Biology subjects

Zhang, X.

Publications and source records attributed to Zhang, X..

At least 163 records · Page 9Linked to original sources

bioRxiv2026

Rapid vegetation reorganization in Hengduan Mountains driven by high climatic oscillations

The accelerating global climate change has been triggering large-scale vegetation reorganizations, yet our understanding of how mountain ecosystems respond to rapid climatic oscillations is critically constrained. Here, we present a high-resolution palynological record from Erhai Lake, southwestern China, revealing eight episodes of rapid vegetation reorganizations in the Hengduan Mountains (HMs) over the past 35,200 years. These reorganizations closely tracked the rhythms of rapid climate oscillations, particularly during the Last Glacial Maximum and the Last Deglacial Period. We find that while the timing of rapid vegetation reorganizations were synchronous with Atlantic Meridional Overturning Circulation (AMOC) anomalies that modulated global climate variability, the magnitude of these reorganizations did not exhibit a linear correlation with AMOC strength; instead, they were governed by local heat and moisture availability mediated through teleconnections. This demonstrates a strong natural regulatory capacity of mountain vegetation in HMs, enabling resilience to intense climatic fluctuations. However, when using the Erhai record as a benchmark, we project that rapid reorganizations under the high-emission pathway (SSP585) will likely surpass the intensities observed during historical events. These findings reveal the high climatic sensitivity and strong natural regulatory capacity of mountain ecosystems, highlighting the critical necessity of climate mitigation actions and nature-based solutions to safeguard subalpine and alpine biodiversity against unprecedented future climate change.

ecology↗

bioRxiv2026

ComplexDesign: sequence-hallucination design of protein binders bridging multiple proteins

MotivationDesigning multichain protein complexes requires coordinating the folding of component proteins with the formation of their interfaces. The existing methods, however, remain limited in their ability to satisfy these requirements simultaneously, especially for trimeric and tetrameric complexes. As an important practical scenario, designing a binder that bridges two target proteins into a ternary complex requires flexibility in the relative arrangement of the two targets, adding an additional challenge to existing design methods. ResultsWe present ComplexDesign, a hallucination-based approach for multichain protein design. ComplexDesign performs structure-prediction-guided sequence optimization to simultaneously fold each protein chain and form inter-chain interactions that bind them together. To provide the flexibility required to appropriately arrange these target proteins, ComplexDesign introduces a specialized masking mechanism that enables exploration of possible relative arrangements rather than being limited to the predefined ones. Across a comprehensive set of benchmarks with various chain lengths, ComplexDesign outperformed existing methods in the unconditional design of dimers, trimers, and tetramers, achieving a high design success rate exceeding 50%, supporting its capability for multichain complex design. Furthermore, in the case of multi-target binder design, ComplexDesign produced high-confidence, self-consistent ternary complexes for 8 out of 10 target pairs. These results establish ComplexDesign as an effective tool for multichain protein design, with particular utility for designing binders that bridge two target proteins. Availability and implementationThe source code of ComplexDesign will be made publicly available upon publication.

bioinformatics↗

bioRxiv2026

Structural and Dynamic Basis of TREM2--DAP12 Stabilization by Small-Molecule Agonist VG-3927

Triggering receptor expressed on myeloid cells 2 (TREM2) is an important microglial receptor implicated in Alzheimers disease, but the structural basis of small-molecule TREM2 agonism remains poorly understood. In particular, the clinical-stage agonist VG-3927 has been reported to promote TREM2-DAP12 complex formation, suggesting a mechanism distinct from conventional recognition at the ectodomain surface. Here, we propose that VG-3927 binds within a membrane-embedded interfacial cavity formed by the transmembrane helices of TREM2, DAP12A, and DAP12B. Our simulations support a stable and reproducible cavity-bound pose that is maintained across independent trajectories and is anchored by a persistent hotspot network dominated by hydrophobic and van der Waals interactions. Rather than merely occupying a pre-existing pocket, VG-3927 reshapes the surrounding transmembrane assembly by reinforcing selected interhelical contacts, strengthening receptor-adaptor hydrogen-bond coupling, and biasing the three-helix bundle toward a more compact, more upright, and more conformationally focused state. Importantly, the ligand-bound ensemble remains within a subdomain of the apo conformational landscape, consistent with stabilization of a pre-existing but less populated signaling-relevant state. Together, these findings support a membrane-asscosiated mechanism in which VG-3927 acts as a transmembrane interfacial molecular glue, biasing the TREM2-DAP12 complex toward a more compact, ordered, and signaling-competent state. This finding can provide a structural framework for understanding small-molecule TREM2 agonism.

