A single-nucleotide polymorphism in WRKY33 promoter is associated with the cold sensitivity in cultivated tomato.

Natural variations in cis-regulatory regions often affect crop phenotypes by altering gene expression. However, the mechanism of how promoter mutations affect gene expression and crop stress tolerance is still poorly understood. In this study, by analyzing RNA-sequencing (RNA-Seq) data and reverse transcription quantitative real-time PCR validation in the cultivated tomato and its wild relatives, we reveal that the transcripts of WRKY33 are almost unchanged in cold-sensitive cultivated tomato Solanum lycopersicum L. 'Ailsa Craig' but are significantly induced in cold-tolerant wild tomato relatives Solanum habrochaites LA1777 and Solanum pennellii LA0716 under cold stress. Overexpression of SlWRKY33 or ShWRKY33 positively regulates cold tolerance in tomato. Variant of the critical W-box in SlWRKY33 promoter results in the loss of self-transcription function of SlWRKY33 under cold stress. Analysis integrating RNA-Seq and chromatin immunoprecipitation sequencing data reveals that SlWRKY33 directly targets and induces multiple kinases, transcription factors, and molecular chaperone genes, such as CDPK11, MYBS3, and BAG6, thus enhancing cold tolerance. In addition, heat- and Botrytis-induced WRKY33s expression in both wild and cultivated tomatoes are independent of the critical W-box variation. Our findings suggest nucleotide polymorphism in cis-regulatory regions is crucial for different cold sensitivity between cultivated and wild tomato plants.

Plant-nematode battle: engagement of complex signaling network.

Plant-parasitic nematodes (PPNs) are widely distributed and highly adaptable. To evade the invasion and infection of PPNs, plants initiate a series of defense responses. In turn, PPNs secrete effectors into the host tissues to suppress plant defense. In this ongoing battle between PPNs and plants, complex signal transduction processes are typically involved. This article aims to review the plant signaling network involved in host perception by the nematode, nematode perception, and downstream activation of plant defense signaling and how nematodes attempt to interfere with this network. Our goal is to establish a foundation for elucidating the signaling and regulatory mechanisms of plant-nematode interactions, and to provide insights and tools for developing PPN-resistant crops and technologies.

Generation of focused drug molecule library using recurrent neural network.

CONTEXT: With the wide application of deep learning in drug research and development, de novo molecular design methods based on recurrent neural network (RNN) have strong advantages in drug molecule generation. The RNN model can be used to learn the internal chemical structure of molecules, which is similar to a natural language processing task. Although techniques for generating target-specific molecular libraries based on RNN models are mature, research related to drug design and screening continues around the clock. Research based on de novo drug design methods to generate larger quantities of valid compounds is necessary. METHODS: In this study, a molecular generation model based on RNN was designed, which abandoned the traditional way of stacked RNN and introduced the Nested long short-term memory network structure. To enrich the library of focused molecules for specific targets, we fine-tuned the model using active molecules from novel coronavirus pneumonia and screened the molecules using machine learning models. Following rigorous screening, the selected molecules underwent molecular docking with the SARS-CoV-2 M-pro receptor using AutoDock2.4 to identify the top 3 potential inhibitors. Subsequently, 100-ns molecular dynamics simulations were conducted using Amber22. Molecule parameterization involved the GAFF2 force field, while the proteins were modeled using the ff19SB force field, with solvation facilitated by a truncated octahedral TIP3P solvent environment. Upon completion of molecular dynamics simulations, stability of ligand-protein complexes was assessed by analysis of RMSD, H-bonds, and MM-GBSA. Reasonable results prove that the model can complete the task of de novo drug design and has the potential to be ideal drug molecules.

Modeling of CSTR flow field for Agaricus bisporus residue fermentation based on CFD numerical simulation.

Agaricus bisporus production gets a lot of residues, which could be fermented by a continuous stirred tank reactor (CSTR). This research was conducted to study the characteristics of the multiphase flow field in the reactor and its influence on the efficiency of biogas production in the CSTR fermentation process of Agaricus bisporus residue by using CFD numerical simulation technique. The aim is to reveal the relationship between the reactor operating conditions, flow field characteristics, and biogas production efficiency at the micro-level. We compared the results of different turbulence models by evaluating the power quotients and flow quotients with the experimental results to derive the most suitable flow field model inside the reactor for the Agaricus bisporus residues. The results showed that, under the condition that the number of grids does not affect the simulation results, and considering the model accuracy and efficiency, the numerical method can be chosen as the multiple reference frame (MRF) method of the second-order upwind discrete scheme with the realizable k - ε model. In this way, we can make use of edible mushroom residue as a substrate for resource utilization and provide basic data and theoretical basis for the design and scale-up with anaerobic fermentation to biogas reactor.

