TOXRIC: a comprehensive database of toxicological data and benchmarks.

The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.

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.

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.

Response of exogenous melatonin on transcription and metabolism of soybean under drought stress.

Amino acid metabolism is an important factor in regulating nitrogen source assimilation and source/sink transport in soybean. Melatonin can improve plant stress resistance, but whether it affects amino acid metabolism is not known. Therefore, this study investigated whether exogenous melatonin had an effect on amino acid metabolism of soybean under drought conditions and explored its relationship with yield. The treatments were normal water supply treatment (WW), drought stress treatment (D), drought stress and melatonin treatment group (D + M), sprayed with 100 µmol/L melatonin. The effects of melatonin on amino acid metabolism and grain filling were studied by physiological and omics experiments using Kangxian 9 (drought-sensitive variety) and Suinong 26 (drought-resistant variety) soybean cultivars. The results showed that drought stress decreased the activity of carbon and nitrogen metabolizing enzymes, which inhibited the accumulation of dry matter and protein, and decreased the yield. In the drought-sensitive soybean variety, glycoenzymes and amino acid synthetases synthetic genes were upregulated in melatonin-treated soybeans, hence carbon and nitrogen metabolism enzyme activity increased, increasing the carbohydrate and amino acid contents simultaneously. This resulted in higher dry matter and yield than drought-stressed soybean not treated with melatonin. In the drought-resistant variety, the grain weight per plant increased by 7.98% and 6.57% in 2020 and 2021, respectively, while it increased by 23.20% and 14.07% in the drought-sensitive variety during the respective years. In conclusion, melatonin treatment can enhance the activity of nitrogen and carbon metabolism and amino acid content by upregulating the expression of soybean metabolic pathway and related genes, thus increasing the yield of soybean under drought stress.

Genome-Wide Identification, Classification, and Expression Analyses of the CsDGAT Gene Family in Cannabis sativa L. and Their Response to Cold Treatment.

Hempseed is a nutrient-rich natural resource, and high levels of hempseed oil accumulate within hemp seeds, consisting primarily of different triglycerides. Members of the diacylglycerol acyltransferase (DGAT) enzyme family play critical roles in catalyzing triacylglycerol biosynthesis in plants, often governing the rate-limiting step in this process. As such, this study was designed to characterize the Cannabis sativa DGAT (CsDGAT) gene family in detail. Genomic analyses of the C. sativa revealed 10 candidate DGAT genes that were classified into four families (DGAT1, DGAT2, DGAT3, WS/DGAT) based on the features of different isoforms. Members of the CsDGAT family were found to be associated with large numbers of cis-acting promoter elements, including plant response elements, plant hormone response elements, light response elements, and stress response elements, suggesting roles for these genes in key processes such as development, environmental adaptation, and abiotic stress responses. Profiling of these genes in various tissues and varieties revealed varying spatial patterns of CsDGAT expression dynamics and differences in expression among C. sativa varieties, suggesting that the members of this gene family likely play distinct functional regulatory functions CsDGAT genes were upregulated in response to cold stress, and significant differences in the mode of regulation were observed when comparing roots and leaves, indicating that CsDGAT genes may play positive roles as regulators of cold responses in hemp while also playing distinct roles in shaping the responses of different parts of hemp seedlings to cold exposure. These data provide a robust basis for further functional studies of this gene family, supporting future efforts to screen the significance of CsDGAT candidate genes to validate their functions to improve hempseed oil composition.

COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data.

Extensive amounts of multi-omics data and multiple cancer subtyping methods have been developed rapidly, and generate discrepant clustering results, which poses challenges for cancer molecular subtype research. Thus, the development of methods for the identification of cancer consensus molecular subtypes is essential. The lack of intuitive and easy-to-use analytical tools has posed a barrier. Here, we report on the development of the COnsensus Molecular SUbtype of Cancer (COMSUC) web server. With COMSUC, users can explore consensus molecular subtypes of more than 30 cancers based on eight clustering methods, five types of omics data from public reference datasets or users' private data, and three consensus clustering methods. The web server provides interactive and modifiable visualization, and publishable output of analysis results. Researchers can also exchange consensus subtype results with collaborators via project IDs. COMSUC is now publicly and freely available with no login requirement at http://comsuc.bioinforai.tech/ (IP address: http://59.110.25.27/). For a video summary of this web server, see S1 Video and S1 File.

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.

Multi-dimensional data integration algorithm based on random walk with restart.

