A proteomic approach to characterize protein shedding

Shedding (i.e. proteolysis of ectodomains of membrane proteins) plays an important pathophysiological role. In order to study the feasibility of identifying shed proteins, we analyzed serum-free media of human mammary epithelial cells by mass spectrometry following induction of shedding by the phorbol ester, 4 beta-phorbol 12-myristate 13-acetate (PMA). Different means of sample preparation, including biotinylation of cell surface proteins, isolation of glycosylated proteins, and preparation of crude protein fractions, were carried out to develop the optimal method of sample processing. The collected proteins were digested with trypsin and analyzed by reversed-phase capillary liquid chromatography interfaced to an ion-trap mass spectrometer. The resulting peptide spectra were interpreted using the program SEQUEST. Analyzing the sample containing the crude protein mixture without chemical modification or separation resulted in the greatest number of identifications, including putatively shed proteins. Overall, 45 membrane-associated proteins were identified including 22 that contain at least one transmembrane domain and 23 that indirectly associate with the extracellular surface of the plasma membrane. Of the 22 transmembrane proteins, 18 were identified by extracellular peptides providing strong evidence they originate from regulated proteolysis or shedding processes. We combined results from the different experiments and used a peptide count method to estimate changes in protein abundance. Using this approach, we identified two proteins, syndecan-4 and hepatoma-derived growth factor, whose abundances increased in media of cells treated with PMA. We also detected proteins whose abundances decreased after PMA treatment such as 78 kDa glucose-regulated protein and lactate dehydrogenase A. Further analysis using immunoblotting validated the abundance changes for syndecan-4 and 78 kDa glucose-regulated protein as a result of PMA treatment. These results demonstrate that tandem mass spectrometry can be used to identify shed proteins and to estimate changes in protein abundance.     

Proteomic approach to characterize mitochondrial complex I from plants

Mitochondrial NADH dehydrogenase complex (complex I) is by far the largest protein complex of the respiratory chain. It is best characterized for bovine mitochondria and known to consist of 45 different subunits in this species. Proteomic analyses recently allowed for the first time to systematically explore complex I from plants. The enzyme is especially large and includes numerous extra subunits. Upon subunit separation by various gel electrophoresis procedures and protein identifications by mass spectrometry, overall 47 distinct types of proteins were found to form part of Arabidopsis complex I. An additional subunit, ND4L, is present but could not be detected by the procedures employed due to its extreme biochemical properties. Seven of the 48 subunits occur in pairs of isoforms, six of which were experimentally proven. Fifteen subunits of complex I from Arabidopsis are specific for plants. Some of these resemble enzymes of known functions, e.g. carbonic anhydrases and l-galactono-1,4-lactone dehydrogenase (GLDH), which catalyzes the last step of ascorbate biosynthesis. This article aims to review proteomic data on the protein composition of complex I in plants. Furthermore, a proteomic re-evaluation on its protein constituents is presented.     

A computational approach to motion perception

In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.     

A novel approach to predicting human ingress motion using an artificial neural network

Due to the increased availability of digital human models, the need for knowing human movement is important in product design process. If the human motion is derived rapidly as design parameters change, a developer could determine the optimal parameters. For example, the optimal design of the door panel of an automobile can be obtained for a human operator to conduct the easiest ingress and egress motion. However, acquiring motion data from existing methods provides only unrealistic motion or requires a great amount of time. This not only leads to an increased time consumption for a product development, but also causes inefficiency of the overall design process. To solve such problems, this research proposes an algorithm to rapidly and accurately predict full-body human motion using an artificial neural network (ANN) and a motion database, as the design parameters are varied. To achieve this goal, this study refers to the processes behind human motor learning procedures. According to the previous research, human generate new motion based on past motion experience when they encounter new environments. Based on this principle, we constructed a motion capture database. To construct the database, motion capture experiments were performed in various environments using an optical motion capture system. To generate full-body human motion using this data, a generalized regression neural network (GRNN) was used. The proposed algorithm not only guarantees rapid and accurate results but also overcomes the ambiguity of the human motion objective function, which has been pointed out as a limitation of optimization-based research. Statistical criteria were utilized to confirm the similarity between the generated motion and actual human motion. Our research provides the basis for a rapid motion prediction algorithm that can include a variety of environmental variables. This research contributes to an increase in the usability of digital human models, and it can be applied to various research fields.     

A cell-based approach to characterize antimicrobial compounds through kinetic dose response

The rapid spread of antibiotic resistance has created a pressing need for the development of novel drug screening platforms. Herein, we report on the use of cell-based kinetic dose response curves for small molecule characterization in antibiotic discovery efforts. Kinetically monitoring bacterial growth at sub-inhibitory concentrations of antimicrobial small molecules generates unique dose response profiles. We show that clustering of profiles by growth characteristics can classify antibiotics by mechanism of action. Furthermore, changes in growth kinetics have the potential to offer insight into the mechanistic action of novel molecules and can be used to predict off-target effects generated through structure–activity relationship studies. Kinetic dose response also allows for detection of unstable compounds early in the lead development process. We propose that this kinetic approach is a rapid and cost-effective means to gather critical information on antimicrobial small molecules during the hit selection and lead development pipeline.     