De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning.

CONTEXT: In recent decades, drug development has become extremely important as different new diseases have emerged. However, drug discovery is a long and complex process with a very low success rate, and methods are needed to improve the efficiency of the process and reduce the possibility of failure. Among them, drug design from scratch has become a promising approach. Molecules are generated from scratch, reducing the reliance on trial and error and prefabricated molecular repositories, but the optimization of its molecular properties is still a challenging multi-objective optimization problem. METHODS: In this study, two stack-augmented recurrent neural networks were used to compose a generative model for generating drug-like molecules, and then reinforcement learning was used for optimization to generate molecules with desirable properties, such as binding affinity and the logarithm of the partition coefficient between octanol and water. In addition, a memory storage network was added to increase the internal diversity of the generated molecules. For multi-objective optimization, we proposed a new approach which utilized the magnitude of different attribute reward values to assign different weights to molecular optimization. The proposed model not only solves the problem that the properties of the generated molecules are extremely biased towards a certain attribute due to the possible conflict between the attributes, but also improves various properties of the generated molecules compared with the traditional weighted sum and alternating weighted sum, among which the molecular validity reaches 97.3%, the internal diversity is 0.8613, and the desirable molecules increases from 55.9 to 92%.

STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks.

With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training process may be unstable, such as gradient disappearance and model collapse, which may lead to the generation of molecules that do not conform to chemical rules or a single style. In this paper, a novel method called STAGAN was proposed to solve the difficulty of model training, by adding a new gradient penalty term in the discriminator and designing a parallel layer of batch normalization used in generator. As an illustration of method, STAGAN generated higher valid and unique molecules than previous models in training datasets from QM9 and ZINC-250K. This indicates that the proposed method can effectively solve the instability problem in the model training process, and can provide more instructive guidance for the further study of molecular graph generation.

Autophagy promotes jasmonate-mediated defense against nematodes.

Autophagy, as an intracellular degradation system, plays a critical role in plant immunity. However, the involvement of autophagy in the plant immune system and its function in plant nematode resistance are largely unknown. Here, we show that root-knot nematode (RKN; Meloidogyne incognita) infection induces autophagy in tomato (Solanum lycopersicum) and different atg mutants exhibit high sensitivity to RKNs. The jasmonate (JA) signaling negative regulators JASMONATE-ASSOCIATED MYC2-LIKE 1 (JAM1), JAM2 and JAM3 interact with ATG8s via an ATG8-interacting motif (AIM), and JAM1 is degraded by autophagy during RKN infection. JAM1 impairs the formation of a transcriptional activation complex between ETHYLENE RESPONSE FACTOR 1 (ERF1) and MEDIATOR 25 (MED25) and interferes with transcriptional regulation of JA-mediated defense-related genes by ERF1. Furthermore, ERF1 acts in a positive feedback loop and regulates autophagy activity by transcriptionally activating ATG expression in response to RKN infection. Therefore, autophagy promotes JA-mediated defense against RKNs via forming a positive feedback circuit in the degradation of JAMs and transcriptional activation by ERF1.

The E3 ubiquitin ligase RING1 interacts with COP9 Signalosome Subunit 4 to positively regulate resistance to root-knot nematodes in Solanum lycopersicum L.

Globally, root-knot nematodes (RKNs) cause massive production losses in all major crops. E3 ubiquitin ligases are involved in plant growth, development and immune response. But their roles in plant defense against RKNs are largely unclear. Here, we show that tomato E3 ubiquitin ligase RING1 interacts with COP9 Signalosome Subunit 4 (CSN4) which is essential for jasmonic acid (JA)-dependent basal defense against RKNs. Tissue-specific expression analysis showed that RING1 expression was the highest in tomato roots and the expression was significantly increased with RKN (Meloidogyne incognita) infection. Compared with the wild-type plants, the number of egg masses in roots significantly increased in the ring1 mutants, while RING1 overexpression conferred resistance against RKNs. Furthermore, RKN infection increased the accumulation of CSN4 protein in the roots of wild-type plants, which was largely compromised in the ring1 mutants but was enhanced in the RING1 overexpressing plants. The RKN-induced transcripts of JA biosynthetic and signaling genes as well as the accumulation of JA and JA-isoleucine were compromised in ring1 mutants but were increased in RING1 overexpressing plants. These results suggest that RING1 positively regulates JA-dependent basal defense against RKNs by interacting with CSN4 proteins.