Discovery of A-910, a Highly Potent and Orally Bioavailable Dual MerTK/Axl-Selective Tyrosine Kinase Inhibitor.

TAM receptor tyrosine kinases have emerged as promising therapeutic targets for cancer treatment due to their roles in both tumor intrinsic survival mechanisms and suppression of antitumor immunity within the tumor microenvironment. Inhibiting MerTK and Axl selectively is believed to hinder cancer cell survival, reverse the protumor myeloid phenotype, and suppress efferocytosis, thereby eliciting an antitumor immune response. In this study, we present the discovery of A-910, a highly potent and selective dual MerTK/Axl inhibitor, achieved through a structure-based medicinal chemistry campaign. The lead compound exhibits favorable oral bioavailability, exceptional kinome selectivity, and significantly improved in vivo target engagement. These findings support the use of A-910 as an orally bioavailable in vivo tool compound for investigating the immunotherapy potential of dual MerTK/Axl inhibition.

Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning.

Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals.

Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model.

Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.

A novel three-layer module BoMYB1R1-BoMYB4b/BoMIEL1-BoDFR1 regulates anthocyanin accumulation in kale.

Anthocyanin is an important pigment responsible for plant coloration and beneficial to human health. Kale (Brassica oleracea var. acephala), a primary cool-season flowers and vegetables, is an ideal material to study anthocyanin biosynthesis and regulation mechanisms due to its anthocyanin-rich leaves. However, the underlying molecular mechanism of anthocyanin accumulation in kale remains poorly understood. Previously, we demonstrated that BoDFR1 is a key gene controlling anthocyanin biosynthesis in kale. Here, we discovered a 369-bp InDel variation in the BoDFR1 promoter between the two kale inbred lines with different pink coloration, which resulted in reduced transcriptional activity of the BoDFR1 gene in the light-pink line. With the 369-bp insertion as a bait, an R2R3-MYB repressor BoMYB4b was identified using the yeast one-hybrid screening. Knockdown of the BoMYB4b gene led to increased BoDFR1 expression and anthocyanin accumulation. An E3 ubiquitin ligase, BoMIEL1, was found to mediate the degradation of BoMYB4b, thereby promoting anthocyanin biosynthesis. Furthermore, the expression level of BoMYB4b was significantly reduced by light signals, which was attributed to the direct repression of the light-signaling factor BoMYB1R1 on the BoMYB4b promoter. Our study revealed that a novel regulatory module comprising BoMYB1R1, BoMIEL1, BoMYB4b, and BoDFR1 finely regulates anthocyanin accumulation in kale. The findings aim to establish a scientific foundation for genetic improvement of leaf color traits in kale, meanwhile, providing a reference for plant coloration studies.

PIF4 interacts with ABI4 to serve as a transcriptional activator complex to promote seed dormancy by enhancing ABA biosynthesis and signaling.

Transcriptional regulation plays a key role in the control of seed dormancy, and many transcription factors (TFs) have been documented. However, the mechanisms underlying the interactions between different TFs within a transcriptional complex regulating seed dormancy remain largely unknown. Here, we showed that TF PHYTOCHROME-INTERACTING FACTOR4 (PIF4) physically interacted with the abscisic acid (ABA) signaling responsive TF ABSCISIC ACID INSENSITIVE4 (ABI4) to act as a transcriptional complex to promote ABA biosynthesis and signaling, finally deepening primary seed dormancy. Both pif4 and abi4 single mutants exhibited a decreased primary seed dormancy phenotype, with a synergistic effect in the pif4/abi4 double mutant. PIF4 binds to ABI4 to form a heterodimer, and ABI4 stabilizes PIF4 at the protein level, whereas PIF4 does not affect the protein stabilization of ABI4. Subsequently, both TFs independently and synergistically promoted the expression of ABI4 and NCED6, a key gene for ABA anabolism. The genetic evidence is also consistent with the phenotypic, physiological and biochemical analysis results. Altogether, this study revealed a transcriptional regulatory cascade in which the PIF4-ABI4 transcriptional activator complex synergistically enhanced seed dormancy by facilitating ABA biosynthesis and signaling.