BACKGROUND: The accumulation of various multi-omics data and computational approaches for data integration can accelerate the development of precision medicine. However, the algorithm development for multi-omics data integration remains a pressing challenge. RESULTS: Here, we propose a multi-omics data integration algorithm based on random walk with restart (RWR) on multiplex network. We call the resulting methodology Random Walk with Restart for multi-dimensional data Fusion (RWRF). RWRF uses similarity network of samples as the basis for integration. It constructs the similarity network for each data type and then connects corresponding samples of multiple similarity networks to create a multiplex sample network. By applying RWR on the multiplex network, RWRF uses stationary probability distribution to fuse similarity networks. We applied RWRF to The Cancer Genome Atlas (TCGA) data to identify subtypes in different cancer data sets. Three types of data (mRNA expression, DNA methylation, and microRNA expression data) are integrated and network clustering is conducted. Experiment results show that RWRF performs better than single data type analysis and previous integrative methods. CONCLUSIONS: RWRF provides powerful support to users to decipher the cancer molecular subtypes, thus may benefit precision treatment of specific patients in clinical practice.

A cytosolic NAD+-dependent GPDH from maize (ZmGPDH1) is involved in conferring salt and osmotic stress tolerance.

BACKGROUND: Plant glycerol-3-phosphate dehydrogenase (GPDH) catalyzes the reduction of dihydroxyacetone phosphate (DHAP) to produce glycerol-3-phosphate (G-3-P), and plays a key role in glycerolipid metabolism as well as stress responses. RESULTS: In this study, we report the cloning, enzymatic and physiological characterization of a cytosolic NAD+-dependent GPDH from maize. The prokaryotic expression of ZmGPDH1 in E.coli showed that the enzyme encoded by ZmGPDH1 was capable of catalyzing the reduction of DHAP in the presence of NADH. The functional complementation analysis revealed that ZmGPDH1 was able to restore the production of glycerol-3-phosphate and glycerol in AtGPDHc-deficient mutants. Furthermore, overexpression of ZmGPDH1 remarkably enhanced the tolerance of Arabidopsis to salinity/osmotic stress by enhancing the glycerol production, the antioxidant enzymes activities (SOD, CAT, APX) and by maintaining the cellular redox homeostasis (NADH/NAD+, ASA/DHA, GSH/GSSG). ZmGPDH1 OE Arabidopsis plants also exhibited reduced leaf water loss and stomatal aperture under salt and osmotic stresses. Quantitative real-time RT-PCR analyses revealed that overexpression of ZmGPDH1 promoted the transcripts accumulation of genes involved in cellular redox homeostasis and ROS-scavenging system. CONCLUSIONS: Together, these data suggested that ZmGPDH1 is involved in conferring salinity and osmotic tolerance in Arabidopsis through modulation of glycerol synthesis, stomatal closure, cellular redox and ROS homeostasis.

Genome-wide characterization and expression profiling of diacylglycerol acyltransferase genes from maize.

Diacylglycerol acyltransferase (DGAT) catalyzes the only rate-limiting step in the pathway of plant oil (TAG) biosynthesis and is involved in plant development. In this study, five DGAT family members were identified from maize genome database. Phylogenetic analysis classified the ZmDGATs into type-I, II, and III clusters. Conserved functional domain analysis revealed that the proteins encoded by ZmDGAT1 contained conserved MBOAT domains, while two ZmDGAT2-encoding proteins harbored LPLAT domains. qRT-PCR analysis showed that ZmDGAT genes exhibited very high relative expression in developing seeds, especially at the early stage of seed development. Under various abiotic stress conditions, differential responses of ZmDGAT genes were observed. An overall significant induction of ZmDGAT genes under cold stress in leaves and a quick and strong response to osmotic stresses in roots were highlighted. This study provides useful information for understanding the roles of DGATs in oil accumulation and stress responses in higher plants.

Molecular regulation of lipid metabolism in Suaeda salsa.

Suaeda salsa is remarkable for its high oil content and abundant unsaturated fatty acids. In this study, the regulatory networks on fatty acid and lipid metabolism were constructed by combining the de novo transcriptome and lipidome data. Differentially expressed genes (DEGs) associated with lipids biosynthesis pathways were identified in the S. salsa transcriptome. DEGs involved in fatty acid and glycerolipids were generally up-regulated in leaf tissues. DEGs for TAG assembly were enriched in developing seeds, while DEGs in phospholipid metabolic pathways were enriched in root tissues. Polar lipids were extracted from S. salsa tissues and analyzed by lipidomics. The proportion of galactolipid MGDG was the highest in S. salsa leaves. The molar percentage of PG was high in the developing seeds, and the other main phospholipids had higher molar percentage in roots of S. salsa. The predominant C36:6 molecular species indicates that S. salsa is a typical 18:3 plant. The combined transcriptomic and lipidomic data revealed that different tissues of S. salsa were featured with DEGs associated with specific lipid metabolic pathways, therefore, represented unique lipid profiles. This study will be helpful on understanding lipid metabolism pathway and exploring the key genes involved in lipid synthesis in S. salsa.

