An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.

INTRODUCTION: Synthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to identify and mechanistically characterize more SL interactions. Artificial intelligence (AI) methods have recently been proposed for SL prediction, but the results of these models are often not interpretable such that deriving the underlying mechanism can be challenging. OBJECTIVES: This study aims to develop an interpretable AI framework for SL prediction and subsequently utilize it to design SL-based synergistic combination therapies. METHODS: We propose a knowledge and data dual-driven AI framework for SL prediction (KDDSL). Specifically, we use gene knowledge related to the SL mechanism to guide the construction of the model and develop a method to identify the most relevant gene knowledge for the predicted results. RESULTS: Experimental and literature-based validation confirmed a good balance between predictive and interpretable ability when using KDDSL. Moreover, we demonstrated that KDDSL could help to discover promising drug combinations and clarify associated biological processes, such as the combination of MDM2 and CDK9 inhibitors, which exhibited significant anti-cancer effects in vitro and in vivo. CONCLUSION: These data underscore the potential of KDDSL to guide SL-based combination therapy design. There is a need for biomedicine-focused AI strategies to combine rational biological knowledge with developed models.

Dihydroartemisinin inhibits plasmid transfer in drug-resistant Escherichia coli via limiting energy supply.

Conjugative transfer of antibiotic resistance genes (ARGs) by plasmids is an important route for ARG dissemination. An increasing number of antibiotic and nonantibiotic compounds have been reported to aid the spread of ARGs, highlighting potential challenges for controlling this type of horizontal transfer. Development of conjugation inhibitors that block or delay the transfer of ARG-bearing plasmids is a promising strategy to control the propagation of antibiotic resistance. Although such inhibitors are rare, they typically exhibit relatively high toxicity and low efficacy in vivo and their mechanisms of action are inadequately understood. Here, we studied the effects of dihydroartemisinin (DHA), an artemisinin derivative used to treat malaria, on conjugation. DHA inhibited the conjugation of the IncI2 and IncX4 plasmids carrying the mobile colistin resistance gene ( mcr-1) by more than 160-fold in vitro in Escherichia coli, and more than two-fold (IncI2 plasmid) in vivo in a mouse model. It also suppressed the transfer of the IncX3 plasmid carrying the carbapenem resistance gene bla NDM-5 by more than two-fold in vitro. Detection of intracellular adenosine triphosphate (ATP) and proton motive force (PMF), in combination with transcriptomic and metabolomic analyses, revealed that DHA impaired the function of the electron transport chain (ETC) by inhibiting the tricarboxylic acid (TCA) cycle pathway, thereby disrupting PMF and limiting the availability of intracellular ATP for plasmid conjugative transfer. Furthermore, expression levels of genes related to conjugation and pilus generation were significantly down-regulated during DHA exposure, indicating that the transfer apparatus for conjugation may be inhibited. Our findings provide new insights into the control of antibiotic resistance and the potential use of DHA.

[The Role and Mechanism of MiR-451 in Multidrug Resistance of Leukemia Cell Line K562/A02].

OBJECTIVE: To detect the differential expressions of miR-451, ABCB1 and ABCC2 in drug-sensitive leukemia cell line K562 and drug-resistant cell line K562/A02, and explore the regulatory relationship between miR-451 and the expressions of ABCB1 and ABCC2 , and the mechanism of miR-451 involved in drug resistance in leukemia. METHODS: CCK-8 assay was used to detect the drug resistance of K562/A02 and K562 cells. Quantitative Real-time PCR (qRT-PCR) was used to verify the differential expressions of miR-451 in K562 and K562/A02 cells. MiR-451 mimic and negative control (miR-NC), miR-451 inhibitor and negative control (miR-inNC) were transfected into K562 and K562/A02 cells respectively, then qRT-PCR and Western blot were used to detect the expression levels of mRNA and protein of ABCB1 and ABCC2 in K562 and K562/A02 cells and the transfected groups. RESULTS: The drug resistance of K562/A02 cells to adriamycin was 177 times higher than that of its parent cell line K562. Compared with K562 cells, the expression of miR-451 in K562/A02 cells was significantly higher (P <0.001), and the mRNA and protein expression levels of ABCB1 and ABCC2 in K562/A02 cells were significantly higher than those in K562 cells (P <0.001). After transfected with miR-451 inhibitor, the expression of miR-451 was significantly down-regulated in K562/A02 cells (P <0.001), the sensitivity to chemotherapy drugs was significantly enhanced (P <0.05), and the mRNA and protein expressions of ABCB1 and ABCC2 were significantly decreased (P <0.01). After transfected with miR-451 mimic, the expression of miR-451 was significantly upregulated in K562 cells (P <0.001), and the mRNA and protein expressions of ABCB1 and ABCC2 were significantly increased (P <0.01). CONCLUSION: There are significant differences in the expressions of miR-451, ABCB1 and ABCC2 between the drug-sensitive leukemia cell line K562 and drug-resistant cell line K562/A02, which suggests that miR-451 may affect the drug resistance of leukemia cells by regulating the expression of ABCB1 and ABCC2.

DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.

Synergistic drug combinations have demonstrated effective therapeutic effects in cancer treatment. Deep learning methods accelerate identification of novel drug combinations by reducing the search space. However, potential adverse drug-drug interactions (DDIs), which may increase the risks for combination therapy, cannot be detected by existing computational synergy prediction methods. We propose DEML, an ensemble-based multi-task neural network, for the simultaneous optimization of five synergy regression prediction tasks, synergy classification, and DDI classification tasks. DEML uses chemical and transcriptomics information as inputs. DEML adapts the novel hybrid ensemble layer structure to construct higher order representation using different perspectives. The task-specific fusion layer of DEML joins representations for each task using a gating mechanism. For the Loewe synergy prediction task, DEML overperforms the state-of-the-art synergy prediction method with an improvement of 7.8% and 13.2% for the root mean squared error and the R2 correlation coefficient. Owing to soft parameter sharing and ensemble learning, DEML alleviates the multi-task learning 'seesaw effect' problem and shows no performance loss on other tasks. DEML has a superior ability to predict drug pairs with high confidence and less adverse DDIs. DEML provides a promising way to guideline novel combination therapy strategies for cancer treatment.

GADRP: graph convolutional networks and autoencoders for cancer drug response prediction.

Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations， and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.

NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.

BACKGROUND: Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge. METHODS: In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions. RESULTS: Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance. CONCLUSIONS: NeRD's feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.

Gypsum scale formation and inhibition kinetics with implications in membrane system.

Water desalination using membrane technology is one of the main technologies to resolve water pollution and scarcity issues. In the membrane treatment process, mineral scale deposition and fouling is a severe challenge that can lead to filtration efficiency decrease, permeate quality compromise, and even membrane damage. Multiple methods have been developed to resolve this problem, such as scale inhibitor addition, product recovery ratio adjustment, periodic membrane surface flushing. The performance of these methods largely depends on the ability to accurately predict the kinetics of mineral scale deposition and fouling with or without inhibitors. Gypsum is one of the most common and troublesome inorganic mineral scales in membrane systems, however, no mechanistic model is available to accurately predict the induction time of gypsum crystallization and inhibition. In this study, a new gypsum crystallization and inhibition model based on the classical nucleation theory and a Langmuir type adsorption isotherm has been developed. Through this model, it is believed that gypsum nucleation may gradually transit from homogeneous to heterogeneous nucleation when the gypsum saturation index (SI) decreases. Such transition is represented by a gradual decrease of surface tension at smaller SI values. This model assumes that the adsorption of inhibitors onto the gypsum nucleus can increase the nucleus superficial surface tension and prolong the induction time. Using the new model, this study accurately predicted the gypsum crystallization induction times with or without nine commonly used scale inhibitors over wide ranges of temperature (25-90 °C), SI (0.04-0.96), and background NaCl concentration (0-6 mol/L). The fitted affinity constants between scale inhibitors and gypsum show a good correlation with those between the same inhibitors and barite, indicating a similar inhibition mechanism via adsorption. Furthermore, by incorporating this model with the two-phase mineral deposition model our group developed previously, this study accurately predicts the gypsum deposition time on the membrane material surfaces reported in the literature. We believe that the model developed in this study can not only accurately predict the gypsum crystallization induction time with or without scale inhibitors, elucidate the gypsum crystallization and inhibition mechanisms, but also optimize the mineral scale control in the membrane filtration system.

Talaromynoids A-E: Five New Fusicoccane Diterpenoids from the Endophytic Fungus Talaromyces sp. DC-26.

Talaromynoids A-E (1-5), five new fusicoccane diterpenoids, were obtained from the endophytic fungus Talaromyces sp. DC-26, which was isolated from a wild leech. Talaromynoid A (1) represents the first fusicoccane diterpenoid bearing an unexpected 5-7-5 tricyclic ring system, which is possibly derived from normal 5-8-5 ones by ring contraction. Talaromynoid E (5) is characterized by an unusual oxygen bridge between C-1 and C-8 that establishes the eight-membered ring B to be a 9-oxo-bicyclo[3.3.1]nonane. Structures of 1-5 with absolute configurations were determined by extensive NMR spectral analyses, electronic circular dichroism (ECD) calculations, X-ray diffraction analyses, and acid hydrolysis.