Multielement simultaneous quantitative analysis of trace elements in stainless steel via full spectrum laser-induced breakdown spectroscopy.

Laser-Induced Breakdown Spectroscopy (LIBS) instruments are increasingly recognized as valuable tools for detecting trace metal elements due to their simplicity, rapid detection, and ability to perform simultaneous multi-element analysis. Traditional LIBS modeling often relies on empirical or machine learning-based feature band selection to establish quantitative models. In this study, we introduce a novel approach-simultaneous multi-element quantitative analysis based on the entire spectrum, which enhances model establishment efficiency and leverages the advantages of LIBS. By logarithmically processing the spectra and quantifying the cognitive uncertainty of the model, we achieved remarkable predictive performance (R2) for trace elements Mn, Mo, Cr, and Cu (0.9876, 0.9879, 0.9891, and 0.9841, respectively) in stainless steel. Our multi-element model shares features and parameters during the learning process, effectively mitigating the impact of matrix effects and self-absorption. Additionally, we introduce a cognitive error term to quantify the cognitive uncertainty of the model. The results suggest that our approach has significant potential in the quantitative analysis of trace elements, providing a reliable data processing method for efficient and accurate multi-task analysis in LIBS. This methodology holds promising applications in the field of LIBS quantitative analysis.

Small-sample stacking model for qualitative analysis of aluminum alloys based on femtosecond laser-induced breakdown spectroscopy.

Material characterization using laser-induced breakdown spectroscopy (LIBS) often relies on extensive data for effective analysis. However, data acquisition can be challenging, and the high dimensionality of raw spectral data combined with a large-scale sample dataset can strain computational resources. In this study, we propose a small sample size stacking model based on femtosecond LIBS to achieve accurate qualitative analysis of aluminum alloys. The proposed three-layer stacking algorithm performs data reconstruction and feature extraction to enhance the analysis. In the first layer, random forest spectral feature selection and specific spectral line spreading are employed to reconstruct the data. The second layer utilizes three heterogeneous classifiers to extract features from the reconstructed spectra in different feature spaces, generating second-level reconstructed data. Finally, the third layer utilizes the reconstructed dataset for qualitative prediction. Results indicate that the Stacking algorithm outperforms traditional methods such as k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), including those combined with principal component analysis (PCA). The Stacking algorithm achieves an impressive 100% recognition rate in classification, with Accuracy, precision, recall, and F1 scores reaching 1.0. Moreover, as the number of samples decreases, the gap between the recognition accuracy of the Stacking algorithm and traditional approaches widens. For instance, using only 15 spectra for training, the Stacking algorithm achieves a recognition accuracy of 96.47%, significantly surpassing the improved RF's accuracy of 71.76%. Notably, the model demonstrates strong robustness compared to traditional modeling approaches, and the qualitative prediction error remains consistently below 5%. These findings underscore the model's enhanced generalization ability and higher prediction accuracy in small sample machine learning. This research contributes significantly to improving the applicability of the LIBS technique for fast detection and analysis of small samples. It provides valuable insights into the development of effective methodologies for material characterization, paving the way for advancements in the field.

The ABI4-RbohD/VTC2 regulatory module promotes reactive oxygen species (ROS) accumulation to decrease seed germination under salinity stress.

Salinity stress enhances reactive oxygen species (ROS) accumulation by activating the transcription of NADPH oxidase genes such as RbohD, thus mediating plant developmental processes, including seed germination. However, how salinity triggers the expression of ROS-metabolism-related genes and represses seed germination has not yet been fully addressed. In this study, we show that Abscisic Acid-Insensitive 4 (ABI4), a key component in abscisic acid (ABA) signaling, directly combines with RbohD and Vitamin C Defective 2 (VTC2), the key genes involved in ROS production and scavenging, to modulate ROS metabolism during seed germination under salinity stress. Salinity-induced ABI4 enhances RbohD expression by physically interacting with its promoter, and subsequently promotes ROS accumulation, thus resulting in cell membrane damage and a decrease in seed vigor. Additional genetic evidence indicated that the rbohd mutant largely rescues the salt-hypersensitive phenotype of ABI4 overexpression seeds. Consistently, the abi4/vtc2 double mutant showed the salt-sensitive phenotype, similar to the vtc2 mutant, suggesting that both RbohD and VTC2 are epistatic to ABI4 genetically. Altogether, these results suggest that the salt-induced RbohD transcription and ROS accumulation is dependent on ABI4, and that the ABI4-RbohD/VTC2 regulatory module integrates both ROS metabolism and cell membrane integrity, ultimately repressing seed germination under salinity stress.

