Identifying and validating the presence of Guanine-Quadruplexes (G4) within the blood fluke parasite Schistosoma mansoni.

Schistosomiasis is a neglected tropical disease that currently affects over 250 million individuals worldwide. In the absence of an immunoprophylactic vaccine and the recognition that mono-chemotherapeutic control of schistosomiasis by praziquantel has limitations, new strategies for managing disease burden are urgently needed. A better understanding of schistosome biology could identify previously undocumented areas suitable for the development of novel interventions. Here, for the first time, we detail the presence of G-quadruplexes (G4) and putative quadruplex forming sequences (PQS) within the Schistosoma mansoni genome. We find that G4 are present in both intragenic and intergenic regions of the seven autosomes as well as the sex-defining allosome pair. Amongst intragenic regions, G4 are particularly enriched in 3´ UTR regions. Gene Ontology (GO) term analysis evidenced significant G4 enrichment in the wnt signalling pathway (p<0.05) and PQS oligonucleotides synthetically derived from wnt-related genes resolve into parallel and anti-parallel G4 motifs as elucidated by circular dichroism (CD) spectroscopy. Finally, utilising a single chain anti-G4 antibody called BG4, we confirm the in situ presence of G4 within both adult female and male worm nuclei. These results collectively suggest that G4-targeted compounds could be tested as novel anthelmintic agents and highlights the possibility that G4-stabilizing molecules could be progressed as candidates for the treatment of schistosomiasis.

The anti-fecundity effect of 5-azacytidine (5-AzaC) on Schistosoma mansoni is linked to dis-regulated transcription, translation and stem cell activities.

Uncontrolled host immunological reactions directed against tissue-trapped eggs precipitate a potentially lethal, pathological cascade responsible for schistosomiasis. Blocking schistosome egg production, therefore, presents a strategy for simultaneously reducing immunopathology as well as limiting disease transmission in endemic or emerging areas. We recently demonstrated that the ribonucleoside analogue 5-azacytidine (5-AzaC) inhibited Schistosoma mansoni oviposition, egg maturation and ovarian development. While these anti-fecundity effects were associated with a loss of DNA methylation, other molecular processes affected by 5-AzaC were not examined at the time. By comparing the transcriptomes of 5-AzaC-treated females to controls, we provide evidence that this ribonucleoside analogue also modulates other crucial aspects of schistosome egg-laying biology. For example, S. mansoni gene products associated with amino acid-, carbohydrate-, fatty acid-, nucleotide- and tricarboxylic acid (TCA)- homeostasis are all dysregulated in 5-AzaC treated females. To validate the metabolic pathway most significantly affected by 5-AzaC, amino acid metabolism, nascent protein synthesis was subsequently quantified in adult schistosomes. Here, 5-AzaC inhibited this process by 68% ±16.7% (SEM) in male- and 81% ±4.8% (SEM) in female-schistosomes. Furthermore, the transcriptome data indicated that adult female stem cells were also affected by 5-AzaC. For instance, 40% of transcripts associated with proliferating schistosome cells were significantly down-regulated by 5-AzaC. This finding correlated with a considerable reduction (95%) in the number of 5-ethynyl-2'-deoxyuridine (EdU) positive cells found in 5-AzaC-treated females. In addition to protein coding genes, the effect that 5-AzaC had on repetitive element expression was also assessed. Here, 46 repeats were found differentially transcribed between 5-AzaC-treated and control females with long terminal repeat (LTR) and DNA transposon classes being amongst the most significant. This study demonstrates that the anti-fecundity activity of 5-AzaC affects more than just DNA methylation in schistosome parasites. Further characterisation of these processes may reveal novel targets for schistosomiasis control.

Killer immunoglobulin-like receptor and human leukocyte antigen-C genotypes in rheumatoid arthritis primary responders and non-responders to anti-TNF-α therapy.

