Search for: All records

Abstract In plant immunity, a well-orchestrated cascade is initiated by the dimerization of receptor-like kinases (RLKs), followed by the phosphorylation of receptor-like cytoplasmic kinases (RLCKs) and subsequent activation of NADPH oxidases for ROS generation. Recent findings by Zhong et al. illustrated that a maize signaling module comprising ZmWAKL-ZmWIK-ZmBLK1-ZmRBOH4 governs quantitative disease resistance to grey leaf spot, a pervasive fungal disease in maize worldwide, unveiling the conservation of this signaling quartet in plant immunity. 

Abstract BackgroundAlternative splicing of precursor mRNAs serves as a crucial mechanism to enhance gene expression plasticity for organismal adaptation. However, the precise regulation and function of alternative splicing in plant immune gene regulation remain elusive. ResultsHere, by deploying in-depth transcriptome profiling with deep genome coverage coupled with differential expression, differential alternative splicing, and differential transcript usage analysis, we reveal profound and dynamic changes in alternative splicing following treatment with microbial pattern flg22 peptides inArabidopsis. Our findings highlight RNA polymerase II C-terminal domain phosphatase-like 3 (CPL3) as a key regulator of alternative splicing, preferentially influencing the splicing patterns of defense genes rather than their expression levels. CPL3 mediates the production of a flg22-induced alternative splicing variant, diacylglycerol kinase 5α (DGK5α), which differs from the canonical DGK5β in its interaction with the upstream kinase BIK1 and subsequent phosphorylation, resulting in reduced flg22-triggered production of phosphatidic acid and reactive oxygen species. Furthermore, our functional analysis suggests that DGK5β, but not DGK5α, contributes to plant resistance against virulent and avirulent bacterial infections. ConclusionsThese findings underscore the role of CPL3 in modulating alternative splicing dynamics of defense genes and DGK5 isoform-mediated phosphatidic acid homeostasis, shedding light on the intricate mechanisms underlying plant immune gene regulation. 

Abstract Processing bodies (PBs) and stress granules (SGs) are membrane-less cellular compartments consisting of ribonucleoprotein complexes. Whereas PBs are more ubiquitous, SGs are assembled mainly in response to stress. PBs and SGs are known to physically interact and molecules exchange between the two have been documented in mammals. However, the molecular mechanisms underpinning these processes are virtually unknown in plants. We have reported recently that tandem CCCH zinc finger 1 (TZF1) protein can recruit MAPK signaling components to SGs. Here we have found that TZF1-MPK3/6-MKK4/5 form a protein-protein interacting network in SGs. The mRNA decapping factor 1 (DCP1) is a core component of PBs. MAPK signaling mediated phosphorylation triggers a rapid reduction of DCP1 partition into PBs, concomitantly associated with an increase of DCP1 assembly into SGs. Furthermore, we have found that plant SG marker protein UBP1b (oligouridylate binding protein 1b) plays a role in maintaining DCP1 in PBs by suppressing the accumulation of MAPK signaling components. Together, we propose that MAPK signaling and UBP1b mediate the dynamics of PBs and SGs in plant cells. 

Field inversion machine learning (FIML) has the advantages of model consistency and low data dependency and has been used to augment imperfect turbulence models. However, the solver-intrusive field inversion has a high entry bar, and existing FIML studies focused on improving only steady-state or time-averaged periodic flow predictions. To break this limit, this paper develops an open-source FIML framework for time-accurate unsteady flow, where both spatial and temporal variations of flow are of interest. We augment a Reynolds-Averaged Navier–Stokes (RANS) turbulence model's production term with a scalar field. We then integrate a neural network (NN) model into the flow solver to compute the above augmentation scalar field based on local flow features at each time step. Finally, we optimize the weights and biases of the built-in NN model to minimize the regulated spatial-temporal prediction error between the augmented flow solver and reference data. We consider the spatial-temporal evolution of unsteady flow over a 45° ramp and use only the surface pressure as the training data. The unsteady-FIML-trained model accurately predicts the spatial-temporal variations of unsteady flow fields. In addition, the trained model exhibits reasonably good prediction accuracy for various ramp angles, Reynolds numbers, and flow variables (e.g., velocity fields) that are not used in training, highlighting its generalizability. The FIML capability has been integrated into our open-source framework DAFoam. It has the potential to train more accurate RANS turbulence models for other unsteady flow phenomena, such as wind gust response, bubbly flow, and particle dispersion in the atmosphere.