Metabolic Footprinting-Based DNA-AuNP Encoders for Extracellular Metabolic Response Profiling.

Metabolic footprinting as a convenient and non-invasive cell metabolomics strategy relies on monitoring the whole extracellular metabolic process. It covers nutrient consumption and metabolite secretion of in vitro cell culture, which is hindered by low universality owing to pre-treatment of the cell medium and special equipment. Here, we report the design and a variety of applicability, for quantifying extracellular metabolism, of fluorescently labeled single-stranded DNA (ssDNA)-AuNP encoders, whose multi-modal signal response is triggered by extracellular metabolites. We constructed metabolic response profiling of cells by detecting extracellular metabolites in different tumor cells and drug-induced extracellular metabolites. We further assessed the extracellular metabolism differences using a machine learning algorithm. This metabolic response profiling based on the DNA-AuNP encoder strategy is a powerful complement to metabolic footprinting, which significantly applies potential non-invasive identification of tumor cell heterogeneity.

Point-and-shoot Strategy based on Enzyme-assisted DNA "Paper-Cutting" to Construct Arbitrary Planar DNA Nanostructures.

DNA self-assembly provides a "bottom-up" route to fabricating complex shapes on the nanometer scale. However, each structure needs to be designed separately and carried out by professionally trained technicians, which seriously restricts its development and application. Herein, a point-and-shoot strategy based on enzyme-assisted DNA "paper-cutting" to construct planar DNA nanostructures using the same DNA origami as the template is reported. Precisely modeling the shapes with high precision in the strategy based on each staple strand of the desired shape structure hybridizes with its nearest neighbor fragments from the long scaffold strand. As a result, some planar DNA nanostructures by one-pot annealing the long scaffold strand and selected staple strands is constructed. The point-and-shoot strategy of avoiding DNA origami staple strands' re-designing based on different shapes breaks through the shape complexity limitation of the planar DNA nanostructures and enhances the simplicity of design and operation. Overall, the strategy's simple operability and great generality enable it to act as a candidate tool for manufacturing DNA nanostructures.

Small Molecule Inhibition of the Autophagy Kinase ULK1 and Identification of ULK1 Substrates.

Many tumors become addicted to autophagy for survival, suggesting inhibition of autophagy as a potential broadly applicable cancer therapy. ULK1/Atg1 is the only serine/threonine kinase in the core autophagy pathway and thus represents an excellent drug target. Despite recent advances in the understanding of ULK1 activation by nutrient deprivation, how ULK1 promotes autophagy remains poorly understood. Here, we screened degenerate peptide libraries to deduce the optimal ULK1 substrate motif and discovered 15 phosphorylation sites in core autophagy proteins that were verified as in vivo ULK1 targets. We utilized these ULK1 substrates to perform a cell-based screen to identify and characterize a potent ULK1 small molecule inhibitor. The compound SBI-0206965 is a highly selective ULK1 kinase inhibitor in vitro and suppressed ULK1-mediated phosphorylation events in cells, regulating autophagy and cell survival. SBI-0206965 greatly synergized with mechanistic target of rapamycin (mTOR) inhibitors to kill tumor cells, providing a strong rationale for their combined use in the clinic.

2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification.

An integrated custom cross-response sensing array has been developed combining the algorithm module's visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 105 ~ 108 CFU/mL for Escherichia coli, 102 ~ 107 CFU/mL for E. coli-ß, 103 ~ 108 CFU/mL for Staphylococcus aureus, 103 ~ 107 CFU/mL for MRSA, 102 ~ 108 CFU/mL for Pseudomonas aeruginosa, 103 ~ 108 CFU/mL for Enterococcus faecalis, 102 ~ 108 CFU/mL for Klebsiella pneumoniae, and 103 ~ 108 CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification. • A molecular response differential profiling (MRDP) was established based on custom cross-response sensor array for rapid and accurate recognition and phenotyping common pathogenic microorganism. • Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array. Each of these sensing elements' performance has competitive reaction with the microorganism. • MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms.

