Biology subjects

Luo, X.

Publications and source records attributed to Luo, X..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Microglial epigenetic memory is associated with accelerated resolution of inflammatory pain induced by prophylactic macrophage-derived small extracellular vesicles

Small extracellular vesicles (sEVs) including exosomes play an important role in intercellular communication and can exert immunomodulatory effects in recipient cells. We have shown that a single prophylactic intrathecal injection of sEVs from RAW 264.7 macrophages two weeks prior, promotes faster resolution of mechanical and thermal hypersensitivity in the complete Freunds adjuvant (CFA) mouse model of inflammatory pain. How this long-term memory develops, and how sEVs regulate immune responses are unknown. Recent studies have shown that priming microglia with inflammatory stimuli can enhance or suppress responses to a delayed secondary insult via epigenetic modifications. We hypothesized that prophylactic intrathecal administration of macrophage-derived sEVs confers accelerated resolution of inflammatory pain by reprogramming epigenetic memory in spinal microglia in recipient CFA model mice. To determine whether prophylactic sEVs could attenuate pain in the absence of microglia when administering sEVs, we ablated microglia using a colony-stimulating factor 1 receptor (CSF1R) inhibitor, PLX5622. sEV-induced pain prophylaxis was completely abolished in PLX5622-fed mice, indicating that microglia are required to be present during sEV administration to confer early resolution of inflammatory pain hypersensitivity. ChIP-seq analysis in spinal microglia 14 days after sEV administration (prior to CFA) revealed an increased number of gene loci enriched for H3K4me1, a hallmark of innate immune memory. Furthermore, inhibiting the H3K4 mono-methyltransferase SETD7 abolished sEV-induced pain attenuation. Our findings indicate that both microglia and its epigenetic reprogramming contribute to pain prophylaxis induced by macrophage-derived sEVs, providing novel insights into the development of non-addictive preventive analgesia.

neuroscience↗

bioRxiv2026

Donor Macrophage Depletion Permits Post-Transplant Tolerance Induction in a Murine Islet Transplant Model

Current strategies for experimental tolerance induction for allogeneic transplantation typically require recipient preparation days to weeks prior to transplantation, making them not applicable to deceased donor transplantation. Developing tolerance strategies feasible for deceased donor transplantation would greatly increase the pool of eligible patients for tolerance induction. Here, we aimed to induce tolerance with post-transplant only interventions in a murine pancreatic islet transplant model. We demonstrated that transplant tolerance induction by recipient infusions of ethylcarbodiimide-treated donor splenocytes (ECDI-SPs) could be reliably delayed to the post-transplant timeframe provided that donor islets were depleted of intra-islet macrophages prior to transplantation. Mechanistically, islet production of CCL3, CCL4, and CCL5 (RANTES) was significantly reduced by intra-islet macrophage depletion. On POD+1, islet allograft depleted of donor intra-islet macrophages exhibited significantly reduced infiltration of recipient innate immune cells, including monocytes, macrophages, and neutrophils. Interestingly, perioperative inhibition of CCR5, the receptor for CCL3, CCL4 and CCL5, also reduced POD+1 innate immune cell infiltration, and similarly permitted tolerance induction by post-transplant donor ECDI-SP infusions. This study thus demonstrates the efficacy of a strategy that would allow transplant tolerance induction by post-transplant-only interventions, thereby expanding the applicability of tolerance induction regimens to additional clinically relevant settings.

immunology↗

bioRxiv2025

Profiles of Aging Based on Cognition, Affect, and Brain Reserve

The aging paradox describes improvements in emotional wellbeing as a function of aging, despite declines in cognition. Conversely, late life depression has been associated with increased cognitive decline in aging. We sought to understand these seemingly contradictory patterns of cognitive and mental health in older age. Building on cognitive reserve, affective reserve, and brain reserve models of aging, we developed three alternative algorithmic approaches to group N=22,686 participants from the UK Biobank into different profiles of aging. Our results revealed that aging profiles identified using our data-driven brain reserve model, which incorporated measures of cognition, neuroticism, and brain volume, achieved the highest validation results. Importantly, only two of the four aging profiles were characterized by the aging paradox (i.e., improved emotionality and decreased cognition with age). We identified one profile characterized by particularly low levels of neuroticism and relative resilience to cognitive decline. Another profile benefited from relatively preserved brain volumes, potentially driven by younger ages and/or higher socioeconomic status. Conversely, we identified two profiles with poorer health characteristics, including one profile with elevated cardiovascular risk. Taken together, these findings enrich our understanding of the emotion paradox and highlight the value of taking a nuanced and stratified approach when studying aging. In the future, aging profiles could be used to target preventative strategies to address modifiable risk factors and improve lifespan and healthspan.

