Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 181 records · Page 10Linked to original sources

bioRxiv2026

Evolutionary history and genomic vulnerability of the extinct giant deer Megaloceros giganteus

The extinct giant deer (Megaloceros giganteus) was one of the most striking megafaunal species of the Late Quaternary, distinguished by its enormous palmated antlers reaching up to 3.5 m across, the largest known among both living and extinct cervids. Despite its iconic status, little is known about its genomic history prior to extinction [~]8 thousand years ago (kya). We generated the first nuclear palaeogenomes for Megaloceros, represented by nine individuals from Germany ([~]40 kya) and Ireland ([~]11 kya), mapped to a new chromosome-level reference genome of the fallow deer (Dama dama). Phylogenomic analyses placed Megaloceros as sister to Dama (divergence [~]3.5 Ma) and revealed evidence of gene flow with ancestral Cervus lineages. Population analyses identified clear differentiation between German and Irish lineages, with higher genetic diversity in the German individuals. Two genes under strong positive selection, BNIPL and SLC10A7, are associated with apoptosis regulation and skeletal development/bone mineralisation, respectively, and may relate to the species large body and antler size. Demographic reconstructions indicate a long-term decline in effective population size, extremely low heterozygosity, little evidence of extensive runs of homozygosity, and an elevated burden of predicted deleterious alleles. Together, these results suggest that Megaloceros entered the terminal Pleistocene in a genomically fragile state, offering new insight into the biology and evolutionary legacy of one of the largest and most distinctive cervids that ever lived.

evolutionary biology↗

bioRxiv2026

Quantitative Analysis of External Urethral Sphincter Stimulation Parameters for Modulating Urinary Output

ObjectiveNeurogenic lower urinary tract dysfunction (NLUTD) impairs bladder control and remains difficult to treat. We aim to define how electrical stimulation (ES) parameters of the external urethral sphincter (EUS) affect urinary leakage thresholds to guide neuromodulation strategies for NLUTD. MethodsWe performed direct EUS stimulation in anesthetized rats using charge-balanced biphasic pulses while systematically varying current amplitude (0.5-3.0 mA), frequency (20-100 Hz), and pulse duration (0.5-3 ms). Urine leakage thresholds were mapped across the multidimensional parameter space. ResultsStimulation parameters exhibited strong nonlinear interdependence in determining leakage onset. At a fixed pulse duration, higher current amplitudes required lower stimulation frequencies to evoke leakage. Increasing pulse duration substantially reduced both current and frequency thresholds. Age and sex caused modest shifts in absolute thresholds but did not alter the fundamental parameter-response relationships. ConclusionPulse duration, current amplitude, and frequency jointly govern urinary leakage thresholds, with pulse duration serving as the dominant modulator of stimulation efficiency. SignificanceThis work establishes a quantitative framework for charge-efficient stimulation parameter selection, enabling the design of energy-aware, precision neuromodulation protocols and implantable systems for NLUTD rehabilitation.

neuroscience↗

bioRxiv2026

Sphk1-S1P signaling drives blood-brain barrier breakdown after intracerebral hemorrhage via HIF-1α-dependent upregulation of Bsg-MMP-9