Discovery of LY3325656: A GPR142 agonist suitable for clinical testing in human.

The discovery and optimization of a novel series of GPR142 agonists are described. These led to the identification of compound 21 (LY3325656), which demonstrated anti-diabetic benefits in pre-clinical studies and ADME/PK properties suitable for human dosing. Compound 21 is the first GPR142 agonist molecule advancing to phase 1 clinic trials for the treatment of Type 2 diabetes.

Activity-balanced GLP-1/GDF15 dual agonist reduces body weight and metabolic disorder in mice and non-human primates.

Obesity is a considerable health concern with limited pharmacotherapy options of low efficacy. Here, we develop a GLP-1/GDF15 fusion protein and explore its weight-lowering potential in animals. The molecule, QL1005, is engineered via fusing GLP-1 and GDF15 analogs by a peptide linker and conjugating it to a fatty acid for time-action extension. In vitro, the potency of QL1005 is superior to the GLP-1 analog semaglutide. In obese mice, QL1005 induces reductions in body weight, food intake, insulin, fasting glucose, and triglycerides. Notably, these metabolic effects come as a result of activities emanating from both GLP-1 and GDF15, in an individual pathway-balanced fashion. In a cynomolgus monkey model of obesity, QL1005 reduces body weight, food intake, insulin, and glucose in a dose-dependent manner with limited incidence of GI side effects. Altogether, this long-acting, dual GLP-1/GDF15 molecule demonstrates the promise of poly-pharmaceutical approaches in metabolic drug discovery and development.

An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials.

Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.

Discovery of ecnoglutide - A novel, long-acting, cAMP-biased glucagon-like peptide-1 (GLP-1) analog.

OBJECTIVE: Glucagon-like peptide (GLP)-1 is an incretin hormone that acts after food intake to stimulate insulin production, enhance satiety, and promote weight loss. Here we describe the discovery and characterization of ecnoglutide (XW003), a novel GLP-1 analog. METHODS: We engineered a series of GLP-1 peptide analogs with an alanine to valine substitution (Ala8Val) and a γGlu-2xAEEA linked C18 diacid fatty acid at various positions. Ecnoglutide was selected and characterized in GLP-1 receptor signaling assays in vitro, as well as in db/db mice and a diet induced obese (DIO) rat model. A Phase 1, double-blind, randomized, placebo-controlled, single (SAD) and multiple ascending dose (MAD) study was conducted to evaluate the safety, tolerability, and pharmacokinetics of subcutaneous ecnoglutide injection in healthy participants. SAD doses ranged from 0.03 to 1.0 mg; MAD doses ranged from 0.2 to 0.6 mg once weekly for 6 weeks (ClinicalTrials.gov Identifier: NCT04389775). RESULTS: In vitro, ecnoglutide potently induced cAMP (EC50 = 0.018 nM) but not GLP-1 receptor internalization (EC50 > 10 µM), suggesting a desirable signaling bias. In rodent models, ecnoglutide significantly reduced blood glucose, promoted insulin induction, and led to more pronounced body weight reduction compared to semaglutide. In a Phase 1 trial, ecnoglutide was generally safe and well tolerated as a once-weekly injection for up to 6 weeks. Adverse events included decreased appetite, nausea, and headache. The half-life at steady state ranged from 124 to 138 h, supporting once-weekly dosing. CONCLUSIONS: Ecnoglutide showed a favorable potency, pharmacokinetic, and tolerability profile, as well as a simplified manufacturing process. These results support the continued development of ecnoglutide for the treatment of type 2 diabetes and obesity.

Correction: A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds.

Correction for 'A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds' by Zhijun Li et al., Nanoscale, 2022, 14, 3087-3096, DOI: 10.1039/D1NR07452K.

A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds.