A novel approach to characterize branching network: Application to termite tunnel patterns

We defined a novel “branch length similarity” (BLS) entropy, S, on a simple network consisting of a single node and branches. This simple network is referred to as “unit branching network” (UBN) because UBNs are components of larger networks. As an application of BLS entropy, we considered the characterization of termite tunnel patterns because termite tunnel patterns can be broken down into a collection of simple units consisting of a single node and branches. These simple units correspond to UBNs. To this end, in additional to the entropy, we introduced the standard deviation (σ) of the difference in S between UBNs connected by a single tunnel branch. Forty simulated tunnel patterns were created for each of two termite species, Reticulitermes flavipes (Kollar) and Coptotermes formosanus Shiraki. These patterns were projected into <S>–σ phase space in order to assess their topological properties. This approach showed that for R. flavipes, their coordinates were relatively more clustered than those of C. formosanus. This result reflected that these two species were differently constrained by emergent property resulting from simple worker's tunneling behavior. We believe that the approach proposed in this study can be a useful tool to explore termite tunnel systems, but not limited to termite system.     

A multi-differential neuromorphic approach to motion detection

This paper presents a multi-differential neuromorphic approach to motion detection. The model is based evidence for a differential operators interpretation of the properties of the cortical motion pathway. We discuss how this strategy, which provides a robust measure of speed for a range of types of image motion using a single computational mechanism, forms a useful framework in which to develop future neuromorphic motion systems. We also discuss both our approaches to developing computational motion models, and constraints in the design strategy for transferring motion models to other domains of early visual processing.     

Different immunoaffinity fractionation strategies to characterize the human plasma proteome

Plasma proteins may often serve as indicators of disease and are a rich source for biomarker discovery. However, the intrinsic large dynamic range of plasma proteins makes the analysis very challenging because a large number of low abundance proteins are often masked by a few high abundance proteins. The use of prefractionation methods, such as depletion of higher abundance proteins before protein profiling, can assist in the discovery and detection of less abundant proteins that may ultimately prove to be informative biomarkers. But there are few studies on comprehensive investigation of the proteins both in the fractions depleted and remainder. In the present study, two different immunoaffinity fractionation columns for the top-6 or the top-12 proteins in plasma were investigated and both the proteins in column-bound and flow-through fractions were subsequently analyzed. A two-dimensional peptide separation strategy, utilizing chromatographic separation techniques, combined with tandem mass spectrometry (MS/MS) was employed for proteomic analysis of the four fractions. Using the established HUPO PPP criteria, a total of 2401 unique plasma proteins were identified. The Multiple Affinity Removal System yielded 921 and 725 unique proteins from the flow-through and bound fractions, respectively, whereas the Seppro MIXED 12 column yielded identification of 897 and 730 unique proteins from the flow-through and bound fractions, respectively. When more stringent criteria, based on searching against the reversed database, were implemented, 529 unique proteins were identified from the four fractions with the confidence in peptide identification increased from 73.6% to 99%. To determine whether the presence of nontarget proteins in the immunoaffinity-bound fraction could be attributed to their interaction with high abundance proteins, co-immunoprecipitation analysis with an antibody to human plasma albumin was performed, which resulted in an identification of 40 unique proteins from the coimmunoprecipitate with the more stringent criteria. This study illustrated that combining the column-bound and flow-through fractions from immunoaffinity separation affords more extensive profiling of the protein content of human plasma. The presence of nontarget proteins in the column-bound fractions may be induced by their binding to the higher abundance proteins targeted by the immunoaffinity column.     

Proteomic approach to characterize the supramolecular organization of photosystems in higher plants

A project to investigate the supramolecular structure of photosystems was initiated, which is based on protein solubilizations by digitonin, protein separations by Blue native (BN)-polyacrylamide gel electrophoresis (PAGE) and protein identifications by mass spectrometry (MS). Under the conditions applied, nine photosystem supercomplexes could be described for chloroplasts of Arabidopsis, which have apparent molecular masses between 600 and 3200 kDa on BN gels. Identities of the supercomplexes were determined on the basis of their subunit compositions as documented by 2D BN/SDS-PAGE and BN/BN-PAGE. Two supercomplexes of 1060 and approximately 1600 kDa represent dimeric and trimeric forms of photosystem I (PSI), which include tightly bound LHCI proteins. Compared to monomeric PSI, these protein complexes are of low abundance. In contrast, photosystem II mainly forms part of dominant supercomplexes of 850, 1000, 1050 and 1300 kDa. According to our interpretation, these supercomplexes contain dimeric PSII, 1-4 LHCII trimers and additionally monomeric LHCII proteins. The 1300-kDa PSII supercomplex (containing four LHCII trimers) is partially converted into the 1000-kDa PSII supercomplex (containing two LHCII trimers) in the presence of dodecylmaltoside on 2D BN/BN gels. Analyses of peptides of the trypsinated 1300-kDa PSII supercomplex by mass spectrometry allowed to identify known subunits of the PSII core complex and additionally LHCII proteins encoded by eight different genes in Arabidopsis. Further application of this experimental approach will allow new insights into the supermolecular organization of photosystems in plants.     

