Incorporating functional inter-relationships into protein function prediction algorithms

<p>Abstract</p> <p>Background</p> <p>Functional classification schemes (e.g. the Gene Ontology) that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches.</p> <p>Results</p> <p>We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the <it>k</it>-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of predictions made over a set of base classes. Finally, we provide qualitative and quantitative evidence that this incorporation of functional inter-relationships enables the discovery of interesting biology in the form of novel functional annotations for several yeast proteins, such as Sna4, Rtn1 and Lin1.</p> <p>Conclusion</p> <p>We implemented and evaluated a methodology for incorporating interrelationships between functional classes into a standard classification-based protein function prediction algorithm. Our results show that this incorporation can help improve the accuracy of such algorithms, and help uncover novel biology in the form of previously unknown functional annotations. The complete source code, a sample data set and the additional files for this paper are available free of charge for non-commercial use at <url>http://www.cs.umn.edu/vk/gaurav/functionalsimilarity/</url>.</p

Genome-scale modeling of the protein secretory machinery in yeast

The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking. Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm was developed which mimics secretory machinery and assigns each secretory protein to a particular secretory class that determines the set of PTMs and transport steps specific to each protein. Protein abundances were integrated with the model in order to gain system level estimation of the metabolic demands associated with the processing of each specific protein as well as a quantitative estimation of the activity of each component of the secretory machinery

Identification of molecular pathways affected by pterostilbene, a natural dimethylether analog of resveratrol

<p>Abstract</p> <p>Background</p> <p>Pterostilbene, a naturally occurring phenolic compound produced by agronomically important plant genera such as <it>Vitis </it>and <it>Vacciunium</it>, is a phytoalexin exhibiting potent antifungal activity. Additionally, recent studies have demonstrated several important pharmacological properties associated with pterostilbene. Despite this, a systematic study of the effects of pterostilbene on eukaryotic cells at the molecular level has not been previously reported. Thus, the aim of the present study was to identify the cellular pathways affected by pterostilbene by performing transcript profiling studies, employing the model yeast <it>Saccharomyces cerevisiae</it>.</p> <p>Methods</p> <p><it>S. cerevisiae </it>strain S288C was exposed to pterostilbene at the IC₅₀concentration (70 μM) for one generation (3 h). Transcript profiling experiments were performed on three biological replicate samples using the Affymetrix GeneChip Yeast Genome S98 Array. The data were analyzed using the statistical methods available in the GeneSifter microarray data analysis system. To validate the results, eleven differentially expressed genes were further examined by quantitative real-time RT-PCR, and <it>S. cerevisiae </it>mutant strains with deletions in these genes were analyzed for altered sensitivity to pterostilbene.</p> <p>Results</p> <p>Transcript profiling studies revealed that pterostilbene exposure significantly down-regulated the expression of genes involved in methionine metabolism, while the expression of genes involved in mitochondrial functions, drug detoxification, and transcription factor activity were significantly up-regulated. Additional analyses revealed that a large number of genes involved in lipid metabolism were also affected by pterostilbene treatment.</p> <p>Conclusion</p> <p>Using transcript profiling, we have identified the cellular pathways targeted by pterostilbene, an analog of resveratrol. The observed response in lipid metabolism genes is consistent with its known hypolipidemic properties, and the induction of mitochondrial genes is consistent with its demonstrated role in apoptosis in human cancer cell lines. Furthermore, our data show that pterostilbene has a significant effect on methionine metabolism, a previously unreported effect for this compound.</p

Dietary supplementation with hydrolyzed yeast and its effect on the performance, intestinal microbiota, and immune response of weaned piglets.

The objective of this study was to evaluate the effects of autolyzed yeast on performance, cecal microbiota, and leukogram of weaned piglets. A total of 96 piglets of commercial line weaned at 21-day-old were used. The experimental design was a randomized block design with four treatments (diets containing 0.0%, 0.3%, 0.6%, and 0.9% autolyzed yeast), eight replicates, and three animals per pen in order to evaluate daily weight gain, daily feed intake, and feed conversion in periods of 0 to 15, 0 to 26, and 0 to 36 days. Quadratic effects of autolyzed yeast inclusion were observed on the feed conversion from 0 to 15 days, on daily weight gain from 0 to 15 days, 0 to 26 days and, 0 to 36 days, indicating an autolyzed yeast optimal inclusion level between 0.4% and 0.5%. No effect from autolyzed yeast addition was observed on piglet daily feed intake, cecal microbiota, and leukogram; however, i.m. application of E. coli lipopolysaccharide reduced the values of total leukocytes and their fractions (neutrophils, eosinophils, lymphocytes, monocytes, and rods). Therefore, autolyzed yeast when provided at levels between 0.4% and 0.5% improved weaned piglets’ performance.info:eu-repo/semantics/publishedVersio

R-SNARE Homolog MoSec22 Is Required for Conidiogenesis, Cell Wall Integrity, and Pathogenesis of Magnaporthe oryzae

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins mediate intracellular vesicle fusion, which is an essential cellular process of the eukaryotic cells. To investigate the role of SNARE proteins in the rice blast fungus Magnaporthe oryzae, MoSec22, an ortholog of Saccharomyces cerevisiae SNARE protein Sec22, was identified and the MoSEC22 gene disrupted. MoSec22 restored a S. cerevisiae sec22 mutant in resistance to cell wall perturbing agents, and the ΔMosec22 mutant also exhibited defects in mycelial growth, conidial production, and infection of the host plant. Treatment with oxidative stress inducers indicated a breach in cell wall integrity, and staining and quantification assays suggested abnormal chitin deposition on the lateral walls of hyphae of the ΔMosec22 mutant. Furthermore, hypersensitivity to the oxidative stress correlates with the reduced expression of the extracellular enzymes peroxidases and laccases. Our study thus provides new evidence on the conserved function of Sec22 among fungal organisms and indicates that MoSec22 has a role in maintaining cell wall integrity affecting the growth, morphogenesis, and virulence of M. oryzae

A new era for understanding amyloid structures and disease

The aggregation of proteins into amyloid fibrils and their deposition into plaques and intracellular inclusions is the hallmark of amyloid disease. The accumulation and deposition of amyloid fibrils, collectively known as amyloidosis, is associated with many pathological conditions that can be associated with ageing, such as Alzheimer disease, Parkinson disease, type II diabetes and dialysis-related amyloidosis. However, elucidation of the atomic structure of amyloid fibrils formed from their intact protein precursors and how fibril formation relates to disease has remained elusive. Recent advances in structural biology techniques, including cryo-electron microscopy and solid-state NMR spectroscopy, have finally broken this impasse. The first near-atomic-resolution structures of amyloid fibrils formed in vitro, seeded from plaque material and analysed directly ex vivo are now available. The results reveal cross-β structures that are far more intricate than anticipated. Here, we describe these structures, highlighting their similarities and differences, and the basis for their toxicity. We discuss how amyloid structure may affect the ability of fibrils to spread to different sites in the cell and between organisms in a prion-like manner, along with their roles in disease. These molecular insights will aid in understanding the development and spread of amyloid diseases and are inspiring new strategies for therapeutic intervention