ASMPKS: an analysis system for modular polyketide synthases

<p>Abstract</p> <p>Background</p> <p>Polyketides are secondary metabolites of microorganisms with diverse biological activities, including pharmacological functions such as antibiotic, antitumor and agrochemical properties. Polyketides are synthesized by serialized reactions of a set of enzymes called polyketide synthase(PKS)s, which coordinate the elongation of carbon skeletons by the stepwise condensation of short carbon precursors. Due to their importance as drugs, the volume of data on polyketides is rapidly increasing and creating a need for computational analysis methods for efficient polyketide research. Moreover, the increasing use of genetic engineering to research new kinds of polyketides requires genome wide analysis.</p> <p>Results</p> <p>We describe a system named ASMPKS (Analysis System for Modular Polyketide Synthesis) for computational analysis of PKSs against genome sequences. It also provides overall management of information on modular PKS, including polyketide database construction, new PKS assembly, and chain visualization. ASMPKS operates on a web interface to construct the database and to analyze PKSs, allowing polyketide researchers to add their data to this database and to use it easily. In addition, the ASMPKS can predict functional modules for a protein sequence submitted by users, estimate the chemical composition of a polyketide synthesized from the modules, and display the carbon chain structure on the web interface.</p> <p>Conclusion</p> <p>ASMPKS has powerful computation features to aid modular PKS research. As various factors, such as starter units and post-processing, are related to polyketide biosynthesis, ASMPKS will be improved through further development for study of the factors.</p

Understanding and improving error-correcting output coding

Error-correcting output coding (ECOC) is a method for converting a k-classsupervised learning problem into a large number L of two-class supervised learningproblems and then combining the results of these L evaluations. Previous researchhas shown that ECOC can dramatically improve the classi cation accuracy of supervisedlearning algorithms that learn to classify data points into one of k 2 classes.An investigation of why the ECOC technique works, particularly when employedwith decision tree learning algorithms, is presented.It is shown that the ECOC method is a compact form of voting amongmultiple hypotheses. The success of the voting depends on that the errors committedby each of the L learned binary functions are substantially uncorrelated.By employing the statistical notions of bias and variance, the generalizationerrors of ECOC are decomposed into bias and variance errors. Like any votingmethod, ECOC reduces variance errors. However, unlike homogeneous voting, whichsimply combines multiple runs of the same learning algorithm, ECOC can alsoreduce bias errors. It is shown that the bias errors in the individual functions are uncorrelated and that this results from non-local behavior of the learning algorithmin splitting the feature space.ECOC is also extended to provide class probability information. The problemof computing these class probabilities can be formulated as an over-constrained systemof linear equations. Least squares methods are applied to solve these equations.Accuracy of the posterior probabilities is demonstrated with overlapping classes anda simple reject option task

Breast Cancer Diagnosis Using a Microfluidic Multiplexed Immunohistochemistry Platform

BACKGROUND: Biomarkers play a key role in risk assessment, assessing treatment response, and detecting recurrence and the investigation of multiple biomarkers may also prove useful in accurate prediction and prognosis of cancers. Immunohistochemistry (IHC) has been a major diagnostic tool to identify therapeutic biomarkers and to subclassify breast cancer patients. However, there is no suitable IHC platform for multiplex assay toward personalized cancer therapy. Here, we report a microfluidics-based multiplexed IHC (MMIHC) platform that significantly improves IHC performance in reduction of time and tissue consumption, quantification, consistency, sensitivity, specificity and cost-effectiveness. METHODOLOGY/PRINCIPAL FINDINGS: By creating a simple and robust interface between the device and human breast tissue samples, we not only applied conventional thin-section tissues into on-chip without any additional modification process, but also attained perfect fluid control for various solutions, without any leakage, bubble formation, or cross-contamination. Four biomarkers, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), progesterone receptor (PR) and Ki-67, were examined simultaneously on breast cancer cells and human breast cancer tissues. The MMIHC method improved immunoreaction, reducing time and reagent consumption. Moreover, it showed the availability of semi-quantitative analysis by comparing Western blot. Concordance study proved strong consensus between conventional whole-section analysis and MMIHC (n = 105, lowest Kendall's coefficient of concordance, 0.90). To demonstrate the suitability of MMIHC for scarce samples, it was also applied successfully to tissues from needle biopsies. CONCLUSIONS/SIGNIFICANCE: The microfluidic system, for the first time, was successfully applied to human clinical tissue samples and histopathological diagnosis was realized for breast cancers. Our results showing substantial agreement indicate that several cancer-related proteins can be simultaneously investigated on a single tumor section, giving clear advantages and technical advances over standard immunohistochemical method. This novel concept will enable histopathological diagnosis using numerous specific biomarkers at a time even for small-sized specimens, thus facilitating the individualization of cancer therapy

Optimizing linear discriminant error correcting output codes using particle swarm optimization

Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multi-class problem. In this paper we consider the optimization of the Linear Discriminant Error Correcting Output Codes framework using Particle Swarm Optimization. In particular, we apply the Particle Swarm Optimization algorithm in order to optimally select the free parameters that control the split of the initial problem's classes into sub-classes. Moreover, by using the Support Vector Machine as classifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance. © 2011 Springer-Verlag

Kikuchi-Fujimoto disease mimicking malignant lymphoma with 2-[F]fluoro-2-deoxy-D-glucose PET/CT in children