Employing a combinatorial expression approach to characterize xylose utilization in Saccharomyces cerevisiae

Fermentation of xylose, a major constituent of lignocellulose, will be important for expanding sustainable biofuel production. We sought to better understand the effects of intrinsic (genotypic) and extrinsic (growth conditions) variables on optimal gene expression of the Scheffersomyces stipitis xylose utilization pathway in Saccharomyces cerevisiae by using a set of five promoters to simultaneously regulate each gene. Three-gene (xylose reductase, xylitol dehydrogenase (XDH), and xylulokinase) and eight-gene (expanded with non-oxidative pentose phosphate pathway enzymes and pyruvate kinase) promoter libraries were enriched under aerobic and anaerobic conditions or with a mutant XDH with altered cofactor usage. Through characterization of enriched strains, we observed (1) differences in promoter enrichment for the three-gene library depending on whether the pentose phosphate pathway genes were included during the aerobic enrichment; (2) the importance of selection conditions, where some aerobically-enriched strains underperform in anaerobic conditions compared to anaerobically-enriched strains; (3) improved growth rather than improved fermentation product yields for optimized strains carrying the mutant XDH compared to the wild-type XDH.     

An integrated approach using orthogonal analytical techniques to characterize heparan sulfate structure

Heparan sulfate (HS), a glycosaminoglycan present on the surface of cells, has been postulated to have important roles in driving both normal and pathological physiologies. The chemical structure and sulfation pattern (domain structure) of HS is believed to determine its biological function, to vary across tissue types, and to be modified in the context of disease. Characterization of HS requires isolation and purification of cell surface HS as a complex mixture. This process may introduce additional chemical modification of the native residues. In this study, we describe an approach towards thorough characterization of bovine kidney heparan sulfate (BKHS) that utilizes a variety of orthogonal analytical techniques (e.g. NMR, IP-RPHPLC, LC-MS). These techniques are applied to characterize this mixture at various levels including composition, fragment level, and overall chain properties. The combination of these techniques in many instances provides orthogonal views into the fine structure of HS, and in other instances provides overlapping / confirmatory information from different perspectives. Specifically, this approach enables quantitative determination of natural and modified saccharide residues in the HS chains, and identifies unusual structures. Analysis of partially digested HS chains allows for a better understanding of the domain structures within this mixture, and yields specific insights into the non-reducing end and reducing end structures of the chains. This approach outlines a useful framework that can be applied to elucidate HS structure and thereby provides means to advance understanding of its biological role and potential involvement in disease progression. In addition, the techniques described here can be applied to characterization of heparin from different sources.     

Proteome approach to characterize the methylmalonate-semialdehyde dehydrogenase that is regulated by gibberellin

Proteins regulated by gibberellin (GA) in rice were determined by proteome analysis. Proteins extracted from suspension culture cells of slr1, a constitutive GA response mutant of rice, were separated by two-dimensional polyacrylamide gel electrophoresis, and three proteins were greatly accumulated in the mutant. The most up-regulated protein was methylmalonate-semialdehyde dehydrogenase (MMSDH), and the amount of protein was 7-fold that of wild type. In this study, the function of MMSDH in rice was analyzed. MMSDH gene expression in suspension culture cells, roots, and leaf sheaths ofslr1 was higher than that in its wild-type. MMSDH expression in wild-type roots was increased by exogenous GA(3). Analyzed by in situ hybridization, MMSDH mRNA was expressed in root primordia of slr1, where cells are undergoing growth. MMSDH gene expression in the root zone of tissue differentiation was higher than in the elongation zone or meristem. Transgenic rice expressing antisense MMSDH showed that its seminal roots were thinner than that of control, and that the leaf sheath elongation was slightly inhibited compared to control. Concentrations of TCA cycle metabolites were decreased in the antisense plants as compared with the control plants, suggesting that acetyl-CoA was reduced in the antisense plants. These results suggest that one of the regulations by GA signal transduction including SLR1 is the expression of MMSDH, and that MMSDH may play a role in root development and leaf sheath elongation in rice.     

TRACE generates fluorescent human reporter cell lines to characterize epigenetic pathways

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Catalytic loop motion in human glutathione synthetase: A molecular modeling approach

Conformational changes of three flexible loops (G, A, and S) in human glutathione synthetase (hGS) arise to accommodate the substrates inside the active site. The crystal structure of hGS, a member of the ATP-grasp superfamily, has been reported only for the product-enzyme complex. To study the function of the hGS loops, molecular dynamics simulations are performed on three different conformational models: unbound enzyme, reactant-enzyme, and product-enzyme complex of hGS. The conformational changes among the three models are analyzed and the roles of the loops during the catalytic process are described. The modeled structures of hGS show that the central portions of the G- and A-loop have a double role in the reactant complex conformation: they bind the substrates and simultaneously interact with each other through an extensive network of hydrogen bonds. The present study proposes that these favorable loop-ligand and loop-loop interactions are required for opening and closing of the active site of hGS. Additionally, this research identifies important amino acid residues and explains their function within the catalytic loops of hGS.