Blood-brain barrier (BBB) breakdown is a critical pathological event driving secondary brain injury and poor outcomes following intracerebral hemorrhage (ICH). However, the mechanisms governing acute BBB breakdown after ICH remain incompletely understood. Here we demonstrate that the Sphk1-S1P-S1PR3 signaling plays a pivotal role in this process. Sphk1 expression was significantly upregulated in the perihematomal endothelial cells of both ICH patients and mice, with levels positively correlating with BBB dysfunction severity and poor clinical outcomes. Using endothelial-specific genetic gain- and loss-of-function approaches, we found that Sphk1 knockdown attenuated BBB leakage, reduced hematoma volume and brain edema, preserved tight junction integrity, and improved neurological function at 1-day post-ICH, whereas Sphk1 overexpression exacerbated these pathological features. Mechanistically, transcriptomic profiling of perihematomal endothelial cells revealed that prior to its established role in Nlrp3-mediated pyroptosis, Sphk1 promotes early BBB breakdown by regulating the Bsg-MMP-9 axis. Endothelial-specific Bsg deletion completely abrogated the deleterious effects of Sphk1, confirming Bsg as an indispensable intermediary through which Sphk1 signals to MMP-9. ATAC-seq and dual-luciferase assays further demonstrated that Sphk1-generated S1P signals through S1PR3 to activate HIF-1, which directly binds the Bsg promoter to drive its transcription, ultimately promoting MMP-9-mediated tight junction degradation. These findings delineate a complete hierarchical signaling cascade from metabolic enzyme to transcriptional regulation and subsequent barrier injury, establishing the Sphk1-Bsg-MMP-9 axis as a promising therapeutic target for ICH. One sentence summaryThis work identifies an early and pivotal mechanism of blood-brain barrier breakdown after intracerebral hemorrhage, demonstrating that Sphk1-generated S1P signals through S1PR3 to activate HIF-1, which directly transactivates Bsg expression, leading to MMP-9-mediated tight junction degradation, thereby establishing a novel hierarchical axis with therapeutic potential.

cell biology↗

bioRxiv2026

Novel linkage disequilibrium-based genotype-by-environmental interaction method for genomic prediction of cotton yield and fibre quality traits

Genomic prediction (GP) across diverse environments has a potential to accelerate genetic gain in cotton breeding programs. A major challenge in GP is modelling genotype-by-environment interactions (GEI), which is essential for selecting stable and high-performing genotypes under variable production conditions. However, incorporating GEI into GP models increases the dimensionality and computational complexity, risking complex models that are impractical to use on commercial breeding-scale data sets because of run times and computational demands. This study addresses two primary aims. Firstly, we evaluate the practical benefits of GEI-informed GP for predicting economically important cotton traits. Second, advanced statistical modelling strategies are developed and assessed for integrating genomic and environmental data at scale. We propose a dimensionality reduction approach that combines linkage disequilibrium network analysis with principal component techniques to reduce redundancy while preserving informative variation. Using this reduced dataset, we implement Bayesian linear regression models and, for comparison, deep residual neural networks for genomic prediction. Analyses were conducted on a large multi-environment dataset from the CSIRO cotton breeding program, comprising 3,236 breeding lines, 54 environmental covariates, and 8,049 yield and fibre quality phenotype records collected over 10 years and 9 locations representing 41 year-location combinations. Results demonstrate that generally Bayesian linear regression approaches outperform BG-BLUP models, with all three linear/linear mixed methods providing clearly more reliable performance than the deep learning models. These findings highlight the value of using interpretable statistical models for integrating genomic and environmental information to support selection decisions under diverse environmental conditions.

plant biology↗

bioRxiv2026

Axonal swelling as a neuron-specific compartment to sequester and expel misfolded proteins

Neurons are long-lived, highly specialized cells with extended neurites, requiring precise control of misfolded proteins over time and space. Yet, where misfolded proteins are directed and how quality-control pathways adapt during aging are still unresolved. Here, we identify a neuron-specific quality-control compartment that emerges in neurites as aggresome function declines with age, which we call SolAS (Soluble Misfolded Proteins-induced Axonal Swellings). These structures not only sequester misfolded proteins but also facilitate their clearance via microvesicles and exophers. During aging, neurite SolAS and soma aggresomes function hierarchically to maintain proteostasis, with aggresomes acting as the primary sequestration sites in young neurons and SolAS taking over this role as their function declines with age. This transition is driven by a shift from a ubiquitin-dominant to a SUMO-dominant balance. Moreover, solid pathogenic amyloids, such as GA50, can be converted into soluble forms and sequestered into SolAS via SUMO fusion, thereby reducing neurotoxicity. Our findings identify a previously unrecognized neuronal quality-control pathway critical for proteostasis during aging.