neuroscience↗

bioRxiv2025

Swine wastewater drives dissemination of high-risk blaNDM-positive Escherichia coli clones

Swine wastewater can accelerate the spread of antibiotic-resistant bacteria among animals, the environment, and humans. Therefore, the molecular epidemiological characteristics of carbapenemase-producing E. coli isolates from swine wastewater was investigated. In this study, a total of 100 no-cloning carbapenemase-producing (blaNDM-1, blaNDM-5 and blaOXA-48-like) E. coli isolates were recovered from 316 swine wastewater samples collected from 29 swine farm in Shandong, China. Of which, two isolates that co-harbored blaOXA-48-like and mcr-1, as well as five isolates that co-harbored blaNDM-5 and tet(X4). All isolates were multidrug-resistant (MDR), with a majority exhibiting resistance to meropenem, ciprofloxacin, imipenem and gentamicin. Whole genome sequencing (WGS) analysis indicated that these isolates were belonged to 12 distinct sequence types (STs), with the most prevalent STs being ST10 and ST5299. Additionally, Phylogenomic analysis revealed that clonal spread of blaNDM-positive ST10 E. coli isolates at swine wastewater in multiple cities, including Binzhou, Dezhou, Heze and Weihai. Meanwhile, a notable divergence in SNPs was observed in isolates from this study and public database, which indicates that these blaNDM-positive E. coli isolates have high genetic diversity. In addition, WGS analysis further revealed that blaNDM co-existed with other antibiotic resistance genes, conferring resistance to twelve classes of antimicrobials. This study underscores the importance of surveillance for blaNDM-harboring microbes in swine wastewater, and continuous monitoring in swine wastewater is essential for ensure human and environmental health. IMPORTANCEThe study described the NDM-positive E. coli strains isolated from swine wastewater, where it was associated with antibiotic exposure. Notably, swine wastewater was increasingly considered to be related to CRE hosts. Pathogens originating from this source can be transmitted to humans via direct and indirect contact, as well as through the ingestion or inhalation of contaminated water or aerosols. In addition, under the selective pressure of antibiotics, the co-harbored of blaOXA-48-like and mcr-1, blaNDM-5 and tet(X4) genes within the same host bacterium signifies the emergence of an extremely MDR pathogen, posing a grave threat to public health. Consequently, the monitoring of blaNDM in swine wastewater is critical for containing the transmission and persistence of carbapenem-resistant pathogens.

microbiology↗

bioRxiv2025

Susceptibility to Task-irrelevant Auditory Distractors in Relation to Visual Working Memory in Children With and Without ADHD

ObjectiveAttention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children and is often associated with increased distractibility. Previous research has reported that susceptibility to task-irrelevant visual distractions is associated with visual working memory (WM) capacity. However, how neural responses to task-irrelevant auditory distractors are related to visual WM capacity in children with and without ADHD remains poorly understood. MethodsIn this study, we collected electroencephalography (EEG) signals from 72 children with ADHD and 88 typically developing (TD) children across two experiments, during which they performed visual tasks of varying difficulty while task-irrelevant auditory distractors were presented. The mismatch negativity (MMN) amplitude was measured as a neural index of susceptibility to auditory distractors, and the K score was assessed as a behavioral indicator of visual WM capacity. ResultsTD children showed opposite patterns across the two experiments: under low audiovisual competition, those with higher visual working memory capacity were more susceptible to task-irrelevant sounds, whereas under high competition, children with lower capacity showed greater susceptibility to distraction. In contrast, children with ADHD showed a stable association, with lower visual WM capacity consistently predicting higher susceptibility to task-irrelevant auditory distractors regardless of competition level. ConclusionsThese findings provide novel evidence that susceptibility to task-irrelevant auditory distraction is closely related to visual WM capacity in both children with and without ADHD, but the patterns of this relationship differ between typical and atypical neurodevelopment and are modulated by audiovisual competition.

neuroscience↗

bioRxiv2025

Systematic structural and functional analysis of metal-binding sites in native proteomes

