Visualising biological data: a semantic approach to tool and database integration

<p>Abstract</p> <p>Motivation</p> <p>In the biological sciences, the need to analyse vast amounts of information has become commonplace. Such large-scale analyses often involve drawing together data from a variety of different databases, held remotely on the internet or locally on in-house servers. Supporting these tasks are <it>ad hoc </it>collections of data-manipulation tools, scripting languages and visualisation software, which are often combined in arcane ways to create cumbersome systems that have been customised for a particular purpose, and are consequently not readily adaptable to other uses. For many day-to-day bioinformatics tasks, the sizes of current databases, and the scale of the analyses necessary, now demand increasing levels of automation; nevertheless, the unique experience and intuition of human researchers is still required to interpret the end results in any meaningful biological way. Putting humans in the loop requires tools to support real-time interaction with these vast and complex data-sets. Numerous tools do exist for this purpose, but many do not have optimal interfaces, most are effectively isolated from other tools and databases owing to incompatible data formats, and many have limited real-time performance when applied to realistically large data-sets: much of the user's cognitive capacity is therefore focused on controlling the software and manipulating esoteric file formats rather than on performing the research.</p> <p>Methods</p> <p>To confront these issues, harnessing expertise in human-computer interaction (HCI), high-performance rendering and distributed systems, and guided by bioinformaticians and end-user biologists, we are building reusable software components that, together, create a toolkit that is both architecturally sound from a computing point of view, and addresses both user and developer requirements. Key to the system's usability is its direct exploitation of semantics, which, crucially, gives individual components knowledge of their own functionality and allows them to interoperate seamlessly, removing many of the existing barriers and bottlenecks from standard bioinformatics tasks.</p> <p>Results</p> <p>The toolkit, named Utopia, is freely available from <url>http://utopia.cs.man.ac.uk/</url>.</p

Biodegradative mechanism of the brown rot basidiomycete Gloeophyllum trabeum: evidence for an extracellular hydroquinone-driven fenton reaction

AbstractWe have identified key components of the extracellular oxidative system that the brown rot fungus Gloeophyllum trabeum uses to degrade a recalcitrant polymer, polyethylene glycol, via hydrogen abstraction reactions. G. trabeum produced an extracellular metabolite, 2,5-dimethoxy-1,4-benzoquinone, and reduced it to 2,5-dimethoxyhydroquinone. In the presence of 2,5-dimethoxy-1,4-benzoquinone, the fungus also reduced extracellular Fe3+ to Fe2+ and produced extracellular H2O2. Fe3+ reduction and H2O2 formation both resulted from a direct, non-enzymatic reaction between 2,5-dimethoxyhydroquinone and Fe3+. polyethylene glycol depolymerization by G. trabeum required both 2,5-dimethoxy-1,4-benzoquinone and Fe3+ and was completely inhibited by catalase. These results provide evidence that G. trabeum uses a hydroquinone-driven Fenton reaction to cleave polyethylene glycol. We propose that similar reactions account for the ability of G. trabeum to attack lignocellulose

A behavioral comparison of male and female adults with high functioning autism spectrum conditions

Autism spectrum conditions (ASC) affect more males than females in the general population. However, within ASC it is unclear if there are phenotypic sex differences. Testing for similarities and differences between the sexes is important not only for clinical assessment but also has implications for theories of typical sex differences and of autism. Using cognitive and behavioral measures, we investigated similarities and differences between the sexes in age- and IQ-matched adults with ASC (high-functioning autism or Asperger syndrome). Of the 83 (45 males and 38 females) participants, 62 (33 males and 29 females) met Autism Diagnostic Interview-Revised (ADI-R) cut-off criteria for autism in childhood and were included in all subsequent analyses. The severity of childhood core autism symptoms did not differ between the sexes. Males and females also did not differ in self-reported empathy, systemizing, anxiety, depression, and obsessive-compulsive traits/symptoms or mentalizing performance. However, adult females with ASC showed more lifetime sensory symptoms (p = 0.036), fewer current socio-communication difficulties (p = 0.001), and more self-reported autistic traits (p = 0.012) than males. In addition, females with ASC who also had developmental language delay had lower current performance IQ than those without developmental language delay (p<0.001), a pattern not seen in males. The absence of typical sex differences in empathizing-systemizing profiles within the autism spectrum confirms a prediction from the extreme male brain theory. Behavioral sex differences within ASC may also reflect different developmental mechanisms between males and females with ASC. We discuss the importance of the superficially better socio-communication ability in adult females with ASC in terms of why females with ASC may more often go under-recognized, and receive their diagnosis later, than males

PhenoFam-gene set enrichment analysis through protein structural information

<p>Abstract</p> <p>Background</p> <p>With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam.</p> <p>Results</p> <p>PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, <it>etc</it>.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins.</p> <p>Conclusions</p> <p>PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.</p

