Inverse Association Between Serum Osteoprotegerin and Bone Mineral Density in Renal Transplant Recipients.

BACKGROUND: Osteoprotegerin (OPG) has pleiotropic effects on bone metabolism as well as endocrine function. Our aim was to evaluate the relationship between bone mineral density (BMD) and serum OPG concentration in renal transplant recipients. METHODS: Fasting blood samples were obtained from 69 renal transplant recipients. BMD was measured in lumbar vertebrae (L2-L4) by dual-energy X-ray absorptiometry. Eight patients (11.6%) had BMD values indicative of osteoporosis, 28 patients (40.6%) had BMD values indicative of osteopenia, and 33 patients had normal BMD values. Increased serum OPG levels (P < .001), decreased body mass index (BMI) (P = .033), and decreased body weight (P = .010) were significantly correlated with low lumbar T-score cut-off points between groups (normal, osteopenia, and osteoporosis). RESULTS: Women had significantly lower lumbar BMD values than men (P = .013). Menopause (P = .005), use of tacrolimus (P = .020), and use of cyclosporine (P = .046) were associated with lower lumbar BMD in renal transplant recipients. Univariate linear regression analysis revealed that lumbar BMD was positively correlated with height (P = .016), body weight (P = .001), and BMI (P = .015) and negatively correlated with age (P = .039) and log-OPG (P = .001). Multivariate linear regression analysis revealed that log-OPG (ß: -0.275, R(2) change = 0.154, P = .014), body weight (ß: 0.334, R(2) change = 0.073, P = .004), and age (ß: -0.285, R(2) change = 0.079, P = .008) were independent predictors of lumbar BMD values in renal transplant recipients. CONCLUSIONS: Serum OPG concentration correlated negatively with lumbar BMD values in renal transplant recipients.

Serum leptin levels positively correlate with peripheral arterial stiffness in kidney transplantation patients.

BACKGROUND: Leptin is a protein predominantly produced by adipocytes that plays a pathophysiologic role in the pathogenesis of hypertension and cardiovascular diseases. The aim of this study was to evaluate the relationship between fasting serum leptin levels and peripheral arterial stiffness among kidney transplant (KT) patients. METHODS: Fasting blood samples were obtained from 74 KT patients. Brachial-ankle pulse wave velocity (baPWV) was measured in the right or left brachial artery to the ankle segments using an automatic pulse wave analyzer (VaSera VS-1000). Plasma leptin levels were measured using a commercial enzyme-linked immunosorbent assay kit. In this study, left or right baPWV values of less than 14.0 m/s were used to define the high arterial stiffness group. RESULTS: Forty KT patients (54.1%) were defined in high arterial stiffness group. Hypertension (P < .010), diabetes (P < .010), age (P = .010), KT duration (P = .013), triglyceride levels (P = .016), systolic blood pressure (P < .001), waist circumference (P = .031), and leptin level (P < .001) were higher, whereas serum high-density lipoprotein cholesterol level (P = .030) was lower in the high arterial stiffness group compared with the low arterial stiffness group. Multivariate logistic regression analysis showed that leptin (odds ratio, 1.033; 95% CI, 1.004-1.062; P = .023), KT duration (odds ratio, 1.023; 95% CI, 1.004-1.044; P = .020), and high-density lipoprotein cholesterol level (odds ratio, 0.925; 95% CI, 0.872-0.982; P = .010) were the independent predictors of peripheral arterial stiffness in KT patients. CONCLUSIONS: Serum fasting leptin level was positively associated with peripheral arterial stiffness among KT patients.

Hypoadiponectinemia correlates with metabolic syndrome in kidney transplantation patients.

Serum adiponectin values correlate inversely with the presence of metabolic syndrome (MS). This study was undertaken to evaluate the relationship between MS and fasting serum adiponectin concentrations in 55 kidney transplantation patients. MS and its components were defined using the diagnostic criteria of the International Diabetes Federation. Thirteen subjects (23.6%) with MS showed negative correlations with adiponectin levels (P = .035), which also negatively correlated with a number of MS criteria (P = .015). Univariate linear regression analysis showed, serum adiponectin values to negatively correlate with waist circumference (r = -0.367; P = .006), body mass index (r = -0.306; P = .023), and body fat mass (r = -0.373; P = .005). Multivariate forward stepwise linear regression analysis of the significant variables revealed that body fat mass (R(2) change = 0.139; P = .035) and waist circumference (R(2) change = 0.067; P = .041) were independent predictors of fasting serum adiponectin concentrations. Thus, serum adiponectin concentrations correlated inversely with MS. Body fat mass and waist circumference were independent predictors of serum adiponectin values in kidney transplant patients.

Cleavage mechanism of the H5N1 hemagglutinin by trypsin and furin.

