Microarray data mining with visual programming

Profiling of carbonyl metabolic fingerprints in urine of Graves' disease patients based on atmospheric ionization mass spectrometry

Graves' disease (GD) is considered among the organ autoimmune diseases and is somewhat linked to other autoimmune and secondary diseases. Commonly used detection methods rely on identifying characteristic clinical features and abnormal biochemical markers, but they have certain limitations and may be affected by patient medication. In this study, a desorption separation ionization (DSI) device coupled with a linear ion trap mass spectrometer is introduced for effective detection and screening of urine from GD patients. To enhance the sensitivity of MS analysis, derivatization reagent is utilized as a labeling method. The MS signal is used for metabolic profiling, through which differential metabolites and pathways are identified. Subsequently, processing the acquired spectra with a machine learning algorithm enables successful differentiation of GD patients and healthy individuals. This method is believed to provide versatile and powerful technical support for effective detection on the scene. Notably, this method offers the advantage of achieving early and rapid diagnosis of thyroid-related diseases.

From pixels to insights: Machine learning and deep learning for bioimage analysis

Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user-friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.

Targeted Analysis of Tears Revealed Specific Altered Metal Homeostasis in Age-Related Macular Degeneration

Purpose

Specific altered metal homeostasis has been investigated in the tear film of age-related macular degeneration (AMD) patients considering that metal dyshomeostasis contributes to the production of free radicals, inflammation, and apoptosis and results in conformational changes of proteins.

Methods

A multitargeted approach based on spectrophotometry and mass spectrometry techniques has been implemented to the multiplexed quantitation of lactoferrin (LF), S100 calcium binding protein A6 (S100A6), metallothionein 1A (MT1A), complement factor H (CFH), clusterin (CLU), amyloid precursor protein (APP), Mg, P, Na, Fe, Cu, Zn, and Ca, in the tear film from 60 subjects, 31 patients diagnosed with the dry form of AMD, and 29 healthy individuals

Results

Significant up-regulations of MT1A (1.9-fold) and S100A6 (1.4-fold) and down-regulations of LF (0.7-fold), Fe (0.6-fold), Mg (0.7-fold), and Cu (0.7-fold) were observed in AMD patients, when compared to control subjects. Of all the studied variables, only APP showed negative correlation with age in the AMD group. Also, positive correlations were observed for the variables Mg and Na, Cu and Mg, and P and Mg in both the AMD and control groups, whereas positive correlations were exclusively determined in the AMD group for Cu and LF, Na and Ca, and Mg and Ca. The panel constituted of MT1A, Na, and Mg predicts AMD disease in 73% of cases.

Conclusions

The different levels of target metals and (metallo-)proteins in the tear film suggest altered metal homeostasis in AMD patients. These observed pathophysiological changes may be related with the anomalous protein aggregation in the macula.

Quasar: Easy Machine Learning for Biospectroscopy

Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets. In our opinion, open-source software with strong community engagement is the way forward. To counter these problems, we develop Quasar, an open-source and user-friendly software, as a solution to these challenges. Here, we present case studies to highlight some Quasar features analyzing infrared spectroscopy data using various machine learning techniques.

Hands-on training about overfitting

Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis.

Every teacher strives for an a-ha moment, a sudden revelation by the student who gained a fundamental insight she will always remember. In the past years, authors of this paper have been tailoring their courses in machine learning to include material that could lead students to such discoveries. We aim to expose machine learning to practitioners–not only computer scientists but also molecular biologists and students of biomedicine, that is, the end-users of bioinformatics’ computational approaches. In this article, we lay out a course that aims to teach about overfitting, one of the key concepts in machine learning that needs to be understood, mastered, and avoided in data science applications. We propose a hands-on approach that uses an open-source workflow-based data science toolbox that combines data visualization and machine learning. In the proposed training about overfitting, we first deceive the students, then expose the problem, and finally challenge them to find the solution. In the paper, we present three lessons in overfitting and associated data analysis workflows and motivate the use of introduced computation methods by relating them to concepts conveyed by instructors.

Systemic Alterations of Immune Response-Related Proteins during Glaucoma Development in the Murine Model DBA/2J

Animal models of glaucoma, a neurodegenerative disease affecting the retina, offer the opportunity to study candidate molecular biomarkers throughout the disease. In this work, the DBA/2J glaucomatous mouse has been used to study the systemic levels of several proteins previously identified as potential biomarkers of glaucoma, along the pre- to post-glaucomatous transition. Serum samples obtained from glaucomatous and control mice at 4, 10, and 14 months, were classified into different experimental groups according to the optic nerve damage at 14 months old. Quantifications of ten serum proteins were carried out by enzyme immunoassays. Changes in the levels of some of these proteins in the transition to glaucomatous stages were identified, highlighting the significative decrease in the concentration of complement C4a protein. Moreover, the five-protein panel consisting of complement C4a, complement factor H, ficolin-3, apolipoprotein A4, and transthyretin predicted the transition to glaucoma in 78% of cases, and to the advanced disease in 89%. Our data, although still preliminary, suggest that disease development in DBA/2J mice is associated with important molecular changes in immune response and complement system proteins and demonstrate the utility of this model in identifying, at systemic level, potential markers for the diagnosis of glaucoma.

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

Deep learning approaches for image preprocessing and analysis offer important advantages, but these are rarely incorporated into user-friendly software. Here the authors present an easy-to-use visual programming toolbox integrating deep-learning and interactive data visualization for image analysis.

scOrange-a tool for hands-on training of concepts from single-cell data analytics

MOTIVATION

Single-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve.

