Ming-Jung Liu

Adaptation to terrestrial environments required the evolution of morphological structures, such as rhizoids and air pores, and robust tolerance mechanisms to environmental stress. While these innovations are well-described, the regulatory logic coordinating development with stress signaling during this transition remains largely unknown. Here, we identify Heat Shock Transcription Factor B1 (HSFB1) as a central evolutionary node that bridges this gap in Marchantia polymorpha. Using single-nucleus RNA sequencing (snRNA-seq) and gene regulatory network analysis, we reveal that MpHSFB1 is required to activate cell-type-specific programs for terrestrial adaptation, functioning via the regulation of cytokinin and reactive oxygen species (ROS) homeostasis. We find that MpHSFB1 functions through a modular logic: it co-opts a specific set of bHLH transcription factors (including MpbHLH21/40) to drive developmental morphogenesis, while recruiting a distinct partner (MpbHLH51) to confer resistance to prolonged heat stress. This functional uncoupling allows a single ancestral regulator to manage disparate physiological needs. By reconstructing HSFB1 regulons across diverse plant lineages, we propose that MpHSFB1 acted as an ancestral pleiotropic hub that was effectively rewired and subfunctionalized in later angiosperm lineages for land plant evolution.

Summary statement: This study introduces BioLumiPi, a novel, low‐cost, and non‐invasive biosensor plant, engineered by combining a phosphate (Pi) deficiency‐induced promoter with a fungal bioluminescence system in tobacco. BioLumiPi plants emit light that accurately reflects endogenous Pi deficiency and responds specifically to Pi levels. Coupled with deep learning analysis, this technology offers a proof‐of‐concept strategy for real‐time visualisation and prediction of nutrient stress to optimise fertiliser application and advance sustainable agriculture.

The recognition of translational initiation sites (TISs) offers complementary insights into identifying genes encoding novel proteins or small peptides. Conventional computational methods primarily identify Ribo-seq-supported TISs and lack the capacity of systematic and global identification of TIS, especially for non-AUG sites in plants. Additionally, these methods are often unsuitable for evaluating the importance of mRNA sequence features for TIS determination. In this study, we present TISCalling, a robust framework that combines machine learning (ML) models and statistical analysis to identify and rank novel TISs across eukaryotes. TISCalling generalized and ranks important features common to multiple plant and mammalian species while identifying kingdom-specific features such as mRNA secondary structures and “G”-nucleotide contents. Furthermore, TISCalling achieved high predictive power for identifying novel viral TISs. Importantly, TISCalling provides prediction scores for putative TIS along plant transcripts, enabling prioritization of those of interest for further validation. We offer TISCalling as a command-line-based package [https://github.com/yenmr/TISCalling], capable of generating prediction models and identifying key sequence features. Additionally, we provide web tools [https://predict.southerngenomics.org/TISCalling/] for visualizing pre-computed potential TISs, making it accessible to users without programming experience. The TISCalling framework offers a sequence-aware and interpretable approach for decoding genome sequences and exploring functional proteins in plants and viruses.

SUMMARY: Monitoring endogenous nutrient levels is crucial for maximizing crop yields and optimizing fertilizer use. Here, focusing on phosphorus, an essential nutrient for plant growth, we developed a low-cost and non-invasive biosensor to visualize and predict early stress signaling in plants. By combining plant phosphate (Pi)-deficiency-induced promoter systems with fungal self-sustained bioluminescence systems genetically engineered into tobacco plants, we created sensor plants that emitted more light when experiencing Pi deficiency. This light emission correlated with the expressions of known phosphate-responsive genes and the total phosphorus content in plants, and decreased during Pi recovery conditions, demonstrating the responsiveness and robustness of the sensor plants in reflecting endogenous phosphorus deficiency. The sensor plants responded primarily to Pi deficiency rather than nitrogen or potassium deficiencies and were sensitive to different ranges of external Pi concentrations. Additionally, when grafted onto tomato and chili pepper plants, the sensor plants responded to external phosphorus deficiency, showing promise for monitoring stress signals in different crop species. Using deep-learning-based image analysis techniques, auto-luminescent signals of sensor plants could be detected and used to predict phosphorus deficiency. This study outlines a strategy of creating a self-luminous biosensor to visualize phosphate dynamics in planta and predict nutrient deficiency for sustainable agriculture.