A matter of life and death: Molecular, physiological, and environmental regulation of seed longevity.

Both seed germination and early seedling establishment are important biological processes in a plant's lifecycle. Seed longevity is a key trait in agriculture, which directly influences seed germination and ultimately determines crop productivity and hence food security. Numerous studies have demonstrated that seed deterioration is regulated by complex interactions between diverse endogenous genetically controlled factors and exogenous environmental cues, including temperature, relative humidity, and oxygen partial pressure during seed storage. The endogenous factors, including the chlorophyll concentration, the structure of the seed coat, the balance of phytohormones, the concentration of reactive oxygen species, the integrity of nucleic acids and proteins and their associated repair systems, are also involved in the control of seed longevity. A precise understanding of the regulatory mechanisms underlying seed longevity is becoming a hot topic in plant molecular biology. In this review, we describe recent research into the regulation of seed longevity and the interactions between the various environmental and genetic factors. Based on this, the current state-of-play regarding seed longevity regulatory networks will be presented, particularly with respect to agricultural seed storage, and the research challenges to be faced in the future will be discussed.

Genome-wide identification of GRF transcription factors in soybean and expression analysis of GmGRF family under shade stress.

BACKGROUND: The Growth-regulating factor (GRF) family encodes plant-specific transcription factors which contain two conserved domains, QLQ and WRC. Members of this family play vital roles in plant development and stress response processes. Although GRFs have been identified in various plant species, we still know little about the GRF family in soybean (Glycine max). RESULTS: In the present study, 22 GmGRFs distributed on 14 chromosomes and one scaffold were identified by searching soybean genome database and were clustered into five subgroups according to their phylogenetic relationships. GmGRFs belonging to the same subgroup shared a similar motif composition and gene structure. Synteny analysis revealed that large-scale duplications played key roles in the expansion of the GmGRF family. Tissue-specific expression data showed that GmGRFs were strongly expressed in growing tissues, including the shoot apical meristems, developing seeds and flowers, indicating that GmGRFs play critical roles in plant growth and development. On the basis of expression analysis of GmGRFs under shade conditions, we found that all GmGRFs responded to shade stress. Most GmGRFs were down-regulated in soybean leaves after shade treatment. CONCLUSIONS: Taken together, this research systematically analyzed the characterization of the GmGRF family and its primary roles in soybean development and shade stress response. Further studies of the function of the GmGRFs in the growth, development and stress tolerance of soybean, especially under shade stress, will be valuable.

DA-6 promotes germination and seedling establishment from aged soybean seeds by mediating fatty acid metabolism and glycometabolism.

Soybean seeds contain higher concentrations of oil (triacylglycerol) and fatty acids than do cereal crop seeds, and the oxidation of these biomolecules during seed storage significantly shortens seed longevity and decreases germination ability. Here, we report that diethyl aminoethyl hexanoate (DA-6), a plant growth regulator, increases germination and seedling establishment from aged soybean seeds by increasing fatty acid metabolism and glycometabolism. Phenotypic analysis showed that DA-6 treatment markedly promoted germination and seedling establishment from naturally and artificially aged soybean seeds. Further analysis revealed that DA-6 increased the concentrations of soluble sugars during imbibition of aged soybean seeds. Consistently, the concentrations of several different fatty acids in DA-6-treated aged seeds were higher than those in untreated aged seeds. Subsequently, quantitative PCR analysis indicated that DA-6 induced the transcription of several key genes involved in the hydrolysis of triacylglycerol to sugars in aged soybean seeds. Furthermore, the activity of invertase in aged seeds, which catalyzes the hydrolysis of sucrose to form fructose and glucose, increased following DA-6 treatment. Taken together, DA-6 promotes germination and seedling establishment from aged soybean seeds by enhancing the hydrolysis of triacylglycerol and the conversion of fatty acids to sugars.

