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Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied. 

Climate change and enhanced pollution levels are subjecting plants and crops to an increased number of different stressors, simultaneously or sequentially, generating conditions of multifactorial stress combination (MFSC). Although MFSC was shown to severely diminish plant growth, yield, and survival, how plants acclimate to increased levels of stress complexity is largely unknown. Here, we reveal that theArabidopsis thalianatranscriptional regulator basic helix-loop-helix 35 (bHLH35) is required for plant acclimation to a specific set of MFSC conditions that includes a combination of salinity, excess light, and heat, occurring simultaneously (but not to each of these stresses applied individually or in any other combination). Under the three-stress combination, bHLH35 interacts with no apical meristem/transcription activator factor/cup-shaped cotyledon 69 (NAC069), binds the promoter oflateral organ boundaries domain 31 (LBD31), and regulates the expression of transcripts involved in flavonoid metabolism and ethylene signaling. Our findings uncover a high degree of specificity in plant responses to stress combination, suggesting that different conditions of MFSC could require the function of specific genetic programs for acclimation. 

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend. 

Abstract FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/ 

Abstract Waterlogging stress (WLS) negatively impacts the growth and yield of crops resulting in heavy losses to agricultural production. Previous studies have revealed that WLS induces a systemic response in shoots that is partially dependent on the plant hormones ethylene and abscisic acid. However, the role of rapid cell-to-cell signaling pathways, such as the reactive oxygen species (ROS) and calcium waves, in systemic responses of plants to WLS is unknown at present. Here, we reveal that an abrupt WLS treatment of Arabidopsis (Arabidopsis thaliana) plants growing in peat moss triggers systemic ROS and calcium wave responses and that the WLS-triggered ROS wave response of Arabidopsis is dependent on the ROS-generating RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD), calcium-permeable channels GLUTAMATE-LIKE RECEPTOR 3.3 and 3.6 (GLR3.3 and GLR3.6), and aquaporin PLASMA MEMBRANE INTRINSIC PROTEIN 2;1 (PIP2;1) proteins. We further show that WLS is accompanied by a rapid systemic transcriptomic response that is evident as early as 10 min following waterlogging initiation, includes many hypoxia-response transcripts, and is partially dependent on RBOHD. Interestingly, the abrupt WLS of Arabidopsis resulted in the triggering of a rapid hydraulic wave response and the transient opening of stomata on leaves. In addition, it induced in plants a heightened state of tolerance to a subsequent submergence stress. Taken together, our findings reveal that the initiation of WLS in plants is accompanied by rapid systemic physiological and transcriptomic responses that involve the ROS, calcium, and hydraulic waves, as well as the induction of hypoxia acclimation mechanisms in systemic tissues. 

Abstract The complexity of environmental factors affecting crops in the field is gradually increasing due to climate change-associated weather events, such as droughts or floods combined with heat waves, coupled with the accumulation of different environmental and agricultural pollutants. The impact of multiple stress conditions on plants was recently termed “multifactorial stress combination” (MFSC) and defined as the occurrence of 3 or more stressors that impact plants simultaneously or sequentially. We recently reported that with the increased number and complexity of different MFSC stressors, the growth and survival of Arabidopsis (Arabidopsis thaliana) seedlings declines, even if the level of each individual stress is low enough to have no significant effect on plants. However, whether MFSC would impact commercial crop cultivars is largely unknown. Here, we reveal that a MFSC of 5 different low-level abiotic stresses (salinity, heat, the herbicide paraquat, phosphorus deficiency, and the heavy metal cadmium), applied in an increasing level of complexity, has a significant negative impact on the growth and biomass of a commercial rice (Oryza sativa) cultivar and a maize (Zea mays) hybrid. Proteomics, element content, and mixOmics analyses of MFSC in rice identified proteins that correlate with the impact of MFSC on rice seedlings, and analysis of 42 different rice genotypes subjected to MFSC revealed substantial genetic variability in responses to this unique state of stress combination. Taken together, our findings reveal that the impacts of MFSC on 2 different crop species are severe and that MFSC may substantially affect agricultural productivity. 

Abstract Climate change is causing an increase in the frequency and intensity of droughts, heat waves, and their combinations, diminishing agricultural productivity and destabilizing societies worldwide. We recently reported that during a combination of water deficit (WD) and heat stress (HS), stomata on leaves of soybean (Glycine max) plants are closed, while stomata on flowers are open. This unique stomatal response was accompanied by differential transpiration (higher in flowers, while lower in leaves) that cooled flowers during a combination of WD + HS. Here, we reveal that developing pods of soybean plants subjected to a combination of WD + HS use a similar acclimation strategy of differential transpiration to reduce internal pod temperature by approximately 4 °C. We further show that enhanced expression of transcripts involved in abscisic acid degradation accompanies this response and that preventing pod transpiration by sealing stomata causes a significant increase in internal pod temperature. Using an RNA-Seq analysis of pods developing on plants subjected to WD + HS, we also show that the response of pods to WD, HS, or WD + HS is distinct from that of leaves or flowers. Interestingly, we report that although the number of flowers, pods, and seeds per plant decreases under conditions of WD + HS, the seed mass of plants subjected to WD + HS increases compared to plants subjected to HS, and the number of seeds with suppressed/aborted development is lower in WD + HS compared to HS. Taken together, our findings reveal that differential transpiration occurs in pods of soybean plants subjected to WD + HS and that this process limits heat-induced damage to seed production. 

Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant–bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.