The Adjustment of Membrane Lipid Metabolism Pathways in Maize Roots Under Saline-Alkaline Stress.

Plants are frequently confronted by diverse environmental stress, and the membrane lipids remodeling and signaling are essential for modulating the stress responses. Saline-alkaline stress is a major osmotic stress affecting the growth and development of crops. In this study, an integrated transcriptomic and lipidomic analysis was performed, and the metabolic changes of membrane lipid metabolism in maize (Zea mays) roots under saline-alkaline stress were investigated. The results revealed that phospholipids were major membrane lipids in maize roots, and phosphatidylcholine (PC) accounts for approximately 40% of the total lipids. Under 100 mmol NaHCO3 treatment, the level of PC decreased significantly (11-16%) and the parallel transcriptomic analysis showed an increased expression of genes encoding phospholipase A and phospholipase D/non-specific phospholipase C, which suggested an activated PC turnover under saline-alkaline stress. The plastidic galactolipid synthesis was also activated, and an abnormal generation of C34:6 galactolipids in 18:3 plants maize implied a plausible contribution from the prokaryotic pathway, which could be partially supported by the up-regulated expression of three putative plastid-localized phosphatidic acid phosphatase/lipid phosphate phosphatase. A comprehensive gene-metabolite network was constructed, and the regulation of membrane lipid metabolism under saline-alkaline stress in maize was discussed.

Characterisation of genes involved in galactolipids and sulfolipids metabolism in maize and Arabidopsis and their differential responses to phosphate deficiency.

Galactolipids (MGDG and DGDG) and sulfolipids (SQDG) are key components of plastidic membranes, and play important roles in plant development and photosynthesis. In this study, the whole families of MGD, DGD and SQD were identified in maize genome, and were designated as ZmMGD1-3, ZmDGD1-5 and ZmSQD1-5 respectively. Based on the phylogenetic analyses, maize and Arabidopsis MGDs, DGDs and SQDs were clearly divided into two major categories (Type A and Type B) along with their orthologous genes from other plant species. Under low-phosphorus condition, the expression of Type B MGD, DGD and SQD genes of maize and Arabidopsis were significantly elevated in both leaf and root tissues. The lipid analysis was also conducted, and an overall increase in non-phosphorus lipids (MGDG, DGDG and SQDG), and a decrease in phosphorus lipids (PC, PE and PA) were observed in maize leaves and roots under phosphate deficiency. Several maize MGD and SQD genes were found involved in various abiotic stress responses. These findings will help for better understanding the specific functions of MGDs, DGDs and SQDs in 18:3 plants and for the generation of improved crops adapted to phosphate starvation and other abiotic stresses.

Biochemical and Transcriptional Regulation of Membrane Lipid Metabolism in Maize Leaves under Low Temperature.

Membrane lipid modulation is one of the major strategies plants have developed for cold acclimation. In this study, a combined lipidomic and transcriptomic analysis was conducted, and the changes in glycerolipids contents and species, and transcriptional regulation of lipid metabolism in maize leaves under low temperature treatment (5°C) were investigated. The lipidomic analysis showed an increase in the phospholipid phosphatidic acid (PA) and a decrease in phosphatidylcholine (PC). And an increase in digalactosyldiacylglycerol and a decrease in monogalactosyldiacylglycerol of the galactolipid class. The results implied an enhanced turnover of PC to PA to serve as precursors for galactolipid synthesis under following low temperature treatment. The analysis of changes in abundance of various lipid molecular species suggested major alterations of different pathways of plastidic lipids synthesis in maize under cold treatment. The synchronous transcriptomic analysis revealed that genes involved in phospholipid and galactolipid synthesis pathways were significantly up-regulated, and a comprehensive gene-metabolite network was generated illustrating activated membrane lipids adjustment in maize leaves following cold treatment. This study will help to understand the regulation of glycerolipids metabolism at both biochemical and molecular biological levels in 18:3 plants and to decipher the roles played by lipid remodeling in cold response in major field crop maize.