaggregation. The drop in platelet numbers was primarily due to aspercetin, a protein of the C-type lectin family which induces von Willebrand factor-mediated platelet aggregation/agglutination. In addition, the effect of class P-III hemorrhagic metalloproteinases on the microvessel wall also contributes to thrombocytopenia since jararhagin, a P-III metalloproteinase, reduced platelet counts. Hypoaggregation was associated with the action of procoagulant and defibrin(ogen)ating proteinases jararacussin-1 (a thrombin-like serine proteinase) and basparin A (a prothrombin activating metalloproteinase). At the doses which induced hypoaggregation, these enzymes caused defibrin(ogen)ation, increments in fibrin(ogen) degradation products and D-dimer and prolongation of the bleeding time. Incubation of B. asper venom with batimastat and α 2-macroglobulin abrogated the hypoaggregating activity, confirming the role of venom proteinases in this effect. Neither aspercetin nor the defibrin(ogen)ating and hypoaggregating components induced hemorrhage upon intravenous injection. However, aspercetin, but not the thrombin-like or the prothrombin-activating proteinases, potentiated the hemorrhagic activity of two hemorrhagic metalloproteinases in the lungs. © 2005 Schattauer GmbH, Stuttgart.
Resumo:
Snake venom proteomes/peptidomes are highly complex and maintenance of their integrity within the gland lumen is crucial for the expression of toxin activities. There has been considerable progress in the field of venom proteomics, however, peptidomics does not progress as fast, because of the lack of comprehensive venom sequence databases for analysis of MS data. Therefore, in many cases venom peptides have to be sequenced manually by MS/MS analysis or Edman degradation. This is critical for rare snake species, as is the case of Bothrops cotiara (BC) and B. fonsecai (BF), which are regarded as near threatened with extinction. In this study we conducted a comprehensive analysis of the venom peptidomes of BC, BF, and B. jararaca (BJ) using a combination of solid-phase extraction and reversed-phase HPLC to fractionate the peptides, followed by nano-liquid chromatography-tandem MS (LC-MS/MS) or direct infusion electrospray ionization-(ESI)-MS/MS or MALDI-MS/MS analyses. We detected marked differences in the venom peptidomes and identified peptides ranging from 7 to 39 residues in length by de novo sequencing. Forty-four unique sequences were manually identified, out of which 30 are new peptides, including 17 bradykinin-potentiating peptides, three poly-histidine-poly-glycine peptides and interestingly, 10 L-amino acid oxidase fragments. Some of the new bradykinin-potentiating peptides display significant bradykinin potentiating activity. Automated database search revealed fragments from several toxins in the peptidomes, mainly from L-amino acid oxidase, and allowed the determination of the peptide bond specificity of proteinases and amino acid occurrences for the P4-P4' sites. We also demonstrate that the venom lyophilization/resolubilization process greatly increases the complexity of the peptidome because of the imbalance caused to the venom proteome and the consequent activity of proteinases on venom components. The use of proteinase inhibitors clearly showed different outcomes in the peptidome characterization and suggested that degradomic-peptidomic analysis of snake venoms is highly sensitive to the conditions of sampling procedures. Molecular & Cellular Proteomics 11: 10.1074/mcp.M112.019331, 1245-1262, 2012.
Resumo:
Phenol oxidase (PO) was isolated as a proenzyme (pro-phenol oxidase, pro-PO) from the hemolymph of Manduca sexta larvae and purified to homogeneity. Pro-PO exhibits a M(r) of 130,000 on gel filtration and two bands with an apparent M(r) of approximately 100,000 on SDS/PAGE, as well as size-exclusion HPLC. Activation of pro-PO was achieved either by specific proteolysis by a cuticular protease or by the detergent cetylpyridinium chloride at a concentration below the critical micellar concentration. A cDNA clone for M. sexta pro-PO was obtained from a larval hemocyte cDNA library. The clone encodes a polypeptide of approximately 80,000 Da that contains two copper-binding sites and shows high sequence similarity to POs, hemocyanins, and storage proteins of arthropods. The M. Sexta pro-PO, together with other arthropod pro-POs, contains a short stretch of amino acids with sequence similarity to the thiol ester region of alpha-macroglobulins and complement proteins C3 and C4.
Resumo:
Many benthic marine invertebrates, like barnacles, have a planktonic larval stage whose primary purpose is dispersal. How these species colonize suitable substrata is fundamental to understanding their evolution, population biology, and wider community dynamics. Unlike larval dispersal, settlement occurs on a relatively small spatial scale and involves larval behavior in response to physical and chemical characteristics of the substratum. Biogenic chemical cues have been implicated in this process. Their identification, however, has proven challenging, no more so than for the chemical basis of barnacle gregariousness, which was first described >50 years ago. We now report that a biological cue to gregarious settlement, the settlement-inducing protein complex (SIPC), of the major fouling barnacle Balanus amphitrite is a previously undescribed glycoprotein. The SIPC shares a 30% sequence homology with the thioester-containing family of proteins that includes the alpha sub(2)-macroglobulins. The cDNA (5.2 kb) of the SIPC encodes a protein precursor comprising 1,547 aa with a 17-residue signal peptide region. A number of structural characteristics and the absence of a thioester bond in the SIPC suggest that this molecule is a previously undescribed protein that may have evolved by duplication from an ancestral alpha sub(2)-macroglobulin gene. Although the SIPC is regarded as an adult cue that is recognized by the cyprid at settlement, it is also expressed in the juvenile and in larvae, where it may function in larva-larva settlement interactions.
Resumo:
The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.