The identification of patients who will respond to anti-tumor necrosis factor alpha (anti-TNF-α) therapy will improve the efficacy, safety, and economic impact of these agents. We investigated whether killer cell immunoglobulin-like receptor (KIR) genes are related to response to anti-TNF-α therapy in patients with rheumatoid arthritis (RA). Sixty-four RA patients and 100 healthy controls were genotyped for 16 KIR genes and human leukocyte antigen-C (HLA-C) group 1/2 using polymerase chain reaction sequence-specific oligonucleotide probes (PCR-SSOP). Each patient received anti-TNF-α therapy (adalimumab, etanercept, or infliximab), and clinical responses were evaluated after 3 months using the disease activity score in 28 joints (DAS28). We investigated the correlations between the carriership of KIR genes, HLA-C group 1/2 genes, and clinical data with response to therapy. Patients responding to therapy showed a significantly higher frequency of KIR2DS2/KIR2DL2 (67.7% R vs. 33.3% NR; P = 0.012). A positive clinical outcome was associated with an activating KIR-HLA genotype; KIR2DS2 (+) HLA-C group 1/2 homozygous. Inversely, non-response was associated with the relatively inhibitory KIR2DS2 (-) HLA-C group 1/2 heterozygous genotype. The KIR and HLA-C genotype of an RA patient may provide predictive information for response to anti-TNF-α therapy.

Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them).

The receiver operating characteristic (ROC) has emerged as the gold standard for assessing and comparing the performance of classifiers in a wide range of disciplines including the life sciences. ROC curves are frequently summarized in a single scalar, the area under the curve (AUC). This article discusses the caveats and pitfalls of ROC analysis in clinical microarray research, particularly in relation to (i) the interpretation of AUC (especially a value close to 0.5); (ii) model comparisons based on AUC; (iii) the differences between ranking and classification; (iv) effects due to multiple hypotheses testing; (v) the importance of confidence intervals for AUC; and (vi) the choice of the appropriate performance metric. With a discussion of illustrative examples and concrete real-world studies, this article highlights critical misconceptions that can profoundly impact the conclusions about the observed performance.

Pseudomonas aeruginosa cystic fibrosis isolates of similar RAPD genotype exhibit diversity in biofilm forming ability in vitro.

BACKGROUND: Pseudomonas aeruginosa is considered to grow in a biofilm in cystic fibrosis (CF) chronic lung infections. Bacterial cell motility is one of the main factors that have been connected with P. aeruginosa adherence to both biotic and abiotic surfaces. In this investigation, we employed molecular and microscopic methods to determine the presence or absence of motility in P. aeruginosa CF isolates, and statistically correlated this with their biofilm forming ability in vitro. RESULTS: Our investigations revealed a wide diversity in the production, architecture and control of biofilm formation. Of 96 isolates, 49% possessed swimming motility, 27% twitching and 52% swarming motility, while 47% were non-motile. Microtitre plate assays for biofilm formation showed a range of biofilm formation ability from biofilm deficient phenotypes to those that formed very thick biofilms. A comparison of the motility and adherence properties of individual strains demonstrated that the presence of swimming and twitching motility positively affected biofilm biomass. Crucially, however, motility was not an absolute requirement for biofilm formation, as 30 non-motile isolates actually formed thick biofilms, and three motile isolates that had both flagella and type IV pili attached only weakly. In addition, CLSM analysis showed that biofilm-forming strains of P. aeruginosa were in fact capable of entrapping non-biofilm forming strains, such that these 'non-biofilm forming' cells could be observed as part of the mature biofilm architecture. CONCLUSIONS: Clinical isolates that do not produce biofilms in the laboratory must have the ability to survive in the patient lung. We propose that a synergy exists between isolates in vivo, which allows "non biofilm-forming" isolates to be incorporated into the biofilm. Therefore, there is the potential for strains that are apparently non-biofilm forming in vitro to participate in biofilm-mediated pathogenesis in the CF lung.

