Biology subjects

Yu, P.

Publications and source records attributed to Yu, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Fine-tuning an intracellular allosteric pocket programs GPCR signaling bias

Biased signaling of G protein-coupled receptors (GPCRs) offers a route for precise therapeutics with fewer adverse effects. Intracellular allosteric ligands can impose such bias, yet how subtle chemical changes within these pockets reshape receptor-transducer coupling and downstream signaling remains unclear. Here, we systematically fine-tuned the intracellular allosteric pocket of neurotensin receptor 1 (NTSR1) through chemical modification of the {beta}-arrestin-biased ligand SBI-553. Minor changes to the ligand scaffold altered transducer-coupling preferences, generating distinct profiles of G-protein and {beta}-arrestin signaling. Cryo-electron microscopy structures of NTSR1-{beta}-arrestin2 complexes bound to designed allosteric probes revealed ligand-dependent remodeling of the intracellular cavity that reshapes the receptor-effector interface. Alanine scanning identified transducer-sensitive residues within and beyond the immediate ligand-contact region. In mice, SBI-553 and N065 produced distinct physiological profiles, with N065 improving survival in a lethal endotoxemia model. Finally, phosphoproteomic profiling revealed partially overlapping but distinct downstream signaling profiles among the three modulators. Together, these findings establish the intracellular allosteric pocket as a tunable determinant of transducer coupling and show that closely related modulators can generate distinct signaling and physiological profiles.

pharmacology and toxicology↗

bioRxiv2026

Anti-defense systems of viral communitie vary with genomic traits, host immunity and environmental stress

Abstract: Background Despite the ecological importance of viral anti-defense systems (ADS), yet how viral ADS repertoires vary across microbial ecosystems remains poorly understood. Here, we integrated global virus datasets with metagenomes from heavy metal-contaminated soils and chromium-stress microcosms to investigate ecological determinants of viral ADS. Results We found that ADS repertoires were constrained by viral genome capacity, with most ADS-carrying viruses encoded a single system, whereas multi-ADS occurred mainly in larger genomes. Viral lifestyle partitioned ADS, with temperate viruses enriched in ADS targeting early-stage defenses and virulent viruses preferentially encoding ADS against mid- and late-stage defense. ADS composition also varied by host lineage and across habitats, linking viral ADS repertoires to host immune landscapes and ecosystem-specific viral strategies. Moreover, along the heavy metal contamination gradient, changes in the prevalence of ADS-carrying viruses occurred alongside shifts in prokaryotic defense profiles. In addition, expanded ADS repertoires may further increase dissemination potential of MGE-borne adaptive genes, highlighting viral ADS as a potential contributor to the adaptive evolution of microbial community. Conclusions These findings reveal that viral ADS are shaped by genomic traits, host immunity and environmental stress, with implications for virus-host interaction and microbial adaptive evolution. More broadly, this study extends anti-defense research beyond individual virus-host systems, offering an integrative, community-level perspective to guide future studies testing whether and how virus-prokaryote immune interactions contribute to microbial community dynamics and evolution in changing environments.

microbiology↗

bioRxiv2026

One-dimensional CNNs for Near-Infrared Prediction of Protein and Moisture in Cereal Grains: The Effects of Architecture and Input Preparation

Near-infrared (NIR) spectroscopy is widely used for the rapid, non-destructive determination of constituents such as protein and moisture in cereal grains, but model comparisons in this field are often confounded by differences in the inputs received by each model. We benchmark a compact one-dimensional CNN derived by one-factor-at-a-time ablation (CNN-Baseline) and a randomly searched CNN (CNN-RS) against PLSR, SVR, XGBoost on six cereal-grain datasets (n=500-5046) under three input conditions: raw spectra, optimal preprocessing, and preprocessing plus wavelength selection. A single-filter with a kernel size of 11 of convolution and batch normalization proved sufficient. Given a unified raw input, CNN-RS was most accurate on five of the six datasets, while CNN-Baseline came within 0.003-0.017 in test R2 on the four larger datasets. PLSR led only on the smallest protein dataset (XDS, n=500) and was the most stable model. Preprocessing had almost no effect on CNN accuracy on the four larger datasets, but improved accuracy by 0.034-0.047 (CNN-Baseline) and 0.017-0.055 (CNN-RS) on the two smallest, and brought the networks to their optimum in a median of 49% fewer epochs. Therefore, replacing explicit preprocessing with learned filters was only justified when sufficient training data were available. Once each model received its own preprocessing and wavelength subset, SVR became most accurate on four of six datasets and XGBoost rose from the weakest model to within 0.01-0.03 of the best model on four of the six datasets, suggesting that the principal advantage of CNNs lied in robustness to raw input data rather than in attainable accuracy. Overall, model selection for NIR calibration of cereal grains depends more on sample size and the input preparation than on network architecture.