Metal-binding proteins (MBPs) are widespread and critical for many biological processes. Proteomics-based approaches have identified MBPs in various systems but typically cannot determine the location of metal-binding sites or shed light on the functional roles of metals. Thus, a systems-wide map of metal binding sites and an understanding of the consequences of metal binding for protein function are still lacking. Here, we used limited proteolysis-coupled mass spectrometry (LiP-MS) combined with chelator treatment to globally map metal-protein interactions and binding sites in native protein lysates. We applied the pipeline, named Metal-LiP, to Escherichia coli, identified 981 peptides on 297 proteins that exhibited dose responsiveness to chelator treatment, and quantified the relative metal-binding affinity. These proteins are strongly enriched for known or predicted MBPs, based on our novel compiled ground truth dataset of previously annotated (n=1013) and predicted (n=850) MBPs. Further, chelator-responsive peptides mapped at or near annotated metal-binding sites in 3D protein structures, showing that Metal-LiP can identify metal-binding sites, and provided novel experimental evidence for previously predicted sites. Notably, Metal-LiP identified 225 novel candidate MBPs and we validated five of these novel MBPs (GatZ, AhpC, GrxB, MsrC, TrhO) using inductively coupled plasma-mass spectrometry to detect metal binding in purified proteins. Finally, we used orthogonal approaches to probe the broad functional consequences of metal binding. Using a combination of Metal-LiP, thermal proteome profiling, size-exclusion chromatography coupled to mass spectrometry, and structural analyses, we identified cases where metal binding affects protein stability, modulates the assembly states of protein complexes, or is likely to affect enzymatic activity. We thereby propose functional roles for metal binding in 98 of our Metal-LiP hits and in 225 proteins in total. In summary, we describe an approach to identify bona fide MBPs with binding site-level resolution across the proteome. We have combined it with other methods to identify 424 candidate MBPs in the E coli proteome, to map known and novel candidate metal-binding sites for 297 of these proteins, and to suggest functional roles of metal binding.

systems biology↗

bioRxiv2025

Benchmarking Real-World Applicability of Molecular Generative Models from De novo Design to Lead Optimization with MolGenBench

Structure-based drug design (SBDD) has been profoundly reshaped by the advent of deep generative models, yet their practical impact on drug discovery remains limited. A central issue is the absence of a rigorous, application-oriented benchmark that mirrors the multi-stage, target-aware workflows of real-world pharmaceutical development. Inspired by recent advances in benchmarking for computer vision and large language models, where systematic evaluation has catalysed rapid progress, we introduce MolGenBench, a comprehensive benchmark designed to close the gap between molecular generation algorithms and tangible drug discovery outcomes. MolGenBench integrates a structurally diverse, large-scale dataset spanning 120 protein targets, 5,433 chemical series comprising 220,005 experimentally confirmed active molecules. Beyond conventional de novo generation, it incorporates a dedicated hit-to-lead (H2L) optimization scenario, which represents a critical phase in hit optimization that is seldom evaluated in existing benchmarks. We further introduce novel, pharmaceutically grounded metrics that assess a models ability to both rediscover target-specific actives or progressively optimize compounds for potency. Through extensive evaluation, MolGenBench uncovers significant gaps between current generative models and the demands of real-world drug development, establishing a foundational resource for building generative models with enhanced practical impact and accelerated translational potential.

bioinformatics↗

bioRxiv2025

Oral delivery of mesenchymal stem cell-derived extracellular vesicles to treat intestinal inflammation

Despite advances in therapy for inflammatory bowel disease (IBD), current treatments are still associated with poor clinical outcomes and severe systemic side effects. Extracellular vesicles derived from mesenchymal stem cells (MSC-EVs) could have therapeutic applications in IBD due to their regenerative potential and immunomodulatory properties. Previous studies investigating the potential of MSC-EVs in IBD have largely administered the vesicles by injection, which does not offer the significant benefits of oral administration, including direct and localised access to the site(s) of intestinal inflammation. Here, we evaluated the stability of MSC-EVs for oral delivery by assessing particle size, concentration and EV markers. The EVs disintegrated in gastrointestinal (GI) fluids, with cryogenic electron microscopy confirming the loss of structural integrity. To address this, we developed a double-coating formulation consisting of chitosan and Eudragit S100 to enhance GI stability and facilitate colon-targeted delivery. We demonstrated that coated EVs were resistant to GI fluids and digestive enzymes, and the formulation released intact vesicles in colonic fluid. Preliminary in vivo studies in a dextran sodium sulfate-induced colitis mouse model showed that coated EVs reduced disease severity, whereas uncoated EVs performed similarly to the control group. Interestingly, coated MSC-EVs elicited a stronger therapeutic response compared to EVs administered intravenously at the same dose. These findings indicate that oral delivery of MSC-EVs could be an effective route of administration, with appropriate formulation, to treat intestinal inflammation.