Attitudes towards diagnostic tests and therapies for dry eye disease.

AIM: The purpose of this study was to survey the attitudes of optometrists and ophthalmologists, located in a number of different countries, towards diagnostic tests and therapies for dry eye disease. METHODS: A web-based questionnaire was used to survey attitudes using forced-choice questions and Likert scales. RESULTS: Sixty-one respondents (23 ophthalmologists and 38 optometrists) reported a wide range of patient dry eye symptoms. A large variation in use of diagnostic tests was noted. Patient symptoms and fluorescein staining were reported to be significantly more valuable and more frequently performed than any other test. Artificial tear supplements and improved lid hygiene were the preferred therapeutic options selected by the entire group. The results demonstrated a wide variation in attitudes in relation to satisfaction with the range of available diagnostic and therapeutic options. CONCLUSIONS: This study indicates that the interest for the issue of dry eye is relatively limited amongst eye professionals, as demonstrated by the poor participation in the questionnaire.

Text mining of full-text journal articles combined with gene expression analysis reveals a relationship between sphingosine-1-phosphate and invasiveness of a glioblastoma cell line.

BACKGROUND: Sphingosine 1-phosphate (S1P), a lysophospholipid, is involved in various cellular processes such as migration, proliferation, and survival. To date, the impact of S1P on human glioblastoma is not fully understood. Particularly, the concerted role played by matrix metalloproteinases (MMP) and S1P in aggressive tumor behavior and angiogenesis remains to be elucidated. RESULTS: To gain new insights in the effect of S1P on angiogenesis and invasion of this type of malignant tumor, we used microarrays to investigate the gene expression in glioblastoma as a response to S1P administration in vitro. We compared the expression profiles for the same cell lines under the influence of epidermal growth factor (EGF), an important growth factor. We found a set of 72 genes that are significantly differentially expressed as a unique response to S1P. Based on the result of mining full-text articles from 20 scientific journals in the field of cancer research published over a period of five years, we inferred gene-gene interaction networks for these 72 differentially expressed genes. Among the generated networks, we identified a particularly interesting one. It describes a cascading event, triggered by S1P, leading to the transactivation of MMP-9 via neuregulin-1 (NRG-1), vascular endothelial growth factor (VEGF), and the urokinase-type plasminogen activator (uPA). This interaction network has the potential to shed new light on our understanding of the role played by MMP-9 in invasive glioblastomas. CONCLUSION: Automated extraction of information from biological literature promises to play an increasingly important role in biological knowledge discovery. This is particularly true for high-throughput approaches, such as microarrays, and for combining and integrating data from different sources. Text mining may hold the key to unraveling previously unknown relationships between biological entities and could develop into an indispensable instrument in the process of formulating novel and potentially promising hypotheses.

Avoiding model selection bias in small-sample genomic datasets.

MOTIVATION: Genomic datasets generated by high-throughput technologies are typically characterized by a moderate number of samples and a large number of measurements per sample. As a consequence, classification models are commonly compared based on resampling techniques. This investigation discusses the conceptual difficulties involved in comparative classification studies. Conclusions derived from such studies are often optimistically biased, because the apparent differences in performance are usually not controlled in a statistically stringent framework taking into account the adopted sampling strategy. We investigate this problem by means of a comparison of various classifiers in the context of multiclass microarray data. RESULTS: Commonly used accuracy-based performance values, with or without confidence intervals, are inadequate for comparing classifiers for small-sample data. We present a statistical methodology that avoids bias in cross-validated model selection in the context of small-sample scenarios. This methodology is valid for both k-fold cross-validation and repeated random sampling.

Instance-based concept learning from multiclass DNA microarray data.

