Metabolomic Analysis of Maize Response to Northern Corn Leaf Blight.

As a major food crop, maize is highly susceptible to pathogenic bacteria, which greatly reduces its yield and quality. Metabolomics reveals physiological and biochemical changes in organisms and aids in analyzing metabolic changes caused by various factors. This study utilized metabolomics to examine maize's metabolic changes after NCLB infestation, aiming to uncover related pathways and potential biomarkers.

X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development.

In single-cell transcriptomics research, accurately predicting cell lineage allocation and identifying differences between lineages are crucial for understanding cell differentiation processes and reducing early pregnancy miscarriages in humans. This paper introduces an explainable PCA-based deep learning attention autoencoder model, X-scPAE (eXplained Single Cell PCA - Attention Auto Encoder), which is built on the Counterfactual Gradient Attribution (CGA) algorithm. The model is designed to predict lineage allocation in human and mouse single-cell transcriptomic data, while identifying and interpreting gene expression differences across lineages to extract key genes.

MORF: Multi-view oblique random forest for hepatotoxicity prediction.

Hepatotoxicity prediction is significant in drug development, so the experts expect to get effective and reliable references. Based on the consideration that the hepatotoxicity data involve multiple types of features, this paper proposes a multi-view oblique random forest (MORF) for hepatotoxicity prediction by considering each type of feature as an independent view. The Householder transformation is employed to get the inclined cut hyperplane in each view.

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.

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.

Genome-Wide Identification, Classification, and Expression Analyses of the Gene Family in 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 () gene family in detail.

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.

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.

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.

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.

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.

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 () roots under saline-alkaline stress were investigated.

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.

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.

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.

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.

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.

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).