Unveiling (-)-Englerin A as a Modulator of L-Type Calcium Channels

Effects of plant extracts and derivatives on cardiac K+, Nav, and Cav channels: a review

Natural products (NPs) are endless sources of compounds for fighting against several pathologies. Many dysfunctions, including cardiovascular disorders, such as cardiac arrhythmias have their modes of action regulation of the concentration of electrolytes inside and outside the cell targeting ion channels. Here, we highlight plant extracts and secondary metabolites' effects on the treatment of related cardiac pathologies on hERG, Nav, and Cav of cardiomyocytes. The natural product's pharmacology of expressed receptors like alpha-adrenergic receptors causes an influx of Ca2+ ions through receptor-operated Ca2+ ion channels. We also examine the NPs associated with cardiac contractions such as myocardial contractility by reducing the L-type calcium current and decreasing the intracellular calcium transient, inhibiting the K+ induced contractions, decreasing amplitude of myocyte shortening and showed negative ionotropic and chronotropic effects due to decreasing cytosolic Ca2+. We examine whether the NPs block potassium channels, particular the hERG channel and regulatory effects on Nav1.7.

Englerin A Inhibits T-Type Voltage-Gated Calcium Channels at Low Micromolar Concentrations

Englerin A (EA) is a potent agonist of tetrameric transient receptor potential canonical (TRPC) ion channels containing TRPC4 and TRPC5 subunits. TRPC proteins form cation channels that are activated by plasma membrane receptors. They convert extracellular signals such as angiotensin II into cellular responses, whereupon Na+ and Ca2+ influx and depolarization of the plasma membrane occur. Via depolarization, voltage-gated Ca2+ (CaV) channels can be activated, further increasing Ca2+ influx. We investigated the extent to which EA also affects the functions of CaV channels using the high-voltage-activated L-type Ca2+ channel CaV1.2 and the low-voltage-activated T-type Ca2+ channels CaV3.1, CaV3.2, and CaV3.3. After expression of cDNAs in human embryonic kidney (HEK293) cells, EA inhibited currents through all T-type channels at half-maximal inhibitory concentrations (IC50) of 7.5 to 10.3 μM. In zona glomerulosa cells of the adrenal gland, angiotensin II-induced elevation of cytoplasmic Ca2+ concentration leads to aldosterone release. We identified transcripts of low- and high-voltage-activated CaV channels and of TRPC1 and TRPC5 in the human adrenocortical (HAC15) zona glomerulosa cell line. Although no EA-induced TRPC activity was measurable, Ca2+ channel blockers distinguished T- and L-type Ca2+ currents. EA blocked 60% of the CaV current in HAC15 cells and T- and L-type channels analyzed at -30 mV and 10 mV were inhibited with IC50 values of 2.3 and 2.6 μM, respectively. Although the T-type blocker Z944 reduced basal and angiotensin II-induced 24-hour aldosterone release, EA was not effective. In summary, we show here that EA blocks CaV1.2 and T-type CaV channels at low-micromolar concentrations. SIGNIFICANCE STATEMENT: In this study we showed that englerin A (EA), a potent agonist of tetrameric transient receptor potential canonical (TRPC)4- or TRPC5-containing channels and currently under investigation to treat certain types of cancer, also inhibits the L-type voltage-gated Ca2+ (CaV) channel CaV1.2 and the T-type CaV channels CaV3.1, CaV3.2, and CaV3.3 channels at low micromolar concentrations.

Englerin A Rewires Phosphosignaling via Hsp27 Hyperphosphorylation to Induce Cytotoxicity in Renal Cancer Cells

Englerin A (EA) is a small-molecule natural product with selective cytotoxicity against renal cancer cells. EA has been shown to induce apoptosis and cell death through cell-cycle arrest and/or insulin signaling pathways. However, its biological mode of action or targets in renal cancer remains enigmatic. In this study, we employed advanced mass spectrometry-based phosphoproteomics approaches to identify EA's functional roles in renal cancer. We identified 10,940 phosphorylation sites, of which 706 sites exhibited EA-dependent phosphorylation changes. Integrated analysis of motifs and interaction networks suggested activation of stress-activated kinases including p38 upon EA treatment. Of note, a downstream target of p38, Hsp27, was found to be hyperphosphorylated on multiple sites upon EA treatment. Among these, a novel site Ser65 on Hsp27, which was further validated by targeted proteomics, was shown to be crucial for EA-induced cytotoxicity in renal cancer cells. Taken together, these data reveal the complex signaling cascade that is induced upon EA treatment and importantly provide insights into its effects on downstream molecular signaling.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms

Plants and associated microorganisms are essential sources of natural products against human cancer diseases, partly exemplified by plant-derived anticancer drugs such as Taxol (paclitaxel). Natural products provide diverse mechanisms of action and can be used directly or as prodrugs for further anticancer optimization. Despite the success, major bottlenecks can delay anticancer lead discovery and implementation. Recent advances in sequencing and omics-related technology have provided a mine of information for developing new therapeutics from natural products. Artificial intelligence (AI), including machine learning (ML), has offered powerful techniques for extensive data analysis and prediction-making in anticancer leads discovery. This review presents an overview of current AI-guided solutions to discover anticancer lead compounds, focusing on natural products from plants and associated microorganisms.

Natural products targeting cancer cell dependency

Precision cancer medicine is a tailored treatment approach for individual cancer patients with different genomic characteristics. Mutated or hyperactive oncogenes have served as main drug targets in current precision cancer medicine, while defective or inactivated tumor suppressors in general have not been considered as druggable targets. Synthetic lethality is one of very few approaches that enable to target defective tumor suppressors with pharmacological agents. Synthetic lethality exploits cancer cell dependency on a protein or pathway, which arises when the function of a tumor suppressor is defective. This approach has been proven to be effective in clinical settings since the successful clinical introduction of BRCA-PARP synthetic lethality for the treatment of breast and ovarian cancer with defective BRCA. Subsequently, large-scale screenings with RNAi, CRISPR/Cas9-sgRNAs, and chemical libraries have been applied to identify synthetic lethal partners of tumor suppressors. Natural products are an important source for the discovery of pharmacologically active small molecules. However, little effort has been made in the discovery of synthetic lethal small molecules from natural products. This review introduces recent advances in the discovery of natural products targeting cancer cell dependency and discusses potentials of natural products in the precision cancer medicine.

Validation strategies for target prediction methods

Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.

Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression

The use of computational tools to identify biological targets of natural products with anticancer properties and unknown modes of action is gaining momentum. We employed self-organizing maps to deconvolute the phenotypic effects of piperlongumine (PL) and establish a link to modulation of the human transient receptor potential vanilloid 2 (hTRPV2) channel. The structure of the PL-bound full-length rat TRPV2 channel was determined by cryo-EM. PL binds to a transient allosteric pocket responsible for a new mode of anticancer activity against glioblastoma (GBM) in which hTRPV2 is overexpressed. Calcium imaging experiments revealed the importance of Arg539 and Thr522 residues on the antagonistic effect of PL and calcium influx modulation of the TRPV2 channel. Downregulation of hTRPV2 reduces sensitivity to PL and decreases ROS production. Analysis of GBM patient samples associates hTRPV2 overexpression with tumor grade, disease progression, and poor prognosis. Extensive tumor abrogation and long term survival was achieved in two murine models of orthotopic GBM by formulating PL in an implantable scaffold/hydrogel for sustained local therapy. Furthermore, in primary tumor samples derived from GBM patients, we observed a selective reduction of malignant cells in response to PL ex vivo. Our results establish a broadly applicable strategy, leveraging data-motivated research hypotheses for the discovery of novel means tackling cancer.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

Investigation of the Selectivity of L-Type Voltage-Gated Calcium Channels 1.3 for Pyrimidine-2,4,6-Triones Derivatives Based on Molecular Dynamics Simulation

Human Ca_v1.3 (hCa_v1.3) is of great interest as a potential target for Parkinson’s disease. However, common medications like dihydropyridines (DHPs), a kind of classic calcium channel blocker, have poor selectivity to hCa_v1.3 in clinical treatment, mainly due to being implicated in cardiovascular side-effects mediated by human Ca_v1.2 (hCa_v1.2). Recently, pyrimidine-2,4,6-triones (PYTs) have received extensive attention as prominent selective inhibitors to hCa_v1.3. In this study, we describe the selectivity mechanism of PYTs for hCa_v1.2 and hCa_v1.3 based on molecular dynamic simulation methods. Our results reveal that the van der Waals (vdW) interaction was the most important force affecting selectivity. Moreover, the hydrophobic interaction was more conducive to the combination. The highly hydrophobic amino acid residues on hCa_v1.3, such as V162 (IR1), L303 (IR2), M481 (IR3), and F484 (IR3), provided the greatest contributions in the binding free energy. On the other hand, the substituents of a halogen-substituted aromatic ring, cycloalkyl and norbornyl on PYTs, which are pertinent to the steric hindrance of the compounds, played core roles in the selectivity and affinity for hCa_v1.3, whereas strong polar substituents needed to be avoided. The findings could provide valuable information for designing more effective and safe medicines for Parkinson’s disease.

