Development of human single-chain antibodies against SARS-associated coronavirus.

The outbreak of severe acute respiratory syndrome (SARS), caused by a distinct coronavirus, in 2003 greatly threatened public health in China, Southeast Asia as well as North America. Over 1,000 patients died of the SARS virus, representing 10% of infected people. Like other coronaviruses, the SARS virus also utilizes a surface glycoprotein, namely the spike protein, to infect host cells. The spike protein of SARS virus consists of 1,255 amino acid residues and can be divided into two sub-domains, S1 and S2. The S1 domain mediates the binding of the virus to its receptor angiotensin-converting enzyme 2, which is abundantly distributed on the surface of human lung cells. The S2 domain mediates membrane fusion between the virus and the host cell. Hence two strategies can be used to block the infection of the SARS virus, either by interfering with the binding of the S1 domain to the receptor or by blocking the fusion of the virus with the cell membrane mediated by the S2 domain. Several antibodies against the S1 domain have been generated and all of them are able to neutralize the virus in vitro and in vivo using animal models. Unfortunately, point mutations have been identified in the S1 domain, so that the virus isolated in the future may not be recognized by these antibodies. As no mutation has been found in the S2 domain indicating that this region is more conserved than the S1 domain, it may be a better target for antibody binding. After predicting the immunogenicity of the epitopes of the S2 domain, we chemically synthesized two peptides and also expressed one of them using a recombinant DNA method. We screened a phage displaying library of human single-chain antibodies (ScFv) against the predicted epitopes and obtained a human ScFv which can recognize the SARS virus in vitro

Absence of association between angiotensin converting enzyme polymorphism and development of adult respiratory distress syndrome in patients with severe acute respiratory syndrome: a case control study

BACKGROUND: It has been postulated that genetic predisposition may influence the susceptibility to SARS-coronavirus infection and disease outcomes. A recent study has suggested that the deletion allele (D allele) of the angiotensin converting enzyme (ACE) gene is associated with hypoxemia in SARS patients. Moreover, the ACE D allele has been shown to be more prevalent in patients suffering from adult respiratory distress syndrome (ARDS) in a previous study. Thus, we have investigated the association between ACE insertion/deletion (I/D) polymorphism and the progression to ARDS or requirement of intensive care in SARS patients. METHOD: One hundred and forty genetically unrelated Chinese SARS patients and 326 healthy volunteers were recruited. The ACE I/D genotypes were determined by polymerase chain reaction and agarose gel electrophoresis. RESULTS: There is no significant difference in the genotypic distributions and the allelic frequencies of the ACE I/D polymorphism between the SARS patients and the healthy control subjects. Moreover, there is also no evidence that ACE I/D polymorphism is associated with the progression to ARDS or the requirement of intensive care in the SARS patients. In multivariate logistic analysis, age is the only factor associated with the development of ARDS while age and male sex are independent factors associated with the requirement of intensive care. CONCLUSION: The ACE I/D polymorphism is not directly related to increased susceptibility to SARS-coronavirus infection and is not associated with poor outcomes after SARS-coronavirus infection

Inferring a protein interaction map of Mycobacterium tuberculosis based on sequences and interologs

Background: Mycobacterium tuberculosis is an infectious bacterium posing serious threats to human health. Due to the difficulty in performing molecular biology experiments to detect protein interactions, reconstruction of a protein interaction map of M. tuberculosis by computational methods will provide crucial information to understand the biological processes in the pathogenic microorganism, as well as provide the framework upon which new therapeutic approaches can be developed.Results: In this paper, we constructed an integrated M. tuberculosis protein interaction network by machine learning and ortholog-based methods. Firstly, we built a support vector machine (SVM) method to infer the protein interactions of M. tuberculosis H37Rv by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying its protein interactions to M. tuberculosis. Moreover, the documented interactions of 14 other species were mapped to the interactome of M. tuberculosis by the interolog method. The ensemble protein interactions were validated by various functional relationships, i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. The accuracy and validation demonstrate the effectiveness and efficiency of our framework.Conclusions: A protein interaction map of M. tuberculosis is inferred from genetic codons and interologs. The prediction accuracy and numerically experimental validation demonstrate the effectiveness and efficiency of our method. Furthermore, our methods can be straightforwardly extended to infer the protein interactions of other bacterial species. © 2012 Liu et al.; licensee BioMed Central Ltd.Link_to_subscribed_fulltex

Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis

Background: Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD). Methods. In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods (partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naïve Bayes classification, neural network and support vector machine). We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNPs) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset. Results: Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine (svmRadial) and random forest (cforest) had the best prediction accuracy whereas models derived from naïve Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes (systolic and diastolic blood pressure, age, age of diagnosis, triglyceride, white blood cell count, total cholesterol, waist to hip ratio, LDL cholesterol, and alcohol intake) and 5 genetic attributes (UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models. Conclusions: Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD. © 2013 Leung et al.; licensee BioMed Central Ltd.Link_to_subscribed_fulltex

Employee perceptions of management relations as influences on job satisfaction and quit intentions

In this paper we use a relational approach to investigate how employee perceptions of their relationships with three types of managers-senior, line, and human resource managers-are related to employees' job satisfaction and intention to quit. Based on an employee survey ( n = 1,533), and manager network data ( n = 140) in ten organizations operating in Australia, we found that the extent of agreement between employees' perceptions of their relations with senior and line management was positively related to these outcome variables. In addition, we found these relationships were strengthened in organizations where HR and line managers reported high-frequency communication between the two groups. Implications of our findings are briefly canvassed