Development of an orally bioavailable mSWI/SNF ATPase degrader and acquired mechanisms of resistance in prostate cancer.

Mammalian switch/sucrose nonfermentable (mSWI/SNF) ATPase degraders have been shown to be effective in enhancer-driven cancers by functioning to impede oncogenic transcription factor chromatin accessibility. Here, we developed AU-24118, an orally bioavailable proteolysis-targeting chimera (PROTAC) degrader of mSWI/SNF ATPases (SMARCA2 and SMARCA4) and PBRM1. AU-24118 demonstrated tumor regression in a model of castration-resistant prostate cancer (CRPC) which was further enhanced with combination enzalutamide treatment, a standard of care androgen receptor (AR) antagonist used in CRPC patients. Importantly, AU-24118 exhibited favorable pharmacokinetic profiles in preclinical analyses in mice and rats, and further toxicity testing in mice showed a favorable safety profile. As acquired resistance is common with targeted cancer therapeutics, experiments were designed to explore potential mechanisms of resistance that may arise with long-term mSWI/SNF ATPase PROTAC treatment. Prostate cancer cell lines exposed to long-term treatment with high doses of a mSWI/SNF ATPase degrader developed SMARCA4 bromodomain mutations and ABCB1 (ATP binding cassette subfamily B member 1) overexpression as acquired mechanisms of resistance. Intriguingly, while SMARCA4 mutations provided specific resistance to mSWI/SNF degraders, ABCB1 overexpression provided broader resistance to other potent PROTAC degraders targeting bromodomain-containing protein 4 and AR. The ABCB1 inhibitor, zosuquidar, reversed resistance to all three PROTAC degraders tested. Combined, these findings position mSWI/SNF degraders for clinical translation for patients with enhancer-driven cancers and define strategies to overcome resistance mechanisms that may arise.

Decoding multilevel relationships with the human tissue-cell-molecule network.

Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.

Tumor-associated macrophages in colorectal cancer metastasis: molecular insights and translational perspectives.

Metastasis is the leading cause of high mortality in colorectal cancer (CRC), which is not only driven by changes occurring within the tumor cells, but is also influenced by the dynamic interaction between cancer cells and components in the tumor microenvironment (TME). Currently, the exploration of TME remodeling and its impact on CRC metastasis has attracted increasing attention owing to its potential to uncover novel therapeutic avenues. Noteworthy, emerging studies suggested that tumor-associated macrophages (TAMs) within the TME played important roles in CRC metastasis by secreting a variety of cytokines, chemokines, growth factors and proteases. Moreover, TAMs are often associated with poor prognosis and drug resistance, making them promising targets for CRC therapy. Given the prognostic and clinical value of TAMs, this review provides an updated overview on the origin, polarization and function of TAMs, and discusses the mechanisms by which TAMs promote the metastatic cascade of CRC. Potential TAM-targeting techniques for personalized theranostics of metastatic CRC are emphasized. Finally, future perspectives and challenges for translational applications of TAMs in CRC development and metastasis are proposed to help develop novel TAM-based strategies for CRC precision medicine and holistic healthcare.

QTL mapping of downy mildew resistance in foxtail millet by SLAF­seq and BSR-seq analysis.

KEY MESSAGE: Key message Three major QTLs for resistance to downy mildew were located within an 0.78 Mb interval on chromosome 8 in foxtail millet. Downy mildew, a disease caused by Sclerospora graminicola, is a serious problem that jeopardizes the yield and quality of foxtail millet. Breeding resistant varieties represents one of the most economical and effective solutions, yet there is a lack of molecular markers related to the resistance. Here, a mapping population comprising of 158 F6:7 recombinant inbred lines (RILs) was constructed from the crossing of G1 and JG21. Based on the specific locus amplified fragment sequencing results, a high-density linkage map of foxtail millet with 1031 bin markers, spanning 1041.66 cM was constructed. Based on the high-density linkage map and the phenotype data in four environments, a total of nine quantitative trait loci (QTL) associated with resistance to downy mildew were identified. Further BSR-seq confirmed the genomic regions containing the potential candidate genes related to downy mildew resistance. Interestingly, a 0.78-Mb interval between C8M257 and C8M268 on chromosome 8 was highlighted because of its presence in three major QTL, qDM8_1, qDM8_2, and qDM8_4, which contains 10 NBS-LRR genes. Haplotype analysis in RILs and natural population suggest that 9 SNP loci on Seita8G.199800, Seita8G.195900, Seita8G.198300, and Seita.8G199300 genes were significantly correlated with disease resistance. Furthermore, we found that those genes were taxon-specific by collinearity analysis of pearl millet and foxtail millet genomes. The identification of these new resistance QTL and the prediction of resistance genes against downy mildew will be useful in breeding for resistant varieties and the study of genetic mechanisms of downy mildew disease resistance in foxtail millet.

