Ting-Ying Wu

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: Plants often encounter simultaneous imbalances in multiple nutrients, but the regulatory logic coordinating their responses remains poorly understood. We aimed to uncover shared transcriptional programs and regulatory nodes underpinning multinutrient adaptation in Arabidopsis thaliana roots. We analyzed publicly available RNA‐seq datasets spanning 15 nutrient and beneficial element conditions using differential expression, co‐expression network (WGCNA), and gene regulatory network analysis. Selected transcription factors (TFs) were validated via root phenotyping, suberin staining, and ionomic profiling under two‐nutrient stress conditions. We identified a core set of 2050 genes responsive to multiple nutrient treatments, enriched for suberin biosynthesis, and structured into modular co‐expression clusters. Eight prioritized candidate TFs (ARR10, GBF3, HHO5, NAC32, NF‐YA3, NF‐YB2, SARD1, and WRKY33) were shown to modulate root system architecture under specific nutrient combinations. WRKY33 and NF‐YB2, in particular, regulated nutrient‐responsive suberin deposition and ionomic plasticity. These findings reveal suberin remodeling as a shared downstream process in multinutrient responses and suggest that plasticity is not a fixed trait but a modular, polygenic, and context‐dependent outcome. Repurposed TFs with pleiotropic functions coordinate structural and physiological traits, providing regulatory entry points for improving nutrient resilience.

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: 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.

Eukaryotic organisms remodel chromatin landscapes to regulate gene expression in response to environmental stress. In plants, heat stress (HS) induces widespread chromatin changes, yet the role of heat-responsive Heat Shock Transcription Factors (HSFs) in chromatin remodeling and their evolutionary conservation remains unclear. Using chromatin accessibility profiling and transcriptomics in Marchantia polymorpha hsf mutants, we identify HSFA1 as a key determinant in positioning cis -regulatory elements (CREs) for HS-induced gene activation, a mechanism conserved across land plants, mice, and human cells. By integrating gene regulatory network modeling, we identify parallel transcription factor subnetworks, with MpWRKY10 and MpABI5B acting as indirect regulators of HS responses via phenylpropanoid pathways and general stress signaling. We further explore crosstalk between HS and abscisic acid (ABA) signaling, showing that while ABA modulates gene expression in an HSFA1-dependent manner, it does not induce broad chromatin remodeling, positioning it as a downstream regulator rather than a primary determinant of chromatin dynamics. To extend these insights, we develop a cross-species and cross-condition machine learning framework that accurately predicts chromatin accessibility and gene expression, demonstrating a conserved regulatory logic of stress-responsive chromatin and transcription dynamics. Our findings provide a conceptual framework for understanding how TFs coordinate chromatin architecture to drive stress adaptation in plants and potentially other eukaryotes.

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.