RESULTS

We propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training.

AVAILABILITY AND IMPLEMENTATION

scOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si.

Modelling gene-environment interaction in first episodes of psychosis

INTRODUCTION

Recent research demonstrates the heterogeneous etiology of psychotic disorders, where gen-environment (GxE) interaction plays a key role. Large genetic studies have linked many genetic variants with schizophrenia, but each variant is only associated with a small effect and the GxE interaction contribution has not been evaluated.

METHODS

The PEPs Project was designed to carefully collect a large amount of genetic and environmental exposure data of 335 FEP patients and 253 matched healthy controls.780single-nucleotide polymorphisms (from 159 candidate genes)and 16 environmental variables previously reported as the main psychosis non-genetic risk factors were analyzed together using entropy-based measures of information gain.

RESULTS

Our analyses identified an interaction between nine SNPs and the exposition to the environmental risk factors of psychosis, showing a clear enrichment of genes linked to serotonin neurotransmission and neurodevelopmental processes.

CONCLUSIONS

This study has allowed the identification of several GxE-environment interactions involved in the risk of presenting a FEP. Our results highlight the importance of serotonin neurotransmission interacting with certain environmental stimuli. The serotoninergic system may be playing a key role in the regulatory network of stress and other systems implicated in the emergence and development of psychotic disorders.

Lipidomics: Prospects from a technological perspective

Over the last two decades, lipidomics has evolved into an 'omics' technology pari passu with benchmarking 'omics' technologies, such as genomics or proteomics. The driving force behind this development was a constant advance in mass spectrometry and related technologies. The aim of this opinion article is to give the interested reader a concise but still comprehensive overview about the technological state of the art in lipidomics, current challenges and perspectives for future development. As such, this article guides through the whole workflow of lipidomics, from sampling to data analysis. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Neuropsychological test selection for cognitive impairment classification: A machine learning approach

INTRODUCTION

Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI), or dementia using a suite of classification techniques.

METHOD

Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis; Clinical Dementia Rating, CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals with CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. A total of 27 demographic, psychological, and neuropsychological variables were available for variable selection.

RESULTS

No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0-99.1%), geometric mean (60.9-98.1%), sensitivity (44.2-100%), and specificity (52.7-100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2-9 variables were required for classification and varied between datasets in a clinically meaningful way.

CONCLUSIONS

The current study results reveal that machine learning techniques can accurately classify cognitive impairment and reduce the number of measures required for diagnosis.

Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder.

Comparative genome and transcriptome analyses of the social amoeba Acytostelium subglobosum that accomplishes multicellular development without germ-soma differentiation

BACKGROUND

Social amoebae are lower eukaryotes that inhabit the soil. They are characterized by the construction of a starvation-induced multicellular fruiting body with a spore ball and supportive stalk. In most species, the stalk is filled with motile stalk cells, as represented by the model organism Dictyostelium discoideum, whose developmental mechanisms have been well characterized. However, in the genus Acytostelium, the stalk is acellular and all aggregated cells become spores. Phylogenetic analyses have shown that it is not an ancestral genus but has lost the ability to undergo cell differentiation.

RESULTS

We performed genome and transcriptome analyses of Acytostelium subglobosum and compared our findings to other available dictyostelid genome data. Although A. subglobosum adopts a qualitatively different developmental program from other dictyostelids, its gene repertoire was largely conserved. Yet, families of polyketide synthase and extracellular matrix proteins have not expanded and a serine protease and ABC transporter B family gene, tagA, and a few other developmental genes are missing in the A. subglobosum lineage. Temporal gene expression patterns are astonishingly dissimilar from those of D. discoideum, and only a limited fraction of the ortholog pairs shared the same expression patterns, so that some signaling cascades for development seem to be disabled in A. subglobosum.

CONCLUSIONS

The absence of the ability to undergo cell differentiation in Acytostelium is accompanied by a small change in coding potential and extensive alterations in gene expression patterns.

Comprehensive lipidome analysis by shotgun lipidomics on a hybrid quadrupole-orbitrap-linear ion trap mass spectrometer

Here we report on the application of a novel shotgun lipidomics platform featuring an Orbitrap Fusion mass spectrometer equipped with an automated nanoelectrospray ion source. To assess the performance of the platform for in-depth lipidome analysis, we evaluated various instrument parameters, including its high resolution power unsurpassed by any other contemporary Orbitrap instrumentation, its dynamic quantification range and its efficacy for in-depth structural characterization of molecular lipid species by quadrupole-based higher-energy collisional dissociation (HCD), and ion trap-based resonant-excitation collision-induced dissociation (CID). This evaluation demonstrated that FTMS analysis with a resolution setting of 450,000 allows distinguishing isotopes from different lipid species and features a linear dynamic quantification range of at least four orders of magnitude. Evaluation of fragmentation analysis demonstrated that combined use of HCD and CID yields complementary fragment ions of molecular lipid species. To support global lipidome analysis, we designed a method, termed MS(ALL), featuring high resolution FTMS analysis for lipid quantification, and FTMS(2) analysis using both HCD and CID and ITMS(3) analysis utilizing dual CID for in-depth structural characterization of molecular glycerophospholipid species. The performance of the MS(ALL) method was benchmarked in a comparative analysis of mouse cerebellum and hippocampus. This analysis demonstrated extensive lipidome quantification covering 311 lipid species encompassing 20 lipid classes, and identification of 202 distinct molecular glycerophospholipid species when applying a novel high confidence filtering strategy. The work presented here validates the performance of the Orbitrap Fusion mass spectrometer for in-depth lipidome analysis.

