The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

Community-wide analysis of microbial genome sequence signatures

Genome signatures are used to identify and cluster sequences de novo from an acid biofilm microbial community metagenomic dataset, revealing information about the low-abundance community members

Insights into the Ecological Roles and Evolution of Methyl-Coenzyme M Reductase-Containing Hot Spring Archaea

Several recent studies have shown the presence of genes for the key enzyme associated with archaeal methane/alkane metabolism, methyl-coenzyme M reductase (Mcr), in metagenome-assembled genomes (MAGs) divergent to existing archaeal lineages. Here, we study the mcr-containing archaeal MAGs from several hot springs, which reveal further expansion in the diversity of archaeal organisms performing methane/alkane metabolism. Significantly, an MAG basal to organisms from the phylum Thaumarchaeota that contains mcr genes, but not those for ammonia oxidation or aerobic metabolism, is identified. Together, our phylogenetic analyses and ancestral state reconstructions suggest a mostly vertical evolution of mcrABG genes among methanogens and methanotrophs, along with frequent horizontal gene transfer of mcr genes between alkanotrophs. Analysis of all mcr-containing archaeal MAGs/genomes suggests a hydrothermal origin for these microorganisms based on optimal growth temperature predictions. These results also suggest methane/alkane oxidation or methanogenesis at high temperature likely existed in a common archaeal ancestor

Microarray transcriptional profiling of Arctic Mesorhizobium strain N33 at low temperature provides insights into cold adaption strategies

Background: Arctic Mesorhizobium strain N33 was isolated from nodules of the legume Oxytropis arctobia in Canada’s eastern Arctic. This symbiotic bacterium can grow at temperatures ranging from 0 to 30 °C, fix nitrogen at 10 °C, and is one of the best known cold-adapted rhizobia. Despite the economic potential of this bacterium for northern regions, the key molecular mechanisms of its cold adaptation remain poorly understood. Results: Using a microarray printed with 5760 Arctic Mesorhizobium genomic clones, we performed a partial transcriptome analysis of strain N33 grown under eight different temperature conditions, including both sustained and transient cold treatments, compared with cells grown at room temperature. Cells treated under constant (4 and 10 °C) low temperatures expressed a prominent number of induced genes distinct from cells treated to short-term cold-exposure (<60 min), but exhibited an intermediate expression profile when exposed to a prolonged cold exposure (240 min). The most prominent up-regulated genes encode proteins involved in metabolite transport, transcription regulation, protein turnover, oxidoreductase activity, cryoprotection (mannitol, polyamines), fatty acid metabolism, and membrane fluidity. The main categories of genes affected in N33 during cold treatment are sugar transport and protein translocation, lipid biosynthesis, and NADH oxidoreductase (quinone) activity. Some genes were significantly down-regulated and classified in secretion, energy production and conversion, amino acid transport, cell motility, cell envelope and outer membrane biogenesis functions. This might suggest growth cessation or reduction, which is an important strategy to adjust cellular function and save energy under cold stress conditions. Conclusion: Our analysis revealed a complex series of changes associated with cold exposure adaptation and constant growth at low temperatures. Moreover, it highlighted some of the strategies and different physiological states that Mesorhizobium strain N33 has developed to adapt to the cold environment of the Canadian high Arctic and has revealed candidate genes potentially involved in cold adaptati

Advancing systems biology of yeast through machine learning and comparative genomics

Synthetic biology has played a pivotal role in accomplishing the production of high value commodities, pharmaceuticals, and bulk chemicals. Fueled by the breakthrough of synthetic biology and metabolic engineering, Saccharomyces cerevisiae and various other yeasts (such as Yarrowia lipolytica, Pichia pastoris) have been proven to be promising microbial cell factories and are frequently used in scientific studies. However, the cellular metabolism and physiological properties for most of the yeast species have not been characterized in detail. To address these knowledge gaps, this thesis aims to leverage the large amounts of data available for yeast species and use state-of-the-art machine learning techniques and comparative genomic analysis to gain a deeper insight into yeast traits and metabolism.In this thesis, machine learning was applied to various unresolved biological problems on yeasts, i.e., gene essentiality, enzyme turnover number (kcat), and protein production. In the first part of the work, machine learning approaches were employed to predict gene essentiality based on sequence features and evolutionary features. It was demonstrated that the essential gene prediction could be substantially improved by integrating evolution-based features. Secondly, a high-quality deep learning model DLKcat was developed to predict kcat\ua0values by combining a graph neural network for substrates and a convolutional neural network for proteins. By predicting kcat profiles for 343 yeast/fungi species, enzyme-constrained models were reconstructed and used to further elucidate the cellular metabolism on a large scale. Lastly, a random forest algorithm was adopted to investigate feature importance analysis on protein production, it was found that post-translational modifications (PTMs) have a relatively higher impact on protein production compared with amino acid composition. In comparative genomics, a comprehensive toolbox HGTphyloDetect was developed to facilitate the identification of horizontal gene transfer (HGT) events. Case studies on some yeast species demonstrated the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, through systematic evolution analysis (e.g., HGT, gene family expansion) and genome-scale metabolic model simulation, the underlying mechanisms for substrate utilization were further probed across large-scale yeast species

STEM Undergraduate Research Symposium 2016 Full Program

Full Program of the 2016 LSSF STEM Undergraduate Research Conference

PhyloSift: Phylogenetic analysis of genomes and metagenomes

Like all organisms on the planet, environmental microbes are subject to the forces of molecular evolution. Metagenomic sequencing provides a means to access the DNA sequence of uncultured microbes. By combining DNA sequencing of microbial communities with evolutionary modeling and phylogenetic analysis we might obtain new insights into microbiology and also provide a basis for practical tools such as forensic pathogen detection. In this work we present an approach to leverage phylogenetic analysis of metagenomic sequence data to conduct several types of analysis. First, we present a method to conduct phylogeny-driven Bayesian hypothesis tests for the presence of an organism in a sample. Second, we present a means to compare community structure across a collection of many samples and develop direct associations between the abundance of certain organisms and sample metadata. Third, we apply new tools to analyze the phylogenetic diversity of microbial communities and again demonstrate how this can be associated to sample metadata. These analyses are implemented in an open source software pipeline called PhyloSift. As a pipeline, PhyloSift incorporates several other programs including LAST, HMMER, and pplacer to automate phylogenetic analysis of protein coding and RNA sequences in metagenomic datasets generated by modern sequencing platforms (e.g., Illumina, 454). © 2014 Darling et al

Fourth Symposium on Chemical Evolution and the Origin and Evolution of Life

This symposium was held at the NASA Ames Research Center, Moffett Field, California, July 24-27, 1990. The NASA exobiology investigators reported their recent research findings. Scientific papers were presented in the following areas: cosmic evolution of biogenic compounds, prebiotic evolution (planetary and molecular), early evolution of life (biological and geochemical), evolution of advanced life, solar system exploration, and the Search for Extraterrestrial Intelligence (SETI)