Molecular Biology↗

bioRxiv2026

Label-free Pathogen Identification with Microscopy Imaging and Deep Learning

Rapid and accurate pathogen identification is crucial for the clinical management of infectious diseases, particularly sepsis and severe respiratory infections, yet standard clinical workflows remain slow and resource-intensive. Here, we developed an automated, high-throughput imaging platform built on standard, clinically accessible bright-field microscopy, and generated a large dataset comprising 24.9 million label-free bacterial cells across six focal pathogens. Leveraging this resource, we trained a neural network (ESKAPe-ResNet) to identify ESKAPe species at the single-bacterium level. The model achieved >92% accuracy in species-level classification and >82% accuracy in quantifying ESKAPe abundance in mock mixtures, with high specificity against non-ESKAPe bacteria. In clinical validation using sputum, bronchoalveolar lavage fluid and blood samples from patients with respiratory infections and sepsis, the approach correctly identified the dominant ESKAPe pathogen in >78% of samples after minimum broth culture enrichment. The imaging-to-identification pipeline was completed in under 10 minutes, and coupled with brief cultivation, the median time to accurate identification was reduced to 5-6 hours, compared with days for conventional blood culture-based workflows. This work establishes the proof-of-principle for label-free, hardware-minimal rapid pathogen identification, providing a clinically deployable workflow to expedite diagnosis and reduce mortality in severe bacterial infections.

microbiology↗

bioRxiv2026

Killer-cell dominance dichotomy governs tumor immune networks and stratifies inflamed cancers

Cancer immunotherapy benefits remain limited, even among "hot" tumors with high killer lymphocyte infiltration. Here, we investigated the population-level architecture of killer cells based on nearly 5,000 pan-cancer scRNA-seq samples, together with orthogonal validation by spectral cytometry and spatial transcriptomics. Unlike the prevailing "hot-cold" paradigm, which assumes coordinated infiltration of multiple cytotoxic lineages, we uncovered a conserved framework wherein terminal cytotoxic immunity in individuals or malignancies diverges into states dominated by either exhausted CD8+ T cells (Tex) or CD56dimCD16hi NK (NK1) cells. Despite the complexity of the tumor microenvironment, Tex-NK1 divergence governs the primary axis of tumor-intrinsic and tumor-extrinsic variance. Distinct from the conventional view that NK cells positively contribute to immunotherapy efficacy, NK1-skewed tumors, although highly cytolytic, are refractory to current immune checkpoint blockade regimens. This killer divergence defines a foundational axis of cancer immunity and provides a resource for prioritizing next-generation targets for NK-directed immunotherapy.

immunology↗

bioRxiv2026

Deep Learning-Based Reconstruction: Model Comparison for Variable-Density GRAPPA 1H MRSI

Proton magnetic resonance spectroscopic imaging (1H MRSI) enables quantitative mapping of brain metabolites, but its clinical use remains limited by long acquisition time. The goal of this work to improve the applicability of high-resolution 1H FID-MRSI at 7T by enhancing GRAPPA-based acceleration through deep learning-driven k-space reconstruction. In particular, compared with conventional GRAPPA, MultiNet PyGRAPPA enables substantially higher in-plane acceleration while suppressing residual lipid aliasing and preserving metabolite map fidelity in non-lipid-suppressed MRSI. Building on the MultiNet PyGRAPPA framework, we introduce a comprehensive comparison of advanced machine-learning models for predicting missing k-space points. Multiple architectures--including multilayer perceptrons, convolutional neural networks, and several U-Net variants--were trained within a variable-density k-space undersampling scheme to support acceleration factors of R = 4, 6, and 7. The proposed U-Net model extends the MultiNet concept by leveraging nonlinear hierarchical feature extraction, thereby improving reconstruction fidelity while maintaining robustness to noise.The methods were evaluated in vivo using retrospectively undersampled 7T 1H FID-MRSI datasets from healthy volunteers and patients. Quantitative analyses demonstrate that the U-Net outperforms the original MultiNet approach, offering improved SNR retention rate, reduced lipid RMSE, and higher structural similarity of major metabolites. Metabolite maps reconstructed with the U-Net showed reduced lipid artifacts and improved anatomical consistency. In conclusion, integrating deep convolutional networks into GRAPPA-based k-space prediction provides a more reliable and higher-fidelity reconstruction pipeline. When combined with variable-density undersampling, this approach enables faster acquisition of high-resolution 1H MRSI without compromising spectral quality or metabolite quantification.