Computational methods, databases and tools for synthetic lethality prediction.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

An enhanced cascade-based deep forest model for drug combination prediction.

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.

Machine learning methods, databases and tools for drug combination prediction.

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

A non-translational role of threonyl-tRNA synthetase in regulating JNK signaling during myogenic differentiation.

Aminoacyl-tRNA synthetases (aaRSs) are house-keeping enzymes that are essential for protein synthesis. However, it has become increasingly evident that some aaRSs also have non-translational functions. Here we report the identification of a non-translational function of threonyl-tRNA synthetase (ThrRS) in myogenic differentiation. We find that ThrRS negatively regulates myoblast differentiation in vitro and injury-induced skeletal muscle regeneration in vivo. This function is independent of amino acid binding or aminoacylation activity of ThrRS, and knockdown of ThrRS leads to enhanced differentiation without affecting the global protein synthesis rate. Furthermore, we show that the non-catalytic new domains (UNE-T and TGS) of ThrRS are both necessary and sufficient for the myogenic function. In searching for a molecular mechanism of this new function, we find the kinase JNK to be a downstream target of ThrRS. Our data further reveal MEKK4 and MKK4 as upstream regulators of JNK in myogenesis and the MEKK4-MKK4-JNK pathway to be a mediator of the myogenic function of ThrRS. Finally, we show that ThrRS physically interacts with Axin1, disrupts Axin1-MEKK4 interaction and consequently inhibits JNK signaling. In conclusion, we uncover a non-translational function for ThrRS in the maintenance of homeostasis of skeletal myogenesis and identify the Axin1-MEKK4-MKK4-JNK signaling axis to be an immediate target of ThrRS action.

Spectanoids A-H: Eight undescribed sesterterpenoids from Aspergillus spectabilis.

Ten sesterterpenoids, including eight undescribed ones named spectanoids A-H and two known analogs, were obtained from Aspergillus spectabilis. Their structures, including absolute configurations, were determined based on HRESIMS, NMR, ECD calculations and single-crystal X-ray diffraction analyses. Spectanoids A-G are tricyclic sesterterpenoids with an unusual 5/12/5 ring system, while spectanoid H possesses a 5/8/6/5 ring system. All of these compounds were evaluated for their cytotoxic activities against three human cancer cells, and spectanoid A, spectanoid C and spectanoid F exhibited moderate cytotoxic activities with IC50 values ranging from 12.1 to 26.1 µM.

Pesimquinolones I-S, eleven new quinolone alkaloids produced by Penicillium simplicissimum and their inhibitory activity on NO production.

Eleven undescribed quinolone alkaloids, pesimquinolones I-S (1-4 and 6-12), as well as eleven known congeners (5 and 13-22), were isolated from the solid culture broth of the fungus Penicillium simplicissimum. Their chemical structures with absolute configurations were established by a combination of NMR spectroscopy, single-crystal X-ray crystallography, and modified Mosher's methods. Pesimquinolones I-K (1-3) represent the first examples of natural occurring quinolone alkaloids that possess a 6/6/6/6 tetracyclic ring system. The anti-inflammatory activities of selected compounds on LPS-induced nitric oxide (NO) production in adherent cells were evaluated. Compounds 1 and 2 showed suppressive effects on the production of NO, with IC50 values of 10.13 and 8.10 µM, respectively.

Erratum to: Polysubstituted Phenyl Glucosides Produced by the Fungus Metarrhizium anisopliae.

The article "Polysubstituted Phenyl Glucosides Produced by the Fungus Metarrhizium anisopliae", written by Wen-jing WANG, Chong DAI, Jian-ping WANG, Hu-cheng ZHU, Chun-mei CHEN, Yong-hui ZHANG, was originally published electronically on the publisher's internet portal on May 2020 without open access. With the author(s)' decision to opt for Open Choice, the copyright of the article is changed to © The Author(s) 2020 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made..The original article has been corrected.

The regeneration of Acer rubrum L. "October Glory" through embryonic callus.

