Novel unimolecular bivalent glycoconjugates were assembled combining several functionalized capsular polysaccharides of Streptococcus pneumoniae and Neisseria meningitidis to a carrier protein by using an effective strategy based on the Ugi 4-component reaction. The development of multivalent glycoconjugates opens new opportunities in the field of vaccine design, but their high structural complexity involves new analytical challenges. Nuclear Magnetic Resonance has found wide applications in the characterization and impurity profiling of carbohydrate-based vaccines. Eight bivalent conjugates were studied by quantitative NMR analyzing the structural identity, the content of each capsular polysaccharide, the ratios between polysaccharides, the polysaccharide to protein ratios and undesirable contaminants. The qNMR technique involves experiments with several modified parameters for obtaining spectra with quantifiable signals. In addition, the achieved NMR results were combined with the results of colorimetric assay and Size Exclusion HPLC for assessing the protein content and free protein percentage, respectively. The application of quantitative NMR showed to be efficient to clear up the new structural complexities while allowing the quantitative assessment of the components.

Salvadora persica L. (toothbrush tree, Miswak) is well recognized in most Middle Eastern and African countries for its potential role in dental care, albeit the underlying mechanism for its effectiveness is still not fully understood. A comparative MS and NMR metabolomics approach was employed to investigate the major primary and secondary metabolites composition of S. persica in context of its organ type viz., root or stem to rationalize for its use as a tooth brush. NMR metabolomics revealed its enrichment in nitrogenous compounds including proline-betaines i.e., 4-hydroxy-stachydrine and stachydrine reported for the first time in S. persica. LC/MS metabolomics identified flavonoids (8), benzylurea derivatives (5), butanediamides (3), phenolic acids (8) and 5 sulfur compounds, with 21 constituents reported for the first time in S. persica. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) of either NMR or LC/MS dataset clearly separated stem from root specimens based on nitrogenous compounds abundance in roots and is justifying for its preference as toothbrush versus stems. The presence of betaines at high levels in S. persica (9−12 μg/mg dry weight) offers novel insights into its functioning as an osmoprotectant that maintains the hydration of oral mucosa. Additionally, the previously described anti-inflammatory activity of stachydrine along with the antimicrobial effects of sulfonated flavonoids, benzylisothiocynate and ellagic acid derivatives are likely contributors to S. persica oral hygiene health benefits. Among root samples, variation in sugars and organic acids levels were the main discriminatory criterion. This study provides the first standardization of S. persica extract using qNMR for further inclusion in nutraceuticals.

Ceramides (CERs) play a major role in skin barrier function and direct replacement of depleted skin CERs,due to skin disorder or aging, has beneficial effects in improving skin barrier function and skin hydration.Though, plants are reliable source of CERs, absence of economical and effective method of hydrolysis toconvert the dominant plant sphingolipid, glucosylceramides (GlcCERs), into CERs remains a challenge.This study aims at exploring alternative GlcCERs sources and chemical method of hydrolysis into CERsfor dermal application. GlcCERs isolated from lupin bean (Lupinus albus), mung bean (Vigna radiate) andnaked barley (Hordium vulgare) were identified using ultra high performance liquid chromatographyhyphenated with atmospheric pressure chemical ionization - high resolution tandem mass spectrometer(UHPLC/APCI-HRMS/MS) and quantified with validated automated multiple development-high perfor-mance thin layer chromatography (AMD-HPTLC) method. Plant GlcCERs were hydrolyzed into CERs withmild acid hydrolysis (0.1 N HCl) after treating them with oxidizing agent, NaIO4,and reducing agent,NaBH4. GlcCERs with 4,8-sphingadienine, 8-sphingenine and 4-hydroxy-8-sphingenine sphingoid baseslinked with C14 to C26 -hydroxylated fatty acids (FAs) were identified. Single GlcCER (m/z 714.5520)was dominant in lupin and mung beans while five major GlcCERs species (m/z 714.5520, m/z 742.5829,m/z 770.6144, m/z 842.6719 and m/z 844.56875) were obtained from naked barley. The GlcCERs con-tents of the three plants were comparable. However, lupin bean contains predominantly (> 98 %) a singleGlcCER (m/z 714.5520). Considering the affordability, GlcCER content and yield, lupin bean would bethe preferred alternative commercial source of GlcCERs. CER species bearing 4,8-sphingadienine and 8-sphingenine sphingoid bases attached to C14 to 24 FAs were found after mild acid hydrolysis. CER specieswith m/z 552.4992 was the main component in the beans while CER with m/z 608.5613 was dominantin the naked barley. However, CERs with 4-hydroxy-8-sphingenine sphingoid base were not detected inUHPLC-HRMS/MS study suggesting that the method works for mainly GlcCERs carrying dihydroxy sph-ingoid bases. The method is economical and effective which potentiates the commercialization of plantCERs for dermal application.