bioengineering↗

bioRxiv2026

A missense mutation in FBXL3 links circadian regulation to out-of-season estrus in sheep

Seasonal breeding is a remarkable adaptive trait, but it constrains efficient production in the sheep industry. Recent studies have shown that seasonal breeding is associated with endogenous circannual rhythms, which are regulated in part by the circadian clock system. FBXL3, a pivotal component of the SCF (SKP1 - CUL1 - F-box) E3 ubiquitin ligase complex, is a known determinant of the mammalian circadian period. In this study, we identified a missense mutation, T183M, in FBXL3 through selective sweep analysis. The allele frequency of this mutation differed significantly between sheep breeds exhibiting year-round estrus and those showing seasonal breeding patterns. The association between the T183M mutation and seasonal breeding was further validated using an ovariectomized, estradiol-implanted sheep model. We then generated mice carrying the homologous T183M mutation and found that they exhibited significantly lengthened circadian periods, accompanied by reduced CRY1 expression and increased CLOCK expression. Co-immunoprecipitation assays confirmed that the mutation reduced the interaction between FBXL3 and CRY1. These findings demonstrate an evolutionarily conserved role of FBXL3 in the circadian clock system. We propose that the T183M mutation disrupts day-length recognition, thereby influencing seasonal estrus in sheep. Author summarySeasonal breeding limits sheep productivity and is regulated by circadian rhythms, yet the key genetic determinants remain poorly understood. Here, we identified an FBXL3 T183M missense mutation whose allele frequency differed markedly between year-round-estrous and seasonally breeding sheep, and validated its association with seasonal reproduction in a sheep population. Functional analyses in mutant mice showed that this variant lengthened the circadian period, disrupted the expression of core clock genes, and weakened the interaction between FBXL3 and CRY1. These findings suggest that the FBXL3 T183M variant impairs day-length perception, thereby modulating seasonal estrus in sheep. Our study reveals a conserved circadian mechanism underlying seasonal breeding and highlights FBXL3 T183M as a promising genetic target for improving reproductive performance in sheep.

genetics↗

bioRxiv2026

Buffered sweat microfluidics with AI-enabled translation for clinically actionable blood urea estimation and renal risk stratification

Kidney-function assessment relies on blood urea as a clinically informative metabolic marker; however, its dependence on venipuncture and centralised laboratory testing limits high-frequency monitoring and delays timely clinical intervention. Here, we report an integrated platform combining a wearable buffered microfluidic patch with a physiology-informed, data-driven calibration framework for real-time, non-invasive estimation of blood urea from microlitre-scale sweat volumes (4.79 L). By precisely regulating the release kinetics of internal buffer salts, the device stabilises the local reaction microenvironment, mitigating variability in sweat pH and flow to ensure reproducible measurement. The resulting signals are processed through an artificial intelligence (AI)-enabled analysis pipeline that integrates sweat urea with patient-specific physiological information to generate clinically interpretable outputs. In multicentre studies, sweat urea shows a strong association with blood urea across diverse cohorts, but with nonlinear and time-lagged relationships that limit direct use. The AI-enabled calibration model compensates for these effects, enabling high-fidelity estimation of blood urea (r = 0.945 versus gold-standard measurements) at clinically relevant concordance levels. The platform further identifies kidney injury with 89.1% accuracy and stratifies disease severity with 83.2% accuracy. Notably, these results demonstrate that the integration of physicochemical stabilisation and AI-enabled data-driven translation establishes sweat as a clinically actionable surrogate for renal monitoring, supporting population-level estimation and highlighting the potential for personalised longitudinal assessment, and enabling a scalable, non-invasive strategy for high-frequency kidney disease management.

biophysics↗

bioRxiv2026

Multi-stage physics-informed neural networks for JAK--STAT5 signaling and ultradian insulin--glucose dynamics: latent-species identifiability and suppression of parameter-induced divergence