Computational models of liver fibrosis progression for hepatitis C virus chronic infection

BACKGROUND

Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was hypothesized to be associated with severity of liver disease. However, no reliable viral markers predicting disease severity have been identified. Here, we report the utility of sequences from 3 HCV 1b genomic regions, Core, NS3 and NS5b, to identify viral genetic markers associated with fast and slow rate of fibrosis progression (RFP) among patients with and without liver transplantation (n = 42).

METHODS

A correlation-based feature selection (CFS) method was used to detect and identify RFP-relevant viral markers. Machine-learning techniques, linear projection (LP) and Bayesian Networks (BN), were used to assess and identify associations between the HCV sequences and RFP.

RESULTS

Both clustering of HCV sequences in LP graphs using physicochemical properties of nucleotides and BN analysis using polymorphic sites showed similarities among HCV variants sampled from patients with a similar RFP, while distinct HCV genetic properties were found associated with fast or slow RFP. Several RFP-relevant HCV sites were identified. Computational models parameterized using the identified sites accurately associated HCV strains with RFP in 70/30 split cross-validation (90-95% accuracy) and in validation tests (85-90% accuracy). Validation tests of the models constructed for patients with or without liver transplantation suggest that the RFP-relevant genetic markers identified in the HCV Core, NS3 and NS5b genomic regions may be useful for the prediction of RFP regardless of transplant status of patients.

CONCLUSIONS

The apparent strong genetic association to RFP suggests that HCV genetic heterogeneity has a quantifiable effect on severity of liver disease, thus presenting opportunity for developing genetic assays for measuring virulence of HCV strains in clinical and public health settings.

Evaluation and construction of diagnostic criteria for inclusion body myositis

OBJECTIVE

To use patient data to evaluate and construct diagnostic criteria for inclusion body myositis (IBM), a progressive disease of skeletal muscle.

METHODS

The literature was reviewed to identify all previously proposed IBM diagnostic criteria. These criteria were applied through medical records review to 200 patients diagnosed as having IBM and 171 patients diagnosed as having a muscle disease other than IBM by neuromuscular specialists at 2 institutions, and to a validating set of 66 additional patients with IBM from 2 other institutions. Machine learning techniques were used for unbiased construction of diagnostic criteria.

RESULTS

Twenty-four previously proposed IBM diagnostic categories were identified. Twelve categories all performed with high (≥97%) specificity but varied substantially in their sensitivities (11%-84%). The best performing category was European Neuromuscular Centre 2013 probable (sensitivity of 84%). Specialized pathologic features and newly introduced strength criteria (comparative knee extension/hip flexion strength) performed poorly. Unbiased data-directed analysis of 20 features in 371 patients resulted in construction of higher-performing data-derived diagnostic criteria (90% sensitivity and 96% specificity).

CONCLUSIONS

Published expert consensus-derived IBM diagnostic categories have uniformly high specificity but wide-ranging sensitivities. High-performing IBM diagnostic category criteria can be developed directly from principled unbiased analysis of patient data.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that published expert consensus-derived IBM diagnostic categories accurately distinguish IBM from other muscle disease with high specificity but wide-ranging sensitivities.

Data visualization in environmental proteomics

From raw data to gene expression profiles, from single cultures to complex microbial communities, environmental proteomics works with data of different complexity levels that need to be interpreted in detail or in its entirety. Although data visualization is closely connected with data analysis approaches, this work will solely focus on data visualization. Complementing traditional tools such as bar charts or line graphs, scientists and visualization professionals have been provided sophisticated visualization tools. Many rules and concerns regarding the display of single but also complex data will be reviewed and discussed. Visual approaches such as microcharts, heat maps, stream graphs, and tree maps will be brought to the reader's attention and demonstrated by utilizing real data sets.

Gene - Gene Interactions Among MCP Genes Polymorphisms in Asthma

Purpose

Monocyte chemoattractant proteins (MCPs) are important cytokines that involved in cellular activation and releasing of inflammatoy mediators by basophils and eosinophils in allergic disease. Some MCP gene variants implicate in asthma and monoclonal antibody for MCP-3 blocks allergic inflammations in the patients with asthma. Detection of interactions between gene and environment or between genes for complex disease such as asthma is important. We searched for an evidence of genetic effect of single nucleotide polymorphisms (SNPs) of MCP genes as well as gene - gene interactions involved in asthma.

Methods

Four hundreds asthmatics and four hundreds normal controls were enrolled. Asthma was defined as a positive bronchodilator response or positive methacholine provocation test with compatible clinical symptoms. Seven MCP gene SNPs (2 SNPs in MCP-1, 1 in MCP-2, and 4 in MCP-3) were included. Association analyses between SNP and asthma, and the tests for gene - gene interaction were performed.

Results

Strong linkage disequilibria were found among 7 MCP gene polymorphisms. There was no SNP that showed a significant association with asthma among 7 SNPs of 3 MCP genes. No haplotype was associated with asthma, either. The combination of MCP1-2518G>A, MCP2+46A>C, and MCP3+563C>T was the best predictive model for asthma as compared to the control in tests for gene - gene interaction. The MCP1-2518G>A and MCP2+46A>C was the second best predictive combination and this had the highest synergistic interaction effect on the subject's status than any other combination of polymorphisms. Complete linkages were not associated with the gene - gene interactions models.