BACKGROUND: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as nearest neighbor (NN) approaches perform remarkably well in comparison to more complex models, and are currently experiencing a renaissance in the analysis of data sets from biology and biotechnology. While binary classification of microarray data has been extensively investigated, studies involving multiclass data are rare. The question remains open whether there exists a significant difference in performance between NN approaches and more complex multiclass methods. Comparative studies in this field commonly assess different models based on their classification accuracy only; however, this approach lacks the rigor needed to draw reliable conclusions and is inadequate for testing the null hypothesis of equal performance. Comparing novel classification models to existing approaches requires focusing on the significance of differences in performance. RESULTS: We investigated the performance of instance-based classifiers, including a NN classifier able to assign a degree of class membership to each sample. This model alleviates a major problem of conventional instance-based learners, namely the lack of confidence values for predictions. The model translates the distances to the nearest neighbors into 'confidence scores'; the higher the confidence score, the closer is the considered instance to a pre-defined class. We applied the models to three real gene expression data sets and compared them with state-of-the-art methods for classifying microarray data of multiple classes, assessing performance using a statistical significance test that took into account the data resampling strategy. Simple NN classifiers performed as well as, or significantly better than, their more intricate competitors. CONCLUSION: Given its highly intuitive underlying principles--simplicity, ease-of-use, and robustness--the k-NN classifier complemented by a suitable distance-weighting regime constitutes an excellent alternative to more complex models for multiclass microarray data sets. Instance-based classifiers using weighted distances are not limited to microarray data sets, but are likely to perform competitively in classifications of high-dimensional biological data sets such as those generated by high-throughput mass spectrometry.

Towards data warehousing and mining of protein unfolding simulation data.

OBJECTIVES: The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. METHODS: To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis. RESULTS: To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse. CONCLUSIONS: Web and grid services, especially pre-defined data mining services that can run on or 'near' the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

Survival trees for analyzing clinical outcome in lung adenocarcinomas based on gene expression profiles: identification of neogenin and diacylglycerol kinase alpha expression as critical factors.

We present survival trees as an exploratory tool for revealing new insights into gene expression profiles in combination with clinical patient data. Survival trees partition the patient data studied into groups with similar survival outcomes and identify characteristic genetic profiles within these groups. We demonstrate the application of survival trees in a study involving the expression profiles of 3,588 genes in 211 lung adenocarcinoma patients. The survival tree identified a group of early-stage cancer patients with relatively low survival rates and another group of advanced-stage patients with remarkably good survival outcome. For both groups, the tree identified characteristic expression profiles of genes that might play a role in cancerogenesis and disease progression, notably the genes for the netrin receptor neogenin and the Ras/Rho kinase modulator diacylglycerol kinase alpha.

Multiclass cancer classification using gene expression profiling and probabilistic neural networks.

Gene expression profiling by microarray technology has been successfully applied to classification and diagnostic prediction of cancers. Various machine learning and data mining methods are currently used for classifying gene expression data. However, these methods have not been developed to address the specific requirements of gene microarray analysis. First, microarray data is characterized by a high-dimensional feature space often exceeding the sample space dimensionality by a factor of 100 or more. In addition, microarray data exhibit a high degree of noise. Most of the discussed methods do not adequately address the problem of dimensionality and noise. Furthermore, although machine learning and data mining methods are based on statistics, most such techniques do not address the biologist's requirement for sound mathematical confidence measures. Finally, most machine learning and data mining classification methods fail to incorporate misclassification costs, i.e. they are indifferent to the costs associated with false positive and false negative classifications. In this paper, we present a probabilistic neural network (PNN) model that addresses all these issues. The PNN model provides sound statistical confidences for its decisions, and it is able to model asymmetrical misclassification costs. Furthermore, we demonstrate the performance of the PNN for multiclass gene expression data sets. Here, we compare the performance of the PNN with two machine learning methods, a decision tree and a neural network. To assess and evaluate the performance of the classifiers, we use a lift-based scoring system that allows a fair comparison of different models. The PNN clearly outperformed the other models. The results demonstrate the successful application of the PNN model for multiclass cancer classification.