Structural optimization of Moracin M as novel selective phosphodiesterase 4 inhibitors for the treatment of idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and high mortality lung disease. Although the antifibrotic drugs pirfenidone and nintedanib could slow the rate of lung function decline, the usual course of the condition is inexorably to respiratory failure and death. Therefore, new approaches and novel therapeutic drugs for the treatment of IPF are urgently needed. And the selective PDE4 inhibitor has in vivo and in vitro anti-fibrotic effects in IPF models. But the clinical application of most PDE4 inhibitors are limited by their unexpected and severe side effects such as nausea, vomiting, and diarrhea. Herein, structure-based optimizations of the natural product Moracin M resulted in a novel a novel series of 2-arylbenzofurans as potent PDE4 inhibitors. The most potent inhibitor L13 has an IC50 of 36 ± 7 nM with remarkable selectivity across the PDE families and administration of L13·citrate (10.0 mg/kg) exhibited comparable anti-pulmonary fibrosis effects to pirfenidone (300 mg/kg) in a bleomycin-induced IPF mice model, indicate that L13 is a potential lead for the treatment of IPF.

Hydrogel immobilized bacteria@MOFs composite towards Bisphenol A degradation and the interconnection mechanism elucidation.

Bisphenol A (BPA) degradation efficiency by bacteria or by metal-organic-frameworks (MOFs) catalyzed persulfate (PMS) oxidation have been studied intensively. However, their synergistic effect on BPA degradation was less reported. In this study, we combined previously synthesized CNT-hemin/Mn-MOF with an BPA degrading bacteria SQ-2 to form a composite (SQ-2@MOFs). CNT-hemin/Mn-MOF in the composite catalyzed little PMS to promote the degradation efficiency of SQ-2 on BPA. Results indicated SQ-2@MOFs significantly accelerated BPA degradation rate than SQ-2 alone. Furthermore, SQ-2@MOFs composite was successfully immobilized in hydrogel to achieve better degradation performance. Immobilized SQ-2@MOFs could almost completely degrade 1-20 mg/L BPA within 24 h and completely degrade 5 mg/L BPA at pH 4-8. Besides, degradation byproducts also reduced by immobilized SQ-2@MOFs, which promoted the cleaner biodegradation of BPA. Metabolomics and multiple chemical characterization results revealed the interconnection mechanism between CNT-hemin/Mn-MOFs, SQ-2 and hydrogel. CNT-hemin/Mn-MOF helped SQ-2 degrade BPA into more biodegradable products, promoted electron transfer, and augmented BPA degradation ability of SQ-2 itself. SQ-2 enabled the surface electronegativity of SQ-2@MOFs more suitable for BPA contact. Meanwhile, SQ-2 avoided the loss of Fe and Mn of CNT-hemin/Mn-MOF. Hydrogel augmented the above synergistic effect. This study provided new perspective for the development of biodegradation materials through interdisciplinary integration.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

BACKGROUND: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. OBJECTIVE: This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. METHODS: In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. RESULTS: A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. CONCLUSIONS: The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

MDSi: Multi-omics Database for Setaria italica.