Machine learning for target discovery in drug development

The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.

Sesquiterpenoids and mycotoxin swainsonine from the locoweed endophytic fungus Alternaria oxytropis

Oxytropiols A-J, ten undescribed guaiane-type sesquiterpenoids, and the mycotoxin swainsonine (SW) were isolated from the locoweed endophytic fungus Alternaria oxytropis. The chemical structures of these sesquiterpenoids were elucidated on the basis of HR-ESI-MS and NMR data including ¹H, ¹³C, HSQC, ¹H-¹H COSY, HMBC, and NOESY spectra, and the absolute configurations of these compounds were determined using a modified Mosher's method and X-ray diffraction spectroscopy. A possible biosynthetic pathway of these guaiane-type sesquiterpenoids is discussed, and proposed that post-modification oxidative enzymes might form these highly polyhydroxylated structures. Compound 1 displayed biological effects on the root growth of Arabidopsis thaliana, and SW displayed cytotoxicity against A549 and HeLa cancer cell lines.

Dissecting celastrol with machine learning to unveil dark pharmacology

By coalescing bespoke machine learning and bioinformatics analyses with cell-based assays, we unveil the pharmacology of celastrol. Celastrol is a direct modulator of the progesterone and cannabinoid receptors, and its effects correlate with the antiproliferative activity. We demonstrate how in silico methods may drive systems biology studies for natural products.

Englerin A-sensing charged residues for transient receptor potential canonical 5 channel activation

The transient receptor potential canonical (TRPC) 5 channel, known as a nonselective cation channel, has a crucial role in calcium influx. TRPC5 has been reported to be activated by muscarinic receptor activation and extracellular pH change and inhibited by the protein kinase C pathway. Recent studies have also suggested that TRPC5 is extracellularly activated by englerin A (EA), but the mechanism remains unclear. The purpose of this study is to identify the EA-interaction sites in TRPC5 and thereby clarify the mechanism of TRPC5 activation. TRPC5 channels are over-expressed in human embryonic kidney (HEK293) cells. TRPC5 mutants were generated by site-directed mutagenesis. The whole-cell patch-clamp configuration was used to record TRPC5 currents. Western analysis was also performed to observe the expression of TRPC5 mutants. To identify the EA-interaction site in TRPC5, we first generated pore mutants. When screening the mutants with EA, we observed the EA-induced current increases of TRPC5 abolished in K554N, H594N, and E598Q mutants. The current increases of other mutants were reduced in different levels. We also examined the functional intactness of the mutants that had no effect by EA with TRPC5 agonists, such as carbachol or GTPγS. Our results suggest that the three residues, Lys-554, His-594, and Glu-598, in TRPC5 might be responsible for direct interaction with EA, inducing the channel activation. We also suggest that although other pore residues are not critical, they could partly contribute to the EA-induced channel activation.

Computational advances in combating colloidal aggregation in drug discovery

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.

Natural product-drug conjugates for modulation of TRPV1-expressing tumors

We report the design, synthesis and biological evaluation of natural product-drug conjugates for treatment of prostate cancers over-expressing the transient receptor potential vanilloid 1 (TRPV1) channel. We validate the relevance of TRPV1 as a target in prostate cancer patients by using a bioinformatics approach and provide proof-of-concept for the drug delivery strategy through bioorthogonal chemistry and stability assays under simulated physiological conditions. In cell-based assays, the constructs displayed modest activity. Moreover, we serendipitously discover that a stoichiometric combination of a TRPV1 agonist with a small, positively charged cytotoxic may provide new research avenues in personalized medicines for prostate cancer.

NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules

Natural products (NPs) remain the most prolific resource for the development of small-molecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates similarity maps, which highlight atoms that contribute significantly to the classification of small molecules as a natural product or synthetic molecule. The method can hence be utilized to (i) identify natural products in large molecular libraries, (ii) quantify the natural product-likeness of small molecules, and (iii) visualize atoms in small molecules that are characteristic of natural products or synthetic molecules. The models are based on random forest classifiers trained on data sets consisting of more than 265,000 to 322,000 natural products and synthetic molecules. Two-dimensional molecular descriptors, MACCS keys and Morgan2 fingerprints were explored. On an independent test set the models reached areas under the receiver operating characteristic curve (AUC) of 0.997 and Matthews correlation coefficients (MCCs) of 0.954 and higher. The method was further tested on data from the Dictionary of Natural Products, ChEMBL and other resources. The best-performing models are accessible as a free web service at http://npscout.zbh.uni-hamburg.de/npscout.

A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.

Drug target prediction using chem- and bioinformatics

The biological pre-validation of natural products (NPs) and their underlying frameworks ensures an unrivaled source of inspiration for chemical probe and drug design. However, the poor knowledge of their drug target counterparts critically hinders the broader exploration of NPs in chemical biology and molecular medicine. Cutting-edge algorithms now provide powerful means for the target deconvolution of phenotypic screen hits and generate motivated research hypotheses. Herein, we present recent progress in artificial intelligence applied to target identification that may accelerate future NP-inspired molecular medicine.

Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor

Using machine learning, targets were identified for β-lapachone.

Using machine learning, targets were identified for β-lapachone. Resorting to biochemical assays, β-lapachone was validated as a potent, ligand efficient, allosteric and reversible modulator of 5-lipoxygenase (5-LO). Moreover, we provide a rationale for 5-LO modulation and show that inhibition of 5-LO is relevant for the anticancer activity of β-lapachone. This work demonstrates the power of machine intelligence to deconvolute complex phenotypes, as an alternative and/or complement to chemoproteomics and as a viable general approach for systems pharmacology studies.

TRPC4/TRPC5 channels mediate adverse reaction to the cancer cell cytotoxic agent (-)-Englerin A

(-)-Englerin A (EA) is a natural product which has potent cytotoxic effects on renal cell carcinoma cells and other types of cancer cell but not non-cancer cells. Although selectively cytotoxic to cancer cells, adverse reaction in mice and rats has been suggested. EA is a remarkably potent activator of ion channels formed by Transient Receptor Potential Canonical 4 and 5 proteins (TRPC4 and TRPC5) and TRPC4 is essential for EA-mediated cancer cell cytotoxicity. Here we specifically investigated the relevance of TRPC4 and TRPC5 to the adverse reaction. Injection of EA (2 mg.kg^-1 i.p.) adversely affected mice for about 1 hour, manifesting as a marked reduction in locomotor activity, after which they fully recovered. TRPC4 and TRPC5 single knockout mice were partially protected and double knockout mice fully protected. TRPC4/TRPC5 double knockout mice were also protected against intravenous injection of EA. Importance of TRPC4/TRPC5 channels was further suggested by pre-administration of Compound 31 (Pico145), a potent and selective small-molecule inhibitor of TRPC4/TRPC5 channels which did not cause adverse reaction itself but prevented adverse reaction to EA. EA was detected in the plasma but not the brain and so peripheral mechanisms were implicated but not identified. The data confirm the existence of adverse reaction to EA in mice and suggest that it depends on a combination of TRPC4 and TRPC5 which therefore overlaps partially with TRPC4-dependent cancer cell cytotoxicity. The underlying nature of the observed adverse reaction to EA, as a consequence of TRPC4/TRPC5 channel activation, remains unclear and warrants further investigation.

Remarkable Progress with Small-Molecule Modulation of TRPC1/4/5 Channels: Implications for Understanding the Channels in Health and Disease

Proteins of the TRPC family can form many homo- and heterotetrameric cation channels permeable to Na⁺, K⁺ and Ca2+. In this review, we focus on channels formed by the isoforms TRPC1, TRPC4 and TRPC5. We review evidence for the formation of different TRPC1/4/5 tetramers, give an overview of recently developed small-molecule TRPC1/4/5 activators and inhibitors, highlight examples of biological roles of TRPC1/4/5 channels in different tissues and pathologies, and discuss how high-quality chemical probes of TRPC1/4/5 modulators can be used to understand the involvement of TRPC1/4/5 channels in physiological and pathophysiological processes.