neuroscience↗

bioRxiv2026

Automated Multimodal Correlative Registration for Organelle-Specific Molecular Imaging

Mapping subcellular drug distribution is essential for understanding trafficking and off-target effects. NanoSIMS enables chemical imaging of labeled therapeutics, but signal interpretation requires ultrastructural correlation with electron microscopy, a manual and laborious process. We present an automated AI-driven pipeline for correlating chemical and ultrastructural images, enabling multiscale, organelle-precise imaging of molecules in cells and tissues. The method integrates bidirectional optical flow, confidence-guided affine transformation, and automated template matching for cross-scale EM alignment. Morphology-rich ion channels (e.g., 32S) estimate transformations that propagate to sparse therapeutic signals (e.g., 79Br, 15N), overcoming low signal-to-noise challenges. We validate this framework across diverse cell and tissue types, tracking oligonucleotide and antibody therapeutics in vitro and in vivo to reveal cell-type- and organelle-specific distribution patterns. This work establishes a generalizable platform for automated multimodal registration and organelle-resolved subcellular pharmacology.

bioinformatics↗

bioRxiv2026

Gut microbiota-derived indole-3-propionic acid preserves dorsal hippocampal catecholamines to prevent post-stroke cognitive impairment

BackgroundGut dysbiosis has been increasingly implicated in post-stroke cognitive impairment (PSCI), yet the causal contribution and therapeutic potential of gut microbiota-derived metabolites remain unclear. This study aimed to identify key microbiota-derived metabolites involved in PSCI and to elucidate their underlying mechanisms. ResultsWe found that both PSCI patients and middle cerebral artery occlusion (MCAO) mice exhibited distinct gut microbial alterations, characterized by a marked reduction in tryptophan-metabolizing bacteria and indole-3-propionic acid (IPA), a gut microbiota-derived tryptophan metabolite. Exogenous IPA administration alleviated PSCI-like phenotypes in MCAO mice. Mechanistically, IPA preserved tyrosine hydroxylase-positive (Th) fibers and catecholamine levels in the dorsal hippocampus. Further analyses showed that IPA binds to the adaptor protein Ywhab, promotes ERK activation, and enhances neuronal survival, thereby counteracting neuronal apoptosis-associated inflammation and subsequent Th fiber degeneration. ConclusionThese findings identify IPA as a gut microbiota-derived neuromodulator that mitigates PSCI by preserving dorsal hippocampal catecholaminergic transmission. IPA may therefore serve as a promising predictive biomarker and therapeutic candidate for PSCI.

neuroscience↗

bioRxiv2026

Fibroblast-encoded inflammatory memory orchestrates recurrent skin inflammation via NNMT-dependent metabolic remodeling

Chronic skin inflammation frequently recurs at the same anatomical sites after therapy withdrawal, implying stromal cells may encode local inflammatory memory. Here, we identified nicotinamide N-methyltransferase (NNMT) as a central metabolic-epigenetic regulator of fibroblast inflammatory memory enabling psoriasis relapse. Single-cell and spatial transcriptomics revealed that dermal fibroblasts acquire a persistent senescence-associated secretory phenotype (SASP) during inflammation, maintaining pro-inflammatory niche in resolved skin that supports CD 8+CD 103+ tissue-resident memory T cell (Trm) differentiation. Multi-omics profiling demonstrated that NNMT depletes S-adenosylmethionine (SAM), reduces H 3K 27me3 deposition, and permits sustained AP-1 occupancy at SASP gene promoters. Fibroblast-specific NNMT ablation or pharmacologic inhibition suppressed SASP activity, limited Trm accumulation, and prevented both initiation and relapse of skin inflammation in mice. These findings establish NNMT as a stromal regulator linking fibroblast metabolism to durable epigenetic memory and propose its targeting to erase inflammatory memory and achieve long-term remission in psoriasis and related immune-mediated diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/721223v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@34b2f4org.highwire.dtl.DTLVardef@1d0bb89org.highwire.dtl.DTLVardef@c8277borg.highwire.dtl.DTLVardef@9825ac_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphic Abstract:C_FLOATNO Proposed mechanism through which NNMT-driven fibroblast SASP facilitates the initiation and recurrence of psoriasis C_FIG