bioinformatics↗

bioRxiv2026

A cross-kingdom interactome predicted by AlphaFold3 reveals a DNF2-centered interface required for symbiotic accommodation

Legumes convert atmospheric nitrogen into ammonium through symbiotic bacteria housed in root nodules, yet the molecular interactions between rhizobial and host proteins inside nodules remain poorly understood. Here we employed AlphaFold3 to construct a cross-kingdom interactome between Medicago truncatula and its symbiont Sinorhizobium meliloti. Screening more than 217,000 protein pairs yielded 7,137 putative interactions, providing a valuable resource for the broader symbiosis community. Within this network, we focused on DEFECTIVE IN NITROGEN FIXATION 2 (DNF2), a host protein required for rhizobial persistence within nodules. We showed that DNF2 localizes to the peribacteroid space and associates with previously uncharacterized secreted rhizobial proteins (SRPs), suggesting it may function as a hub for host-symbiont communication. Notably, knockout of two DNF2-interacting proteins, SRP86 and SRP485, results in white, nitrogen-fixation-deficient nodules with abnormal symbiosomes and elevated expression of senescence-associated genes, closely phenocopying the dnf2 loss-of-function mutant. Together, our findings define a DNF2-SRP molecular framework underlying symbiotic accommodation, and illustrate the potential of AI-guided interactome mapping to uncover molecular mechanisms of plant-microbe interactions with relevance to sustainable agriculture.

plant biology↗

bioRxiv2026

Cell type-resolved chromatin accessibility clocks for brain aging

Aging is a progressive decline in biological function that is proposed to be driven by the accumulation of epigenetic noise and the loss of epigenetic information. Among epigenetic readouts, DNA methylation has been extensively used to develop aging clocks, machine learning models that predict age from molecular data. However, DNA methylation clocks are relatively difficult to interpret and remain distant from gene regulatory networks, a gap that can be complemented by clocks built from another epigenetic layer: chromatin accessibility profiled by ATAC-seq. Existing chromatin accessibility clocks predict age from bulk ATAC-seq data, thereby averaging over the epigenetic heterogeneity across cells that drives aging. We hypothesized that a chromatin accessibility clock trained at the level of individual cell types, using pseudobulk profiles derived from single-nucleus ATAC-seq (snATAC-seq) data, would be particularly useful for characterizing cell type-specific aging. We focused on the brain, a highly heterogeneous tissue whose diverse cell types age asynchronously, and assessed how well cell type-specific accessibility clocks can predict chronological age, capture rejuvenation from genetic perturbation, and detect age acceleration in age-associated neurodegenerative disease. To this end, we introduce a set of cell type-specific and all-cell aging clocks built from snATAC-seq profiles of the prefrontal cortex (PFC) of 357 human donors (15 to 100 years), which generalize to accurately predict age across brain regions and species. Beyond healthy aging, these PFC clocks captured the rejuvenating effects of SIRT6 overexpression in mouse liver and cell type-specific age acceleration in Alzheimer's disease (AD) and Parkinson's disease, with microglial age acceleration correlating most strongly with pathology among major cell types, and with female oligodendrocytes and OPCs showing the largest sex differences in age acceleration. Interpreting the clocks further revealed the regulatory elements, genes, pathways, and motifs underlying these signals across species, disease, and perturbation, including repression of the NF-kB pathway in SIRT6 transgenic mice, upregulation of immune and inflammatory pathways in severe AD, and conserved age-predictive peaks related to histone regulation, metabolism, and neuronal survival across brain regions and species. Together, these results establish PFC snATAC-seq aging clocks as a generalizable tool that accurately predicts age and captures cell type-specific perturbation effects of rejuvenation and disease on the epigenetic landscape, providing both a means to evaluate perturbations and insight into the epigenetic mechanisms of aging and disease.

