In light of recent climate change, with its rising temperatures and precipitation changes, we are facing the need to increase the valuable crop’s tolerance against unfavorable environmental conditions. Emmer wheat is a cereal crop with high nutritional value. We investigated the possibility of improving the stress tolerance of emmer wheat by activating the synthesis of the stress hormone jasmonate by overexpressing two genes of the jasmonate biosynthetic pathway from Arabidopsis thaliana, ALLENE OXIDE SYNTHASE (AtAOS) and OXOPHYTODIENOATE REDUCTASE 3 (AtOPR3). Analyses of jasmonates in intact and mechanically wounded leaves of non-transgenic and transgenic plants showed that the overexpression of each of the two genes resulted in increased wounding-induced levels of jasmonic acid and jasmonate-isoleucine. Against all expectations, the overexpression of AtAOS, encoding a chloroplast-localized enzyme, does not lead to an increased level of the chloroplast-formed 12-oxo-phytodienoic acid (OPDA), suggesting an effective conversion of OPDA to downstream products in wounded emmer wheat leaves. Transgenic plants overexpressing AtAOS or AtOPR3 with increased jasmonate levels show a similar phenotype, manifested by shortening of the first and second leaves and elongation of the fourth leaf, as well as increased tolerance to osmotic stress induced by the presence of the polyethylene glycol (PEG) 6000.

The molecule (2S)-naringenin is a scaffold molecule with several nutraceutical properties. Currently, (2S)-naringenin is obtained through chemical synthesis and plant isolation. However, these methods have several drawbacks. Thus, heterologous biosynthesis has emerged as a viable alternative to its production. Recently, (2S)-naringenin production studies in Escherichia coli have used different tools to increase its yield up to 588 mg/L. In this study, we designed and assembled a bio-factory for (2S)-naringenin production. Firstly, we used several parametrized algorithms to identify the shortest pathway for producing (2S)-naringenin in E. coli, selecting the genes phenylalanine ammonia lipase (pal), 4-coumarate: CoA ligase (4cl), chalcone synthase (chs), and chalcone isomerase (chi) for the biosynthetic pathway. Then, we evaluated the effect of oxygen transfer on the production of (2S)-naringenin at flask (50 mL) and bench (4 L culture) scales. At the flask scale, the agitation rate varied between 50 rpm and 250 rpm. At the bench scale, the dissolved oxygen was kept constant at 5% DO (dissolved oxygen) and 40% DO, obtaining the highest (2S)-naringenin titer (3.11 ± 0.14 g/L). Using genome-scale modeling, gene expression analysis (RT-qPCR) of oxygen-sensitive genes was obtained.

Organotin(IV) carboxylates are a class of compounds explored as alternatives to platinum-containing chemotherapeutics due to propitious in vitro and in vivo results, and distinct mechanisms of action. In this study, triphenyltin(IV) derivatives of non-steroidal anti-inflammatory drugs (indomethacin (HIND) and flurbiprofen (HFBP)) are synthesized and characterized, namely [Ph3Sn(IND)] and [Ph3Sn(FBP)]. The crystal structure of [Ph3Sn(IND)] reveals penta-coordination of the central tin atom with almost perfect trigonal bipyramidal geometry with phenyl groups in the equatorial positions and two axially located oxygen atoms belonging to two distinct carboxylato (IND) ligands leading to formation of a coordination polymer with bridging carboxylato ligands. Employing MTT and CV probes, the antiproliferative effects of both organotin(IV) complexes, indomethacin, and flurbiprofen were evaluated on different breast carcinoma cells (BT-474, MDA-MB-468, MCF-7 and HCC1937). [Ph3Sn(IND)] and [Ph3Sn(FBP)], unlike the inactive ligand precursors, were found extremely active towards all examined cell lines, demonstrating IC50 concentrations in the range of 0.076–0.200 µM. Flow cytometry was employed to examine the mode of action showing that neither apoptotic nor autophagic mechanisms were triggered within the first 48 h of treatment. However, both tin(IV) complexes inhibited cell proliferation potentially related to the dramatic reduction in NO production, resulting from downregulation of nitric oxide synthase (iNOS) enzyme expression.

Meroterpenoids are secondary metabolites formed due to mixed biosynthetic pathways which are produced in part from a terpenoid co-substrate. These mixed biosynthetically hybrid compounds are widely produced by bacteria, algae, plants, and animals. Notably amazing chemical diversity is generated among meroterpenoids via a combination of terpenoid scaffolds with polyketides, alkaloids, phenols, and amino acids. This review deals with the isolation, chemical diversity, and biological effects of 452 new meroterpenoids reported from natural sources from January 2016 to December 2020. Most of the meroterpenoids possess antimicrobial, cytotoxic, antioxidant, anti-inflammatory, antiviral, enzyme inhibitory, and immunosupressive effects.

Ozoroa insignis Del. is an ethnobotanical plant widely used in traditional medicine for various ailments, including schistosomiasis, tapeworm, and hookworm infections. From the so far not investigated fruits of Ozoroa insignis, the anthelmintic principles could be isolated through bioassay-guided isolation using Caenorhabditis elegans and identified by NMR spectroscopic analysis and mass spectrometric studies. Isolated 6-[8(Z)-pentadecenyl] anacardic (1), 6-[10(Z)-heptadecenyl] anacardic acid (2), and 3-[7(Z)-pentadecenyl] phenol (3) were evaluated against the 5 parasitic organisms Schistosoma mansoni (adult and newly transformed schistosomula), Strongyloides ratti, Heligmosomoides polygyrus, Necator americanus, and Ancylostoma ceylanicum, which mainly infect humans and other mammals. Compounds 1–3 showed good activity against Schistosoma mansoni, with compound 1 showing the best activity against newly transformed schistosomula with 50% activity at 1µM. The isolated compounds were also evaluated for their cytotoxic properties against PC-3 (human prostate adenocarcinoma) and HT-29 (human colorectal adenocarcinoma) cell lines, whereby compounds 2 and 3 showed antiproliferative activity in both cancer cell lines, while compound 1 exhibited antiproliferative activity only on PC-3 cells. With an IC50 value of 43.2 µM, compound 3 was found to be the most active of the 3 investigated compounds.

Ranunculus muricatus L. is a spiny fruit buttercup that is used in various traditional medicinal systems. In the current investigation of R. muricatus, the new chalcone 4-benzyloxylonchocarpin (1), the new anthraquinone muracatanes A (2), the new-to-nature anthraquinone muracatanes B (3), and the new naphthalene analog muracatanes C (4) were isolated, in addition to the three previously reported compounds, 4-methoxylonchocarpin (5), β-sitosterol (6), and β-sitosterol β-D-glucopyranoside (7). Their structures were elucidated using 1D (1H and 13C) and 2D (COSY, HSQC, and HMBC) NMR spectroscopy and HR-ESI-MS. Chalcone 1 showed potent acetylcholinesterase inhibitory effects with Ki of 5.39 µM and Ki′ of 3.54 µM, but none of the isolated compounds showed inhibitory activity towards butyrylcholinesterase. Anthraquinone 3 illustrated α-glucosidase inhibitory effects with IC50-values of 164.46 ± 83.04 µM. Compound 5 displayed moderate cytotoxic activity towards ovarian carcinoma (A2780, IC50 = 25.4 µM), colorectal adenocarcinoma (HT29, IC50 = 20.2 µM), breast cancer (MCF7, IC50 = 23.7 µM), and thyroid carcinoma (SW1736, IC50 = 26.2 µM) while it was inactive towards pharynx carcinoma (FaDu: IC50 > 30 µM).

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.

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.