SUMMARY: The recognition of translational initiation sites (TISs) offers complementary insights into identifying genes encoding novel proteins or small peptides. Conventional computational methods primarily identify Ribo-seq-supported TISs and lack the capacity of systematical and global identification of TIS, especially for non-AUG sites in plants. Additionally, these methods are often unsuitable for evaluating the importance of mRNA sequence features for TIS determination. In this study, we present TISCalling , a robust framework that combines machine learning (ML) models and statistical analysis to identify and rank novel TISs across eukaryotes. TISCalling generalized and ranks important features common to multiple plant and mammalian species while identifying kingdom-specific features such as mRNA secondary structures and G-contents. Furthermore, TISCalling achieved high predictive power for identifying novel viral TISs. Importantly, TISCalling provides prediction scores for putative TIS along plant transcripts, enabling prioritization of those of interest for further validation. We offer TISCalling as a command-line-based package [ https://github.com/yenmr/TIScalling ], capable of generating prediction models and identifying key sequence features. Additionally, we provide web tools [ https://predict.southerngenomics.org/TIScalling ] for visualizing pre-computed potential TISs, making it accessible to users without programming experience. The TISCalling framework offers a sequence-aware and interpretable approach for decoding genome sequences and exploring functional proteins in plants and viruses.

The recent growth in global warming, soil contamination, and climate instability have widely disturbed ecosystems, and will have a significant negative impact on the growth of plants that produce grains, fruits and woody biomass. To conquer this difficult situation, we need to understand the molecular bias of plant environmental responses and promote development of new technologies for sustainable maintenance of crop production. Accumulated molecular biological data have highlighted the importance of RNA-based mechanisms for plant stress responses. Here, we report the most advanced plant RNA research presented in the 33rd International Conference on Arabidopsis Research (ICAR2023), held as a hybrid event on June 5–9, 2023 in Chiba, Japan, and focused on “Arabidopsis for Sustainable Development Goals”. Six workshops/concurrent sessions in ICAR2023 targeted plant RNA biology, and many RNA-related topics could be found in other sessions. In this meeting report, we focus on the workshops/concurrent sessions targeting RNA biology, to share what is happening now at the forefront of plant RNA research.

Cells respond to environmental stimuli through transcriptional responses, orchestrated by transcription factors (TFs) that interpret the gene cis -regulatory DNA sequences, determining gene expression dynamics timing and locations. Diversification in TFs and cis -regulatory element (CRE) interactions result in unique gene regulatory networks (GRNs) that underpin plant adaptation. A primary challenge is identifying Transcription Factor Binding Motifs (TFBMs) for temporal and condition-specific gene expressions in plants. While the Multiple EM for Motif Elicitation (MEME) suite identifies stress-responsive CREs in Arabidopsis, its predictive power for gene expression remains uncertain. Alternatively, the k -mer approach identifies CRE sites and consensus TF motifs, thereby improving gene expression prediction models. In this study, we harnessed the power of a k -mer pipeline to address sequence-to-expression prediction problems across diverse abiotic stresses, in both bryophytic and vascular plants, including monocots and dicots. Moreover, we characterized both un-gapped and gapped CREs and, coupled with GRN analyses, pinpointed key TFs within transcriptional cascades. Lastly, we developed the P redictive R egulatory E lement D atabase for Identifying C is -regulatory elements and T ranscription factors (PREDICT), a web tool for efficient k -mer identification. This advancement will enrich our understanding of the cis -regulatory code landscape that shapes gene regulation in plant adaptation. PREDICT web tool is available at [ http://predict.southerngenomics.org/kmers/kmers.php ].

MRNA translation relies on identifying translation initiation sites (TISs) in mRNAs. Alternative TISs are prevalent across plant transcriptomes, but the mechanisms for their recognition are unclear. Using ribosome profiling and machine learning, we developed models for predicting alternative TISs in the tomato (Solanum lycopersicum). Distinct feature sets were predictive of AUG and nonAUG TISs in 5′ untranslated regions and coding sequences, including a novel CU-rich sequence that promoted plant TIS activity, a translational enhancer found across dicots and monocots, and humans and viruses. Our results elucidate the mechanistic and evolutionary basis of TIS recognition, wherebycis-regulatory RNA signatures affect start site selection. The TIS prediction model provides global estimates of TISs to discover neglected protein-coding genes across plant genomes. The prevalence ofcis-regulatory signatures across plant species, humans, and viruses suggests their broad and critical roles in reprogramming the translational landscape.

MRNA translation relies on identifying translation initiation sites (TISs) in mRNAs. Alternative TISs are prevalent across plant transcriptomes, but the mechanisms for their recognition are unclear. Using ribosome profiling and machine learning, we developed models for predicting alternative TISs in Arabidopsis thaliana and tomato ( Solanum lycopersicum ). Distinct feature sets were predictive of AUG and non-AUG TISs in 5′ untranslated regions and coding sequences, including a novel CU-rich sequence that promoted plant TIS activity, a translational enhancer found across dicots and monocots and also in humans and viruses. Our results elucidate the mechanistic and evolutionary basis of TIS recognition, whereby cis -regulatory RNA signatures affect start site selection. The TIS prediction model provides global estimates of TISs to discover neglected protein-coding genes across plant genomes. The prevalence of cis -regulatory signatures across eukaryotes and viruses suggests their broad, critical roles in reprogramming the translational landscape in the plant–virus arms race. Teaser: New insights into how plant ribosomes distinguish AUG and non-AUG triplets for protein synthesis via a conserved eukaryotic cis -regulatory strategy.