Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.     

Sequence heterogeneity and anomalous electrophoretic mobility of kinetoplast minicircle DNA from Leishmania tarentolae

Several unit-length minicircles from the kinetoplast DNA of Leishmania tarentolae were cloned into pBR322 and into M13 phage vectors. The complete nucleotide sequences of three different partially homologous minicircles were obtained. The molecules contained a region of approx. 80% sequence homology extending for 160–270 bp and a region unique to each minicircle. A 14-mer was found to be conserved in all kinetoplast minicircle sequences reported to date. The frequency distributions of various minicircle sequence classes in L. tarentolae were obtained by quantitative gel electrophoresis and by examination of the “T ladder” patterns of minicircles randomly cloned into M13 at several sites. By these methods we could assign approx. 50% of the total minicircle DNA into a minimum of five sequence classes. A sequence-dependent polyacrylamide gel migration abnormality was observed with several minicircle fragments both cloned and uncloned. The abnormality was dependent on the presence of a portion of the conserved region of the minicircle.     

MODY 2: Mutation identification and molecular ancestry in a Brazilian family

Maturity Onset Diabetes of the Young (MODY) is a heterogeneous group of genetic diseases characterized by a primary defect in insulin secretion and hyperglycemia, non-ketotic disease, monogenic autosomal dominant mode of inheritance, age at onset less than 25 years, and lack of auto-antibodies. It accounts for 2–5% of all cases of non-type 1 diabetes. MODY subtype 2 is caused by mutations in the glucokinase (GCK) gene. In this study, we sequenced the GCK gene of two volunteers with clinical diagnosis for MODY2 and we were able to identify four mutations including one for a premature stop codon (c.76C>T). Based on these results, we have developed a specific PCR-RFLP assay to detect this mutation and tested 122 related volunteers from the same family. This mutation in the GCK gene was detected in 21 additional subjects who also had the clinical features of this genetic disease. In conclusion, we identified new GCK gene mutations in a Brazilian family of Italian descendance, with one due to a premature stop codon located in the second exon of the gene. We also developed a specific assay that is fast, cheap and reliable to detect this mutation. Finally, we built a molecular ancestry model based on our results for the migration of individuals carrying this genetic mutation from Northern Italy to Brazil.     

Structure-wise discrimination of adenine and guanine by proteins on the basis of their nonbonded interactions

We have analyzed the nonbonded interactions of the structurally similar moieties, adenine and guanine forming complexes with proteins. The results comprise (a) the amino acid–ligand atom preferences, (b) solvent accessibility of ligand atoms before and after complex formation with proteins, and (c) preferred amino acid residue atoms involved in the interactions. We have observed that the amino acid preferences involved in the hydrogen bonding interactions vary for adenine and guanine. The structural variation between the purine atoms is clearly reflected by their burial tendency in the solvent environment. Correlation of the mean amino acid preference values show the variation that exists between adenine and guanine preferences of all the amino acid residues. All our observations provide evidence for the discriminating nature of the proteins in recognizing adenine and guanine.     

The Mechanism of Supercoiled DNA Recognition by Eukaryotic Type I Topoisomerases: II. A Comparison of the Enzyme Interaction with Specific and Nonspecific Oligonucleotides

Data on the interaction of DNA type I topoisomerases from the murine and human placenta cells with specific and nonspecific oligonucleotides of various structures and lengths are summarized. The relative contributions of various contacts between the enzymes and DNA that have previously been detected by X-ray analysis to the total affinity of the topoisomerases for DNA substrates are estimated. Factors that determine the differences in the enzyme interactions with specific and nonspecific single- and double-stranded DNAs are revealed. The results of the X-ray analysis of human DNA topoisomerase I are interpreted taking into account data on the comprehensive thermodynamic and kinetic analysis of the enzyme interaction with the specific and nonspecific DNAs.     

The role of nutrition on epigenetic modifications and their implications on health

Nutrition plays a key role in many aspects of health and dietary imbalances are major determinants of chronic diseases including cardiovascular disease, obesity, diabetes and cancer. Adequate nutrition is particularly essential during critical periods in early life (both pre- and postnatal). In this regard, there is extensive epidemiologic and experimental data showing that early sub-optimal nutrition can have health consequences several decades later.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

A comparative Proteomics Analysis Identified Differentially Expressed Proteins in Pancreatic Cancer–Associated Stellate Cell Small Extracellular Vesicles

Human pancreatic stellate cells (HPSCs) are an essential stromal component and mediators of pancreatic ductal adenocarcinoma (PDAC) progression. Small extracellular vesicles (sEVs) are membrane-enclosed nanoparticles involved in cell-to-cell communications and are released from stromal cells within PDAC. A detailed comparison of sEVs from normal pancreatic stellate cells (HPaStec) and from PDAC-associated stellate cells (HPSCs) remains a gap in our current knowledge regarding stellate cells and PDAC. We hypothesized there would be differences in sEVs secretion and protein expression that might contribute to PDAC biology. To test this hypothesis, we isolated sEVs using ultracentrifugation followed by characterization by electron microscopy and Nanoparticle Tracking Analysis. We report here our initial observations. First, HPSC cells derived from PDAC tumors secrete a higher volume of sEVs when compared to normal pancreatic stellate cells (HPaStec). Although our data revealed that both normal and tumor-derived sEVs demonstrated no significant biological effect on cancer cells, we observed efficient uptake of sEVs by both normal and cancer epithelial cells. Additionally, intact membrane-associated proteins on sEVs were essential for efficient uptake. We then compared sEV proteins isolated from HPSCs and HPaStecs cells using liquid chromatography–tandem mass spectrometry. Most of the 1481 protein groups identified were shared with the exosome database, ExoCarta. Eighty-seven protein groups were differentially expressed (selected by 2-fold difference and adjusted p value ≤0.05) between HPSC and HPaStec sEVs. Of note, HPSC sEVs contained dramatically more CSE1L (chromosome segregation 1–like protein), a described marker of poor prognosis in patients with pancreatic cancer. Based on our results, we have demonstrated unique populations of sEVs originating from stromal cells with PDAC and suggest that these are significant to cancer biology. Further studies should be undertaken to gain a deeper understanding that could drive novel therapy.