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

Na+ entry through heteromeric TRPC4/C1 channels mediates (-)Englerin A-induced cytotoxicity in synovial sarcoma cells

The sesquiterpene (-)Englerin A (EA) is an organic compound from the plant Phyllanthus engleri which acts via heteromeric TRPC4/C1 channels to cause cytotoxicity in some types of cancer cell but not normal cells. Here we identified selective cytotoxicity of EA in human synovial sarcoma cells (SW982 cells) and investigated the mechanism. EA induced cation channel current (Icat) in SW982 cells with biophysical characteristics of heteromeric TRPC4/C1 channels. Inhibitors of homomeric TRPC4 channels were weak inhibitors of the Icat and EA-induced cytotoxicity whereas a potent inhibitor of TRPC4/C1 channels (Pico145) strongly inhibited Icat and cytotoxicity. Depletion of TRPC1 converted Icat into a current with biophysical and pharmacological properties of homomeric TRPC4 channels and depletion of TRPC1 or TRPC4 suppressed the cytotoxicity of EA. A Na⁺/K⁺-ATPase inhibitor (ouabain) potentiated EA-induced cytotoxicity and direct Na⁺ loading by gramicidin-A caused Pico145-resistant cytotoxicity in the absence of EA. We conclude that EA has a potent cytotoxic effect on human synovial sarcoma cells which is mediated by heteromeric TRPC4/C1 channels and Na⁺ loading.

Englerins: A Comprehensive Review

In the decade since the discovery of englerin A (1) and its potent activity in cancer models, this natural product and its analogues have been the subject of numerous chemical, biological, and preclinical studies by many research groups. This review summarizes published findings and proposes further research directions required for entry of an englerin analogue into clinical trials for kidney cancer and other conditions.

Englerin A induces an acute inflammatory response and reveals lipid metabolism and ER stress as targetable vulnerabilities in renal cell carcinoma

Renal cell carcinoma (RCC) is among the top ten most common forms of cancer and is the most common malignancy of the kidney. Clear cell renal carcinoma (cc-RCC), the most common type of RCC, is one of the most refractory cancers with an incidence that is on the rise. Screening of plant extracts in search of new anti-cancer agents resulted in the discovery of englerin A, a guaiane sesquiterpene with potent cytotoxicity against renal cancer cells and a small subset of other cancer cells. Though a few cellular targets have been identified for englerin A, it is still not clear what mechanisms account for the cytotoxicity of englerin A in RCC, which occurs at concentrations well below those used to engage the targets previously identified. Unlike any prior study, the current study used a systems biology approach to explore the mechanism(s) of action of englerin A. Metabolomics analyses indicated that englerin A profoundly altered lipid metabolism by 24 h in cc-RCC cell lines and generated significant levels of ceramides that were highly toxic to these cells. Microarray analyses determined that englerin A induced ER stress signaling and an acute inflammatory response, which was confirmed by quantitative PCR and Western Blot analyses. Additionally, fluorescence confocal microscopy revealed that englerin A at 25 nM disrupted the morphology of the ER confirming the deleterious effect of englerin A on the ER. Collectively, our findings suggest that cc-RCC is highly sensitive to disruptions in lipid metabolism and ER stress and that these vulnerabilities can be targeted for the treatment of cc-RCC and possibly other lipid storing cancers. Furthermore, our results suggest that ceramides may be a mediator of some of the actions of englerin A. Lastly, the acute inflammatory response induced by englerin A may mediate anti-tumor immunity.

Concise, Enantioselective, and Versatile Synthesis of (-)-Englerin A Based on a Platinum-Catalyzed [4C+3C] Cycloaddition of Allenedienes

A practical synthesis of (-)-englerin A was accomplished in 17 steps and 11 % global yield from commercially available achiral precursors. The key step consists of a platinum-catalyzed [4C+3C] allenediene cycloaddition that directly delivers the trans-fused guaiane skeleton with complete diastereoselectivity. The high enantioselectivity (99 % ee) stems from an asymmetric ruthenium-catalyzed transfer hydrogenation of a readily assembled diene-ynone. The synthesis also features a highly stereoselective oxygenation, and a late-stage cuprate alkylation that enables the preparation of previously inaccessible structural analogues.