PurposeKikuchi-Fujimoto disease (KFD) is a benign disease, which is characterized by a cervical lymphadenopathy with fever, and it often mimics malignant lymphoma (ML). 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) is a powerful imaging modality for the diagnosis, staging and monitoring of ML, with the limitations including the nonspecific FDG uptake in infectious or inflammatory processes. This study compared clinical manifestations and PET/CT findings between KFD and ML patients.MethodsWe retrospectively reviewed the medical records of 23 patients with KFD and 33 patients with ML, diagnosed histopathologically, between January 2000 and May 2013 at the Department of Pediatrics, Yeungnam University Medical Center. Among them, we analyzed the clinical manifestations, laboratory findings and characteristics, and the amount of 18F-FDG uptake between 8 KFD and 9 ML patients who had 18F-FDG PET/CT.ResultsThe 18F-FDG PET/CT maximum standardized uptake values (SUVmax) ranged from 8.3 to 22.5 (mean, 12.0) in KFDs, and from 5.8 to 34.3 (mean, 15.9) in MLs. There were no significant differences in SUVmax between KFDs and MLs. 18F-FDG PET/CT with ML patients showed hot uptakes in the extranodal organs, such as bone marrow, small bowel, thymus, kidney, orbit and pleura. However, none of the KFD cases showed extranodal uptake (P<0.001). 18F-FDG PET/CT findings of KFD with nodal involvement only were indistinguishable from those of ML.ConclusionPatients who had extranodal involvement on PET/CT were more likely to have malignancy than KFD

Synergistic Autophagy Effect of miR-212-3p in Zoledronic Acid-Treated In Vitro and Orthotopic In Vivo Models and in Patient-Derived Osteosarcoma Cells

Osteosarcoma (OS) originates from osteoid bone tissues and is prone to metastasis, resulting in a high mortality rate. Although several treatments are available for OS, an effective cure does not exist for most patients with advanced OS. Zoledronic acid (ZOL) is a third-generation bisphosphonate that inhibits osteoclast-mediated bone resorption and has shown efficacy in treating bone metastases in patients with various types of solid tumors. Here, we sought to clarify the mechanisms through which ZOL inhibits OS cell proliferation. ZOL treatment inhibited OS cell proliferation, viability, and colony formation. Autophagy inhibition by RNA interference against Beclin-1 or ATG5 inhibited ZOL-induced OS cell death. ZOL induced autophagy by repressing the protein kinase B/mammalian target of rapamycin/p70S6 kinase pathway and extracellular signal-regulated kinase signaling-dependent autophagy in OS cell lines and patient-derived OS cells. Microarrays of miRNA showed that ZOL increased the levels of miR-212-3p, which is known to play an important role in autophagy, in OS in vitro and in vivo systems. Collectively, our data provided mechanistic insight into how increased miR-212-3p through ZOL treatment induces autophagy synergistically in OS cells, providing a preclinical rationale for conducting a broad-scale clinical evaluation of ZOL + miR-212-3p in treating OS

Mitochondrial DNA Haplogroup Analysis Reveals no Association between the Common Genetic Lineages and Prostate Cancer in the Korean Population

Mitochondrial DNA (mtDNA) variation has recently been suggested to have an association with various cancers, including prostate cancer risk, in human populations. Since mtDNA is haploid and lacks recombination, specific mutations in the mtDNA genome associated with human diseases arise and remain in particular genetic backgrounds referred to as haplogroups. To assess the possible contribution of mtDNA haplogroup-specific mutations to the occurrence of prostate cancer, we have therefore performed a population-based study of a prostate cancer cases and corresponding controls from the Korean population. No statistically significant difference in the distribution of mtDNA haplogroup frequencies was observed between the case and control groups of Koreans. Thus, our data imply that specific mtDNA mutations/lineages did not appear to have a significant effect on a predisposition to prostate cancer in the Korean population, although larger sample sizes are necessary to validate our results

Sex-related impact on clinical outcomes of patients treated with drug-eluting stents according to clinical presentation: Patient-level pooled analysis from the GRAND-DES registry

Background: The contribution of sex and initial clinical presentation to the long-term outcomes in patients undergoing percutaneous coronary intervention (PCI) is still debated. Methods: Individual patient data from 5 Korean-multicenter drug-eluting stent (DES) registries (The GRAND-DES) were pooled. A total of 17,286 patients completed 3-year follow-up (5216 women and 12,070 men). The median follow-up duration was 1125 days (interquartile range 1097–1140 days), and the primary endpoint was cardiac death at 3 years. Results: The clinical indication for PCI was stable angina pectoris (SAP) in 36.8%, unstable angina pectoris (UAP) or non-ST-segment elevation myocardial infarction (NSTEMI) in 47.4%, and STEMI in 15.8%. In all groups, women were older and had a higher proportion of hypertension and diabetes mellitus compared with men. Women presenting with STEMI were older than women with SAP, with the opposite seen in men. There was no sex difference in cardiac death for SAP or UAP/NSTEMI. In STEMI patients, the incidence of cardiac death (7.9% vs. 4.4%, p = 0.001), all-cause mortality (11.1% vs. 6.9%, p = 0.001), and minor bleeding (2.2% vs. 1.2%, p = 0.043) was significantly higher in women. After multivariable adjustment, cardiac death was lower in women for UAP/NSTEMI (HR 0.69, 95% CI 0.53–0.89, p = 0.005), while it was similar for STEMI (HR 0.97, 95% CI 0.65–1.44, p = 0.884). Conclusions: There was no sex difference in cardiac death after PCI with DES for SAP and UAP/NSTEMI patients. In STEMI patients, women had worse outcomes compared with men; however, after the adjustment of confounders, female sex was not an independent predictor of mortality