bioengineering↗

bioRxiv2025

Predicting SARS-CoV-2 evolution dynamics with spatiotemporal resolution by DMS-empowered protein language model

Early identification of emerging dominant SARS-CoV-2 variants is essential for effective pandemic preparedness, yet existing methodologies face significant limitations. Experimental characterizations are costly and not feasible for real-time surveillance, whereas existing computational approaches cannot achieve satisfactory precision in predicting future dominant lineages and fail to capture the spatiotemporal dynamics of fitness under evolving host immune pressures. Here, we introduce DeepCoV (DMS-Empowered Evolution Prediction of CoronaVirus), a deep-learning framework for the dynamic identification of novel variants with high potential to become prevelent. It integrates deep mutational scanning (DMS)-derived mutation phenotypes with epidemiological surveillence data reflecting historical viral evolution and the dynamic fitness landscape. DeepCoV accurately forecasted the dominance of recently circulating lineages a month in advance, achieving a 90% reduction in false discovery rate while capturing temporal and geographic dynamics of variant spread and reconstructing their regional prevalence trajectories. Moreover, DeepCoV identified mutational hotspots of Omicron-derived backbones in silico, revealing convergent evolution trends. This scalable solution enables timely identification of immune-evasive variants and prospective alert of critical mutations, providing actionable insights for vaccine updates and pandemic surveillance.

microbiology↗

bioRxiv2025

Spacetime concordance in the primate cortex

Understanding the functional organization of the primate cortex requires metrics that capture both the temporal and topological dimensions of functional connectivity. Here we propose the spatial connectivity for local homogeneity in cortex (SoHo), a vertex-wise, continuous metric that quantifies the degree to which a cortical vertex and its immediate neighbors share similar spatial profiles of whole-brain functional connectivity. We validated SoHo using large-scale wakeful resting-state fMRI datasets from the Human Connectome Project (HCP) and the NIH Marmoset Brain Mapping Project. In humans, SoHo values showed a striking correspondence with the parcellation boundaries of the HCP multimodal atlas, with low-value regions consistently aligning with areal boundaries. Higher-order association areas exhibited lower SoHo values (functional diversity), while primary sensorimotor areas demonstrated higher values (functional uniformity). Cross-species SoHo mapping revealed that this primary-to-association gradient is evolutionarily conserved across primates, alongside species-specific adaptations in frontoparietal and motor regions. By capturing the local concordance of spatial fingerprints of whole-brain connectivity, SoHo bridges discrete parcellation schemes and continuous models of brain function, offering new insights into primate brain organization and evolution.

neuroscience↗

bioRxiv2025

Oncogenic KRAS regulates secretion of extracellular vesicles and surface membrane charge via regulation of phosphatidylserine in pancreatic cancer

Extracellular vesicles (EVs) have the potential to be used as a liquid biopsy for cancer detection and treatment response assessment. Although the potential of EVs as disease-specific biomarkers has been promising, the rapid and specific enrichment of EVs from body fluids in the clinical setting remains challenging. To address this limitation, we developed a Microfluidic Electrophoresis (MEP) device, that allows label-free enrichment of EVs based on charge from the serum of patients with pancreatic cancer (PaCa). The ability of MEP to enrich anionic EVs was validated using cell line-derived EVs and PaCa serum-derived EVs. We observed a positive correlation between the KRAS status, secretion of EVs, and net negative zeta potential ({zeta}-potential) of EVs. Further analyses identified phosphatidylserine (PS) and extraluminal DNA as molecular determinants of the enhanced anionic nature of PaCa-derived EVs. Overall, this work introduces a new microfluidic device to enrich cancer EVs in circulation with potential for further rapid detection of PaCa.

bioengineering↗

bioRxiv2025

Nanophotonic neural probes for in vivo photostimulation, electrophysiology, and microfluidic delivery