Conclusions

MCP gene polymorphisms probably interact with each other; thus, these findings may help in developing a possible genetic marker to predict asthma.

Acyclic nucleoside phosphonates: a study on cytochrome P450 gene expression

1. Nucleotide analogues comprise an important class of drugs used in treatment of viral infections but also cancer. These drugs affect the structural integrity of DNA and activate different pathways and processes in the cell and may directly or indirectly influence the drug metabolizing system. Adefovir dipivoxil (AD) and tenofovir disoproxil (TD) are nucleotide analogues approved for the treatment of chronic hepatitis B and/or HIV/AIDS infection. 2. To evaluate the risk of their drug-drug interactions on the level of drug metabolism, an effect of both compounds on cytochromes P450 expression was studied using cDNA microarrays, real-time RT-PCR and immunoblotting. Mice were given intraperitoneally 25 mg/kg of AD or TD, respectively. As a positive control, a combination of prototypic cytochromes P450 (CYP) inducers, phenobarbital and β-naphthoflavone was chosen. 3. The data obtained showed a significant CYP induction in the positive control group, but no clinically significant induction of CYP genes by AD or TD was observed. Our results support the evidence of safety of AD and TD with respect to drug-drug interactions based on enzyme induction. These findings are important as a plethora of new antivirals of different types are being tested and introduced to clinical practice, mostly to be used in combinations.

Different CHD chromatin remodelers are required for expression of distinct gene sets and specific stages during development of Dictyostelium discoideum

Control of chromatin structure is crucial for multicellular development and regulation of cell differentiation. The CHD (chromodomain-helicase-DNA binding) protein family is one of the major ATP-dependent, chromatin remodeling factors that regulate nucleosome positioning and access of transcription factors and RNA polymerase to the eukaryotic genome. There are three mammalian CHD subfamilies and their impaired functions are associated with several human diseases. Here, we identify three CHD orthologs (ChdA, ChdB and ChdC) in Dictyostelium discoideum. These CHDs are expressed throughout development, but with unique patterns. Null mutants lacking each CHD have distinct phenotypes that reflect their expression patterns and suggest functional specificity. Accordingly, using genome-wide (RNA-seq) transcriptome profiling for each null strain, we show that the different CHDs regulate distinct gene sets during both growth and development. ChdC is an apparent ortholog of the mammalian Class III CHD group that is associated with the human CHARGE syndrome, and GO analyses of aberrant gene expression in chdC nulls suggest defects in both cell-autonomous and non-autonomous signaling, which have been confirmed through analyses of chdC nulls developed in pure populations or with low levels of wild-type cells. This study provides novel insight into the broad function of CHDs in the regulation development and disease, through chromatin-mediated changes in directed gene expression.

Identification of factors associated with sural nerve regeneration and degeneration in diabetic neuropathy

OBJECTIVE

Patients with diabetic neuropathy (DN) demonstrate variable degrees of nerve regeneration and degeneration. Our aim was to identify risk factors associated with sural nerve degeneration in patients with DN.

RESEARCH DESIGN AND METHODS

Demographic, anthropometric, biochemical, and anatomical data of subjects with DN from a 52-week trial of acetyl-L-carnitine were retrospectively examined. Based on the change in sural nerve myelinated fiber density (ΔMFD%), subjects were divided into three groups: regenerator (top 16 percentiles, n = 67), degenerator (bottom 16 percentiles, n = 67), and intermediate (n = 290), with dramatically increased, decreased, and steady ΔMFD%, respectively. ANOVA, Fisher exact test, and multifactorial logistic regression were used to evaluate statistical significance.

RESULTS

ΔMFD%s were 35.6 ± 17.4 (regenerator), -4.8 ± 12.1 (intermediate), and -39.8 ± 11.0 (degenerator). HbA1c at baseline was the only factor significantly different across the three groups (P = 0.01). In multifactorial logistic regression, HbA1c at baseline was also the only risk factor significantly different between regenerator (8.3 ± 1.6%) and degenerator (9.2 ± 1.8%) (odds ratio 0.68 [95% CI 0.54-0.85]; P < 0.01). Support Vector Machine classifier using HbA1c demonstrated 62.4% accuracy of classifying subjects into regenerator or degenerator. A preliminary microarray experiment revealed that upregulated genes in the regenerator group are enriched with cell cycle and myelin sheath functions, while downregulated genes are enriched in immune/inflammatory responses.

CONCLUSIONS

These data, based on the largest cohort with ΔMFD% information, suggest that HbA1c levels predict myelinated nerve fiber regeneration and degeneration in patients with DN. Therefore, maintaining optimal blood glucose control is likely essential in patients with DN to prevent continued nerve injury.

