Mitochondrial rate variation among lineages of passerine birds

The order Passeriformes comprises the majority of extant avian species. Analyses of molecular data have provided important insights into the evolution of this diverse order. However, molecular estimates of the evolutionary and demographic timescales of passerine species have been hindered by a lack of reliable calibrations. This has led to a reliance on the application of standard substitution rates to mitochondrial DNA data, particularly rates estimated from analyses of the gene encoding cytochrome b (CYTB). To investigate patterns of rate variation across passerine lineages, we used a Bayesian phylogenetic approach to analyse the protein‐coding genes of 183 mitochondrial genomes. We found that the most commonly used mitochondrial marker, CYTB, has low variation in rates across passerine lineages. This lends support to its widespread use as a molecular clock in birds. However, we also found that the patterns of among‐lineage rate variation in CYTB are only weakly related to the evolutionary rate of the mitochondrial genome as a whole. Our analyses confirmed the presence of mutational saturation at third codon positions across the protein‐coding genes of the mitochondrial genome, reinforcing the view that these sites should be excluded in studies of deep passerine relationships. The results of our analyses have provided information that will be useful for molecular‐clock studies of passerine evolution.     

Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells

Data‐driven materials discovery has become increasingly important in identifying materials that exhibit specific, desirable properties from a vast chemical search space. Synergic prediction and experimental validation are needed to accelerate scientific advances related to critical societal applications. A design‐to‐device study that uses high‐throughput screens with algorithmic encodings of structure–property relationships is reported to identify new materials with panchromatic optical absorption, whose photovoltaic device applications are then experimentally verified. The data‐mining methods source 9431 dye candidates, which are auto‐generated from the literature using a custom text‐mining tool. These candidates are sifted via a data‐mining workflow that is tailored to identify optimal combinations of organic dyes that have complementary optical absorption properties such that they can harvest all available sunlight when acting as co‐sensitizers for dye‐sensitized solar cells (DSSCs). Six promising dye combinations are shortlisted for device testing, whereupon one dye combination yields co‐sensitized DSSCs with power conversion efficiencies comparable to those of the high‐performance, organometallic dye, N719. These results demonstrate how data‐driven molecular engineering can accelerate materials discovery for panchromatic photovoltaic or other applications.     

Synthesis of a green polyurethane foam from a biopolyol obtained by enzymatic glycerolysis and its use for immobilization of lipase NS-40116

The use of green sources for materials synthesis has gained popularity in recent years. This work investigated the immobilization of lipase NS-40116 (Thermomyces lanuginosus lipase) in polyurethane foam (PUF) using a biopolyol obtained through the enzymatic glycerolysis between castor oil and glycerol, catalyzed by the commercial lipase Novozym 435 for the PUF formation. The reaction was performed to obtain biopolyol resulting in the conversion of 64% in mono- and diacylglycerol, promoting the efficient use of the reaction product as biopolyol to obtain polyurethane foam. The enzymatic derivative with immobilized lipase NS-40116 presented apparent density of 0.19 ± 0.03 g/cm³ and an immobilization yield was 94 ± 4%. Free and immobilized lipase NS-40116 were characterized in different solvents (methanol, ethanol, and propanol), temperatures (20, 40, 60 and 80 °C), pH (3, 5, 7, 9 and 11) and presence of ions Na⁺, Mg⁺⁺, and Ca⁺⁺. The support provided higher stability to the enzyme, mainly when subjected to acid pH (free lipase lost 80% of relative activity after 360 h of contact, when the enzymatic derivative lost around 22%) and high-temperature free lipase lost 50% of relative activity, while the immobilized remained 95%. The enzymatic derivative was also used for esterification reactions and conversions around 66% in fatty acid methyl esters, using abdominal chicken fat as feedstock, were obtained in the first use, maintaining this high conversion until the fourth reuse, proving that the support obtained using environmentally friendly techniques is applicable.

     

Identification of novel high-frequency DNA methylation changes in breast cancer

Recent data have revealed that epigenetic alterations, including DNA methylation and chromatin structure changes, are among the earliest molecular abnormalities to occur during tumorigenesis. The inherent thermodynamic stability of cytosine methylation and the apparent high specificity of the alterations for disease may accelerate the development of powerful molecular diagnostics for cancer. We report a genome-wide analysis of DNA methylation alterations in breast cancer. The approach efficiently identified a large collection of novel differentially DNA methylated loci (approximately 200), a subset of which was independently validated across a panel of over 230 clinical samples. The differential cytosine methylation events were independent of patient age, tumor stage, estrogen receptor status or family history of breast cancer. The power of the global approach for discovery is underscored by the identification of a single differentially methylated locus, associated with the GHSR gene, capable of distinguishing infiltrating ductal breast carcinoma from normal and benign breast tissues with a sensitivity and specificity of 90% and 96%, respectively. Notably, the frequency of these molecular abnormalities in breast tumors substantially exceeds the frequency of any other single genetic or epigenetic change reported to date. The discovery of over 50 novel DNA methylation-based biomarkers of breast cancer may provide new routes for development of DNA methylation-based diagnostics and prognostics, as well as reveal epigenetically regulated mechanism involved in breast tumorigenesis.     

Applications of a new subspace clustering algorithm (COSA) in medical systems biology

A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies. Doris Damian, Matej Orešič, and Elwin Verheij contributed equally to this work.