Keeping Pace with Your Eating: Visual Feedback Affects Eating Rate in Humans

Deliberately eating at a slower pace promotes satiation and eating quickly has been associated with a higher body mass index. Therefore, understanding factors that affect eating rate should be given high priority. Eating rate is affected by the physical/textural properties of a food, by motivational state, and by portion size and palatability. This study explored the prospect that eating rate is also influenced by a hitherto unexplored cognitive process that uses ongoing perceptual estimates of the volume of food remaining in a container to adjust intake during a meal. A 2 (amount seen; 300ml or 500ml) x 2 (amount eaten; 300ml or 500ml) between-subjects design was employed (10 participants in each condition). In two ‘congruent’ conditions, the same amount was seen at the outset and then subsequently consumed (300ml or 500ml). To dissociate visual feedback of portion size and actual amount consumed, food was covertly added or removed from a bowl using a peristaltic pump. This created two additional ‘incongruent’ conditions, in which 300ml was seen but 500ml was eaten or vice versa. We repeated these conditions using a savoury soup and a sweet dessert. Eating rate (ml per second) was assessed during lunch. After lunch we assessed fullness over a 60-minute period. In the congruent conditions, eating rate was unaffected by the actual volume of food that was consumed (300ml or 500ml). By contrast, we observed a marked difference across the incongruent conditions. Specifically, participants who saw 300ml but actually consumed 500ml ate at a faster rate than participants who saw 500ml but actually consumed 300ml. Participants were unaware that their portion size had been manipulated. Nevertheless, when it disappeared faster or slower than anticipated they adjusted their rate of eating accordingly. This suggests that the control of eating rate involves visual feedback and is not a simple reflexive response to orosensory stimulatio

Light-induced transcriptional responses associated with proteorhodopsin-enhanced growth in a marine flavobacterium

Proteorhodopsin (PR) is a photoprotein that functions as a light-driven proton pump in diverse marine Bacteria and Archaea. Recent studies have suggested that PR may enhance both growth rate and yield in some flavobacteria when grown under nutrient-limiting conditions in the light. The direct involvement of PR, and the metabolic details enabling light-stimulated growth, however, remain uncertain. Here, we surveyed transcriptional and growth responses of a PR-containing marine flavobacterium during carbon-limited growth in the light and the dark. As previously reported (Gómez-Consarnau et al., 2007), Dokdonia strain MED134 exhibited light-enhanced growth rates and cell yields under low carbon growth conditions. Inhibition of retinal biosynthesis abolished the light-stimulated growth response, supporting a direct role for retinal-bound PR in light-enhanced growth. Among protein-coding transcripts, both PR and retinal biosynthetic enzymes showed significant upregulation in the light. Other light-associated proteins, including bacterial cryptochrome and DNA photolyase, were also expressed at significantly higher levels in the light. Membrane transporters for Na+/phosphate and Na+/alanine symporters, and the Na+-translocating NADH-quinone oxidoreductase (NQR) linked electron transport chain, were also significantly upregulated in the light. Culture experiments using a specific inhibitor of Na+-translocating NQR indicated that sodium pumping via NQR is a critical metabolic process in the light-stimulated growth of MED134. In total, the results suggested the importance of both the PR-enabled, light-driven proton gradient, as well as the generation of a Na+ ion gradient, as essential components for light-enhanced growth in these flavobacteria.Gordon and Betty Moore FoundationNational Science Foundation (U.S.) (NSF Science and Technology Center Award EF0424599.)Japan Society for the Promotion of Science (Postdoctoral Fellowships for Research Abroad

Investigation of G72 (DAOA) expression in the human brain

<p>Abstract</p> <p>Background</p> <p>Polymorphisms at the G72/G30 locus on chromosome 13q have been associated with schizophrenia or bipolar disorder in more than ten independent studies. Even though the genetic findings are very robust, the physiological role of the predicted G72 protein has thus far not been resolved. Initial reports suggested G72 as an activator of D-amino acid oxidase (DAO), supporting the glutamate dysfunction hypothesis of schizophrenia. However, these findings have subsequently not been reproduced and reports of endogenous human G72 mRNA and protein expression are extremely limited. In order to better understand the function of this putative schizophrenia susceptibility gene, we attempted to demonstrate G72 mRNA and protein expression in relevant human brain regions.</p> <p>Methods</p> <p>The expression of G72 mRNA was studied by northern blotting and semi-quantitative SYBR-Green and Taqman RT-PCR. Protein expression in human tissue lysates was investigated by western blotting using two custom-made specific anti-G72 peptide antibodies. An in-depth <it>in silico </it>analysis of the G72/G30 locus was performed in order to try and identify motifs or regulatory elements that provide insight to G72 mRNA expression and transcript stability.</p> <p>Results</p> <p>Despite using highly sensitive techniques, we failed to identify significant levels of G72 mRNA in a variety of human tissues (e.g. adult brain, amygdala, caudate nucleus, fetal brain, spinal cord and testis) human cell lines or schizophrenia/control post mortem BA10 samples. Furthermore, using western blotting in combination with sensitive detection methods, we were also unable to detect G72 protein in a number of human brain regions (including cerebellum and amygdala), spinal cord or testis. A detailed <it>in silico </it>analysis provides several lines of evidence that support the apparent low or absent expression of G72.</p> <p>Conclusion</p> <p>Our results suggest that native G72 protein is not normally present in the tissues that we analysed in this study. We also conclude that the lack of demonstrable G72 expression in relevant brain regions does not support a role for G72 in modulation of DAO activity and the pathology of schizophrenia via a DAO-mediated mechanism. <it>In silico </it>analysis suggests that G72 is not robustly expressed and that the transcript is potentially labile. Further studies are required to understand the significance of the G72/30 locus to schizophrenia.</p