immunology↗

bioRxiv2026

Multidimensional Mechanistic Study of Panax Notoginseng Saponins in the Treatment of Alcohol-Induced Osteonecrosis of the Femoral Head: Integrating Network Pharmacology, Molecular Dynamics Simulation and In Vivo Validation

BackgroundAlcohol-induced osteonecrosis of the femoral head (AIONFH) is an orthopedic disorder from chronic alcohol abuse, characterized by disrupted femoral head blood supply, osteocyte death and structural collapse. Current hip-preserving therapy is unsatisfactory, and most patients eventually require total hip arthroplasty. Panax Notoginseng Saponins (PNS), the core active component of Panax notoginseng, exerts pro-angiogenic and anti-osteocyte apoptosis effects, but its specific therapeutic mechanism remains unclear. ObjectiveThis study used network pharmacology, molecular dynamics simulation and animal experiments to identify PNSs active components, core targets and key pathways for AIONFH, verify its in vivo efficacy, and provide a scientific basis for clinical application. MethodsPNS active components, their targets and AIONFH-related targets were screened from databases; intersection targets constructed an interaction network, core targets were screened by three machine learning algorithms, with concurrent GO and KEGG analysis. Molecular docking was performed between core targets and PNS components; Gromacs 2022 conducted 100 ns simulation to evaluate complex stability. AIONFH rat models were grouped with 4-week intragastric intervention; pathology, immunofluorescence and PCR were used for detection. Results and DiscussionNetwork pharmacology identified 127 PNS targets and 18 intersections with 672 AIONFH targets. Six core targets (including FGF2, HSD11B1) were screened; KEGG indicated VEGF pathway as key. Ginsenoside Re bound HSD11B1 with the lowest binding energy (-12.4 kcal/mol), and 100 ns simulation confirmed complex stability. Animal experiments showed PNS improved trabecular structure and regulated osteocyte activity. PNS treats AIONFH via multi-component, multi-target mode, core mechanism being osteocyte apoptosis inhibition. Results and DiscussionNetwork pharmacology screening identified 127 potential targets of PNS, and 18 potential intersection targets were obtained by overlapping with 672 AIONFH-related targets. Six core targets including FGF2 and HSD11B1 were screened out by machine learning, and KEGG analysis indicated that the VEGF pathway and other pathways were the key signaling pathways for PNS action. Molecular docking showed that Ginsenoside Re had the lowest binding energy with HSD11B1 (-12.4 kcal/mol), and 100 ns molecular dynamics simulation confirmed the stable conformation of this complex. Animal experiments demonstrated that PNS could improve trabecular bone structure and regulate osteocyte activity. In summary, PNS exerts a therapeutic effect on AIONFH through a multi-component, multi-target and multi-pathway mode, with the core mechanism of inhibiting osteocyte apoptosis.

pharmacology and toxicology↗

bioRxiv2026

ExMODE: A Multi-Omics Repository for Extremophile Adaptation and Bioprospecting

Extreme environments, though hostile to most life forms, host specialized extremophile communities that have redefined biological cognition and emerged as vital biotechnological resources, with their unique adaptive traits and bioactive molecules driving advances in multiple scientific and industrial fields. However, research on extremophiles is hindered by limitations in culture-based methods, fragmented multi-omics data with non-uniform annotation standards across repositories, the lack of cross-extreme comparative research in existing resources, and the singularity of data dimensionality that neglects key structural information, all of which restrict the functional interpretation of extremophile microbes and the exploitation of their bioprospecting potential. To tackle these challenges, we developed ExMODE (https://db.genomics.cn/exmode/), a comprehensive multi-omics database platform dedicated to extremophiles. It centrally integrates multi-omics data from diverse extreme habitats with a standardized annotation framework, resolving data fragmentation and enabling systematic cross-environment comparative analyses to elucidate extremophile adaptive mechanisms. Moreover, ExMODE aggregates multi-dimensional datasets including genes, genomes, secondary metabolite sequences and protein structures, overcoming the constraints of single-dimensional data and significantly improving the efficiency of biotechnological resource discovery from extreme microorganisms.