The cleavage property of hemagglutinin (HA) by different proteases was the prime determinant for influenza A virus pathogenicity. In order to understand the cleavage mechanism, molecular modeling tools were utilized to study the coupled model systems of the proteases, i.e., trypsin and furin and peptides of the cleavage sites specific to H5N1 and H1 HAs, which constitute models of HA precursor in complex with cleavage proteases. The peptide segments 'RERRRKKR downward arrow G' and 'SIQSR downward arrow G' from the high pathogenic H5N1 H5 and the low pathogenic H1N1 H1 cleavage sites were docking to the trypsin and furin active pockets, respectively. It was observed through the docking studies that trypsin was able to recognize and cleave both the high pathogenic and low pathogenic hemagglutinin, while furin could only cleave the high pathogenic hemagglutinin. An analysis of binding energies indicated that furin got most of its selectivity due to the interactions with P(1), P(4), and P(6), while having less interaction with P(2) and little interactions with P(3), P(5), P(7), and P(8). Some mutations of H5N1 H5 cleavage sequence fitted less well into furin and would reduce high pathogenicity of the virus. These findings hint that we should focus at the subsites P(1), P(4), and P(6) for developing drugs against H5N1 viruses.

Estimating residue evolutionary conservation by introducing von Neumann entropy and a novel gap-treating approach.

Evolutionary conservation derived from a multiple sequence alignment is a powerful indicator of the functional significance of a residue, and it can help to predict active sites, ligand-binding sites, and protein interaction interfaces. The results of the existing algorithms in identifying the residue's conservation strongly depend on the sequence alignment, making the results highly variable. Here, by introducing the amino acid similarity matrix, we propose a novel gap-treating approach by combining the evolutionary information and von Neumann entropies to compute the residue conservation scores. It is indicated through a series of tested results that the new approach is quite encouraging and promising and may become a useful tool in complementing the existing methods.

Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction.

With the avalanche of newly-found protein sequences emerging in the post genomic era, it is highly desirable to develop an automated method for fast and reliably identifying their subcellular locations because knowledge thus obtained can provide key clues for revealing their functions and understanding how they interact with each other in cellular networking. However, predicting subcellular location of eukaryotic proteins is a challenging problem, particularly when unknown query proteins do not have significant homology to proteins of known subcellular locations and when more locations need to be covered. To cope with the challenge, protein samples are formulated by hybridizing the information derived from the gene ontology database and amphiphilic pseudo amino acid composition. Based on such a representation, a novel ensemble hybridization classifier was developed by fusing many basic individual classifiers through a voting system. Each of these basic classifiers was engineered by the KNN (K-Nearest Neighbor) principle. As a demonstration, a new benchmark dataset was constructed that covers the following 18 localizations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cyanelle, (5) cytoplasm, (6) cytoskeleton, (7) endoplasmic reticulum, (8) extracell, (9) Golgi apparatus, (10) hydrogenosome, (11) lysosome, (12) mitochondria, (13) nucleus, (14) peroxisome, (15) plasma membrane, (16) plastid, (17) spindle pole body, and (18) vacuole. To avoid the homology bias, none of the proteins included has > or =25% sequence identity to any other in a same subcellular location. The overall success rates thus obtained via the 5-fold and jackknife cross-validation tests were 81.6 and 80.3%, respectively, which were 40-50% higher than those performed by the other existing methods on the same strict dataset. The powerful predictor, named "Euk-PLoc", is available as a web-server at http://202.120.37.186/bioinf/euk . Furthermore, to support the need of people working in the relevant areas, a downloadable file will be provided at the same website to list the results predicted by Euk-PLoc for all eukaryotic protein entries (excluding fragments) in Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The large-scale results will be updated twice a year to include the new entries of eukaryotic proteins and reflect the continuous development of Euk-PLoc.

Prediction of linear B-cell epitopes using amino acid pair antigenicity scale.

Identification of antigenic sites on proteins is of vital importance for developing synthetic peptide vaccines, immunodiagnostic tests and antibody production. Currently, most of the prediction algorithms rely on amino acid propensity scales using a sliding window approach. These methods are oversimplified and yield poor predicted results in practice. In this paper, a novel scale, called the amino acid pair (AAP) antigenicity scale, is proposed that is based on the finding that B-cell epitopes favor particular AAPs. It is demonstrated that, using SVM (support vector machine) classifier, the AAP antigenicity scale approach has much better performance than the existing scales based on the single amino acid propensity. The AAP antigenicity scale can reflect some special sequence-coupled feature in the B-cell epitopes, which is the essence why the new approach is superior to the existing ones. It is anticipated that with the continuous increase of the known epitope data, the power of the AAP antigenicity scale approach will be further enhanced.

Using ensemble classifier to identify membrane protein types.

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.