Gastroesophageal reflux leads to esophageal cancer in a surgical model with mice

<p>Abstract</p> <p>Background</p> <p>Esophago-gastroduodenal anastomosis with rats mimics the development of human Barrett's esophagus and esophageal adenocarcinoma by introducing mixed reflux of gastric and duodenal contents into the esophagus. However, use of this rat model for mechanistic and chemopreventive studies is limited due to lack of genetically modified rat strains. Therefore, a mouse model of esophageal adenocarcinoma is needed.</p> <p>Methods</p> <p>We performed reflux surgery on wild-type, <it>p53</it>^{<it>A</it>135<it>V </it>}transgenic, and <it>INK4a/Arf</it>^+/-mice of A/J strain. Some mice were also treated with omeprazole (1,400 ppm in diet), iron (50 mg/kg/m, <it>i.p</it>.), or gastrectomy plus iron. Mouse esophagi were harvested at 20, 40 or 80 weeks after surgery for histopathological analysis.</p> <p>Results</p> <p>At week 20, we observed metaplasia in wild-type mice (5%, 1/20) and <it>p53</it>^{<it>A</it>135<it>V </it>}mice (5.3%, 1/19). At week 40, metaplasia was found in wild-type mice (16.2%, 6/37), <it>p53</it>^{<it>A</it>135<it>V </it>}mice (4.8%, 2/42), and wild-type mice also receiving gastrectomy and iron (6.7%, 1/15). Esophageal squamous cell carcinoma developed in <it>INK4a/Arf</it>^+/-mice (7.1%, 1/14), and wild-type mice receiving gastrectomy and iron (21.4%, 3/14). Among 13 wild-type mice which were given iron from week 40 to 80, twelve (92.3%) developed squamous cell carcinoma at week 80. None of these mice developed esophageal adenocarcinoma.</p> <p>Conclusion</p> <p>Surgically induced gastroesophageal reflux produced esophageal squamous cell carcinoma, but not esophageal adenocarcinoma, in mice. Dominant negative <it>p53 </it>mutation, heterozygous loss of <it>INK4a/Arf</it>, antacid treatment, iron supplementation, or gastrectomy failed to promote esophageal adenocarcinoma in these mice. Further studies are needed in order to develop a mouse model of esophageal adenocarcinoma.</p

Gene ontology based transfer learning for protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as <it>GO</it>, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the <it>GO </it>terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.</p> <p>Results</p> <p>In this paper, we propose a Gene Ontology Based Transfer Learning Model (<it>GO-TLM</it>) for large-scale protein subcellular localization. The model transfers the signature-based homologous <it>GO </it>terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false <it>GO </it>terms that are resulted from evolutionary divergence. We derive three <it>GO </it>kernels from the three aspects of gene ontology to measure the <it>GO </it>similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate <it>GO-TLM </it>performance against three baseline models: <it>MultiLoc, MultiLoc-GO </it>and <it>Euk-mPLoc </it>on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that <it>GO-TLM </it>achieves substantial accuracy improvement against the baseline models: 80.38% against model <it>Euk-mPLoc </it>67.40% with <it>12.98% </it>substantial increase; 96.65% and 96.27% against model <it>MultiLoc-GO </it>89.60% and 89.60%, with <it>7.05% </it>and <it>6.67% </it>accuracy increase on dataset <it>MultiLoc plant </it>and dataset <it>MultiLoc animal</it>, respectively; 97.14%, 95.90% and 96.85% against model <it>MultiLoc-GO </it>83.70%, 90.10% and 85.70%, with accuracy increase <it>13.44%</it>, <it>5.8% </it>and <it>11.15% </it>on dataset <it>BaCelLoc plant</it>, dataset <it>BaCelLoc fungi </it>and dataset <it>BaCelLoc animal </it>respectively. For <it>BaCelLoc </it>independent sets, <it>GO-TLM </it>achieves 81.25%, 80.45% and 79.46% on dataset <it>BaCelLoc plant holdout</it>, dataset <it>BaCelLoc plant holdout </it>and dataset <it>BaCelLoc animal holdout</it>, respectively, as compared against baseline model <it>MultiLoc-GO </it>76%, 60.00% and 73.00%, with accuracy increase <it>5.25%</it>, <it>20.45% </it>and <it>6.46%</it>, respectively.</p> <p>Conclusions</p> <p>Since direct homology-based <it>GO </it>term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, <it>GO-TLM</it>) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based <it>GO </it>term transfer and explicitly weighing the <it>GO </it>kernels substantially improve the prediction performance.</p