ABI4 regulates the floral transition independently of ABI5 and ABI3.

Emerging evidence suggests that the stress hormone abscisic acid (ABA) is also involved in the floral transition control. The transcription factors ABA INSENSITIVE4 (ABI4) and ABI5 negatively regulate flowering by directly promoting FLOWERING LOCUS C expression, and ABI3 also negatively regulates the floral transition. However, the genetic relationships between ABI4 and both ABI5 and ABI3 remain elusive. Here, we generated transgenic plants overexpressing ABI4 in the abi5 (OE-ABI4::abi5) and abi3 backgrounds (OE-ABI4::abi3). The flowering phenotypic analysis demonstrated that OE-ABI4::abi5 and OE-ABI4::abi3 plants exhibited delayed flowering. These findings suggest that ABI4 independently regulates floral transition but not through ABI5 and ABI3 cascades.

Discovery and optimization of novel constrained pyrrolopyridone BET family inhibitors.

Novel conformationally constrained BET bromodomain inhibitors have been developed. These inhibitors were optimized in two similar, yet distinct chemical series, the 6-methyl-1H-pyrrolo[2,3-c]pyridin-7(6H)-ones (A) and the 1-methyl-1H-pyrrolo[2,3-c]pyridin-7(6H)-ones (B). Each series demonstrated excellent activity in binding and cellular assays, and lead compounds from each series demonstrated significant efficacy in in vivo tumor xenograft models.

Exogenous auxin represses soybean seed germination through decreasing the gibberellin/abscisic acid (GA/ABA) ratio.

Auxin is an important phytohormone which mediates diverse development processes in plants. Published research has demonstrated that auxin induces seed dormancy. However, the precise mechanisms underlying the effect of auxin on seed germination need further investigation, especially the relationship between auxins and both abscisic acid (ABA) and gibberellins (GAs), the latter two phytohormones being the key regulators of seed germination. Here we report that exogenous auxin treatment represses soybean seed germination by enhancing ABA biosynthesis, while impairing GA biogenesis, and finally decreasing GA1/ABA and GA4/ABA ratios. Microscope observation showed that auxin treatment delayed rupture of the soybean seed coat and radicle protrusion. qPCR assay revealed that transcription of the genes involved in ABA biosynthetic pathway was up-regulated by application of auxin, while expression of genes involved in GA biosynthetic pathway was down-regulated. Accordingly, further phytohormone quantification shows that auxin significantly increased ABA content, whereas the active GA1 and GA4 levels were decreased, resulting insignificant decreases in the ratiosGA1/ABA and GA4/ABA.Consistent with this, ABA biosynthesis inhibitor fluridone reversed the delayed-germination phenotype associated with auxin treatment, while paclobutrazol, a GA biosynthesis inhibitor, inhibited soybean seed germination. Altogether, exogenous auxin represses soybean seed germination by mediating ABA and GA biosynthesis.

The SUV4-20 inhibitor A-196 verifies a role for epigenetics in genomic integrity.

Protein lysine methyltransferases (PKMTs) regulate diverse physiological processes including transcription and the maintenance of genomic integrity. Genetic studies suggest that the PKMTs SUV420H1 and SUV420H2 facilitate proficient nonhomologous end-joining (NHEJ)-directed DNA repair by catalyzing the di- and trimethylation (me2 and me3, respectively) of lysine 20 on histone 4 (H4K20). Here we report the identification of A-196, a potent and selective inhibitor of SUV420H1 and SUV420H2. Biochemical and co-crystallization analyses demonstrate that A-196 is a substrate-competitive inhibitor of both SUV4-20 enzymes. In cells, A-196 induced a global decrease in H4K20me2 and H4K20me3 and a concomitant increase in H4K20me1. A-196 inhibited 53BP1 foci formation upon ionizing radiation and reduced NHEJ-mediated DNA-break repair but did not affect homology-directed repair. These results demonstrate the role of SUV4-20 enzymatic activity in H4K20 methylation and DNA repair. A-196 represents a first-in-class chemical probe of SUV4-20 to investigate the role of histone methyltransferases in genomic integrity.