genomics↗

bioRxiv2026

Macronutrient Distribution and Protein Secondary Structure of Cool-Season Oats Revealed by Synchrotron-Based Mid-IR Spectroscopy and FTIR Chemical Imaging: Effects of Variety and Steam-Pressure Toasting Duration

This study aims to investigate changes in protein secondary structures (-helix, -sheet, random coils, and -turn) and macronutrient distribution in different cool-season oat varieties and steam-pressure toasting durations using synchrotron-based mid-infrared (Mid-IR) spectroscopy and Fourier Transform Infrared spectroscopy (FTIR) imaging. All oat samples, provided by the Crop Development Center at the University of Saskatchewan, were harvested over three consecutive years (2018, 2019, and 2020). The first experiment compared four oat varieties (CDC Arborg, CDC Nasser, CDC Haymaker, and Summit), while the second examined CDC Nasser oats subjected to steam-pressure toasting (SPT) at 121 for 0, 30, 60, 90, and 120 minutes. FTIR chemical imaging revealed that carbohydrates, proteins and lipids in the four oat varieties were mainly concentrated in the endosperm, aleurone layer and embryo, crease region, and remained unchanged after SPT. Peak-fitting deconvolution of the Amide I band (1700-1600 cm-1) and subsequent quantitative analysis revealed that the four oat varieties exhibited broadly similar protein secondary structure profiles, with statistically significant but subtle variety effects detected for -helix (P = 0.026), -turn (P = 0.047), and the -helix to -sheet ratio (P = 0.048), however, -sheet and random coil proportions did not differ significantly among varieties. In contrast, SPT induced pronounced structural rearrangements, with significant increase in -sheet proportion (P = 0.003) and significant decreases in random coil content (P = 0.026). Notably, 30 minutes of toasting was sufficient to significantly increase -sheet and decrease the random coil contents. These changes are consistent with heat-induced protein denaturation and intermolecular -sheet aggregation, where thermal energy breaks the hydrogen bonds that stabilize the disordered random coil conformation, causing the unfolded polypeptide chains to reassemble into highly ordered -sheet aggregates. After SPT, the peak centers of Amide I and II bands shifted to lower wavenumbers and both bands broadened while their intensities were maintained, reflecting the reorganization of the remaining protein into -sheet aggregates rather than any loss of amide-active protein. These findings suggest that, although genotype has a relatively minor effect on the protein secondary structure of oats, hydrothermal treatments fundamentally reorganize the protein matrix from a disordered to an ordered conformation, which may have implications for protein digestibility, solubility, and nutritional function.

biochemistry↗

bioRxiv2026

Synaptic Development of Fine Spatial Scale Organization of Neuronal Orientation Tuning in Mouse Primary Visual Cortex

Primary sensory cortices often organize neurons with similar stimulus preference into spatially functional maps. Recent work in mouse primary visual cortex (V1) has established that neuronal tuning to the orientation of visual grating stimuli is organized into micro-clusters, where physically close neuron pairs (~ 20 {micro}m) share highly similar orientation preferences, but the organization is unstructured beyond this narrow range. This fine-scale organization is seemingly at odds with the underlying intracortical circuitry in mouse V1 whose spatial extent is an order of magnitude broader (100 ~ 200 {micro}m). In this study, we explore an activity-dependent synaptic plasticity model of spatially structured thalamo-cortical connectivity. We develop theory under asymptotic conditions specific for mouse V1, and derive concrete circuit conditions under which micro-clusters naturally develop. In particular, the recurrent interaction among V1 neurons requires an additional component over a micro-spatial scale, while the spatial profiles of balanced excitation and inhibition support an effective micro-scale interaction. Together, our results provide a developmental mechanism and analytical framework linking thalamo-cortical development, recurrent circuit structure, and the emergence of functional organization in primary visual cortex.

