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Objectives: To investigate the incidence of active tuberculosis (TB) among COPD patients using fluticasone/salmeterol or budesonide/formoterol, and to identify any differences between these two groups of patients.Methods: The study enrolled COPD patients from Taiwan NHIRD who received treatment with fluticasone/salmeterol or budesonide/formoterol for > 90 days between 2004 and 2011. The incidence of active TB was the primary outcome.Results: Among the intention-to-treat population prior to matching, the incidence rates of active TB were 0.94 and 0.61% in the fluticasone/salmeterol and budesonide/formoterol groups, respectively. After matching, the fluticasone/salmeterol group had significantly higher rates of active TB (adjusted HR, 1.41, 95% CI, 1.17-1.70) compared with the budesonide/formoterol group. The significant difference between these two groups remained after a competing risk analysis (HR, 1.45, 95% CI, 1.21-1.74). Following propensity score matching, the fluticasone/salmeterol group had significantly higher rates of active TB compared with the budesonide/formoterol group (adjusted HR, 1.45, 95% CI, 1.14-1.85). A similar trend was observed after a competing risk analysis (HR, 1.44, 95% CI, 1.19-1.75). A higher risk of active TB was observed in the fluticasone/salmeterol group compared with the budesonide/formoterol group across all subgroups, but some differences did not reach statistical significance.Conclusion: Fluticasone/salmeterol carried a higher risk of active TB compared with budesonide/formoterol among COPD patients. [ABSTRACT FROM AUTHOR]

Background: MicroRNAs are very small non-coding RNAs that interact with microRNA recognition elements (MREs) on their target messenger RNAs. Varying the concentration of a given microRNA may influence the expression of many target proteins. Yet, the expression of a specific target protein can be fine-tuned by alternative cleavage and polyadenylation to the corresponding mRNA., Results: This study showed that alternative splicing of mRNA is a fine-tuning mechanism in the cellular regulatory network. The splicing-regulated MREs are often highly repressive MREs. This phenomenon was observed not only in the hsa-miR-148a-regulated DNMT3B gene, but also in many target genes regulated by hsa-miR-124, hsa-miR-1, and hsa-miR-181a. When a gene contains multiple MREs in transcripts, such as the VEGF gene, the splicing-regulated MREs are again the highly repressive MREs. Approximately one-third of the analysable human MREs in MiRTarBase and TarBase can potentially perform the splicing-regulated fine-tuning. Interestingly, the high (+30%) repression ratios observed in most of these splicing-regulated MREs indicate associations with functions. For example, the MRE-free transcripts of many oncogenes, such as N-RAS and others may escape microRNA-mediated suppression in cancer tissues., Conclusions: This fine-tuning mechanism revealed associations with highly repressive MRE. Since high-repression MREs are involved in many important biological phenomena, the described association implies that splicing-regulated MREs are functional. A possible application of this observed association is in distinguishing functionally relevant MREs from predicted MREs.

Background: In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations., Results: This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis., Conclusions: This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.

Background: Genome-wide disease-gene finding approaches may sometimes provide us with a long list of candidate genes. Since using pure experimental approaches to verify all candidates could be expensive, a number of network-based methods have been developed to prioritize candidates. Such tools usually have a set of parameters pre-trained using available network data. This means that re-training network-based tools may be required when existing biological networks are updated or when networks from different sources are to be tried., Results: We developed a parameter-free method, interconnectedness (ICN), to rank candidate genes by assessing the closeness of them to known disease genes in a network. ICN was tested using 1,993 known disease-gene associations and achieved a success rate of ~44% using a protein-protein interaction network under a test scenario of simulated linkage analysis. This performance is comparable with those of other well-known methods and ICN outperforms other methods when a candidate disease gene is not directly linked to known disease genes in a network. Interestingly, we show that a combined scoring strategy could enable ICN to achieve an even better performance (~50%) than other methods used alone., Conclusions: ICN, a user-friendly method, can well complement other network-based methods in the context of prioritizing candidate disease genes.

Background: Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression., Results: In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production systems to improve the chance of high soluble protein production., Conclusion: In this paper, we show that a machine learning approach to the prediction of the efficacy of a vector for a target protein in a recombinant protein production system is promising and may compliment traditional knowledge-driven study of the efficacy. We will release our program to share with other labs in the public domain when this paper is published.

Background: Schizophrenia is a complex disease with multiple factors contributing to its pathogenesis. In addition to environmental factors, genetic factors may also increase susceptibility. In other words, schizophrenia is a highly heritable disease. Some candidate genes have been deduced on the basis of their known function with others found on the basis of chromosomal location. Individuals with multiple candidate genes may have increased risk. However it is not clear what kind of gene combinations may produce the disease phenotype. Their collective effect remains to be studied., Results: Most pathways except metabolic pathways are rich in protein-protein interactions (PPIs). Thus, the PPI network contains pathway information, even though the upstream-downstream relation of PPI is yet to be explored. Here we have constructed a PPI sub-network by extracting the nearest neighbour of the 36 reported candidate genes described in the literature. Although these candidate genes were discovered by different approaches, most of the proteins formed a cluster. Two major protein interaction modules were identified on the basis of the pairwise distance among the proteins in this sub-network. The large and small clusters might play roles in synaptic transmission and signal transduction, respectively, based on gene ontology annotation. The protein interactions in the synaptic transmission cluster were used to explain the interaction between the NRG1 and CACNG2 genes, which was found by both linkage and association studies. This working hypothesis is supported by the co-expression analysis based on public microarray gene expression., Conclusion: On the basis of the protein interaction network, it appears that the NRG1-triggered NMDAR protein internalization and the CACNG2 mediated AMPA receptor recruiting may act together in the glutamatergic signalling process. Since both the NMDA and AMPA receptors are calcium channels, this process may regulate the influx of Ca2+. Reducing the cation influx might be one of the disease mechanisms for schizophrenia. This PPI network analysis approach combined with the support from co-expression analysis may provide an efficient way to propose pathogenetic mechanisms for various highly heritable diseases.

Background: The mosquito, Armigeres subalbatus, mounts a distinctively robust innate immune response when infected with the nematode Brugia malayi, a causative agent of lymphatic filariasis. In order to mine the transcriptome for new insight into the cascade of events that takes place in response to infection in this mosquito, 6 cDNA libraries were generated from tissues of adult female mosquitoes subjected to immune-response activation treatments that lead to well-characterized responses, and from aging, naïve mosquitoes. Expressed sequence tags (ESTs) from each library were produced, annotated, and subjected to comparative analyses., Results: Six libraries were constructed and used to generate 44,940 expressed sequence tags, of which 38,079 passed quality filters to be included in the annotation project and subsequent analyses. All of these sequences were collapsed into clusters resulting in 8,020 unique sequence clusters or singletons. EST clusters were annotated and curated manually within ASAP (A Systematic Annotation Package for Community Analysis of Genomes) web portal according to BLAST results from comparisons to Genbank, and the Anopheles gambiae and Drosophila melanogaster genome projects., Conclusion: The resulting dataset is the first of its kind for this mosquito vector and provides a basis for future studies of mosquito vectors regarding the cascade of events that occurs in response to infection, and thereby providing insight into vector competence and innate immunity.