Analysis of lipid experiments (ALEX): a software framework for analysis of high-resolution shotgun lipidomics data

Global lipidomics analysis across large sample sizes produces high-content datasets that require dedicated software tools supporting lipid identification and quantification, efficient data management and lipidome visualization. Here we present a novel software-based platform for streamlined data processing, management and visualization of shotgun lipidomics data acquired using high-resolution Orbitrap mass spectrometry. The platform features the ALEX framework designed for automated identification and export of lipid species intensity directly from proprietary mass spectral data files, and an auxiliary workflow using database exploration tools for integration of sample information, computation of lipid abundance and lipidome visualization. A key feature of the platform is the organization of lipidomics data in ”database table format” which provides the user with an unsurpassed flexibility for rapid lipidome navigation using selected features within the dataset. To demonstrate the efficacy of the platform, we present a comparative neurolipidomics study of cerebellum, hippocampus and somatosensory barrel cortex (S1BF) from wild-type and knockout mice devoid of the putative lipid phosphate phosphatase PRG-1 (plasticity related gene-1). The presented framework is generic, extendable to processing and integration of other lipidomic data structures, can be interfaced with post-processing protocols supporting statistical testing and multivariate analysis, and can serve as an avenue for disseminating lipidomics data within the scientific community. The ALEX software is available at www.msLipidomics.info.

Identifying novel phenotypes of vulnerability and resistance to activity-based anorexia in adolescent female rats

OBJECTIVE

Activity-based anorexia is a translational rodent model that results in severe weight loss, hyperactivity, and voluntary self-starvation. The goal of our investigation was to identify vulnerable and resistant phenotypes of activity-based anorexia in adolescent female rats.

METHOD

Sprague-Dawley rats were maintained under conditions of restricted access to food (N = 64; or unlimited access, N = 16) until experimental exit, predefined as a target weight loss of 30-35% or meeting predefined criteria for animal health. Nonlinear mixed effects statistical modeling was used to describe wheel running behavior, time to event analysis was used to assess experimental exit, and a regressive partitioning algorithm was used to classify phenotypes.

RESULTS

Objective criteria were identified for distinguishing novel phenotypes of activity-based anorexia, including a vulnerable phenotype that conferred maximal hyperactivity, minimal food intake, and the shortest time to experimental exit, and a resistant phenotype that conferred minimal activity and the longest time to experimental exit.

DISCUSSION

The identification of objective criteria for defining vulnerable and resistant phenotypes of activity-based anorexia in adolescent female rats provides an important framework for studying the neural mechanisms that promote vulnerability to or protection against the development of self-starvation and hyperactivity during adolescence. Ultimately, future studies using these novel phenotypes may provide important translational insights into the mechanisms that promote these maladaptive behaviors characteristic of anorexia nervosa.

PRC2 overexpression and PRC2-target gene repression relating to poorer prognosis in small cell lung cancer

Small cell lung cancer (SCLC) is a subtype of lung cancer with poor prognosis. Expression array analysis of 23 SCLC cases and 42 normal tissues revealed that EZH2 and other PRC2 members were highly expressed in SCLC. ChIP-seq for H3K27me3 suggested that genes with H3K27me3(+) in SCLC were extended not only to PRC2-target genes in ES cells but also to other target genes such as cellular adhesion-related genes. These H3K27me3(+) genes in SCLC were repressed significantly, and introduction of the most repressed gene JUB into SCLC cell line lead to growth inhibition. Shorter overall survival of clinical SCLC cases correlated to repression of JUB alone, or a set of four genes including H3K27me3(+) genes. Treatment with EZH2 inhibitors, DZNep and GSK126, resulted in growth repression of SCLC cell lines. High PRC2 expression was suggested to contribute to gene repression in SCLC, and may play a role in genesis of SCLC.

ABC transporters in Dictyostelium discoideum development

ATP-binding cassette (ABC) transporters can translocate a broad spectrum of molecules across the cell membrane including physiological cargo and toxins. ABC transporters are known for the role they play in resistance towards anticancer agents in chemotherapy of cancer patients. There are 68 ABC transporters annotated in the genome of the social amoeba Dictyostelium discoideum. We have characterized more than half of these ABC transporters through a systematic study of mutations in their genes. We have analyzed morphological and transcriptional phenotypes for these mutants during growth and development and found that most of the mutants exhibited rather subtle phenotypes. A few of the genes may share physiological functions, as reflected in their transcriptional phenotypes. Since most of the abc-transporter mutants showed subtle morphological phenotypes, we utilized these transcriptional phenotypes to identify genes that are important for development by looking for transcripts whose abundance was unperturbed in most of the mutants. We found a set of 668 genes that includes many validated D. discoideum developmental genes. We have also found that abcG6 and abcG18 may have potential roles in intercellular signaling during terminal differentiation of spores and stalks.

Concordance of white matter and gray matter abnormalities in autism spectrum disorders: a voxel-based meta-analysis study

There are at least two fundamental unanswered questions in the literature on autism spectrum disorders (ASD): Are abnormalities in white (WM) and gray matter (GM) consistent with one another? Are WM morphometric alterations consistent with alterations in the GM of regions connected by these abnormal WM bundles and vice versa? The aim of this work is to bridge this gap. After selecting voxel-based morphometry and diffusion tensor imaging studies comparing autistic and normally developing groups of subjects, we conducted an activation likelihood estimation (ALE) meta-analysis to estimate consistent brain alterations in ASD. Multidimensional scaling was used to test the similarity of the results. The ALE results were then analyzed to identify the regions of concordance between GM and WM areas. We found statistically significant topological relationships between GM and WM abnormalities in ASD. The most numerous were negative concordances, found bilaterally but with a higher prevalence in the right hemisphere. Positive concordances were found in the left hemisphere. Discordances reflected the spatial distribution of negative concordances. Thus, a different hemispheric contribution emerged, possibly related to pathogenetic factors affecting the right hemisphere during early developmental stages. Besides, WM fiber tracts linking the brain structures involved in social cognition showed abnormalities, and most of them had a negative concordance with the connected GM regions. We interpreted the results in terms of altered brain networks and their role in the pervasive symptoms dramatically impairing communication and social skills in ASD patients.