microbiology↗

bioRxiv2026

Extracellular Vesicles Enable CircRNA Delivery via in situ Biogenesis and Sorting

Extracellular vesicles (EVs) are promising vehicles for nucleic acid delivery, yet efficient delivery of circular RNA (circRNA) remains challenging due to inefficient loading and limited intracellular expression. Here, we establish an EV-based platform that enables efficient circRNA delivery via in situ biogenesis and sorting. By optimizing intracellular circularization and translation through vector design, we markedly enhance circRNA expression. By combining this with Snu13-mediated EV sorting and enhanced vesicle biogenesis, we achieve efficient packaging of circRNA without compromising vesicle integrity. This integrated strategy enables robust and sustained protein expression following EV-based circRNA delivery. By leveraging this platform, we demonstrate a dendritic cell-targeting circRNA vaccine that elicits strong antigen-specific CD8+ T cell responses and antitumor efficacy. We further show that systemic delivery of BNP-encoding circRNA attenuates doxorubicin-induced myocardial fibrosis. Together, this work establishes a generalizable platform for circRNA therapeutics by overcoming key barriers in circRNA expression and EV-mediated delivery. TeaserEngineered EVs enable efficient circRNA delivery for sustained protein expression and therapy.

bioengineering↗

bioRxiv2026

Reciprocal repulsions enforce heterotypic dendrite segregation in an olfactory circuit

How dendrites of different neurons segregate into discrete spatial domains during neural circuit assembly is poorly understood. Here, using the Drosophila olfactory system, we found that heterophilic interactions between two cell-surface proteins Teneurin-m (Ten-m) and Capricious (Caps) drive dendrite segregation. Ten-m and Caps are expressed in largely inverse patterns across projection neuron (PN) types when PNs are establishing their dendritic territories. Loss of Ten-m in Ten-m+ PNs causes their dendrites to invade Caps+ territories, whereas loss of Caps in Caps+ PNs causes dendrite invasion into Ten-m+ territories. Structure-guided mutations that abolish Ten- m-Caps binding disrupt dendrite segregation, whereas the same mutation on Ten-m preserves its homophilic attraction in a synaptic partner matching assay. These results support a model in which mutual repulsions between two inversely expressed cell-surface proteins drive dendrite segregation into discrete glomerular territories.

neuroscience↗

bioRxiv2026

OneGenomeRice (OGR): A Genomic Foundation Model for Rice

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress has been observed in all-life and human-centric models, plant species, particularly for the staple crops, remains hindered by a lack of models. Here we introduce OneGenome-Rice (OGR), a rice (Oryza sativa) genomic foundation model pre-trained on a genomic dataset comprising 422 high-quality genomes of cultivated and wild rice. OGR is engineered upon a Mixture of Experts (MoE) transformer architecture with 1.25-billion parameters and supports an ultra-long context window of up to 1 million base (Mb) pairs at single-nucleotide resolution. A comprehensive benchmark demonstrated that OGR significantly outperforms existing state-of-the-art multi-plant or all-life genome models in 11 categories (e.g. motif identification, sweep detection, etc). We further demonstrated the utility of OGR in several downstream applications, such as indica-japonica subspecies introgression analysis, identification of agronomy trait-associated functional loci and prediction of gene expression from DNA sequences. These results establish OGR as a promising foundational computational infrastructure for rice functional genomics and precision breeding. The OGR and its fine-tuned models, including pretrained weights, training code and the rice genomic benchmark suit, have been fully opened.

