Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L ₁-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the “Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project” show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.     

Robust Prediction of Anti-Cancer Drug Sensitivity and Sensitivity-Specific Biomarker

The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc.) and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.     

Increase of cyclin B by overexpression of cystatin α

Degradation of cyclin B was effectively suppressed when cells were treated with ALLN (N-acetylleucylleucylnorleucinal) which inhibits proteasome, calpain and cysteine proteinase cathepsins. In order to examine which protease degrades cyclin B, the effect of a cathepsin inhibitor, cystatin α, was investigated. The cystatin α gene was inserted into an inducible expression vector, pMSG, and transfected into NIH3T3 mouse fibroblasts. The expression of cystatin α was induced effectively in the transfected cells after treatment with dexamethasone. Overexpression of cystatin α resulted in an increase of the amount of cyclin B, suggesting that cysteine proteinase cathepsins might be involved in the degradation of cyclin B.     

Antiobesity and emetic effects of a short-length peptide YY analog and its PEGylated and alkylated derivatives

Neuropeptide Y2 receptor (Y2R) agonism is an important anorectic signal and a target of antiobesity drug discovery. Recently, we synthesized a short-length Y2R agonist, PYY-1119 (4-imidazolecarbonyl-[d-Hyp²⁴,Iva²⁵,Pya(4)²⁶,Cha^27,36,γMeLeu²⁸,Lys³⁰,Aib³¹]PYY(23–36), 1) as an antiobesity drug candidate. Compound 1 induced marked body weight loss in diet-induced obese (DIO) mice; however, 1 also induced severe vomiting in dogs at a lower dose than the minimum effective dose administered to DIO mice. The rapid absorption of 1 after subcutaneous administration caused the severe vomiting. Polyethylene glycol (PEG)- and alkyl-modified derivatives of 1 were synthesized to develop Y2R agonists with improved pharmacokinetic profiles, i.e., lower maximum plasma concentration (C_max) and longer time at maximum concentration (T_max). Compounds 5 and 10, modified with 20?kDa PEG at the N-terminus and eicosanedioic acid at the Lys³⁰ side chain of 1, respectively, showed high Y2R binding affinity and induced significant body weight reduction upon once-daily administration to DIO mice. Compounds 5 and 10, with their relatively low C_max and long T_max, partially attenuated emesis in dogs compared with 1. These results indicate that optimization of pharmacokinetic properties of Y2R agonists is an effective strategy to alleviate emesis induced by Y2R agonism.     

Flavonoid metabolism: the interaction of metabolites and gut microbiota

Abstract

Several dietary flavonoids exhibit anti-oxidative, anti-inflammatory, and anti-osteoporotic activities relevant to prevention of chronic diseases, including lifestyle-related diseases. Dietary flavonoids (glycoside forms) are enzymatically hydrolyzed and absorbed in the intestine, and are conjugated to their glucuronide/sulfate forms by phase II enzymes in epithelial cells and the liver. The intestinal microbiota plays an important role in the metabolism of flavonoids found in foods. Some specific products of bacterial transformation, such as ring-fission products and reduced metabolites, exhibit enhanced properties. Studies on the metabolism of flavonoids by the intestinal microbiota are crucial for understanding the role of these compounds and their impact on our health. This review focused on the metabolic pathways, bioavailability, and physiological role of flavonoids, especially metabolites of quercetin and isoflavone produced by the intestinal microbiota.     

Structural Basis of Heterochromatin Formation by Human HP1

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Battery of tests for profiling abnormalities of vitamin K-dependent coagulation factors in drug-toxicity studies in rats.

A battery of simple tests for profiling abnormalities of vitamin K-dependent coagulation factors encountered in drug-toxicity studies was verified in rats treated with warfarin (3 and 10 mg/kg, p.o). The thrombotest, or hepaplastin-test, is useful as a follow-up test after routine screening tests for coagulation abnormalities based on PT and APTT, to rule out other coagulation-factor abnormalities. Measurement of coagulation factor activities (factors II, VII, IX and X) using factor-deficient human plasmas provides direct evidence of decreased activities of vitamin K-dependent factors. Furthermore, Echis carinatus venom coagulation time, together with factor II activity, allows us to confirm the generation of PIVKA-II.