Sterol homeostasis requires regulated degradation of squalene monooxygenase by the ubiquitin ligase Doa10/Teb4

Sterol homeostasis is essential for the function of cellular membranes and requires feedback inhibition of HMGR, a rate-limiting enzyme of the mevalonate pathway. As HMGR acts at the beginning of the pathway, its regulation affects the synthesis of sterols and of other essential mevalonate-derived metabolites, such as ubiquinone or dolichol. Here, we describe a novel, evolutionarily conserved feedback system operating at a sterol-specific step of the mevalonate pathway. This involves the sterol-dependent degradation of squalene monooxygenase mediated by the yeast Doa10 or mammalian Teb4, a ubiquitin ligase implicated in a branch of the endoplasmic reticulum (ER)-associated protein degradation (ERAD) pathway. Since the other branch of ERAD is required for HMGR regulation, our results reveal a fundamental role for ERAD in sterol homeostasis, with the two branches of this pathway acting together to control sterol biosynthesis at different levels and thereby allowing independent regulation of multiple products of the mevalonate pathway. DOI:http://dx.doi.org/10.7554/eLife.00953.001.

Computational models for prediction of yeast strain potential for winemaking from phenotypic profiles

Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40°C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naïve Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 µg/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection procedures.

KNODWAT: a scientific framework application for testing knowledge discovery methods for the biomedical domain

Background

Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem.

Results

A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations.

Conclusions

The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework.

Generalized query-based active learning to identify differentially methylated regions in DNA

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

Development and comparative assessment of Raman spectroscopic classification algorithms for lesion discrimination in stereotactic breast biopsies with microcalcifications

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. Here, we develop and compare different approaches for developing Raman classification algorithms to diagnose invasive and in situ breast cancer, fibrocystic change and fibroadenoma that can be associated with microcalcifications. In this study, Raman spectra were acquired from tissue cores obtained from fresh breast biopsies and analyzed using a constituent-based breast model. Diagnostic algorithms based on the breast model fit coefficients were devised using logistic regression, C4.5 decision tree classification, k-nearest neighbor (k -NN) and support vector machine (SVM) analysis, and subjected to leave-one-out cross validation. The best performing algorithm was based on SVM analysis (with radial basis function), which yielded a positive predictive value of 100% and negative predictive value of 96% for cancer diagnosis. Importantly, these results demonstrate that Raman spectroscopy provides adequate diagnostic information for lesion discrimination even in the presence of microcalcifications, which to the best of our knowledge has not been previously reported.

Transcriptional profiling of Dictyostelium with RNA sequencing

Transcriptional profiling methods have been utilized in the analysis of various biological processes in Dictyostelium. Recent advances in high-throughput sequencing have increased the resolution and the dynamic range of transcriptional profiling. Here we describe the utility of RNA sequencing with the Illumina technology for production of transcriptional profiles. We also describe methods for data mapping and storage as well as common and specialized tools for data analysis, both online and offline.

The balance in the delivery of ER components and the vacuolar proton pump to the phagosome depends on myosin IK in Dictyostelium

In Dictyostelium, the cytoskeletal proteins Actin binding protein 1 (Abp1) and the class I myosin MyoK directly interact and couple actin dynamics to membrane deformation during phagocytosis. Together with the kinase PakB, they build a regulatory switch that controls the efficiency of uptake of large particles. As a basis for further functional dissection, exhaustive phagosome proteomics was performed and established that about 1300 proteins participate in phagosome biogenesis. Then, quantitative and comparative proteomic analysis of phagosome maturation was performed to investigate the impact of the absence of MyoK or Abp1. Immunoblots and two-dimensional differential gel electrophoresis of phagosomes isolated from myoK-null and abp1-null cells were used to determine the relative abundance of proteins during the course of maturation. Immunoblot profiling showed that absence of Abp1 alters the maturation profile of its direct binding partners such as actin and the Arp2/3 complex, suggesting that Abp1 directly regulates actin dynamics at the phagosome. Comparative two-dimensional differential gel electrophoresis analysis resulted in the quantification of mutant-to-wild type abundance ratios at all stages of maturation for over one hundred identified proteins. Coordinated temporal changes in these ratio profiles determined the classification of identified proteins into functional groups. Ratio profiling revealed that the early delivery of ER proteins to the phagosome was affected by the absence of MyoK and was coupled to a reciprocal imbalance in the delivery of the vacuolar proton pump and Rab11 GTPases. As direct functional consequences, a delayed acidification and a reduced intraphagosomal proteolysis were demonstrated in vivo in myoK-null cells. In conclusion, the absence of MyoK alters the balance of the contributions of the ER and an endo-lysosomal compartment, and slows down phagosome acidification as well as the speed and efficiency of particle degradation inside the phagosome.

The genomic and proteomic blueprint of mouse megakaryocytes derived from embryonic stem cells

BACKGROUND

Platelets are specialized cells, produced by megakaryocytes (MKs) in the bone marrow, which represent the first defense against hemorrhage. There are many diseases where platelet production or function is impaired, with severe consequences for patients. Therefore, new insights into the process of MK differentiation and platelet formation would have a major impact on both basic and clinical research.