BACKGROUND: Foxtail millet (Setaria italica) harbors the small diploid genome (~ 450 Mb) and shows the high inbreeding rate and close relationship to several major foods, feed, fuel and bioenergy grasses. Previously, we created a mini foxtail millet, xiaomi, with an Arabidopsis-like life cycle. The de novo assembled genome data with high-quality and an efficient Agrobacterium-mediated genetic transformation system made xiaomi an ideal C4 model system. The mini foxtail millet has been widely shared in the research community and as a result there is a growing need for a user-friendly portal and intuitive interface to perform exploratory analysis of the data. RESULTS: Here, we built a Multi-omics Database for Setaria italica (MDSi, http://sky.sxau.edu.cn/MDSi.htm ), that contains xiaomi genome of 161,844 annotations, 34,436 protein-coding genes and their expression information in 29 different tissues of xiaomi (6) and JG21 (23) samples that can be showed as an Electronic Fluorescent Pictograph (xEFP) in-situ. Moreover, the whole-genome resequencing (WGS) data of 398 germplasms, including 360 foxtail millets and 38 green foxtails and the corresponding metabolic data were available in MDSi. The SNPs and Indels of these germplasms were called in advance and can be searched and compared in an interactive manner. Common tools including BLAST, GBrowse, JBrowse, map viewer, and data downloads were implemented in MDSi. CONCLUSION: The MDSi constructed in this study integrated and visualized data from three levels of genomics, transcriptomics and metabolomics, and also provides information on the variation of hundreds of germplasm resources that can satisfies the mainstream requirements and supports the corresponding research community.

Mechanical loading and autophagy: A study on the BoNT-A injection-induced condylar cartilage degeneration.

Botulinum toxin A (BoNT-A) has emerged as a treatment option for temporomandibular disorder (TMD). By injecting BoNT-A into the masseter muscle, it is possible to reduce mechanical loading on the temporomandibular joint (TMJ). However, numerous prior studies have indicated excessive reduction in mechanical loading can have detrimental effects on TMJ cartilage. This study proposes that autophagy, a process influenced by mechanical loading, could play a role in BoNT-A-induced mandibular condyle cartilage degeneration. To explore this hypothesis, we employed both BoNT-A injection and an excessive biting model to induce variations in mechanical loading on the condyle cartilage of C57BL/6 mice, thereby simulating an increase and decrease in mechanical loading, respectively. Results showed a significant reduction in cartilage thickness and downregulation of Runt-related transcription factor 2 (Runx2) expression in chondrocytes following BoNT-A injection. In vitro experiments demonstrated that the reduction of Runx2 expression in chondrocytes is associated with autophagy, possibly dependent on decreased YAP expression induced by low mechanical loading. This study reveals the potential involvement of the YAP/LC3/Runx2 signaling pathway in BoNT-A mediated mandibular condylar cartilage degeneration.

Ultrasound-guided stellate ganglion block for the treatment of migraine in elderly patients: A retrospective and observational study.

OBJECTIVES: This study aimed to assess the effectiveness and safety of ultrasound-guided stellate ganglion block (SGB) in the prophylactic treatment of migraine in the elderly. BACKGROUND: Treatment of migraine in the elderly is often difficult and troublesome due to multiple comorbidities, drug interactions, and adverse effects (AEs). SGB may be an effective treatment approach for migraine in the elderly as its clinical use is rarely limited by concomitant diseases and age-related physiological changes; however, no studies have evaluated the effectiveness of SGB in the treatment of migraine in the elderly population. METHODS: This is a retrospective observational case series study. We retrospectively analyzed patients with migraine aged ≥65 years, who underwent ultrasound-guided SGB for headache management between January 2018 and November 2022. Pain intensity using the numerical rating scale (NRS, 0-10), number of days with headache per month, duration of headache, and the consumptions of acute medications were recorded before SGB treatment, and at 1, 2, and 3 months after the last SGB. Safety assessment included thorough documentation of serious and minor AEs related to SGB. RESULTS: Of 71 patients, 52 were analyzed in this study. After the final SGB, the NRS scores decreased significantly from a mean (± standard deviation [SD]) of 7.3 (1.2) at baseline to 3.3 (1.4), 3.1 (1.6), and 3.6 (1.6) at 1, 2, and 3 months, respectively (vs. baseline, p < 0.001). The mean (SD) number of headache days per month significantly reduced from 23.1 (5.5) to 10.9 (7.1) (p < 0.001), 12.7 (6.5) (p = 0.001), and 14.0 (6.8) days (p = 0.001) at the 1-, 2-, and 3-month follow-ups, respectively. The values of headache duration were also significantly lower at the 1-month (mean [SD] 12.5 [15.8] h, p = 0.001), 2-month (mean [SD] 11.3 [15.9] h, p = 0.001), and 3-month follow-ups (mean [SD] 14.3 [16.0] h, p = 0.001) compared to pre-treatment baseline (mean [SD] 22.7 [17.1] h). There were 33/52 (64%) patients experiencing at least a 50% reduction in acute medications consumption 3 months after the final SGB treatment. The overall AEs rate associated with ultrasound-guided SGB was 9.0% (26/290 SGBs). There were no serious AEs; all reported AEs were minor and transient. CONCLUSIONS: Stellate ganglion block treatment could reduce pain intensity, headache frequency, and duration of migraine, thereby reducing the need for adjunctive medications in elderly patients. Ultrasound-guided SGB might be a safe and effective intervention for the treatment of migraine in elderly patients.

