Promising activity of Anthemis austriaca Jacq. on the endometriosis rat model and isolation of its active constituents

© 2019, The Author(s). Heparin and heparan sulfate (Hp/HS) are linear complex glycosaminoglycans which are involved in diverse biological processes. The structural complexity brings difficulties in separation, making the study of structure-function relationships challenging. Here we present a separation method for Hp/HS oligosaccharide fractionation with cross-compatible solvent and conditions, combining size exclusion chromatography (SEC), ion-pair reversed phase chromatography (IPRP), and hydrophilic interaction chromatography (HILIC) as three orthogonal separation methods that do not require desalting or extensive sample handling. With this method, the final eluent is suitable for structure-function relationship studies, including tandem mass spectrometry and microarray printing. Our data indicate that high resolution is achieved on both IPRP and HILIC for Hp/HS isomers. In addition, the fractions co-eluted in IPRP could be further separated by HILIC, with both separation dimensions capable of resolving some isomeric oligosaccharides. We demonstrate this method using both unpurified reaction products from isomeric synthetic hexasaccharides and an octasaccharide fraction from enoxaparin, identifying isomers resolved by this multi-dimensional separation method. We demonstrate both structural analysis by MS, as well as functional analysis by microarray printing and screening using a prototypical Hp/HS binding protein: basic-fibroblast growth factor (FGF2). Collectively, this method provides a strategy for efficient Hp/HS structure-function characterization

The regression of endometriosis with glycosylated flavonoids isolated from Melilotus officinalis (L.) Pall. in an endometriosis rat model

© 2020 Objective: Melilotus officinalis (L.) Pall. is commonly used for treating bronchitis, painful menstruation, hemorrhoids, kidney stones, ulcers of the eyes, earache, and hardening and swelling of uterus. The European Medicines Agency reported the use of M. officinalis orally against stomach ache, gastric ulcer, and disorders of the liver and uterus in folk medicine. The present study aimed to appraise the activity of M. (L.) Pall. aerial parts in endometriosis rat model. Materials and methods: The endometriosis rat model was used to evaluate the potential activity of M. officinalis aerial parts based on its folkloric usage. The aerial parts of M. officinalis were extracted with n-hexane, ethyl acetate (EtOAc), and methanol (MeOH), respectively. The adhesion scores, endometrial foci areas, and cytokine levels were measured in all treated groups. After the biological activity studies, phytochemical studies were performed on the active extract and the fractions obtained from the active extract. Results: The MeOH extract significantly decreased the endometrial foci areas and cytokine levels in rats with endometriosis. Fractionation was performed on the MeOH extract to achieve bioactive molecules. Following the fractionation, the fractions obtained from the MeOH extract were tested. Fraction C showed the highest activity in the rat endometriosis model. Phytochemical investigation of the active fraction (Fraction C) resulted in isolation and elucidation of some quercetin and kaempferol glucoside derivatives. Conclusion: Fraction C obtained from the MeOH extract of M. officinalis showed the highest activity, yielding four glycosylated flavonoids

Sparsity-aware Robust Community Detection (SPARCODE)

Community detection refers to finding densely connected groups of nodes in graphs. In important applications, such as cluster analysis and network modelling, the graph is sparse but outliers and heavy-tailed noise may obscure its structure. We propose a new method for Sparsity-aware Robust Community Detection (SPARCODE). Starting from a densely connected and outlier-corrupted graph, we first extract a preliminary sparsity improved graph model where we optimize the level of sparsity by mapping the coordinates from different clusters such that the distance of their embedding is maximal. Then, undesired edges are removed and the graph is constructed robustly by detecting the outliers using the connectivity of nodes in the improved graph model. Finally, fast spectral partitioning is performed on the resulting robust sparse graph model. The number of communities is estimated using modularity optimization on the partitioning results. We compare the performance to popular graph and cluster-based community detection approaches on a variety of benchmark network and cluster analysis data sets. Comprehensive experiments demonstrate that our method consistently finds the correct number of communities and outperforms existing methods in terms of detection performance, robustness and modularity score while requiring a reasonable computation time

Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation

The Fiedler vector of a connected graph is the eigenvector associated with the algebraic connectivity of the graph Laplacian and it provides substantial information to learn the latent structure of a graph. In real-world applications, however, the data may be subject to heavy-tailed noise and outliers which results in deteriorations in the structure of the Fiedler vector estimate. We design a Robust Regularized Locality Preserving Indexing (RRLPI) method for Fiedler vector estimation that aims to approximate the nonlinear manifold structure of the Laplace Beltrami operator while minimizing the negative impact of outliers. First, an analysis of the effects of two fundamental outlier types on the eigen-decomposition for block affinity matrices which are essential in cluster analysis is conducted. Then, an error model is formulated and a robust Fiedler vector estimation algorithm is developed. An unsupervised penalty parameter selection algorithm is proposed that leverages the geometric structure of the projection space to perform robust regularized Fiedler estimation. The performance of RRLPI is benchmarked against existing competitors in terms of detection probability, partitioning quality, image segmentation capability, robustness and computation time using a large variety of synthetic and real data experiments

SPADIS: an algorithm for selecting predictive and diverse SNPs in GWAS

Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identified in genome-wide association studies (GWAS). Many methods have been developed to select a subset of variant loci, which are associated with or predictive of the phenotype. Selecting connected SNPs on SNP-SNP networks have been proven successful in finding biologically interpretable and predictive SNPs. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. In this paper, we propose a novel method called SPADIS that favors the selection of remotely located SNPs in order to account for their complementary effects in explaining a phenotype. SPADIS selects a diverse set of loci on a SNP-SNP network. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor approximation to the optimal solution. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana with continuous flowering time phenotypes. SPADIS has better average phenotype prediction performance in 15 out of 17 phenotypes when the same number of SNPs are selected and provides consistent improvements across multiple networks and settings on average. Moreover, it identifies more candidate genes and runs faster

A tool for detecting complementary single nucleotide polymorphism pairs in genome-wide association studies for epistasis testing

Detecting interacting loci pairs has been instrumental to understand disease etiology when single locus associations do not fully account for the underlying heritability. However, the number of loci to test is prohibitively large. Epistasis test prioritization algorithms rank likely epistatic single nucleotide polymorphism (SNP) pairs to limit the number of statistical tests. Potpourri detects epistatic SNP pairs by diversifying the selected SNPs' genomic regions and investigating their co-occurrence patterns over the case cohort. It can also input and further prioritize SNPs in regulatory or coding regions. The program identifies and returns a list of prioritized SNP pairs for epistasis testing. This article describes how to use the program and the details of the input and output data

Potpourri: an epistasis test prioritization algorithm via diverse SNP selection

Genome-wide association studies (GWAS) explain a fraction of the underlying heritability of genetic diseases. Investigating epistatic interactions between two or more loci help to close this gap. Unfortunately, the sheer number of loci combinations to process and hypotheses prohibit the process both computationally and statistically. Epistasis test prioritization algorithms rank likely epistatic single nucleotide polymorphism (SNP) pairs to limit the number of tests. However, they still suffer from very low precision. It was shown in the literature that selecting SNPs that are individually correlated with the phenotype and also diverse with respect to genomic location leads to better phenotype prediction due to genetic complementation. Here, we propose that an algorithm that pairs SNPs from such diverse regions and ranks them can improve prediction power. We propose an epistasis test prioritization algorithm that optimizes a submodular set function to select a diverse and complementary set of genomic regions that span the underlying genome. The SNP pairs from these regions are then further ranked w.r.t. their co-coverage of the case cohort. We compare our algorithm with the state of the art on three GWAS and show that (1) we substantially improve precision (from 0.003 to 0.652) while maintaining the significance of selected pairs, (2) decrease the number of tests by 25-fold, and (3) decrease the runtime by 4-fold. We also show that promoting SNPs from regulatory/coding regions improves the performance (up to 0.8). Potpourri is available at http:/ciceklab.cs.bilkent.edu.tr/potpourri