neuroscience↗

bioRxiv2026

METTL1 regulates glioma proliferation through internal m7G methylation of EPHA2

BackgroundMethyltransferase-like 1 (METTL1) is highly expressed in organs like the pancreas but less so in the brain. The METTL1-WDR4 complex catalyzes N7-methylguanosine (m7G) methylation in tRNA, miRNA, mRNA, and rRNA, which impacts RNA stability and function. These modifications affect mRNA translation and tRNA functionality, influencing protein production and cellular activities. Such modifications can regulate tumor growth, invasion, and metabolism by selectively controlling protein expression. MethodGene expression data from public databases were analyzed to compare METTL1 expression in normal and tumor tissues. Western blot (WB) and immunohistochemistry (IHC) were used to quantify METTL1 levels in glioma samples and assess their prognostic significance. Cell viability, migration, invasion, and proliferation were evaluated using Cell Counting Kit-8 (CCK-8), wound healing, Transwell, cell cycle analysis, and colony formation assays. RNA immunoprecipitation PCR (RIP-PCR) identified m7G methylation sites on EPHA2 mRNA, and RNA stability was assessed with actinomycin D. ResultsBioinformatics analysis revealed that METTL1 is overexpressed in gliomas, correlating with poor prognosis. Knockdown of METTL1 significantly affected cell proliferation, migration, and invasion. RNA sequencing (RNA-seq) and m7G analysis identified EPHA2 as a downstream target, influencing the cell cycle via the AKT pathway. RIP and methylated RNA immunoprecipitation (MeRIP) confirmed two m7G sites on EPHA2 mRNA regulated by METTL1. Small interfering RNA (siRNA)-mediated METTL1 knockdown in EPHA2 mutants affected mRNA stability. Rescue experiments restored cell proliferation and AKT pathway gene expression. ConclusionMETTL1 methylates EPHA2 mRNA, enhancing its stability and expression, which activates the AKT signaling pathway and influences glioma cell proliferation. METTL1 could be a potential therapeutic target in glioma treatment.

cell biology↗

bioRxiv2026

Methodological pitfalls in plant pangenome gene family identification may lead to biased evolutionary inferences

Pangenome-level gene family identification often applies sequence similarity clustering without phylogenetic or synteny information, which risks biologically misleading evolutionary inferences. Using five transcription factor families (bHLH, MYB, NAC, WRKY, MADS-box) across 401 rice pangenome accessions, we compared clustering strategies: OrthoFinder alone, cd-hit alone, MMseqs2 alone, and OrthoFinder-informed refinement by cd-hit or MMseqs2. Methods solely based on sequence similarity merged distinct orthogroups and generated fewer orthogroups than approaches incorporating graph-based orthology. Conflicting cluster assignments, measured against OrthoFinder, varied strongly among families, from approximately 14% in MADS-box to approximately 57% in MYB, and were associated with protein length differences. Core, shell, and cloud gene classifications shifted substantially depending on the method, especially in MYB, NAC, and WRKY families. Critically, Ka/Ks distributions for core genes were highly method-sensitive, with orthology-aware methods yielding more convergent and less variable estimates of selective pressure, whereas noncore gene estimates remained robust. These findings demonstrate that neglecting graph-based orthogroup inference inflates methodological artifacts. We recommend a two-step strategy: initial graph-based orthogroup delineation followed by sequence similarity refinement to balance evolutionary accuracy and resolution in pangenome-scale gene family studies.

genomics↗

bioRxiv2026

Machine learning-based Personalized Dietary Recommendations to Achieve Desired Gut Microbial Compositions