genomics↗

bioRxiv2026

Autoluminescence characteristics of 25 Phalaenopsis orchids following transient transformation with eFBP2

Engineering autoluminescent plants, especially horticultural crops, has recently emerged as a promising research area, with one current approach involving the transgenic introduction of fungal bioluminescence pathway genes. Although autoluminescent plants such as tobacco and petunia have been created, it remains unclear whether all horticultural plants have the potential to be autoluminescent after genetic modification, especially those having flowers with dark colors and thick cuticles. To understand whether, and what if any, floral traits affect autoluminescence potential, we assess the autoluminescence characteristics of 25 representative Phalaenopsis cultivars after transient transformation of fungal bioluminescence pathway genes, alongside several key morphological and biochemical traits. Our results demonstrate that autoluminescence characteristics are correlated with floral color lightness, organ textures and epidermal cell types. In contrast, the content of the substrates of luciferin--caffeic acid and tyrosine, and the infiltration ease of inoculation solution into floral organs after injection, have limited effects on autoluminescence characteristics. Autoluminescence intensity can be reasonably predicted using five floral traits investigated, as 80.4% of variation can be explained by these traits. Our study not only identifies specific Phalaenopsis cultivars with high potential for developing autoluminescent lines but also provides a selection framework applicable to other horticultural crops.

plant biology↗

bioRxiv2026

Development of a Fully Non-Viral 1XX-enhanced BCMA CAR-T Cell Therapy for Multiple Myeloma

Multiple myeloma (MM) is a clonal plasma cell malignancy characterized by bone marrow infiltration, monoclonal immunoglobulin production, and microenvironmental dysregulation that leads to systemic organ damage. The advent of B-cell maturation antigen (BCMA)-directed chimeric antigen receptor (CAR) T-cell therapy has induced unprecedented responses and durability for patients with relapsed/refractory MM. These outcomes are rarely observed with prior salvage strategies, although relapse remains the predominant long-term challenge for most patients. The two currently approved BCMA CAR-T cell products use viral vectors to semi-randomly insert the CAR gene, which results in heterogeneous genomic composition and variability in efficacy, safety, and product consistency. To address these challenges, we integrated targeted CRISPR genome engineering with precise CAR transgene insertion at the T-cell receptor alpha constant (TRAC) locus, 1XX CAR signaling architecture to enhance potency and durability, and non-viral manufacturing with a single-stranded DNA repair template to improve efficiency and yield. This approach confers physiological CAR expression, reduces insertional mutagenesis, and improves persistence by mitigating tonic signaling and exhaustion. Our GMP manufacturing process consistently achieved high CAR integration (37.7-72.7%) and yields across all full-scale runs and met predefined release criteria for identity, purity, safety, and quality. In NSG mouse models of MM, the UCCT-BCMA-1 product exhibited exceptionally potent tumor control, CAR-T cell expansion 100-1000-fold greater than that of lentiviral constructs, and durable clearance of myeloma cells after multiple rechallenges. These findings establish a CRISPR-edited, fully non-viral manufacturing platform for next-generation 1XX-BCMA CAR-T therapies with enhanced persistence, safety, and efficacy. One Sentence SummaryCRISPR-engineered, TRAC-targeted 1XX-BCMA CAR-T therapy with improved safety, potency, and persistence in relapsed and refractory multiple myeloma.

cancer biology↗

bioRxiv2026

A sequence-based proactive intelligence for influenza antigenic profiling improves vaccine strain selection

Seasonal influenza viruses accumulate antigenic changes, eroding population immunity and necessitating recurrent vaccine updates. Hemagglutination inhibition (HI) assays are the standard for measuring antigenic relationships between circulating and vaccine strains; however, their limited throughput constrains the scale and timeliness of surveillance. Here, we present fluProfiler, a foundation-model-based framework that learns a stable mapping from viral sequences to antigenic space and uses this representation to support influenza antigenic prediction, vaccine strain evaluation, and diversity-driven sampling. fluAgPredictor aligns hemagglutinin (HA) and neuraminidase (NA) sequence representations with HI-derived antigenicity, enabling accurate and consistent inference of pairwise antigenic distances across surveillance-aligned evaluation settings. Without prior annotation of antigenic sites, it identifies key residues in immunodominant epitopes and reveals the cooperative contributions of HA and NA to antigenic variation. Building on this antigenic-space representation, fluVacSelector provides antigenic coverage scores that are concordant with World Health Organization (WHO) vaccine recommendations while also flagging potential candidates that may offer broader coverage ahead of formal consultations. fluAgEnhancer further leverages the same representation to prioritize antigenically informative and diverse strains for experimental characterization, achieving comparable predictive accuracy with approximately 25% fewer HI measurements than random sampling. Together, these modules provide a high-throughput and interpretable complement to HI testing, converting routine genomic surveillance into a more proactive, data-driven support system for antigenic monitoring and vaccine strain selection.

bioinformatics↗

bioRxiv2026

A global metagenomic atlas uncovers ubiquitous biosynthetic potential linked to adaptation in extreme environments

Extreme environments impose severe physicochemical stresses that drive microorganisms to evolve specialized survival strategies. Microbial secondary metabolites determined by biosynthetic gene clusters (BGCs) are recognized as important mediators of microbial adaptation to environmental stress. However, their ecological roles, particularly habitat-dependent preferences across different environments, remain poorly understood. Although extreme environments provide opportunities to mine microbiomes for unique adaptations, such research is hampered by a lack of systematic overview of its genomic diversity, BGC diversity, and the relationships between them. Here, we constructed a standardized extremophilic genomic catalogue (SEGC) from 1,462 metagenomic samples spanning seven representative extreme habitats. The catalogue comprised 54,661 metagenome-assembled genomes representing 21,805 species, 66.1% of which were previously uncharacterized. With this catalogue, we identified 162,855 BGCs distributed across 81.5% of MAGs. Gene cluster family analysis showed the strong habitat dependence largely explained by species-level habitat specificity. Terpene biosynthetic pathways illustrated habitat-linked adaptive strategies, with hopan-22-ol biosynthesis enriched in acid mine, deep sea and hydrothermal plume environments, while retinal-based phototrophy predominated in cryosphere and saline-alkaline habitats. Metatranscriptomic analyses supported in situ activity of these pathways. In conclusion, we presented a global atlas of biosynthetic potential across extreme-environment microbiota and revealed habitat-dependent patterns of secondary metabolism linked to microbial survival.

microbiology↗

bioRxiv2026

The Euler Characteristic Transform Enables Classification of Complex Plant Shapes and Prediction of Leaf Venation from Blade Geometry

(1) RationaleQuantifying and predicting plant morphology is central to understanding development and evolution, yet many plant forms lack homologous features required for traditional morphometrics. We apply the Euler Characteristic Transform (ECT), an injective descriptor from topological data analysis, to encode 2D plant shapes. The ECT converts contours into image-like representations that preserve shape information while enabling deep learning. (2) MethodsWe computed ECTs for large datasets of leaf and pavement cell shapes and used convolutional neural networks (CNNs) for classification. We also trained CNNs to approximate the inverse mapping, predicting leaf shape masks from radial ECTs. (3) Key resultsECT-based models achieved high classification accuracy, surpassing previous approaches on millions of herbarium-derived leaves. Notably, grapevine leaf venation was predicted from blade geometry alone, demonstrating that vascular structure is encoded in the outline. (4) Main conclusionThe ECT provides a compact, information-preserving representation of biological shape that integrates naturally with deep learning. It enables both accurate classification and predictive reconstruction, revealing latent morphological information and offering new opportunities to study plant form across scales.

plant biology↗