Rapid and accurate identification of the mechanisms of action (MoAs) of antibacterial compounds remains a challenge for the development of antibacterial compounds. Computational inference methods for determining the MoAs of antibacterial compounds have been developed in recent years. In particular, approaches combining machine learning technology enable precisely recognizing the MoA of antibacterial compounds. However, these methods heavily rely on the big data resulting from multiplexed experiments. As such, these approaches tend to produce minimal throughput and are not comprehensive enough to be adapted to widespread industrial applications. Here, we present a machine learning approach based on a customized array sensor for directly identifying the MoAs of antibacterial compounds. The array sensor consists of different two-dimensional nanomaterial fluorescence quenchers with different fluorescence-labeled single-stranded DNAs (ssDNAs). By mapping the subtle difference of the physicochemical properties on the bacterial surface treated with different antibacterial compound stimuli, the array sensor ensures visualizing the recognition process. Moreover, the customized array sensor produces a high volume of the MoA database, overcoming the dependence on big data. We further use the array sensor to build a chemical-response unique "fingerprint" database of MoAs. By combining a neural network-based genetic algorithm (NNGA), we rapidly discriminate the MoAs of four antibiotics with an overall accuracy of 100%. Furthermore, a new screening antibacterial peptide has been discovered and evaluated by our approach for determining the MoA with high accuracy proven by other techniques.

Self-assembly of DNA nanogels with endogenous microRNA toehold self-regulating switches for targeted gene regulation therapy.

Herein, a smart nanohydrogel with endogenous microRNA-21 toehold is developed to encapsulate gemcitabine-loaded mesoporous silica nanoparticles for targeted pancreatic cancer therapy. This toehold mediated strand displacement method can simultaneously achieve specific drug release and miRNA-21 silencing, resulting in the up-regulation of the expression of tumor suppressor genes PTEN and PDCD4.

Corrigendum: Progression and Regression of Hepatic Lesions in a Mouse Model of NASH Induced by Dietary Intervention and Its Implications in Pharmacotherapy.

[This corrects the article DOI: 10.3389/fphar.2018.00410.].

Investigate the role of PTEN in chemotaxis of human breast cancer cells.

Chemotaxis plays an important role in metastasis of cancer cells. In the current study, we investigated the role of PTEN, a tumor suppressor, in chemotaxis of human breast cancer cells. Over-expression of PTEN inhibited EGF-induced chemotaxis, probably due to an overall reduction of PIP(3) levels. Disruption of PTEN by siRNA caused a marked decrease in chemokinesis, cell adhesion, and membrane spreading, resulting in a severe defect in chemotaxis. In PTEN disrupted cells, PDK1, AKT, and PKCzeta exhibited elevated basal activities, which prevented EGF-induced further activation of these molecules. In the absence of EGF, active PDK1 was detected on multiple directions of the plasma membranes of PTEN disrupted cells, which competed against EGF-induced gradient sensing. To confirm the biological relevance of in vitro studies, both PTEN disrupted cells and its parental human breast cancer cells were injected into tail veins of SCID mice. Mice injected with PTEN disrupted cancer cells showed a marked decrease in lung metastasis. Taken together, our data show that PTEN plays a non-redundant role in EGF-induced chemotaxis of human breast cancer cells, and an optimal level of PTEN is required in these responses.

Chemical genetic-based phenotypic screen reveals novel regulators of gluconeogenesis in human primary hepatocytes.

Insulin resistance is a pathophysiological hallmark of type 2 diabetes and nonalcoholic fatty liver disease. Under the condition of fat accumulation in the liver, suppression of hepatic glucose production by insulin is diminished. In order to gain deeper understanding of dysregulation of glucose production in metabolic diseases, in the present study, we performed an unbiased phenotypic screening in primary human hepatocytes to discover novel mechanisms that regulate gluconeogenesis in the presence of insulin. To optimize phenotypic screening process, we used a chemical genetic screening approach by building a small-molecule library with prior knowledge of activity-based protein profiling. The "positive hits" result from the screen will be small molecules with known protein targets. This makes downstream deconvolution process (i.e., determining the relevant biological targets) less time-consuming. To unbiasedly decipher the molecular targets, we developed a novel statistical method and discovered a set of genes, including DDR3 and CACNA1E that suppressed gluconeogenesis in human hepatocytes. Further investigation, including transcriptional profiling and gene network analysis, was performed to understand the molecular functions of DRD3 and CACNA1E in human hepatocytes.