Dietary intervention is an effective way to alter the gut microbiome to promote human health. Yet, due to our limited knowledge of diet-microbe interactions and the highly personalized gut microbial compositions, an efficient method to prescribe personalized dietary recommendations to achieve desired gut microbial compositions is still lacking. Here, we propose a machine learning framework to resolve this challenge. Our key idea is to implicitly learn the diet-microbe interactions by training a machine learning model using paired gut microbiome and dietary intake data from a population-level cohort. The well-trained machine learning model enables us to predict the microbial composition of any given species collection and dietary intake. Next, we prescribe personalized dietary recommendations by solving an optimization problem to achieve the desired microbial compositions. We systematically validated this Machine learning-based Personalized Dietary Recommendation (MPDR) framework using synthetic data generated from an established microbial consumer-resource model. We then validated MPDR using real data collected from a diet-microbiome association study. The presented MPDR framework demonstrates the potential of machine learning for personalized nutrition.

bioinformatics↗

bioRxiv2026

Microbiome-mediated lauric acid turnover contributes to colonization resistance against Ralstonia in the tomato rhizosphere

Host-associated lipids shape plant-microbiome interactions, but their microbiome-mediated turnover and role in pathogen exclusion are not fully understood. In tomato, lauric acid (C12:0) occurs across root-associated compartments, and its abundance at the root-soil interface is linked to soil-dependent differences in rhizosphere community composition and Ralstonia solanacearum colonization. Lauric acid promotes Ralstonia motility and virulence in vitro while inhibiting some beneficial lauric-acid-sensitive taxa, especially Gram-positive antagonists. In disease-suppressive soils, rhizosphere communities enriched in lauric-acid-degrading taxa are associated with lower local lauric acid levels and reduced pathogen colonization, suggesting community-dependent buffering of the rhizosphere lauric acid pool alongside other protective microbiome functions. Soil perturbation, microbiome transplantation, gnotobiotic SynCom reconstruction, and isotope tracing provide convergent evidence that microbiome-mediated lauric acid turnover contributes to colonization resistance and helps explain how rhizosphere lipid chemistry influences pathogen invasion outcomes in tomato, revealing an ecologically grounded protective mechanism for pathogen management.

microbiology↗

bioRxiv2026

Prevotella copri alleviates hyperuricemia via uricolysis and gut microbiota reconstruction

Despite the therapeutic promise of uric acid-metabolizing microbes for hyperuricemia, their application is hindered by the scarcity of highly efficacious strains. To identify uric acid-metabolizing microbes, we enriched gut microbiota from cynomolgus monkeys under uric acid-supplemented conditions. This selection increased the abundance of Prevotellaceae_UCG-001 and norank_f_Prevotellaceae, implicating Prevotella species as key uric acid degraders. In vitro validation confirmed efficient uric acid consumption by two new Prevotella isolates and the reference strain Prevotella copri DSM 18205 (P. copri). Oral administration of P. copri in hyperuricemic mice reduced uric acid levels in serum and kidney tissues and alleviated renal fibrosis by suppressing the TGF-{beta}/Smad pathway and downstream fibrogenic genes. Moreover, P. copri administration restored the depleted population of Faecalibaculum, and further assays demonstrated that Faecalibaculum rodentium directly metabolizes uric acid. Collectively, P. copri alleviates hyperuricemia via a dual mechanism: direct uric acid catabolism and enrichment of commensal uric acid-consuming bacteria. These findings establish P. copri as a promising live biotherapeutic agent for hyperuricemia and highlight microbial collaboration as a therapeutic strategy for metabolic diseases.

microbiology↗

bioRxiv2026

In Situ Landscape of Focal Adhesions and Cytoskeletal Integration Revealed by Cryo-Electron Tomography

Focal adhesions (FAs) are dynamic hubs for mechanotransduction, linking the extracellular matrix to actin fibers, intermediate filaments, and microtubules. Using cryo-electron tomography combined with 3-dimensional segmentation and subtomogram averaging, we visualize in situ the structural architecture of the FA environment at the leading edge of human fibroblasts. Our analysis reveals a rich architectural diversity within the FA landscape, where the spatial organisation and interplay of FA protein clusters, actin, vimentin, and microtubules change from the actin bundle core to its tip and across adjacent regions. Notably, we reveal diverse arrangements and connections of vimentin filaments, supporting their multifaceted role in the control and mechanics of adhesions. Together, these findings establish a structural framework for FA maturation and cytoskeletal integration, extending classical lamellipodial adhesion models and providing mechanistic insight into how FAs coordinate force transmission during cell migration.