Revealing Trade Potential for Reversing Regional Freshwater Boundary Exceedance.

Applying the planetary boundary for the freshwater framework at the regional level is important in supporting local water management but is subject to substantial uncertainty. Previous estimates have not fully investigated the potential of trade in mitigating regional freshwater boundary (RFB) exceedance. Here, we estimate RFB based on the average results of 15 different hydrological models to reduce uncertainty. We then propose a framework to divide the RFB exceedance/maintenance into contributions from both consumption and trade and further identify trade contribution into six types. We applied the framework to China's provinces, which are characterized by intensive interprovincial trade and a significant mismatch in water resource supply and demand. We found that the current trade pattern limits the role of trade to mitigate RFB exceedance. For the importing provinces exceeding RFBs, 78% of their imported goods and services came from other RFB exceeding provinces. Scenario analysis showed that relying on increased imports alone, even to its greatest extent, will not reverse RFB exceedance in most importing provinces. Increased imports, however, will have an aggregate effect on the trade partners, leading to the exceedance of the national freshwater boundary. We also found that promoting export of goods and services from non-RFB exceeding provinces and reducing their water intensity will help address the imbalance both locally and, in the aggregate, nationally.

As a potential predictor of pan-cancer, UBE2S is related to tumor-associated macrophage infiltration.

Background: At the pan-cancer level, exploring the expression and prognostic significance of a gene, such as UBE2S, will help to gain insight into the role of the gene and its feasibility for cancer screening, prognosis assessment and even gene therapy. Methods: The Cancer Genome Atlas, Human Protein Atlas, Kaplan-Meier, Tumor Immunology Estimation Resource and other databases were used to analyze the expression of UBE2S at the pan-cancer level, its prognosis and the role of the immune microenvironment. Immunohistochemistry samples of tumor tissue collected in our clinic were taken as verification. Results: UBE2S is significantly overexpressed in pan-cancer and is closely associated with malignant clinical features, poor prognosis and tumor-associated macrophages. Conclusion: UBE2S may be a potential diagnostic and prognostic marker for pan-cancer and is associated with tumor-associated macrophages.

Role of SiPHR1 in the Response to Low Phosphate in Foxtail Millet via Comparative Transcriptomic and Co-Expression Network Analyses.

Enhancing the absorption and utilization of phosphorus by crops is an important aim for ensuring food security worldwide. However, the gene regulatory network underlying phosphorus use in foxtail millet remains unclear. In this study, the molecular mechanism underlying low-phosphorus (LP) responsiveness in foxtail millet was evaluated using a comparative transcriptome analysis. LP reduced the chlorophyll content in shoots, increased the anthocyanin content in roots, and up-regulated purple acid phosphatase and phytase activities as well as antioxidant systems (CAT, POD, and SOD). Finally, 13 differentially expressed genes related to LP response were identified and verified using transcriptomic data and qRT-PCR. Two gene co-expression network modules related to phosphorus responsiveness were positively correlated with POD, CAT, and PAPs. Of these, SiPHR1, functionally annotated as PHOSPHATE STARVATION RESPONSE 1, was identified as an MYB transcription factor related to phosphate responsiveness. SiPHR1 overexpression in Arabidopsis significantly modified the root architecture. LP stress caused cellular, physiological, and phenotypic changes in seedlings. SiPHR1 functioned as a positive regulator by activating downstream genes related to LP tolerance. These results improve our understanding of the molecular mechanism underlying responsiveness to LP stress, thereby laying a theoretical foundation for the genetic modification and breeding of new LP-tolerant foxtail millet varieties.