Progression and Regression of Hepatic Lesions in a Mouse Model of NASH Induced by Dietary Intervention and Its Implications in Pharmacotherapy.

Understanding of the temporal changes of hepatic lesions in the progression and regression of non-alcoholic steatohepatitis (NASH) is vital to elucidation of the pathogenesis of NASH, and critical to the development of a strategy for NASH pharmacotherapy. There are challenges in studying hepatic lesion progression and regression in NASH patients due to the slow development of NASH in humans, one being the requirement for multiple biopsies during the longitudinal follow-up. Here we studied lesion progression and regression in the diet-induced animal model of NASH by application or removal of the pathogenic diet for multiple time periods. Male C57BL/6 mice fed Western diet developed progressive hepatic steatosis/macrovesicular vacuolation, inflammation, and hepatocyte degeneration, as well as perisinusoidal fibrosis and occasionally portal fibrosis as early as 2 months after initiation of the Western diet. In the same period, the mice exhibited elevated ALT (alanine aminotransferase) and AST (aspartate aminotransferase) enzyme activities, CK18 (cytokeratin-18), PIIINP (N-terminal propeptide of type III collagen), and TIMP-1 (tissue inhibitor of metalloproteinase-1). Hepatic steatosis diminished rapidly when the Western diet was replaced by normal rodent chow diet and hepatic inflammation and hepatocyte degeneration were also reduced. Interestingly, perisinusoidal fibrosis and portal fibrosis regressed 8 months after chow diet replacement. To understand pharmacotherapy for NASH, mice with established NASH hepatic lesions were treated with either FXR agonist obeticholic acid (Ocaliva), or CCR2/5 antagonist Cenicriviroc. Similar to the diet replacement, metabolic modulator Ocaliva markedly reduced steatosis/macrovesicular vacuolation, hepatic inflammation, and hepatocyte degeneration effectively, but exhibited no significant effect on liver fibrosis. Anti-inflammation drug Cenicriviroc, on the other hand, markedly decreased inflammation and hepatocyte degeneration, and mildly decreased liver fibrosis, but exhibited no effect on hepatic steatosis/macrovesicular vacuolation. In conclusion, we found the progression of NASH hepatic steatosis/macrovesicular vacuolation, and inflammation eventually lead to hepatocyte death and fibrosis. Life style change and current pharmacotherapies in development may be effective in treating NASH, but their effects on NASH-induced fibrosis may be mild. Since fibrosis is known to be an independent risk for decompensated cirrhosis, cardiovascular events, and mortality, our study suggests that effective anti-fibrosis therapy should be an essential component of the combined pharmacotherapy for advanced NASH.

Efficient synthesis of maleimides and carbazoles via Zn(OTf)(2)-catalyzed tandem annulations of isonitriles and allenic esters.

Lewis acid Zn(OTf)(2)-catalyzed tandem annulations of isonitriles and allenic esters which lead to efficient and flexible syntheses of a range of biologically significant maleimides and carbazoles and related compounds are reported. A mechanistic rationale is proposed to account for the observed reactivity.

Benzodiazepinone derivatives protect against endoplasmic reticulum stress-mediated cell death in human neuronal cell lines.