cell biology↗

bioRxiv2026

Sustained Relief of Chronic Pain via a Nav1.7_Targeting ASO_siRNA Conjugate

Chronic pain affects billions globally, yet safe, long-lasting, and non-addictive analgesics remain lacking. Nav1.7 is a genetically validated pain target, but traditional small molecules have repeatedly failed. Therapeutic oligonucleotides-antisense oligonucleotides (ASOs) and siRNAs-offer selective, durable silencing. We developed N02C0702, an ASO-siRNA conjugate (ASC), achieving robust Nav1.7 knockdown and sustained analgesia without additional delivery vehicles. N02C0702 outperformed individual ASO (N02A114) and siRNA (N02S154) moieties at mRNA and protein levels and in pain relief. In CFA-induced inflammatory pain, a single intrathecal dose exceeded naproxen and suzetrigine, while in SNL neuropathic pain, efficacy persisted up to 56 days, comparable to or surpassing pregabalin. Genome-wide RNA sequencing confirmed minimal off-target effects. N02C0702 highlights Nav1.7 as a key analgesic target and demonstrates the ASC platforms potential for chronic pain and other CNS-related pathologies, offering durable, selective, and safe therapeutic effects.

neuroscience↗

bioRxiv2026

Metabolic control of mitochondrial plasticity and extracellular vesicle biology drives Cryptococcus neoformans virulence

Metabolic adaptation to nutrient stress is a key but poorly understood driver of fungal virulence. Here, we show that a dominant East Asian lineage of Cryptococcus neoformans (VNIa-5), which disproportionately infects immunocompetent hosts, has undergone lineage-specific rewiring of glucose-responsive stress pathways. Integrating population genomics, transcriptomics, and experimental infection models, we demonstrate that VNIa-5s clinical dominance is not explained by environmental prevalence. Instead, selective activation of Snf1 signalling links glucose limitation to mitochondrial tubularisation, extracellular vesicle remodelling, and enhanced melanization. Under low-glucose conditions, VNIa-5 exhibits marked mitochondrial plasticity and extracellular vesicle compositional shifts resembling hypervirulent outbreak lineages of Cryptococcus gattii. Following experimental induction of dormancy, VNIa-5 shows significantly increased virulence in vivo compared with the closely related but clinically rare VNIa-31 subclade, with host survival tightly correlated with mitochondrial morphology. These findings identify metabolic stress integration as a central mechanism shaping cryptococcal virulence and disease in immunocompetent human hosts.

microbiology↗

bioRxiv2026

Untargeted metabolomic profiling reveals mTORC1-dependent regulation of amino acid utilization in lymphatic endothelial cells

The lymphatic vascular system plays essential roles in tissue fluid drainage, dietary fat absorption and transport, and immune cell trafficking. To support these physiological functions, the lymphatic vasculature forms an extensive and highly organized network throughout the body. We have recently discovered that the mechanistic target of rapamycin complex 1 (mTORC1), with RAPTOR as an indispensable component, directs glycolysis and glutaminolysis in lymphatic endothelial cells (LECs) to promote lymphatic vessel formation. However, the role of mTORC1 in regulating LEC metabolism remains incompletely understood. Here, by conducting untargeted metabolomic profiling of control and RAPTOR-deficient LECs, we uncover a global impact of mTORC1 inhibition on amino acid utilization. Specifically, RAPTOR deficiency impairs the conversion of glutamine to glutamic acid, resulting in decreased levels of glutamic acid and aspartic acid, as well as reduced abundance of N-acetyl-glutamic acid and N-acetyl-aspartic acid--two metabolites unexpectedly detected in LECs. Integrated metabolomic and transcriptomic analyses further reveal that impaired glutaminolysis in RAPTOR-depleted LECs is accompanied by an increase in intracellular asparagine, arginine, and metabolites associated with arginine catabolism, potentially driven by upregulation of their respective transporters. In addition, RAPTOR depletion results in abnormal accumulation of branched-chain amino acids (BCAAs) and other essential amino acids primarily involved in protein synthesis. Mechanistically, our data suggest that defective BCAA catabolism and impaired translational control contribute to these metabolic alterations. Collectively, these findings reveal an important role of mTORC1 signaling in coordinating amino acid utilization and suggest that this regulation is critical for lymphatic vessel formation.