BACKGROUND: Tissue culture and rapid propagation technology is an important way to solve the difficulties of plant propagation. This experiment aims to explore the appropriate conditions at each stage of the red maple's tissue culture process and to obtain plantlets, thus providing a theoretical basis for the establishment of the red maple's tissue culture system. RESULTS: The results showed that the stem segment is the most suitable explant for inducing embryogenic callus. The MS (Murashige&Skoog) + 0.8 mg/L TDZ (Thidiazuron) + 1.0 mg/L 6-BA (6-Benzylaminopurine) + 0.5 mg/L IAA(Indole-3-acetic acid) + 35 g/L sucrose+ 7.5 g/L semi-fixed medium was the best for callus formation. When selecting type VI callus as embryonic callus induction material, MS + 0.6 mg/L TDZ + 0.5 mg/L 6-BA + 2.0 mg/L IAA + 35 g/L sucrose+ 7.5 g/L semi-fixed medium can get embryonic callus. The optimal medium for adventitious bud induction is MS + 1.0 mg/L TDZ + 3.0 mg/L 6-BA+ 0.2 mg/L NAA (1-Naphthaleneacetic acid) + 1.2 mg/L IAA + 35 g/L sucrose+ 7.5 g/L semi-fixed medium. The induction rate of adventitious roots in MS + 0.6 mg/L TDZ + 1.0 mg/L 6-BA+ 3 mg/L NAA + 35 g/L sucrose+ 7.5 g/L semi-fixed medium was the highest, reaching 76%. CONCLUSIONS: In the course of our research, we found that PGRs play an important role in the callus induction stage, and the effect of TDZ is particularly obvious; The callus cells grow and proliferate according to the "S" growth curve, and can be sub-cultured when the highest growth point is reached to maintain the rapid proliferation of the callus cells and to avoid inactivation of callus caused by tight niche.

Polysubstituted Phenyl Glucosides Produced by the Fungus Metarrhizium anisopliae.

Metarhizosides A-G (1-7), seven new polysubstituted phenyl glucosides, were isolated from the extracts of solid rice medium of a marine-derived fungus Metarrhizium anisopliae. Compounds 1-7 all contain a polysubstituted phenyl group and the sugar unit is identified as 4'-O-methyl-ß-D-glucopyranose. Their structures were elucidated by NMR spectroscopy and chemical method. These compounds were evaluated for anti-inflammatory activity by using LPS-stimulated murine macrophage RAW 264.7 cells and the cytotoxicities against four human cancer cell lines.

Pesimquinolones produced by Penicillium simplicissimum and their inhibitory activity on nitric oxide production.

Eight undescribed quinolone alkaloids, pesimquinolones A-H, as well as six known compounds, were isolated from the solid culture broth of the fungus Penicillium simplicissimum. Their chemical structures were characterized by combined analyses of NMR spectroscopy and single-crystal X-ray crystallography. Pesimquinolones A-G are the first examples of naturally occurring quinolone alkaloids possessing a limonene moiety. Their anti-inflammatory activities on LPS-induced nitric oxide (NO) production in adherent cells were evaluated. Pesimquinolones A, E, G, and H showed promising suppressive effect on the production of NO with IC50 values of 1.94, 1.29, 1.20, and 1.23 µM, respectively.

Nontranslational function of leucyl-tRNA synthetase regulates myogenic differentiation and skeletal muscle regeneration.

Aside from its catalytic function in protein synthesis, leucyl-tRNA synthetase (LRS) has a nontranslational function in regulating cell growth via the mammalian target of rapamycin (mTOR) complex 1 (mTORC1) pathway by sensing amino acid availability. mTOR also regulates skeletal myogenesis, but the signaling mechanism is distinct from that in cell growth regulation. A role of LRS in myogenesis has not been reported. Here we report that LRS negatively regulated myoblast differentiation in vitro. This function of LRS was independent of its regulation of protein synthesis, and it required leucine-binding but not tRNA charging activity of LRS. Local knock down of LRS accelerated muscle regeneration in a mouse injury model, and so did the knock down of Rag or Raptor. Further in vitro studies established a Rag-mTORC1 pathway, which inhibits the IRS1-PI3K-Akt pathway, to be the mediator of the nontranslational function of LRS in myogenesis. BC-LI-0186, an inhibitor reported to disrupt LRS-Rag interaction, promoted robust muscle regeneration with enhanced functional recovery, and this effect was abolished by cotreatment with an Akt inhibitor. Taken together, our findings revealed what we believe is a novel function for LRS in controlling the homeostasis of myogenesis, and suggested a potential therapeutic strategy to target a noncanonical function of a housekeeping protein.

Niduterpenoids A and B: Two Sesterterpenoids with a Highly Congested Hexacyclic 5/5/5/5/3/5 Ring System from the Fungus Aspergillus nidulans.

Niduterpenoids A (1) and B (2), two sesterterpenoids with a highly congested hexacyclic 5/5/5/5/3/5 carbon skeleton but no unsaturated functional group, were isolated from Aspergillus nidulans. Their structures were determined by a combination of spectroscopic data and single-crystal X-ray diffraction analyses. Compounds 1 and 2 present the first examples of sesterterpenoids with a hexacyclic carbon ring system. Compound 1 showed no cytotoxicity but abolished 17-estradiol-induced cell proliferation (IC50 = 11.42 ± 0.85 µM).