Virtual screening for finding natural inhibitor against cathepsin-L for SARS therapy.

Recently Simmons et al. reported a new mechanism for SARS virus entry into target cells, where MDL28170 was identified as an efficient inhibitor of CTSL-meditated substrate cleavage with IC(50) of 2.5 nmol/l. Based on the molecule fingerprint searching method, 11 natural molecules were found in the Traditional Chinese Medicines Database (TCMD). Molecular simulation indicates that the MOL376 (a compound derived from a Chinese medicine herb with the therapeutic efficacy on the human body such as relieving cough, removing the phlegm, and relieving asthma) has not only the highest binding energy with the receptor but also the good match in geometric conformation. It was observed through docking studies that the van der Waals interactions made substantial contributions to the affinity, and that the receptor active pocket was too large for MDL21870 but more suitable for MOL736. Accordingly, MOL736 might possibly become a promising lead compound for CTSL inhibition for SARS therapy.

Predicting secretory protein signal sequence cleavage sites by fusing the marks of global alignments.

A newly synthesized secretory protein in cells bears a special sequence, called signal peptide or sequence, which plays the role of "address tag" in guiding the protein to wherever it is needed. Such a unique function of signal sequences has stimulated novel strategies for drug design or reprogramming cells for gene therapy. To realize these new ideas and plans, however, it is important to develop an automated method for fast and accurately identifying the signal sequences or their cleavage sites. In this paper, a new method is developed for predicting the signal sequence of a query secretory protein by fusing the results from a series of global alignments through a voting system. The very high success rates thus obtained suggest that the novel approach is very promising, and that the new method may become a useful vehicle in identifying signal sequence, or at least serve as a complementary tool to the existing algorithms of this field.

Anti-SARS drug screening by molecular docking.

Starting from a collection of 1386 druggable compounds obtained from the 3D pharmacophore search, we performed a similarity search to narrow down the scope of docking studies. The template molecule is KZ7088 (Chou et al., 2003, Biochem Biophys Res Commun 308: 148-151). The MDL MACCS keys were used to fingerprint the molecules. The Tanimoto coefficient is taken as the metric to compare fingerprints. If the similarity threshold was 0.8, a set of 50 unique hits and 103 conformers were retrieved as a result of similarity search. The AutoDock 3.011 was used to carry out molecular docking of 50 ligands to their macromolecular protein receptors. Three compounds, i.e., C(28)H(34)O(4)N(7)Cl, C(21)H(36)O(5)N(6), and C(21)H(36)O(5)N(6), were found that may be promising candidates for further investigation. The main feature shared by these three potential inhibitors as well as the information of the involved side chains of SARS Cov Mpro may provide useful insights for the development of potent inhibitors against SARS enzyme.

Using cellular automata images and pseudo amino acid composition to predict protein subcellular location.

The avalanche of newly found protein sequences in the post-genomic era has motivated and challenged us to develop an automated method that can rapidly and accurately predict the localization of an uncharacterized protein in cells because the knowledge thus obtained can greatly speed up the process in finding its biological functions. However, it is very difficult to establish such a desired predictor by acquiring the key statistical information buried in a pile of extremely complicated and highly variable sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246-255), the approach of cellular automata image is introduced to cope with this problem. Many important features, which are originally hidden in the long amino acid sequences, can be clearly displayed through their cellular automata images. One of the remarkable merits by doing so is that many image recognition tools can be straightforwardly applied to the target aimed here. High success rates were observed through the self-consistency, jackknife, and independent dataset tests, respectively.

Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter.

With the avalanche of new protein sequences we are facing in the post-genomic era, it is vitally important to develop an automated method for fast and accurately determining the subcellular location of uncharacterized proteins. In this article, based on the concept of pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43: 246-255), three pseudo amino acid components are introduced via Lyapunov index, Bessel function, Chebyshev filter that can be more efficiently used to deal with the chaos and complexity in protein sequences, leading to a higher success rate in predicting protein subcellular location.

Using string kernel to predict signal peptide cleavage site based on subsite coupling model.

Owing to the importance of signal peptides for studying the molecular mechanisms of genetic diseases, reprogramming cells for gene therapy, and finding new drugs for healing a specific defect, it is in great demand to develop a fast and accurate method to identify the signal peptides. Introduction of the so-called {-3,-1, +1} coupling model (Chou, K. C.: Protein Engineering, 2001, 14-2, 75-79) has made it possible to take into account the coupling effect among some key subsites and hence can significantly enhance the prediction quality of peptide cleavage site. Based on the subsite coupling model, a kind of string kernels for protein sequence is introduced. Integrating the biologically relevant prior knowledge, the constructed string kernels can thus be used by any kernel-based method. A Support vector machines (SVM) is thus built to predict the cleavage site of signal peptides from the protein sequences. The current approach is compared with the classical weight matrix method. At small false positive ratios, our method outperforms the classical weight matrix method, indicating the current approach may at least serve as a powerful complemental tool to other existing methods for predicting the signal peptide cleavage site. The software that generated the results reported in this paper is available upon requirement, and will appear at http://www.pami.sjtu.edu.cn/wm.