cell biology↗

bioRxiv2026

Arabidopsis GLK transcription factors interact with ABI4 to modulate cotyledon greening in light-exposed etiolated seedlings

During seedling etiolation in darkness, the biosynthesis of protochlorophyllide (Pchlide) and the development of etioplasts must be strictly controlled to prevent photooxidative damage upon light exposure. The transcription factors GLK1 and GLK2 are central regulators of chlorophyll biosynthesis and chloroplast biogenesis. Here, we show that GLK1 and GLK2 interact with ABSCISIC ACID INSENSITIVE 4 (ABI4). We reveal that GLKs and ABI4 have antagonistic functions in cotyledon greening of light-exposed etiolated seedlings: compared to the wild type, abi4 mutants, similar to a transgenic line overexpressing GLK2, accumulated more Pchlide in dark-grown seedlings, while glk1 glk2 mutants contained less Pchlide. The high Pchlide levels in etiolated abi4 mutants and GLK2 overexpressors were inefficiently photoreduced upon light exposure, leading to a significant accumulation of 1O2 in the cotyledons after the dark-to-light transition. This corresponded to low cotyledon greening rates and low seedling survival. Additionally, we identified eight PhANGs involved in Pchlide biosynthesis and etioplast development, whose transcript accumulation patterns may contribute to the photobleaching of etiolated abi4 mutants and GLK2 overexpressors. Importantly, the high Pchlide content, low cotyledon greening rate, high 1O2 level and high PhANG induction in abi4 mutant seedlings were fully dependent on GLK1 and GLK2, indicating that ABI4 acts upstream of GLKs. Since ABI4 does not regulate GLK transcript level, and ABI4 physically interacts with GLK proteins, these data suggest that ABI4 inhibits GLK1 and GLK2 activities in etiolated seedlings to prevent high Pchlide accumulation which would lead to high 1O2 levels and seedling death upon exposure to light. Significance statementThe transition from darkness to light is a critical moment that determines seedling establishment and survival. We report here that the transcription factor ABI4 inhibits GLK1 and GLK2 activities during seedling etiolation to repress Pchlide biosynthesis in darkness, thereby limiting singlet oxygen accumulation and seedling death upon exposure to light.

plant biology↗

bioRxiv2026

The MexMAGIC population reveals the genetic architecture of clinal trait variation in Mexican native maize

Defining the genetic basis of local adaptation is fundamental to evolutionary biology and crop improvement. Theory predicts that when selective pressures track differences in the environment, a cline will be established. Such clines might be exploited to uncover adaptive variation by association of alleles with environmental stressors. However, monotonic phenotypic change over a cline is not necessarily mirrored by adaptive genetic variants. Furthermore, population structure can complicate the interpretation of genotype-environment association. To test the assumptions of genotype-environment association in a crop species, we developed a multi-parent advanced generation inter-cross (MAGIC) population using eight Mexican native maize varieties sourced from distinct agroecological zones. We mapped two clinal traits (tassel branching and flowering time) differing in genetic architecture. Variation in tassel branch number was dominated by a single QTL with allele effects that aligned well with a negative elevational cline. In contrast, we mapped 11 flowering time QTL with allele effects that were not consistently correlated with any one source environmental factor and distinct loci donated by highland and lowland early maturing varieties. Our observations support the theoretical result that genotype-environment association will be strongest under simple genetic architecture, although identification of adaptive alleles may still be confounded by population structure. Plain Language: Nine thousand years of careful selection and cultivation by indigenous farmers has generated a rich diversity of native Mexican maize (corn) varieties, grown from sea level to high mountains, and from jungle to semidesert. By crossing native varieties adapted to different locations, we can uncover important genetic variants conferring tolerance to environmental stressors.

genetics↗