A study of the native-denatured (N in equilibrium with D) transition in lysozyme. II. Kinetic analysis of protease digestion.

Kinetic analyses of the protease digestion of several chemical derivatives of lysozyme [EC 3.2.1.17] showed that only the D(denatured) state of the protein is digested and that the reaction velocity is proportional to the equilibrium constant (KD) of the N in equilibrium with D transition of the protein. Alteration of the net charge of lysozyme by acetylation caused a shift of the N in equilibrium with D transition to the right (ten-fold increase in KD compared to that of native enzyme). Both the formation of a lysozyme-inhibitor complex and the introduction of a covalent bond in the lysozyme molecule restricted the transition. The magnitude of the N in equilibrium with D transition is related to the susceptibility of lysozyme to protease digestion and it is estimated that the N in equilibrium with D transition in proteins is generally important in the intracellular catabolism of proteins.     

Inhibitory effect of the extract from Zizyphus jujuba leaves on sweet taste responses of the chorda tympani in the rat and hamster

1. One of the fractions obtained from the extract of Zizyphus jujuba leaves suppressed the response of the chorda tympani to sucrose, both in the rat and hamster. 2. In the rat and man, suppressive effect was found to be significant in responses to various sugars and artificial sweeteners but not in some sweet amino acids.     

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.     

Simultaneous Detection of Both Single Nucleotide Variations and Copy Number Alterations by Next-Generation Sequencing in Gorlin Syndrome

Gorlin syndrome (GS) is an autosomal dominant disorder that predisposes affected individuals to developmental defects and tumorigenesis, and caused mainly by heterozygous germline PTCH1 mutations. Despite exhaustive analysis, PTCH1 mutations are often unidentifiable in some patients; the failure to detect mutations is presumably because of mutations occurred in other causative genes or outside of analyzed regions of PTCH1, or copy number alterations (CNAs). In this study, we subjected a cohort of GS-affected individuals from six unrelated families to next-generation sequencing (NGS) analysis for the combined screening of causative alterations in Hedgehog signaling pathway-related genes. Specific single nucleotide variations (SNVs) of PTCH1 causing inferred amino acid changes were identified in four families (seven affected individuals), whereas CNAs within or around PTCH1 were found in two families in whom possible causative SNVs were not detected. Through a targeted resequencing of all coding exons, as well as simultaneous evaluation of copy number status using the alignment map files obtained via NGS, we found that GS phenotypes could be explained by PTCH1 mutations or deletions in all affected patients. Because it is advisable to evaluate CNAs of candidate causative genes in point mutation-negative cases, NGS methodology appears to be useful for improving molecular diagnosis through the simultaneous detection of both SNVs and CNAs in the targeted genes/regions.     

Blood diagnostic biomarkers for major depressive disorder using multiplex DNA methylation profiles: discovery and validation

Aberrant DNA methylation in the blood of patients with major depressive disorder (MDD) has been reported in several previous studies. However, no comprehensive studies using medication-free subjects with MDD have been conducted. Furthermore, the majority of these previous studies has been limited to the analysis of the CpG sites in CpG islands (CGIs) in the gene promoter regions. The main aim of the present study is to identify DNA methylation markers that distinguish patients with MDD from non-psychiatric controls. Genome-wide DNA methylation profiling of peripheral leukocytes was conducted in two set of samples, a discovery set (20 medication-free patients with MDD and 19 controls) and a replication set (12 medication-free patients with MDD and 12 controls), using Infinium HumanMethylation450 BeadChips. Significant diagnostic differences in DNA methylation were observed at 363 CpG sites in the discovery set. All of these loci demonstrated lower DNA methylation in patients with MDD than in the controls, and most of them (85.7%) were located in the CGIs in the gene promoter regions. We were able to distinguish patients with MDD from the control subjects with high accuracy in the discriminant analysis using the top DNA methylation markers. We also validated these selected DNA methylation markers in the replication set. Our results indicate that multiplex DNA methylation markers may be useful for distinguishing patients with MDD from non-psychiatric controls.     

Bi-directional sex change and gonad structure in the gobiid fish <Emphasis Type="Italic">Trimma yanagitai</Emphasis>

Bi-directional sex change in the deep-water gobiid fish Trimma yanagitai was examined. The gonads of all individuals consisted of ovarian and testicular elements, and an accessory gonadal structure. In no gonads were both testicular and ovarian parts simultaneously active. Bi-directional sex changes occurred during the rearing experiments in aquaria under conditions of which there was co-existence of two males or plural females. The sex of individuals could be determined by their relative body size or social dominance: the largest individuals acting as male and the remainder as female.