Implantable silicon neural probes with integrated optical emitters and electrodes are emerging tools for simultaneous optogenetic stimulation and electrophysiological recording in deep brain regions. In parallel, neural probes with microfluidic channels have been developed for localized drug delivery and neurochemical sampling. However, thus far, such fluidic probes have lacked optical and electrical functionalities or been limited to a low number of optical emitters and/or electrodes, constraining their utility in multimodal investigations of neural circuits. Here, we introduce foundry-fabricated silicon nanophotonic neural probes with monolithically integrated microfluidics. Each probe has 16 silicon nitride grating coupler emitters, 18 titanium nitride microelectrodes, and one embedded microfluidic channel. We evaluate the photonic, electrophysiological, and microfluidic functionalities in vivo in optogenetic, blue-light-sensitive mice. With our multifunctional neural probes, we demonstrate local suppression of epileptic seizure activity (induced by microfluidic injection of 4-aminopyridine) using photostimulation. Through foundry-compatible microfluidics integration, this work advances the versatility of nanophotonic neural probes and presents new possibilities for multimodal neuroscience experiments leveraging this scalable neurotechnology.

neuroscience↗

bioRxiv2025

HerbComb: an integrated database for the discovery of novel combinational therapies from herbal medicines

Drug combinations have gained increasing interest due to their potential for optimal efficacy and the ability to overcome drug resistance. Herbal medicine is a complex system with multiple ingredients and has illustrated apparent therapeutic effects through thousands of years of clinical experience, offering a rich resource for combinational therapies. However, the underlying mechanism of their synergistic therapeutic effects for disease treatment remains obscure for most herbal medicines. To fill this gap, we developed an integrated database named HerbComb (https://herbcomb.com/) to discover combinatorial therapies in herbal medicine, featuring three unique functions: drug prepositioning, customized combinatorial analysis, and biological and pharmacological features. Additionally, transcriptional gene signatures and ADMET properties were also interrogated as interactive functionalities to facilitate combinational analysis. Using Tongxinluo for Stroke as a case study, we identified and validated Oleanolic acid and Ferulic acid as synergistic ingredients. In summary, HerbComb is a versatile data exploration platform designed to characterize synergistic interactions among herbal medicines, enhancing the understanding of synergistic mechanisms and facilitating the discovery of effective drug combinations.

pharmacology and toxicology↗

bioRxiv2025

RNA-RNA Interactome Approaches Provide in vivo Evidence for a Critical Role of the Hfq Rim Face in sRNA-mRNA Pairing

Most bacterial small regulatory RNAs (sRNAs) modulate gene expression by forming complementary base pairs with target mRNAs, dependent upon the RNA chaperone Hfq. Hfq has three RNA-binding faces (proximal, rim, and distal), facilitating simultaneous binding of sRNAs and target mRNAs. Here, we systematically examined the functional impact of point mutations in the RNA binding faces on in vivo pairing, using the RNA Interaction by Ligation and Sequencing (RIL-seq) approach. The distal and proximal Hfq binding face mutants retained substantial numbers of RNA-RNA interactions (significant chimera counts or S-chimeras). However, the rim face mutant R16A showed a near-complete loss of S-chimeras, although Hfq R16A retained partial RNA binding activity as well as partial regulatory activity. Intracellular RIL-seq (iRIL-seq), a method with fewer in vitro processing steps, led to more S-chimeras in R16A but there were still fewer than for wild type Hfq and somewhat different sets of prevalent RNA-RNA pairs. Our analysis provides insights into how the RNA-binding faces of Hfq contribute to pairing in vivo, document the key role for the rim face in stabilizing RNA pairs on Hfq, and highlight intriguing differences captured by different RNA-RNA interactome approaches. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/660752v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@99569aorg.highwire.dtl.DTLVardef@104d130org.highwire.dtl.DTLVardef@141b117org.highwire.dtl.DTLVardef@1d19611_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Advancing Ligand Binding Affinity Prediction with Cartesian Tensor-Based Deep Learning

0.We present PBCNet2.0, a cartesian tensor-based Siamese Neural Network for protein-ligand relative binding affinity prediction. Trained on 8.6 million protein-ligand complex structure pairs, PBCNet2.0 achieves zero-shot performance comparable to computationally intensive physics-based simulations. Our prioritization experiments show that PBCNet2.0 speeds up binding affinity optimization by 718% while reducing resource use by 41%. Through extensive retrospective experiments, we demonstrate that PBCNet2.0 intrinsically comprehends protein-ligand interactions, showing high sensitivity to intermolecular interactions and exceptional perception of spatial geometric information. Strikingly, PBCNet2.0 exhibits an emergent capability to predict affinity changes induced by binding residue variations, highlighting its potential for identifying resistance mutation. We prospectively validated these capabilities on two targets ENPP1 and ALDH1B1, where PBCNet2.0 successfully identified affinity shifts arising from subtle molecular interactions and conformational differences, and pinpointed critical binding residues with an 83% hit rate. This combination of computational efficiency, spatial geometric perception of binding site, and generalizable affinity prediction establishes PBCNet2.0 as a transformative tool for developing pharmacological probes for all human proteins.