OBJECTIVES

Embryonic stem (ES) cells represent a good in vitro model to study the differentiation of MKs, with the possibility of being genetically engineered and constituting an unlimited source of MKs. However, lack of knowledge about the molecular identity of ES-derived MKs (ES-MKs) may prevent any further development and application of this model.

METHODS

This paper presents the first comprehensive transcriptional and proteome profile analyses of mouse ES-MKs in comparison with MKs derived from mouse fetal liver progenitors (FL-MKs).

RESULTS

In ES-MKs we found a down-regulation of cytoskeleton proteins, specific transcription factors and membrane receptors at both transcriptional and protein levels. At the phenotypic level, this molecular blueprint was displayed by ES-MKs' lower polyploidy, lower nuclear/cytoplasm ratio and reduced capacity to form proplatelets and releasing platelets.

CONCLUSIONS

Overall our data demonstrate that ES-MKs represent a useful model to clarify many aspects of both MK physiology and pathological conditions where impaired MK functions are related to defective MK development, as in inherited thrombocytopenias and primary myelofibrosis.

Sequence dependence of isothermal DNA amplification via EXPAR

Isothermal nucleic acid amplification is becoming increasingly important for molecular diagnostics. Therefore, new computational tools are needed to facilitate assay design. In the isothermal EXPonential Amplification Reaction (EXPAR), template sequences with similar thermodynamic characteristics perform very differently. To understand what causes this variability, we characterized the performance of 384 template sequences, and used this data to develop two computational methods to predict EXPAR template performance based on sequence: a position weight matrix approach with support vector machine classifier, and RELIEF attribute evaluation with Naïve Bayes classification. The methods identified well and poorly performing EXPAR templates with 67–70% sensitivity and 77–80% specificity. We combined these methods into a computational tool that can accelerate new assay design by ruling out likely poor performers. Furthermore, our data suggest that variability in template performance is linked to specific sequence motifs. Cytidine, a pyrimidine base, is over-represented in certain positions of well-performing templates. Guanosine and adenosine, both purine bases, are over-represented in similar regions of poorly performing templates, frequently as GA or AG dimers. Since polymerases have a higher affinity for purine oligonucleotides, polymerase binding to GA-rich regions of a single-stranded DNA template may promote non-specific amplification in EXPAR and other nucleic acid amplification reactions.

BzpF is a CREB-like transcription factor that regulates spore maturation and stability in Dictyostelium

The cAMP response element-binding protein (CREB) is a highly conserved transcription factor that integrates signaling through the cAMP-dependent protein kinase A (PKA) in many eukaryotes. PKA plays a critical role in Dictyostelium development but no CREB homologue has been identified in this system. Here we show that Dictyostelium utilizes a CREB-like protein, BzpF, to integrate PKA signaling during late development. bzpF(-) mutants produce compromised spores, which are extremely unstable and germination defective. Previously, we have found that BzpF binds the canonical CRE motif in vitro. In this paper, we determined the DNA binding specificity of BzpF using protein binding microarray (PBM) and showed that the motif with the highest specificity is a CRE-like sequence. BzpF is necessary to activate the transcription of at least 15 PKA-regulated, late-developmental target genes whose promoters contain BzpF binding motifs. BzpF is sufficient to activate two of these genes. The comparison of RNA sequencing data between wild type and bzpF(-) mutant revealed that the mutant fails to express 205 genes, many of which encode cellulose-binding and sugar-binding proteins. We propose that BzpF is a CREB-like transcription factor that regulates spore maturation and stability in a PKA-related manner.

Obesity resistant mechanisms in the Lean polygenic mouse model as indicated by liver transcriptome and expression of selected genes in skeletal muscle

Background

Divergently selected Lean and Fat mouse lines represent unique models for a polygenic form of resistance and susceptibility to obesity development. Previous research on these lines focused mainly on obesity-susceptible factors in the Fat line. This study aimed to examine the molecular basis of obesity-resistant mechanisms in the Lean line by analyzing various fat depots and organs, the liver transcriptome of selected metabolic pathways, plasma and lipid homeostasis and expression of selected skeletal muscle genes.

Results

Expression profiling using our custom Steroltalk v2 microarray demonstrated that Lean mice exhibit a higher hepatic expression of cholesterol biosynthesis genes compared to the Fat line, although this was not reflected in elevation of total plasma or liver cholesterol. However, FPLC analysis showed that protective HDL cholesterol was elevated in Lean mice. A significant difference between the strains was also found in bile acid metabolism. Lean mice had a higher expression of Cyp8b1, a regulatory enzyme of bile acid synthesis, and the Abcb11 bile acid transporter gene responsible for export of acids to the bile. Additionally, a higher content of blood circulating bile acids was observed in Lean mice. Elevated HDL and upregulation of some bile acids synthesis and transport genes suggests enhanced reverse cholesterol transport in the Lean line - the flux of cholesterol out of the body is higher which is compensated by upregulation of endogenous cholesterol biosynthesis. Increased skeletal muscle Il6 and Dio2 mRNA levels as well as increased activity of muscle succinic acid dehydrogenase (SDH) in the Lean mice demonstrates for the first time that changes in muscle energy metabolism play important role in the Lean line phenotype determination and corroborate our previous findings of increased physical activity and thermogenesis in this line. Finally, differential expression of Abcb11 and Dio2 identifies novel strong positional candidate genes as they map within the quantitative trait loci (QTL) regions detected previously in crosses between the Lean and Fat mice.