Comparative metabolomic and transcriptomic analysis reveals a coexpression network of the carotenoid metabolism pathway in the panicle of Setaria italica.

BACKGROUND: The grains of foxtail millet are enriched in carotenoids, which endow this plant with a yellow color and extremely high nutritional value. However, the underlying molecular regulation mechanism and gene coexpression network remain unclear. METHODS: The carotenoid species and content were detected by HPLC for two foxtail millet varieties at three panicle development stages. Based on a homologous sequence BLAST analysis, these genes related to carotenoid metabolism were identified from the foxtail millet genome database. The conserved protein domains, chromosome locations, gene structures and phylogenetic trees were analyzed using bioinformatics tools. RNA-seq was performed for these samples to identify differentially expressed genes (DEGs). A Pearson correlation analysis was performed between the expression of genes related to carotenoid metabolism and the content of carotenoid metabolites. Furthermore, the expression levels of the key DEGs were verified by qRT-PCR. The gene coexpression network was constructed by a weighted gene coexpression network analysis (WGCNA). RESULT: The major carotenoid metabolites in the panicles of DHD and JG21 were lutein and ß-carotene. These carotenoid metabolite contents sharply decreased during the panicle development stage. The lutein and ß-carotene contents were highest at the S1 stage of DHD, with values of 11.474 µg /100 mg and 12.524 µg /100 mg, respectively. Fifty-four genes related to carotenoid metabolism were identified in the foxtail millet genome. Cis-acting element analysis showed that these gene promoters mainly contain 'plant hormone', 'drought stress resistance', 'MYB binding site', 'endosperm specific' and 'seed specific' cis-acting elements and especially the 'light-responsive' and 'ABA-responsive' elements. In the carotenoid metabolic pathways, SiHDS, SiHMGS3, SiPDS and SiNCED1 were more highly expressed in the panicle of foxtail millet. The expression of SiCMT, SiAACT3, SiPSY1, SiZEP1/2, and SiCCD8c/8d was significantly correlated with the lutein content. The expression of SiCMT, SiHDR, SiIDI2, SiAACT3, SiPSY1, and SiZEP1/2 was significantly correlated with the content of ß-carotene. WGCNA showed that the coral module was highly correlated with lutein and ß-carotene, and 13 structural genes from the carotenoid biosynthetic pathway were identified. Network visualization revealed 25 intramodular hub genes that putatively control carotenoid metabolism. CONCLUSION: Based on the integrative analysis of the transcriptomics and carotenoid metabonomics, we found that DEGs related to carotenoid metabolism had a stronger correlation with the key carotenoid metabolite content. The correlation analysis and WGCNA identified and predicted the gene regulation network related to carotenoid metabolism. These results lay the foundation for exploring the key target genes regulating carotenoid metabolism flux in the panicle of foxtail millet. We hope that these target genes could be used to genetically modify millet to enhance the carotenoid content in the future.

Folate metabolic profiling and expression of folate metabolism-related genes during panicle development in foxtail millet (Setaria italica (L.) P. Beauv).