Using cellular automata to generate image representation for biological sequences.

A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419-424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their "fingerprint". It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246-255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.

Vibrational properties of CO at the Pt(111)-solution interface: the anomalous Stark-tuning slope.

Vibrational properties of CO have been studied on Pt(111) in acid and alkaline electrolytes by synchronous measurements of CO oxidation current (0.5 mV/s) and IRAS spectra (one spectrum for every 1 mV). We found that in acid solutions the frequency-tuning rate (dnu(CO)/dE) as well as the potential-dependent bandwidth (dDeltanu1/2/dE) deviates from expected linear relationships. This unusual potential-dependent behavior is interpreted in terms of compression/dissipation of CO islands during the CO oxidation, engendered by competitive adsorption between inactive anions from a supporting electrolyte and the reactive OH species.

Using complexity measure factor to predict protein subcellular location.

Recent advances in large-scale genome sequencing have led to the rapid accumulation of amino acid sequences of proteins whose functions are unknown. Because the functions of these proteins are closely correlated with their subcellular localizations, it is vitally important to develop an automated method as a high-throughput tool to timely identify their subcellular location. Based on the concept of the pseudo amino acid composition by which a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers (Chou, K. C., Proteins: Structure, Function, and Genetics, 2001, 43: 246-255), the complexity measure approach is introduced. The advantage by incorporating the complexity measure factor as one of the pseudo amino acid components for a protein is that it can more effectively reflect its overall sequence-order feature than the conventional correlation factors. With such a formulation frame to represent the samples of protein sequences, the covariant-discriminant predictor (Chou, K. C. and Elrod, D. W., Protein Engineering, 1999, 12: 107-118) was adopted to conduct prediction. High success rates were obtained by both the jackknife cross-validation test and independent dataset test, suggesting that introduction of the concept of the complexity measure into prediction of protein subcellular location is quite promising, and might also hold a great potential as a useful vehicle for the other areas of molecular biology.

Effect of human dietary exposure levels of genistein during gestation and lactation on long-term reproductive development and sperm quality in mice.

The objective of the present study was to determine the long-term reproductive effects of gestational and lactational exposure (0, 0.1, 0.5, 2.5 and 10 mg/kg/day) to genistein on male mice at levels comparable to or greater than human dietary exposures. Testicular growth, sperm count and motility, and sperm fertilizing ability in vitro was assessed in male offspring on postnatal days (PND) 105 and 315. Selected genes were also examined by real-time PCR to determine whether genistein caused changes in gene expression similar to those previously observed with diethylstilbestrol (DES). No significant treatment-related effects on male offspring body weight, anogenital distance, seminal vesicle weight or testis weight were observed. There were also no significant effects on sperm count, the percent of motile sperm or the number of motile sperm at any age. The in vitro fertilizing ability of epididymal sperm was increased significantly in the high-dose group approximately 17% (P < 0.001) on PND 105 and 315. The results indicate that developmental exposure of mice to genistein at human exposure levels does not induce adverse effects on sperm quality or changes in testicular gene expression similar to DES.

[W206R]-procaspase 3: an inactivatable substrate for caspase 8.

We report here the cloning and high-level expression of a soluble proform of human caspase 3 (Ser(24)-H(277)) engineered to contain a short stretch of N-terminal sequence (MTISDSPREQD) from the prosegment of procaspase 8 and a C-terminal heptahistidine tag. The precursor protein isolated from extracts of recombinant Escherichia coli by immobilized metal-ion affinity chromatography was predominantly unprocessed and migrated as a 32-kDa polypeptide on sodium dodecyl sulfate-polyacrylamide gels. Incubation of this protein with recombinant human caspase 8 produced fragments characteristic of the properly processed caspase 3, but the product was inactive. Amino-terminal sequence analysis of the caspase 3 polypeptides proved that caspase 8 had specifically cleaved the Asp(175)-Ser(176) bond to yield the expected p18 and p12 subunits, with partial cleavage at the Asp(28)-Ser(29) bond to release the prosegment. The lack of caspase 3 activity was found to be the result of a fortuitous mutation in which Trp(206) in the S4 subsite was replaced by arginine (W206R). This mutant procaspase 3, which we call m-pro3, serves as a useful reagent with which to test the efficacy of caspase 8 inhibitors in blocking processing of the natural polypeptide substrate of this enzyme and may be valuable as a source of "proenzyme" for crystallographic analysis.