Conclusion

We identified novel candidate molecular targets and metabolic changes which can at least in part explain resistance to obesity development in the Lean line. The major difference between the Lean and Fat mice was in increased liver cholesterol biosynthesis gene mRNA expression, bile acid metabolism and changes in selected muscle genes' expression in the Lean line. The liver Abcb11 and muscle Dio2 were identified as novel positional candidate genes to explain part of the phenotypic difference between the Lean and Fat lines.

Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection

Within this study, we have used a set of computational techniques to relate the genotypes and phenotypes of natural populations of Saccharomyces cerevisiae, using allelic information from 11 microsatellite loci and results from 24 phenotypic tests. A group of 103 strains was obtained from a larger S. cerevisiae winemaking strain collection by clustering with self-organizing maps. These strains were further characterized regarding their allelic combinations for 11 microsatellites and analysed in phenotypic screens that included taxonomic criteria (carbon and nitrogen assimilation tests, growth at different temperatures) and tests with biotechnological relevance (ethanol resistance, H(2)S or aromatic precursors formation). Phenotypic variability was rather high and each strain showed a unique phenotypic profile. The results, expressed as optical density (A(640)) after 22 h of growth, were in agreement with taxonomic data, although with some exceptions, since few strains were capable of consuming arabinose and ribose to a small extent. Based on microsatellite allelic information, naïve Bayesian classifier correctly assigned (AUC = 0.81, p < 10(-8)) most of the strains to the vineyard from where they were isolated, despite their close location (50-100 km). We also identified subgroups of strains with similar values of a phenotypic feature and microsatellite allelic pattern (AUC > 0.75). Subgroups were found for strains with low ethanol resistance, growth at 30 degrees C and growth in media containing galactose, raffinose or urea. The results demonstrate that computational approaches can be used to establish genotype-phenotype relations and to make predictions about a strain's biotechnological potential.

Sex-specific effects of CNTF, IL6 and UCP2 polymorphisms on weight gain

The human proteins ciliary neurotrophic factor (CNTF) and interleukin-6 (IL6) and their receptors share structural homology with leptin and its receptor. In addition, uncoupling protein-2 (UCP2) has been shown to participate the regulation of leptin on food intake. All three proteins are active in the hypothalamus. Experiments have shown that CNTF and IL6, like leptin, can influence body weight in humans and animals, while the effect of UCP2 is not consistent. In a Dutch general population (n=545) we investigated associations of CNTF (null G/A, rs1800169), IL6 (174 G/C, rs1800795) and UCP2 (A55V, rs660339 and del/ins) polymorphisms with weight gain using interaction graphs and logistic regression analysis. The average follow-up period was 6.9 years. Individuals who gained weight (n=264) were compared with individuals who remained stable in weight (n=281). In women the CNTF polymorphism (odds ratio (OR)=2.15, 95%CI: 1.27-3.64, p=0.004) and in men the IL6 polymorphism by itself (OR=2.26, 95%CI: 1.08-4.75, p=0.03) or in combination with the CNTF polymorphism, were associated with weight gain. Furthermore, CNTF and IL6 polymorphisms in interaction with UCP2 polymorphisms had similar strong effects on weight gain in women and men, respectively. All observed effects were statistically shown to be independent of serum leptin level. These results are incorporated in a biological model for weight regulation with upstream effects of CNTF and IL6, and downstream effects of UCP2. The results of this study suggest a novel mechanism for weight regulation that is active in both women and men, but strongly influenced by sex.

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface

Background

Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability.

Results

We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment.

Conclusion

dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms.

Association and interactions between DNA repair gene polymorphisms and adult glioma

It is generally accepted that glioma develops through accumulation of genetic alterations. We hypothesized that polymorphisms of candidate genes involved in the DNA repair pathways may contribute to susceptibility to glioma. To address this possibility, we conducted a study on 373 Caucasian glioma cases and 365 cancer-free Caucasian controls to assess associations between glioma risk and 18 functional single-nucleotide polymorphisms in DNA repair genes. We evaluated potential gene-gene and gene-environment interactions using a multianalytic strategy combining logistic regression, multifactor dimensionality reduction and classification and regression tree approaches. In the single-locus analysis, six single-nucleotide polymorphisms [ERCC1 3' untranslated region (UTR), XRCC1 R399Q, APEX1 E148D, PARP1 A762V, MGMT F84L, and LIG1 5'UTR] showed a significant association with glioma risk. In the analysis of cumulative genetic risk of multiple single-nucleotide polymorphisms, a significant gene-dosage effect was found for increased glioma risk with increasing numbers of adverse genotypes involving the aforementioned six single-nucleotide polymorphisms (P(trend) = 0.0004). Furthermore, the multifactor dimensionality reduction and classification and regression tree analyses identified MGMT F84L as the predominant risk factor for glioma and revealed strong interactions among ionizing radiation exposure, PARP1 A762V, MGMT F84L, and APEX1 E148D. Interestingly, the risk for glioma was dramatically increased in ionizing radiation exposure individuals who had the wild-type genotypes of MGMT F84L and PARP1 A762V (adjusted odds ratios, 5.95; 95% confidence intervals, 2.21-16.65). Taken together, these results suggest that polymorphisms in DNA repair genes may act individually or together to contribute to glioma risk.