Infectious diseases caused by viruses like HIV and SARS-COV-2 (COVID-19) pose serious public health threats. In search for new antiviral small molecules from chemically underexplored Hypericum species, a previously undescribed atropisomeric C8-C8’ linked dimeric coumarin named bichromonol (1) was isolated from the stem bark of Hypericum roeperianum. The structure was elucidated by MS data and NMR spectroscopy. The absolute configuration at the biaryl axis was determined by comparing the experimental ECD spectrum with those calculated for the respective atropisomers. Bichromonol was tested in cell-based assays for cytotoxicity against MT-4 (CC50 ¼ 54 mM) cells and anti-HIV activity in infected MT-4 cells. It exhibits significant activity at EC50 ¼ 6.6–12.0 mM against HIV-1 wild type and its clinically relevant mutant strains. Especially, against the resistant variants A17 and EFVR, bichromonol is more effective than the commercial drug nevirapine and might thus have potential to serve as a new anti-HIV lead.

Chemical investigation of the aerial parts of Astragalus lehmannianus Bunge (Leguminosae) led to the isolation and identification of a new cycloartane triterpene glycoside – lehmanniaside (2\'-O-acetyl-3-β-O-D-xylopyranosyl-3β,6α,16β,24α-tetrahydroxy-20,25-epoxycycloartane). Its structure was elucidated by means of spectroscopic analysis (HR-MS, 1D and 2D NMR). Bioassays showed that lehmanniaside exhibits weak anthelmintic, antifungal, and cytotoxic activities.

An extensive phytochemical study of a foliose lichen from Indonesia, Parmelia cetrata, resulted in the successful isolation of 13 phenol and depside derivatives (1–13) including the previously unreported depsides 3′-hydroxyl-5′-pentylphenyl 2,4-dihydroxyl-6-methylbenzoate (7), 3′-hydroxyl-5′-propylphenyl 2,4-dihydroxyl-6-methylbenzoate (8) and 3′-hydroxyl-5′-methylphenyl 2-hydroxyl-4-methoxyl-6-propylbenzoate (9). The anti-infective activity of isolated compounds was evaluated against the gram-negative bacterium Aliivibrio fischeri and the nematode Caenorhabditis elegans. 2,4-Dihydroxyl-6-pentylbenzoate (5) and lecanoric acid (6) induced growth inhibition of A. fischeri with inhibition values of 49% and 100% at a concentration of 100 µM, respectively. The antibacterial activity might be due to their free carboxyl group. A phenolic group at C4 also contributed to the antimicrobial activity of the depsides as shown for compounds 7 and 8, which caused 89% and 96% growth inhibition at 100 µM, respectively. Lecanoric acid (6) in addition possesses significant anthelmintic effects causing 80% mortality of C. elegans at 100 µg/mL.

A series of new 11-keto-β-boswellic acid were partially-synthesized by modifying the hydroxyl and carboxylic acid functional groups of ring A. The structures of the new analogs were confirmed by detailed spectral data analysis. Compounds 4, 5 and 9 exhibited potent anti-cancer results against two human tumor cancer cell lines having IC50 value of MCF-7 (breast) and LNCaP (prostate): 123.6, 9.6 and 88.94 μM and 9.6, 44.12 and 12.03 μM, respectively. Additionally, a maximum nuclear fragmentation was observed for 4 (78.44%) in AKBA treated cells after 24 hr followed by 5 and 9 with (74.25 and 66.9% respectively). This study suggests that the presence of hydrazone functionality (4 and 9) has effectively improved the potency of AKBA. Interestingly, compound 5 with a lost carboxylic acid group of ring A showed comparable potent activity. Highly selective AKBA requires further modification to improve its bioavailability and solubility inside the cancer cells.

The chemical investigation of the root barks leaves and stem barks of Brucea antidysenterica J. F. Mill. (Simaroubaceae) led to the isolation of a new pregnane glycoside, named Bruceadysentoside A or 3-O-β-L-arabinopyranosyl-pregn-5-en-20-one (1) together with seventeen known compounds. Their structures were established from spectral data, mainly HRESIMS, 1 D and 2 D NMR and by comparison with literature data. Compounds 1, 2, 5, 6, 8, 10, 12 and 13 were tested in vitro for their effects on the viability of two different human cancer cell lines, namely prostate PC-3 adenocarcinoma cells and colorectal HT-29 adenocarcinoma cells. No substantial activities were recorded for 2, 10, 12 and 13 (up to 10 μM concentration). 1, 5 and 8 did not show strong anti-proliferative effects up to 100 μM, however, 6 exhibited a stronger anti-proliferative effect with IC50 values of ∼ 100 μM against PC-3 and ∼ 200 μM against HT-29.

In the current investigation, a series of heterocyclic derivatives of boswellic acids were prepared along with new monomers of 3-O-acetyl-11-keto-β-boswellic acid (AKBA, 1) 11-keto-β-boswellic acid (KBA, 2) and several new bis-AKBA and KBA homodimers and AKBA-KBA heterodimers. The effects of these compounds on the proliferation of different human cancer cell lines, viz., FaDu (pharynx carcinoma), A2780 (ovarian carcinoma), HT29 (colon adenocarcinoma), and A375 (malignant melanoma), have been evaluated. Thus, KBA homodimer 21 effectively inhibited the growth of FaDu, A2780, HT29, and A375 cells with EC50 values below 9 μM. In addition, compounds 7, 8, 11, 12, 15, 16, and 17 also exhibited cytotoxic effects for A2780, HT29, and A375 cancer cells. In particular, the pyrazine analog 8 was highly cytotoxic for A375 cancer cells with an EC50 value of 2.1 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.

The hydrodistilled essential oil obtained from the dried leaves of Myrtus communis, collected in Yemen, was analysed by GC–MS. Forty-one compounds were identified, representing 96.3% of the total oil. The major constituents of essential oil were oxygenated monoterpenoids (87.1%), linalool (29.1%), 1,8-cineole (18.4%), α-terpineol (10.8%), geraniol (7.3%) and linalyl acetate (7.4%). The essential oil was assessed for its antimicrobial activity using a disc diffusion assay and resulted in moderate to potent antibacterial and antifungal activities targeting mainly Bacillus subtilis, Staphylococcus aureus and Candida albicans. The oil moderately reduced the diphenylpicrylhydrazyl radical (IC50 = 4.2 μL/mL or 4.1 mg/mL). In vitro cytotoxicity evaluation against HT29 (human colonic adenocarcinoma cells) showed that the essential oil exhibited a moderate antitumor effect with IC50 of 110 ± 4 μg/mL. Hierarchical cluster analysis of M. communis has been carried out based on the chemical compositions of 99 samples reported in the literature, including Yemeni sample.

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.