Knipholone (1) and knipholone anthrone (2), isolated from the Ethiopian medicinal plant Kniphofia foliosa Hochst. are two phenyl anthraquinone derivatives, a compound class known for biological activity. In the present study, we describe the activity of both 1 and 2 in several biological assays including cytotoxicity against four human cell lines (Jurkat, HEK293, SH-SY5Y and HT-29), antiplasmodial activity against Plasmodium falciparum 3D7 strain, anthelmintic activity against the model organism Caenorhabditis elegans, antibacterial activity against Aliivibrio fischeri and Mycobacterium tuberculosis and anti-HIV-1 activity in peripheral blood mononuclear cells (PBMCs) infected with HIV-1c. In parallel, we investigated the stability of knipholone (2) in solution and in culture media. Compound 1 displays strong cytotoxicity against Jurkat, HEK293 and SH-SY5Y cells with growth inhibition ranging from approximately 62–95% when added to cells at 50 μM, whereas KA (2) exhibits weak to strong activity with 26, 48 and 70% inhibition of cell growth, respectively. Both 1 and 2 possess significant antiplasmodial activity against Plasmodium falciparum 3D7 strain with IC50 values of 1.9 and 0.7 μM, respectively. These results complement previously reported data on the cytotoxicity and antiplasmodial activity of 1 and 2. Furthermore, compound 2 showed HIV-1c replication inhibition (growth inhibition higher than 60% at tested concentrations 0.5, 5, 15 and 50 μg/ml and an EC50 value of 4.3 μM) associated with cytotoxicity against uninfected PBMCs. The stability study based on preincubation, HPLC and APCI-MS (atmospheric-pressure chemical ionization mass spectrometry) analysis indicates that compound 2 is unstable in culture media and readily oxidizes to form compound 1. Therefore, the biological activity attributed to 2 might be influenced by its degradation products in media including 1 and other possible dimers. Hence, bioactivity results previously reported from this compound should be taken with caution and checked if they differ from those of its degradation products. To the best of our knowledge, this is the first report on the anti-HIV activity and stability analysis of compound 2.

BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

BackgroundOntology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.ResultsWe describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.ConclusionsBiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

BackgroundUntargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing.ResultsWe implemented the software package IPO (‘Isotopologue Parameter Optimization’) which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments.IPO optimizes XCMS peak picking parameters by using natural, stable 13C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third.ConclusionsIPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data.The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO. The training sets and test sets can be downloaded from https://health.joanneum.at/IPO.

BackgroundFor three decades, sequence logos are the de facto standard for the visualization of sequence motifs in biology and bioinformatics. Reasons for this success story are their simplicity and clarity. The number of inferred and published motifs grows with the number of data sets and motif extraction algorithms. Hence, it becomes more and more important to perceive differences between motifs. However, motif differences are hard to detect from individual sequence logos in case of multiple motifs for one transcription factor, highly similar binding motifs of different transcription factors, or multiple motifs for one protein domain.ResultsHere, we present DiffLogo, a freely available, extensible, and user-friendly R package for visualizing motif differences. DiffLogo is capable of showing differences between DNA motifs as well as protein motifs in a pair-wise manner resulting in publication-ready figures. In case of more than two motifs, DiffLogo is capable of visualizing pair-wise differences in a tabular form. Here, the motifs are ordered by similarity, and the difference logos are colored for clarity. We demonstrate the benefit of DiffLogo on CTCF motifs from different human cell lines, on E-box motifs of three basic helix-loop-helix transcription factors as examples for comparison of DNA motifs, and on F-box domains from three different families as example for comparison of protein motifs.ConclusionsDiffLogo provides an intuitive visualization of motif differences. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches.