Three novel complexes of deprotonated diflunisal (dif) with neocuproine (neo) were synthesized and characterized via elemental, spectral (UV-vis, FTIR, fluorescence, and mass spectrometry), and single-crystal X-ray diffraction analyses. Although the compounds shared a similar composition of [MCl(dif)(neo)], where M represents Zn(II) (1), Co(II) (2) and Cu(II) (3), only 1 and 2 were isostructural, while 3 differed in both the molecular and supramolecular structures. In all three complex molecules, the central atom is coordinated by two nitrogen atoms of neo in a bidentate chelate mode, and one chlorido ligand and dif is bonded in either a monodentate mode via one oxygen atom of the carboxylate in 1 and 2 or in a bidentate chelate mode via both carboxylate oxygen atoms in 3. All three compounds demonstrated remarkable antiproliferative activity against human prostate (PC-3), colon (HCT116) and breast (MDA-MB-468) cancer cell lines with IC50 values in the nanomolar range, with the lowest values observed in the case of PC-3 and MDA-MB-468 with 2 (20.0 nM) and 3 (31.1 nM), respectively. Moreover, complex 2, as the most active, was further investigated for its potential to induce perturbations in the cell cycle of PC-3 cells. The results indicated an induction of caspase-independent apoptosis. The interaction of the complexes with genomic DNA isolated from the respective cancer cell lines was evaluated for the intercalative mode, with binding strength correlated with the antiproliferative activity against PC-3 and MDA-MB-468 cancer cell lines.

For the development of anticancer drugs with higher activity and reduced toxicity, two approaches were combined: preparation of platinum(IV) complexes exhibiting higher stability compared to their platinum(II) counterparts and loading them into mesoporous silica SBA-15 with the aim to utilise the passive enhanced permeability and retention (EPR) effect of nanoparticles for accumulation in tumour tissues. Three conjugates based on a cisplatin scaffold bearing the anti-inflammatory drugs naproxen, ibuprofen or flurbiprofen in the axial positions (1, 2 and 3, respectively) were synthesised and loaded into SBA-15 to afford the mesoporous silica nanoparticles (MSNs) SBA-15|1, SBA-15|2 and SBA-15|3. Superior antiproliferative activity of both free and immobilised conjugates in a panel of four breast cancer cell lines (MDA-MB-468, HCC1937, MCF-7 and BT-474) with markedly increased cytotoxicity with respect to cisplatin was demonstrated. All compounds exhibit highest activity against the triple-negative cell line MDA-MB-468, with conjugate 1 being the most potent. However, against MCF-7 and BT-474 cell lines, the most notable improvement was found, with IC50 values up to 240-fold lower than cisplatin. Flow cytometry assays clearly show that all compounds induce apoptotic cell death elevating the levels of both early and late apoptotic cells. Furthermore, autophagy as well as formation of reactive oxygen species (ROS) and nitric oxide (NO) were elevated to a similar or greater extent than with cisplatin.

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.

SBA-15|Sn3, a mesoporous silica-based material (derivative of SBA-15) loaded with an organotin compound Ph3Sn(CH2)3OH (Sn3), possesses improved antitumor potential against the A2780 high-grade serous ovarian carcinoma cell line in comparison to Sn3. It is demonstrated that both the compound and the nanostructured material are internalized by the A2780 cells. A similar mode of action of Sn3 and SBA-15|Sn3 against the A2780 cell line was found. Explicitly, induction of apoptosis, caspase 2, 3, 8 and 9 activation, accumulation of cells in the hypodiploid phase as well as accumulation of ROS were observed. Interestingly, Sn3 loaded in the mesoporous silica-based material needed to reach a concentration 3.5 times lower than the IC50 value of the Sn3 compound, pointing out a higher effect of the SBA-15|Sn3 than Sn3 alone. Clonogenic potential, growth in 3D culture as well as mobility of cells were disturbed in the presence of SBA-15|Sn3. Such behavior could be associated with the suppression of p-38 MAPK. Less profound effect of Sn3 compared to SBA-15|Sn3 could be attributed to a different regulation of p-38 and STAT-3, which are mainly responsible for an appropriate cellular response to diverse stimuli or metastatic properties.

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.

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.

Correction for ‘Synthesis, cytotoxic and hydrolytic studies of titanium complexes anchored by a tripodal diamine bis(phenolate) ligand’ by Sónia Barroso et al., Dalton Trans., 2014, 43, 17422–17433.

The reactivity, cytotoxic studies and hydrolytic behaviour of diamine bis(phenolate) titanium complexes are reported. The reactions of [Ti(tBu2O2NN′)Cl]2(μ-O) (1) with LiOiPr or HOiPr in the presence of NEt3, aiming at the synthesis of the alkoxido derivative of 1 led to no reaction or to the synthesis of the monomeric complex [Ti(tBu2O2NN′)(OiPr)2] (3), respectively. A small amount of the alkoxidotitanium dimer [Ti(tBu2O2NN′)(OiPr)]2(μ-O) (2) crystallized out of a solution of 3 and DFT calculations showed that the transformation of 1 into 3 is a thermodynamically favorable process in the presence of a base (NEt3) (ΔG = −14.7 kcal mol−1). 2 was quantitatively obtained through the direct reaction of the ligand precursor H2(tBu2O2NN′) with titanium tetra(isopropoxido). Further reaction of 2 with an excess of TMSCl was revealed to be the most suitable method for the preparation of [Ti(tBu2O2NN′)Cl2] (4). 1 and 3 disclosed cytotoxic activity towards HeLa, Fem-x, MDA-MB-361 and K562 cells and 1 exhibited moderate binding affinity to FS-DNA. 1H NMR hydrolysis studies attested the fast decomposition of 4 in the presence of D2O. The hydrolysis of 3 is slower and proceeds through the formation of [Ti(tBu2O2NN′)(OH)]2(μ-O) (5) that was crystallographically characterized. Upon D2O addition 1 immediately forms complex new species, stable in solution for long periods (weeks).