Exploration of diverse hinge-binding scaffolds for selective Aurora kinase inhibitors.

Four hinge-binding scaffolds have been explored for novel selective Aurora kinase inhibitors. The structure activity relationship, selectivity and pharmacokinetic profiles have been evaluated.

Discovery of potent and selective thienopyrimidine inhibitors of Aurora kinases.

In an effort to discover Aurora kinase inhibitors, an HTS hit revealed an amide containing pyrrolopyrimidine compound. Replacement of the pyrrolopyrimidine residue with a thienopyrimidine moiety led to a series of potent and selective Aurora inhibitors.

Platelet-derived growth factor receptor tyrosine kinase inhibitors: a review of the recent patent literature.

IMPORTANCE OF THE FIELD: Platelet-derived growth factor receptor (PDGFR) is a compelling target for developing therapeutic agents to treat diseases associated with overactivated platelet-derived growth factor (PDGF) signaling and has proved to be particularly encouraging for cancer treatment. The efforts in this area have been greatly enhanced by the approval of tyrosine kinase inhibitors with PDGFR inhibitory activity such as imatinib, sunitinib and sorafenib. AREAS COVERED IN THIS REVIEW: This review surveys the small molecule PDGFR inhibitors reported in patent literature over the past 5 years (2005 - 2009). WHAT THE READER WILL GAIN: The reader will gain an overview of the chemical scaffolds and the activity/selectivity of the newly discovered PDGFR inhibitors. TAKE HOME MESSAGE: Targeting PDGFR kinase with small molecule inhibitors has remained a very active area. Many new and novel PDGFR inhibitors with different selectivity profiles are being discovered and evaluated. In cancer therapy, the identification of novel and potent PDGFR inhibitors with preferred kinase inhibitory profiles that deliver superior antitumor efficacy, yet have manageable side effects and toxicities, will continue to be the key for success. Additionally, interest in targeting PDGF signaling for intervention of various vascular diseases and fibrotic conditions is expected to continue to grow.

Identification of aminopyrazolopyridine ureas as potent VEGFR/PDGFR multitargeted kinase inhibitors.

Tumor angiogenesis is mediated by KDR and other VEGFR and PDGFR kinases. Their inhibition presents an attractive approach for developing anticancer therapeutics. Here, we report a series of aminopyrazolopyridine ureas as potent VEGFR/PDGFR multitargeted kinase inhibitors. A number of compounds have been identified to be orally bioavailable and efficacious in the mouse edema model.

Discovery of N-(4-(3-amino-1H-indazol-4-yl)phenyl)-N'-(2-fluoro-5-methylphenyl)urea (ABT-869), a 3-aminoindazole-based orally active multitargeted receptor tyrosine kinase inhibitor.

In our continued efforts to search for potent and novel receptor tyrosine kinase (RTK) inhibitors as potential anticancer agents, we discovered, through a structure-based design, that 3-aminoindazole could serve as an efficient hinge-binding template for kinase inhibitors. By incorporating an N,N'-diaryl urea moiety at the C4-position of 3-aminodazole, a series of RTK inhibitors were generated, which potently inhibited the tyrosine kinase activity of the vascular endothelial growth factor receptor and the platelet-derived growth factor receptor families. A number of compounds with potent oral activity were identified by utilizing an estradiol-induced mouse uterine edema model and an HT1080 human fibrosarcoma xenograft tumor model. In particular, compound 17p (ABT-869) was found to possess favorable pharmacokinetic profiles across different species and display significant tumor growth inhibition in multiple preclinical animal models.