molecular biology↗

bioRxiv2025

Developing a general AI model for integrating diverse genomic modalities and comprehensive genomic knowledge

Advances in next-generation sequencing technologies have vastly expanded the availability of diverse genomic, epigenomic and transcriptomic data, presenting the opportunity to develop a general AI model that integrates comprehensive genomic knowledge into a unified model. Unlike previous predictive models, which are typically specialized to certain tasks, our general AI model unifies a wide range of genomic modalities, such as nascent RNA and ultra-high-resolution chromatin organization, within a multi-task architecture. Using ATAC-seq and DNA sequences as inputs, we incorporated diverse genomic modalities as output, and the model exhibits strong generalizability across different cell types and tissues in all tasks we trained. It accurately predicts gene-level transcription measured by various nascent RNA assays, and effectively captures enhancer-associated transcription. Additionally, it also accurately captures the potential functions of non-coding genetic variants and regulatory elements. Additionally, we extended the model trained on human data to a mouse general model, achieving accurate predictions of genomic modalities, such as high resolution chromatin contact maps with limited data availability, which are further validated using an established mouse inner-ear study. This comprehensive approach offers a powerful tool for understanding genome regulation in both human and mouse species.

bioinformatics↗

bioRxiv2025

Strain-level metagenomic profiling using pangenome graphs with PanTax

Microbes are omnipresent, thriving in a range of habitats, from oceans to soils, and even within our gastrointestinal tracts. They play a vital role in maintaining ecological equilibrium and promoting the health of their hosts. Consequently, understanding the diversity in terms of strains in microbial communities is crucial, as variations between strains can lead to different phenotypic expressions or diverse biological functions. However, current methods for taxonomic classification from metagenomic sequencing data have several limitations, including their reliance solely on species resolution, support for either short or long reads, or their confinement to a given single species. Most notably, most existing strain-level taxonomic classifiers rely on the sequence representation of multiple linear reference genomes, which fails to capture the sequence correlations among these genomes, potentially introducing ambiguity and biases in metagenomic profiling. Here, we present PanTax, a pangenome graph-based taxonomic profiler that overcomes the short-comings of sequence-based approaches, because pangenome graphs possess the capability to depict the full range of genetic variability present across multiple evolutionarily or environmentally related genomes. PanTax provides a comprehensive solution to taxonomic classification for strain resolution, compatibility with both short and long reads, and compatibility with single or multiple species. Extensive benchmarking results demonstrate that PanTax drastically outperforms state-of-the-art approaches, primarily evidenced by its significantly higher precision or recall at strain level, while maintaining comparable or better performance in other aspects across various datasets. PanTax is an open-source user-friendly tool that is publicly available at https://github.com/LuoGroup2023/PanTax.

bioinformatics↗

bioRxiv2025

Auto-downregulation of the florigen FT production prevents precocious flowering in plants

In many flowering plants, the developmental switch from vegetative growth to reproduction (flowering) is timed by seasonal changes in the length of daylight (photoperiod). Under inductive day lengths, the photoperiod pathway typically generates rhythmic expression of a transcriptional activator for florigen production. In the facultative long-day plant Arabidopsis thaliana, long-day exposure results in increasing buildup of the CONSTANS (CO) protein towards the end of daylight, and CO activates the expression of the major florigen gene FLOWERING LOCUS T (FT) to confer long-day induction of flowering. CO-mediated FT activation must be properly controlled to prevent an excessive florigen production and precocious flowering under inductive long days, but the underlying mechanism remains elusive. Here, we report an auto-repression mechanism to prevent excessive FT production in inductive photoperiods. We show that the transcription factor FD is expressed in leaf veins and complexes with FT to recognize several cis-regulatory DNA motifs in FT promoter. FT-FD antagonizes CO-mediated FT activation to feedback down-regulate FT expression and thus prevent its excessive induction by long-day signals, thereby precluding precocious transition to flowering. Furthermore, we found that in the facultative short-day plant soybean, an FT homolog directly represses its own expression. Thus, the auto-repression of FT or an FT homolog is a conserved mechanism to prevent excessive production of this potent floral regulator in plants, ensuring the floral transition at a proper time to balance vegetative growth with reproductive success and maximize plant production.

plant biology↗