BACKGROUND: Foxtail millet grain has higher folate content than other cereal crops. However, the folate metabolite content and the expression patterns of folate metabolite-related genes are unknown. RESULTS: Liquid chromatography-mass spectrometry was used to investigate 12 folate metabolites in a foxtail millet panicle. The content of total folate and derivatives gradually decreased during panicle development. Polyglutamate 5-formyl-tetrahydrofolate was the major form. Twenty-eight genes involved in the folate metabolic pathway were identified through bioinformatic analysis. These genes in Setaria italica, S. viridis and Zea mays showed genomic collinearity. Phylogenetic analysis revealed that the folate-related genes were closely related among the C4 plants compared to C3 plants. The gene expressions were then studied at three panicle development stages. The gene expression patterns were classified into two groups, namely SiADCL1 and SiGGH as two key enzymes, which are responsible for folate synthesis and degradation; their expression levels were highest at the early panicle development stage, up to 179.11- and 163.88-fold, respectively. Their expression levels had a similar downward trend during panicle development and were significantly positively correlated with the concentration of total folate and folate derivatives. However, SiSHMT3 expression levels were significantly negatively correlated with total folate concentration. CONCLUSION: Besides being the major determinants of folate and folate derivatives accumulation, SiADCL1 and SiGGH expression levels are key limiting factors in the foxtail millet panicle. Therefore, SiADCL1 and SiGGH expression levels can be targeted in genetic modification studies to improve folate content in foxtail millet seeds in the future. © 2021 Society of Chemical Industry.

Comparative transcriptomic analysis reveals the regulatory mechanism of the gibberellic acid pathway of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn.) dwarf mutants.

BACKGROUND: Tartary buckwheat is an important minor crop species with high nutritional and medicinal value and is widely planted worldwide. Cultivated Tartary buckwheat plants are tall and have hollow stems that lodge easily, which severely affects their yield and hinders the development of the Tartary buckwheat industry. METHODS: Heifeng No. 1 seeds were treated with ethylmethanesulfonate (EMS) to generate a mutant library. The dwarf mutant ftdm was selected from the mutagenized population, and the agronomic characteristics giving rise to the dwarf phenotype were evaluated. Ultra-fast liquid chromatography-electrospray ionization tandem mass spectrometry (UFLC-ESI-MS/MS) was performed to determine the factors underlying the different phenotypes between the wild-type (WT) and ftdm plants. In addition, RNA sequencing (RNA-seq) was performed via the HiSeq 2000 platform, and the resulting transcriptomic data were analysed to identify differentially expressed genes (DEGs). Single-nucleotide polymorphism (SNP) variant analysis revealed possible sites associated with dwarfism. The expression levels of the potential DEGs between the WT and ftdm mutant were then measured via qRT-PCR and fragments per kilobase of transcript per million mapped reads (FPKM). RESULT: The plant height (PH) of the ftdm mutant decreased to 42% of that of the WT, and compared with the WT, the mutant and had a higher breaking force (BF) and lower lodging index (LI). Lower GA4 and GA7 contents and higher contents of jasmonic acid (JA), salicylic acid (SA) and brassinolactone (BR) were detected in the stems of the ftdm mutant compared with the WT. Exogenous application of GAs could not revert the dwarfism of the ftdm mutant. On the basis of the transcriptomic analysis, 146 homozygous SNP loci were identified. In total, 12 DEGs with nonsynonymous mutations were ultimately identified, which were considered potential candidate genes related to the dwarf trait. When the sequences of eight genes whose expression was downregulated and four genes whose expression was upregulated were compared, SKIP14, an F-box protein whose sequence is 85% homologous to that of SLY1 in Arabidopsis, presented an amino acid change (from Ser to Asn) and was expressed at a lower level in the stems of the ftdm mutant compared with the WT. Hence, we speculated that this amino acid change in SKIP14 resulted in a disruption in GA signal transduction, indirectly decreasing the GA content and downregulating the expression of genes involved in GA biosynthesis or the GA response. Further studies are needed to determine the molecular basis underlying the dwarf phenotype of the ftdm mutant. CONCLUSION: We report a Tartary buckwheat EMS dwarf mutant, ftdm, suitable for high-density planting and commercial farming. A significant decrease in GA4 and GA7 levels was detected in the ftdm mutant, and 12 DEGs expressed in the stems of the ftdm mutant were selected as candidates of the dwarfing gene. One nonsynonymous mutation was detected in the SKIP14 gene in the ftdm mutant, and this gene had a lower transcript level compared with that in the WT.

VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models.

