Determining a new alignment scoring matrix for disordered proteins

Intrinsically disordered proteins (IDPs) are polypeptide sequences that do not form a rigid three dimensional structure when isolated in the cytosol of the cell. These sequences are very common in most genomes, and usually are involved in many protein-protein interactions. IDPs also play a key role in many diseases, including cancer and Huntington\u27s disease. However, IDPs are difficult to study because of their amorphous shape, high mutation rate, and unique amino acid composition. These obstacles make homology studies especially difficult. This study focused on generating a new substitution matrix designed to aid in homology studies of IDPs. The matrix was generated using a genetic algorithm (GA). GAs are alternative hill-climbing methods for finding solutions in complex problem spaces. To achieve this goal, a GA models the evolutionary process found in nature by breeding solutions to the problem until one of sufficient quality is produced. The GA implemented in this study produced a substitution matrix for use in differentiation between homologous and non-homologous proteins containing disordered regions. The matrix showed some correlation to the patterns of evolution found in disordered proteins and their general sequence makeup. However, when compared to a commonly used substitution matrix, BLOSUM, the GA\u27s solution did not show significant improvement. But the results here do show a general proof of concept, and that given modifications to the GA, more time, or more resources, a substitution matrix capable of out-performing BLOSUM is potentially possible

A comparison of the stimulus delay procedure and simultaneous stimulus presentation.

A comparison of a time delay and paired prompt (simultaneous stimulus presentation) procedure was conducted involving the teaching of a color identification task to two autistic adolescents. rime delay involved a gradual increase in the Lime between presentation of an instruction and prompt. Simultaneous stimulus presentation involved the concurrent presentation of instruct, ion and prompt. One subject met criterion with the color identification task assigned to the simultaneous stimulus presentation condition. The remaining subject failed to meet criterion with either procedure

Food Waste to Bio-Products

The goal of this project was to design and evaluate a project for the collection and processing of food waste and spent oil in Philadelphia. The project was designed to handle 5% of the total commercial waste generated in Philadelphia. This amounted to approximately 9,700 tons/year of food waste and 73,000 gallons/year of spent oil. The process was designed to utilize a BIOFerm™ Dry Fermentation Digestion System. Following the digestion, the biogas produced is passed through a Caterpillar CG132-12 Generator Set, producing electricity to be sold back to the local grid. The digestate from the anaerobic digestion is used to produce compost, providing an additional revenue stream. In addition to handling the solid food waste, the project is designed to convert the collected spent oil into biodiesel using prepackaged processing units by Springboard Biodiesel. The facility is anticipated to annually produce 2,541 tons of biogas, 5,184,000 kWh of electricity, 14,756 tons of compost, and 59,616 gallons of biodiesel. A rigorous profitability analysis was conducted in order to project cash flows for fifteen years. The total capital investment of the plant is $5.6MM and the expected NPV of the project is -($682,000). The estimated IRR of the project is 12% and the 3-year ROI is 7%. Given the project’s negative NPV, our recommendation is to adopt such a process solely for environmentally beneficial waste management purposes. A key takeway is that in order for such a project to be profitable it would need to target more than just 5% of the total commercial food waste produced

The Alzheimer\u27s Biomarker Consortium-Down Syndrome: Rationale and methodology

Introduction: Adults with Down syndrome (DS) are at exceptionally high risk for Alzheimer\u27s disease (AD), with virtually all individuals developing key neuropathological features by age 40. Identifying biomarkers of AD progression in DS can provide valuable insights into pathogenesis and suggest targets for disease modifying treatments. Methods: We describe the development of a multi-center, longitudinal study of biomarkers of AD in DS. The protocol includes longitudinal examination of clinical, cognitive, blood and cerebrospinal fluid-based biomarkers, magnetic resonance imaging and positron emission tomography measures (at 16-month intervals), as well as genetic modifiers of AD risk and progression. Results: Approximately 400 individuals will be enrolled in the study (more than 370 to date). The methodological approach from the administrative, clinical, neuroimaging, omics, neuropathology, and statistical cores is provided. Discussion: This represents the largest U.S.-based, multi-site, biomarker initiative of AD in DS. Findings can inform other multidisciplinary networks studying AD in the general population

LENS: Web-based lens for enrichment and network studies of human proteins

Background: Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition. Results: We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia. Conclusion: LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS

Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction

Background\ud Protein-protein interaction (PPI) networks (interactomes) of most organisms, except for some model organisms, are largely unknown. Experimental methods including high-throughput techniques are highly resource intensive. Therefore, computational discovery of PPIs can accelerate biological discovery by presenting "most-promising" pairs of proteins that are likely to interact. For many bacteria, genome sequence, and thereby genomic context of proteomes, is readily available; additionally, for some of these proteomes, localization and functional annotations are also available, but interactomes are not available. We present here a method for rapid development of computational system to predict interactome of bacterial proteomes. While other studies have presented methods to transfer interologs across species, here, we propose transfer of computational models to benefit from cross-species annotations, thereby predicting many more novel interactions even in the absence of interologs. Mycobacterium tuberculosis (Mtb) and Clostridium difficile (CD) have been used to demonstrate the work.\ud \ud Results\ud We developed a random forest classifier over features derived from Gene Ontology annotations and genetic context scores provided by STRING database for predicting Mtb and CD interactions independently. The Mtb classifier gave a precision of 94% and a recall of 23% on a held out test set. The Mtb model was then run on all the 8 million protein pairs of the Mtb proteome, resulting in 708 new interactions (at 94% expected precision) or 1,595 new interactions at 80% expected precision. The CD classifier gave a precision of 90% and a recall of 16% on a held out test set. The CD model was run on all the 8 million protein pairs of the CD proteome, resulting in 143 new interactions (at 90% expected precision) or 580 new interactions (at 80% expected precision). We also compared the overlap of predictions of our method with STRING database interactions for CD and Mtb and also with interactions identified recently by a bacterial 2-hybrid system for Mtb. To demonstrate the utility of transfer of computational models, we made use of the developed Mtb model and used it to predict CD protein-pairs. The cross species model thus developed yielded a precision of 88% at a recall of 8%. To demonstrate transfer of features from other organisms in the absence of feature-based and interaction-based information, we transferred missing feature values from Mtb orthologs into the CD data. In transferring this data from orthologs (not interologs), we showed that a large number of interactions can be predicted.\ud \ud Conclusions\ud Rapid discovery of (partial) bacterial interactome can be made by using existing set of GO and STRING features associated with the organisms. We can make use of cross-species interactome development, when there are not even sufficient known interactions to develop a computational prediction system. Computational model of well-studied organism(s) can be employed to make the initial interactome prediction for the target organism. We have also demonstrated successfully, that annotations can be transferred from orthologs in well-studied organisms enabling accurate predictions for organisms with no annotations. These approaches can serve as building blocks to address the challenges associated with feature coverage, missing interactions towards rapid interactome discovery for bacterial organisms.\ud \ud Availability\ud The predictions for all Mtb and CD proteins are made available at: http://severus.dbmi.pitt.edu/TB and http://severus.dbmi.pitt.edu/CD respectively for browsing as well as for download

Moderation of antipsychotic-induced weight gain by energy balance gene variants in the RUPP autism network risperidone studies.

Second-generation antipsychotic exposure, in both children and adults, carries significant risk for excessive weight gain that varies widely across individuals. We queried common variation in key energy balance genes (FTO, MC4R, LEP, CNR1, FAAH) for their association with weight gain during the initial 8 weeks in the two NIMH Research Units on Pediatric Psychopharmacology Autism Network trials (N=225) of risperidone for treatment of irritability in children/adolescents aged 4-17 years with autism spectrum disorders. Variants in the cannabinoid receptor (CNR)-1 promoter (P=1.0 × 10(-6)), CNR1 (P=9.6 × 10(-5)) and the leptin (LEP) promoter (P=1.4 × 10(-4)) conferred robust-independent risks for weight gain. A model combining these three variants was highly significant (P=1.3 × 10(-9)) with a 0.85 effect size between lowest and highest risk groups. All results survived correction for multiple testing and were not dependent on dose, plasma level or ethnicity. We found no evidence for association with a reported functional variant in the endocannabinoid metabolic enzyme, fatty acid amide hydrolase, whereas body mass index-associated single-nucleotide polymorphisms in FTO and MC4R showed only trend associations. These data suggest a substantial genetic contribution of common variants in energy balance regulatory genes to individual antipsychotic-associated weight gain in children and adolescents, which supersedes findings from prior adult studies. The effects are robust enough to be detected after only 8 weeks and are more prominent in this largely treatment naive population. This study highlights compelling directions for further exploration of the pharmacogenetic basis of this concerning multifactorial adverse event