Endoplasmic reticulum (ER) stress causes neuronal dysfunction followed by cell death and is recognized as a feature of many neurodegenerative diseases. Using a phenotypic screen, we recently identified benzodiazepinone derivatives that reduce ER stress-mediated apoptosis in a rat neuronal progenitor cell line (CSM14.1). Herein we describe how structure-activity relationship (SAR) studies around these screening hits led to compounds that display robust cytoprotective activity against thapsigargin-induced ER stress in SH-SY5Y and H4 human neuronal cell lines. We demonstrate that the most potent of these derivatives, compound 4hh, inhibits the activation of p38 MAP kinase (p38) and c-Jun N-terminal kinase (JNK), protein kinases that are downstream signal effectors of the unfolded protein response (UPR). Compound 4hh specifically protects against thapsigargin-induced cell death and displays no protection against other insults known to induce cellular stress or activate p38. However, compound 4hh provides moderate inhibition of p38 activity stimulated by compounds that disrupt calcium homeostasis. Our data indicate that probe compound 4hh is a valuable small molecule tool that can be used to investigate the effects of ER stress on human neurons. This approach may provide the basis for the future development of therapeutics for the treatment of neurodegenerative diseases.

Benzo[e]isoindole-1,3-diones as potential inhibitors of glycogen synthase kinase-3 (GSK-3). Synthesis, kinase inhibitory activity, zebrafish phenotype, and modeling of binding mode.

Benzo[e]isoindole-1,3-dione derivatives were synthesized, and the effects on GSK-3beta activity and zebrafish embryo growth were evaluated. A series of derivatives show obvious inhibitory activity against GSK-3beta. The most potent inhibitor, 7,8-dimethoxy-5-methylbenzo[e]isoindole-1,3-dione (8a), shows nanomolar IC(50) and obvious phenotype on zebrafish embryo growth associated with the inhibition of GSK-3beta at low micromolar concentration. The interaction mode between 8a and GSK-3beta was characterized by computational modeling.

Characterization and development of novel small-molecules inhibiting GSK3 and activating Wnt signaling.

Glycogen synthase kinase 3 (GSK3) is an essential component of the Wnt signaling pathway and plays important roles in regulating cell proliferation, differentiation, and apoptosis. As GSK3 is abnormally upregulated in several diseases including type II diabetes, Alzheimer's disease and cancer, it has been regarded as a potential drug target. During zebrafish development, inhibition of GSK3 leads to ectopic activation of the Wnt pathway, resulting in a headless embryo. Using this phenotype as an assay we screened a chemical library of 4000 compounds and identified one novel compound, 3F8, which specifically inhibits eye and forebrain formation in zebrafish embryos, resembling a typical Wnt overexpression phenotype. Cell reporter assays, chemical informatics analysis and in vitro kinase experiments revealed that 3F8 is a selective GSK3 inhibitor, which is more potent than SB216763, a commonly used GSK3 inhibitor. Based on the structure of 3F8, a new generation of compounds inhibiting GSK3 was synthesized and validated by biological assays. Together, 3F8 and its derivatives could be useful as new reagents and potential therapeutic candidates for GSK3 related diseases.

Identifying tumor cell growth inhibitors by combinatorial chemistry and zebrafish assays.

Cyclin-dependent kinases (CDKs) play important roles in regulating cell cycle progression, and altered cell cycles resulting from over-expression or abnormal activation of CDKs observed in many human cancers. As a result, CDKs have become extensive studied targets for developing chemical inhibitors for cancer therapies; however, protein kinases share a highly conserved ATP binding pocket at which most chemical inhibitors bind, therefore, a major challenge in developing kinase inhibitors is achieving target selectivity. To identify cell growth inhibitors with potential applications in cancer therapy, we used an integrated approach that combines one-pot chemical synthesis in a combinatorial manner to generate diversified small molecules with new chemical scaffolds coupled with growth inhibition assay using developing zebrafish embryos. We report the successful identification of a novel lead compound that displays selective inhibitory effects on CDK2 activity, cancer cell proliferation, and tumor progression in vivo. Our approaches should have general applications in developing cell proliferation inhibitors using an efficient combinatorial chemical genetic method and integrated biological assays. The novel cell growth inhibitor we identified should have potential as a cancer therapeutic agent.