SUMMARY: Although many quantitative structure-activity relationship (QSAR) models are trained and evaluated for their predictive merits, understanding what models have been learning is of critical importance. However, the interpretation and visualization of QSAR model results remain challenging, especially for 'black box' models such as deep neural network (DNN). Here, we take a step forward to interpret the learned chemical features from DNN QSAR models, and present VISAR, an interactive tool for visualizing the structure-activity relationship. VISAR first provides functions to construct and train DNN models. Then VISAR builds the activity landscapes based on a series of compounds using the trained model, showing the correlation between the chemical feature space and the experimental activity space after model training, and allowing for knowledge mining from a global perspective. VISAR also maps the gradients of the chemical features to the corresponding compounds as contribution weights for each atom, and visualizes the positive and negative contributor substructures suggested by the models from a local perspective. Using the web application of VISAR, users could interactively explore the activity landscape and the color-coded atom contributions. We propose that VISAR could serve as a helpful tool for training and interactive analysis of the DNN QSAR model, providing insights for drug design, and an additional level of model validation. AVAILABILITY AND IMPLEMENTATION: The source code and usage instructions for VISAR are available on github https://github.com/qid12/visar. CONTACT: shaoli@mail.tsinghua.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NIP1;2 is a plasma membrane-localized transporter mediating aluminum uptake, translocation, and tolerance in Arabidopsis.

Members of the aquaporin (AQP) family have been suggested to transport aluminum (Al) in plants; however, the Al form transported by AQPs and the roles of AQPs in Al tolerance remain elusive. Here we report that NIP1;2, a plasma membrane-localized member of the Arabidopsis nodulin 26-like intrinsic protein (NIP) subfamily of the AQP family, facilitates Al-malate transport from the root cell wall into the root symplasm, with subsequent Al xylem loading and root-to-shoot translocation, which are critical steps in an internal Al tolerance mechanism in Arabidopsis We found that NIP1;2 transcripts are expressed mainly in the root tips, and that this expression is enhanced by Al but not by other metal stresses. Mutations in NIP1;2 lead to hyperaccumulation of toxic Al3+ in the root cell wall, inhibition of root-to-shoot Al translocation, and a significant reduction in Al tolerance. NIP1;2 facilitates the transport of Al-malate, but not Al3+ ions, in both yeast and Arabidopsis We demonstrate that the formation of the Al-malate complex in the root tip apoplast is a prerequisite for NIP1;2-mediated Al removal from the root cell wall, and that this requires a functional root malate exudation system mediated by the Al-activated malate transporter, ALMT1. Taken together, these findings reveal a critical linkage between the previously identified Al exclusion mechanism based on root malate release and an internal Al tolerance mechanism identified here through the coordinated function of NIP1;2 and ALMT1, which is required for Al removal from the root cell wall, root-to-shoot Al translocation, and overall Al tolerance in Arabidopsis.

Platelet-Derived Growth Factor D Is a Prognostic Biomarker and Is Associated With Platinum Resistance in Epithelial Ovarian Cancer.

OBJECTIVE: This study aimed to investigate whether platelet-derived growth factor D (PDGF-D) is a prognostic biomarker and is associated with platinum resistance in epithelial ovarian cancer, which has not been studied by others previously. METHODS: In this study, we detected expression of PDGF-D in ovarian cancer tissues through immunohistochemistry and Western blotting. Furthermore, we analyzed the association between PDGF-D expression and clinicopathological features including prognosis in epithelial ovarian cancer. Statistical analyses were performed by using χ test, log-rank test, Cox regression test, and Kaplan-Meier method. RESULTS: High PDGF-D expression is positively correlated with International Federation of Gynecology and Obstetrics stage (P < 0.001), histologic grade (P < 0.001), lymph node metastasis (P = 0.022), and poor prognosis (P < 0.001). Platelet-derived growth factor D in platinum-resistant cases is overexpressed compared with that in platinum-sensitive cases (P < 0.001). Obstetrics stage (P = 0.029) and PDGF-D overexpression (P < 0.001) are independently correlated with platinum resistance. CONCLUSIONS: Our study indicates that PDGF-D overexpression is an independent predictor of platinum-based chemotherapy resistance and that it may also be a potential biomarker for targeted therapy and poor prognosis.