Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage.

One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11-q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis

Statistical significance of cis-regulatory modules

BACKGROUND: It is becoming increasingly important for researchers to be able to scan through large genomic regions for transcription factor binding sites or clusters of binding sites forming cis-regulatory modules. Correspondingly, there has been a push to develop algorithms for the rapid detection and assessment of cis-regulatory modules. While various algorithms for this purpose have been introduced, most are not well suited for rapid, genome scale scanning. RESULTS: We introduce methods designed for the detection and statistical evaluation of cis-regulatory modules, modeled as either clusters of individual binding sites or as combinations of sites with constrained organization. In order to determine the statistical significance of module sites, we first need a method to determine the statistical significance of single transcription factor binding site matches. We introduce a straightforward method of estimating the statistical significance of single site matches using a database of known promoters to produce data structures that can be used to estimate p-values for binding site matches. We next introduce a technique to calculate the statistical significance of the arrangement of binding sites within a module using a max-gap model. If the module scanned for has defined organizational parameters, the probability of the module is corrected to account for organizational constraints. The statistical significance of single site matches and the architecture of sites within the module can be combined to provide an overall estimation of statistical significance of cis-regulatory module sites. CONCLUSION: The methods introduced in this paper allow for the detection and statistical evaluation of single transcription factor binding sites and cis-regulatory modules. The features described are implemented in the Search Tool for Occurrences of Regulatory Motifs (STORM) and MODSTORM software

Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes

Abstract Background Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes. Results We describe a new triad pattern algorithm that predicts strong promoter candidates in annotated bacterial genomes by matching specific patterns for the group I σ70 factors of Escherichia coli RNA polymerase. It detects promoter-specific motifs by consecutively matching three patterns, consisting of an UP-element, required for interaction with the α subunit, and then optimally-separated patterns of -35 and -10 boxes, required for interaction with the σ70 subunit of RNA polymerase. Analysis of 43 bacterial genomes revealed that the frequency of candidate sequences depends on the A+T content of the DNA under examination. The accuracy of in silico prediction was experimentally validated for the genome of a hyperthermophilic bacterium, Thermotoga maritima, by applying a cell-free expression assay using the predicted strong promoters. In this organism, the strong promoters govern genes for translation, energy metabolism, transport, cell movement, and other as-yet unidentified functions. Conclusion The triad pattern algorithm developed for predicting strong bacterial promoters is well suited for analyzing bacterial genomes with an A+T content of less than 62%. This computational tool opens new prospects for investigating global gene expression, and individual strong promoters in bacteria of medical and/or economic significance.</p

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

Optimization of Canolol Production from Canola Meal Using Microwave Digestion as a Pre-Treatment Method

Canola meal, the by-product of canola oil refining, is a rich source of phenolic compounds and protein. The meal, however, is primarily utilized as animal feed but represents an invaluable source of nutraceuticals. Of particular interest are the sinapates, sinapine and sinapic acid, with the decarboxylation of the latter to form canolol. Extracting these phenolics has been carried out using a variety of different methods, although there is an urgent need for environmentally safe and sustainable methods. Microwave-assisted solvent extraction (MAE), as a green extraction method, is receiving considerable interest. Its ease of use makes MAE one of the best methods for studying multiple solvents. The formation of canolol, from sinapine and sinapic acid, is primarily dependent on temperature, which favors the decarboxylation reaction. The application of MAE, using the MultiwaveTM 500 microwave system with green extractants, was undertaken to assess its ability to enhance the yield of sinapates and canolol. This study examined the effects of different pre-treatment temperature-time combinations of 140, 150, 160, and 170 °C for 5, 10, 15, 20, and 30 min on the extraction of canolol and other canola endogenous phenolic compounds. Total phenolic content (TPC), total flavonoid content (TFC), as well as metal ion chelation (MIC) and DPPH radical activity of the different extracts were assessed. The results confirmed that extractability of canolol was optimized with methanol at 151 °C and with ethanol at 170 °C with pre-treatment times of 15.43 min and 19.31 min, respectively. Furthermore, there was a strong positive correlation between TPC and TFC (p TM 500 microwave instrument, to enhance the yield of canolol. This was accompanied by substantial improvements in the antioxidant activity of the different extracts and further established the efficacy of the current MAE method for isolating important natural phenolic derivatives for utilization by the nutraceutical industry

Application of Supercritical Fluid Extraction (SFE) of Tocopherols and Carotenoids (Hydrophobic Antioxidants) Compared to Non-SFE Methods

Natural antioxidants have renewed value for human health and the food industry. Green labeling is becoming an important attribute for consumers and is impacting food processing and formulations. Clean label is another attribute that ranked third after the &ldquo;free-from&rdquo; claims and &ldquo;a good source&rdquo; of nutrient claims. Clean label attributes also are ranked higher than local, seasonal, and organic. Techniques that are able to preserve the valuable characteristics of natural antioxidants, while eliminating even trace amounts of solvent residues from their extraction and processing, are important. Supercritical fluids (SCF) are an effective green technology that can be adopted for extraction of natural antioxidants. This review is focused on the application of supercritical carbon dioxide (SCCO2) for extracting hydrophobic antioxidant compounds with an emphasis on oilseed crops and carrots. The information provided about extraction parameters helps to guide optimization of the yield of tocopherols and carotenoids. Pressure is the most effective parameter for the extraction yield of tocopherol among the other parameters, such as temperature, time, and CO2 flow rate. For carotenoid extraction, both pressure and temperature have a large impact on extraction yield. Higher yields of antioxidants, greater purity of the extracts, and larger retention of bioactivity are the main advantages of supercritical fluid extraction (SFE) in comparison to other conventional techniques. The benefits of SCF technology may open new opportunities for extracting valuable, natural and effective antioxidant compounds from food processing co-streams for use as bioactive compounds