Classification of non-coding variants with high pathogenic impact

Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribute to a wide range of diseases, most patients who receive a WGS-based diagnosis today harbour a protein-coding mutation. Functional interpretation and prioritization of non-coding variants represents a persistent challenge, and disease-causing non-coding variants remain largely unidentified. Depending on the disease, WGS fails to identify a candidate variant in 20–80% of patients, severely limiting the usefulness of sequencing for personalised medicine. Here we present FINSURF, a machine-learning approach to predict the functional impact of non-coding variants in regulatory regions. FINSURF outperforms state-of-the-art methods, owing in particular to optimized control variants selection during training. In addition to ranking candidate variants, FINSURF breaks down the score for each variant into contributions from individual annotations, facilitating the evaluation of their functional relevance. We applied FINSURF to a diverse set of 30 diseases with described causative non-coding mutations, and correctly identified the disease-causative non-coding variant within the ten top hits in 22 cases. FINSURF is implemented as an online server to as well as custom browser tracks, and provides a quick and efficient solution to prioritize candidate non-coding variants in realistic clinical settings.     

Simple clinical and laboratory predictors to improve empirical treatment strategies in areas of high scrub typhus and dengue endemicity,central Vietnam

BackgroundDengue fever is highly endemic in Vietnam, but scrub typhus—although recognized as an endemic disease—remains underappreciated. These diseases together are likely to account for more than half of the acute undifferentiated fever burden in Vietnam. Scrub typhus (ST) is a bacterial disease requiring antimicrobial treatment, while dengue fever (DF) is of viral etiology and does not. The access to adequate diagnostics and the current understanding of empirical treatment strategies for both illnesses remain limited. In this study we aimed to contribute to the clinical decision process in the management of these two important etiologies of febrile illness in Vietnam.MethodsUsing retrospective data from 221 PCR-confirmed scrub typhus cases and 387 NS1 protein positive dengue fever patients admitted to five hospitals in Khanh Hoa province (central Vietnam), we defined predictive characteristics for both diseases that support simple clinical decision making with potential to inform decision algorithms in future. We developed models to discriminate scrub typhus from dengue fever using multivariable logistic regression (M-LR) and classification and regression trees (CART). Regression trees were developed for the entire data set initially and pruned, based on cross-validation. Regression models were developed in a training data set involving 60% of the total sample and validated in the complementary subsample. Probability cut points for the distinction between scrub typhus and dengue fever were chosen to maximise the sum of sensitivity and specificity.ResultsUsing M-LR, following seven predictors were identified, that reliably differentiate ST from DF; eschar, regional lymphadenopathy, an occupation in nature, increased days of fever on admission, increased neutrophil count, decreased ratio of neutrophils/lymphocytes, and age over 40. Sensitivity and specificity of predictions based on these seven factors reached 93.7% and 99.5%, respectively. When excluding the “eschar” variable, the values dropped to 76.3% and 92.3%, respectively.The CART model generated one further variable; increased days of fever on admission, when eschar was included, the sensitivity and specificity was 95% and 96.9%, respectively. The model without eschar involved the following six variables; regional lymphadenopathy, increased days of fever on admission, increased neutrophil count, increased lymphocyte count, platelet count ≥ 47 G/L and age over 28 years as predictors of ST and provided a sensitivity of 77.4% and a specificity of 90.7%.ConclusionsThe generated algorithms contribute to differentiating scrub typhus from dengue fever using basic clinical and laboratory parameters, supporting clinical decision making in areas where dengue and scrub typhus are co-endemic in Vietnam.     

The Booly aliasing resource: a database of grouped biological identifiers

Redundancy among sequence identifiers is a recurring problem in bioinformatics. Here, we present a rapid and efficient method of fingerprinting identifiers to ascertain whether two or more aliases are identical. A number of tools and approaches have been developed to resolve differing names for the same genes and proteins, however, these methods each have their own limitations associated with their various goals. We have taken a different approach to the aliasing problem by simplifying the way aliases are stored and curated with the objective of simultaneously achieving speed and flexibility. Our approach (Booly-hashing) is to link identifiers with their corresponding hash keys derived from unique fingerprints such as gene or protein sequences. This tool has proven invaluable for designing a new data integration platform known as Booly, and has wide applicability to situations in which a dedicated efficient aliasing system is required. Compared with other aliasing techniques, Booly-hashing methodology provides 1) reduced run time complexity, 2) increased flexibility (aliasing of other data types, e.g. pharmaceutical drugs), 3) no required assumptions regarding gene clusters or hierarchies, and 4) simplicity in data addition, updating, and maintenance. The new Booly-hashing aliasing model has been incorporated as a central component of the Booly data integration platform we have recently developed and shoud be broadly applicable to other situations in which an efficient streamlined aliasing systems is required. This aliasing tool and database, which allows users to quickly group the same genes and proteins together can be accessed at: http://booly.ucsd.edu/alias. AVAILABILITY: The database is available for free at http://booly.ucsd.edu/alias.     

Ursolic acid is a PPAR-α agonist that regulates hepatic lipid metabolism

In this study, we confirmed that ursolic acid, a plant triterpenoid, activates peroxisome proliferator-activated receptor (PPAR)-α in vitro. Surface plasmon resonance and time-resolved fluorescence resonance energy transfer analyses do not show direct binding of ursolic acid to the ligand-binding domain of PPAR-α; however, ursolic acid enhances the binding of PPAR-α to the peroxisome proliferator response element in PPAR-α-responsive genes, alters the expression of key genes in lipid metabolism, significantly reducing intracellular triglyceride and cholesterol concentrations in hepatocytes. Thus, ursolic acid is a PPAR-α agonist that regulates the expression of lipid metabolism genes, but it is not a direct ligand of PPAR-α.     

Innovative method for quantification of cell-cell adhesion in 96-well plates

Cell adhesion is an important part of many complex biological processes. It plays crucial roles in cancer, development and maintenance of stem cell compartment. The measurement of adhesion under experimental conditions might provide important information for cell biology. There are several protocols to measure adhesion, usually based on washing or shaking to remove non-adherent cells. Here, we describe a quantification method based on gravitational force to measure adhesion in a 96-well format. Non-adherent cells are separated and only vital cells are quantified with a colorimetric assay. This assay can be used especially when the “anti-adhesion” effect is present only for a short period of time like is the case of peptides or cytokines since it provides a trap for non-adherent cells in a way that they can not touch again the adherent surface. As examples we provide the quantification of cell-cell interaction with blocking antibodies anti-CD44 in hematopoietic stem cells and the effect of the stromal cell derived factor-1 (SDF-1) in the Jurkat cell line when they are in contact with mesenchymal stromal cells. This method facilitates fast and reliable measurement of cell adhesion in multiwell format for screening assays.Key words: adhesion assay, adhesion, SDF-1, CD44, hematopoietic stem cells, leukemia cell lines     

Hypolipidaemic Effects of (24R)-4α-methyl-5α-stigmasta-7,22-dien-3β-ol Derived from Aurantiochytrium mangrovei BT3 in the HEPG2 Cell Line

Applied Biochemistry and Microbiology - Aurantiochytrium mangrovei BT3, a heterotrophic marine microalga, has the ability to produce high amounts of polyunsaturated fatty acids and bioactive...