Coupled diffusion-reaction partial differential equations (PDEs) describe biochemical network dynamics but are difficult to solve for realistic multi-species systems without combining mechanism and data. We present a multi-stage physics-informed neural network (PINN) for multi-species diffusion-reaction PDEs and apply it to two ordinary-differential-equation (ODE) reference systems: the Boehm et al. JAK-STAT5 signaling pathway and the Sturis ultradian insulin-glucose model. For STAT5 we pose a latent-species identifiability test: given sparse observations of eight species, a ten-species model that retains two deliberately withheld but mechanistically standard components--an active receptor-JAK complex and the SOCS negative-feedback inhibitor--recovers the reference trajectory and reduces mean root-mean-square error 3.1-fold relative to an eight-species model that omits them, whereas a PDE-only solution without data anchoring diverges. Because the reference is itself ODE-generated, this demonstrates identifiability against synthetic data, not the discovery of new biology. For the insulin-glucose model the same framework reproduces the [~]120-minute oscillation to 1.0% mean relative error as a benchmark on a stiff, multi-timescale oscillator; its spatial dimension is treated as a numerical construct, not a physical transport setting. A Lyapunov analysis of the STAT5 ODE returns a maximal exponent statistically indistinguishable from zero ({lambda}max {approx} 3.61 x 10-5 min-1, 5/8 trials positive; Lyapunov time [~]1.9 x 104 min, far exceeding the 240-720 min horizon), so the system is effectively non-chaotic and the relevant instability is a bounded, parameter-induced trajectory divergence. Anchoring the solution to baseline data suppresses this divergence, with the reduction growing monotonically with sampling density--from [~]15-19% at eight time points to [~]88-97% at sixty-four, depending on perturbation magnitude. The framework thus offers a data-anchored route to latent-species identifiability and divergence suppression in biochemical ODE/PDE systems, demonstrated here against synthetic reference data. Inside cells, a three-dimensional chemistry of diffusing, reacting molecules drives signaling and rhythm--dynamics that, for realistic networks, strain conventional solvers. Here a multi-stage physics-informed neural network--machine learning constrained by the governing equations--solves stiff, multi-species reaction systems from sparse data. In the JAK-STAT5 signaling pathway, a model that retains two standard but unobserved components (an active receptor complex and a negative-feedback brake) recovers a reference trajectory that a reduced model cannot--a controlled test of whether sparse data can pin down withheld pecies, not a claim of new biology. The same framework reproduces the roughly two-hour insulin-glucose rhythm to within 1% as a benchmark on a stiff oscillator. And anchoring the solution to a few dozen baseline measurements collapses parameter-induced trajectory divergence, turning a parametrically sensitive simulation into a stable one. Where mechanism and data meet, sparse measurements can constrain the structure a model would otherwise leave undetermined.

biochemistry↗

bioRxiv2026

OmicOS: A Comprehensive Omics Ecosystem Infrastructure and Agent System for the AI Era

Biology has accumulated a vast ecosystem of omics methods, but much of this ecosystem remains built for expert humans rather than scientific agents. Methods are scattered across Python packages, R/Bioconductor and CRAN workflows, command-line tools, incompatible data containers and implicit object states, making even routine analyses difficult for an AI system to choose, execute and verify reliably. Here we introduce OmicOS, a comprehensive omics ecosystem infrastructure and agent system that turns OmicVerse V2, an open-source omics community, into an executable foundation for agentic biology. OmicVerse V2 provides the community substrate: scalable AnnDataOOM-compatible rust backends, agent-friendly Python algorithms for single-cell, spatial, bulk and multi-omics analysis, interfaces to single-cell foundation models, and Python-native reconstructions of historically R-centred Bioconductor/CRAN-style workflows. OmicOS makes this substrate actionable by registering analytical functions as state-aware capability contracts, allowing agents to inspect live data objects, select valid methods, execute controlled workflows and record provenance. The result is not a fixed pipeline, but a programmable omics environment in which agents compose real analyses from verified community methods rather than inventing tools. Across external and purpose-built benchmarks, OmicOS ranked first among the evaluated systems, reaching 81.2% on BiomniBench. Adding OmicVerse to a minimal agent improved task completion by up to 34.2 percentage points with qwen-3.6-35b, and controlled ablations showed that the gains came from registry-grounded execution rather than from larger models, documentation retrieval or unrestricted tool exposure. The same infrastructure scaled to atlas-sized data, reproduced R-centred workflows in Python and converted external pathology software into agent-usable skills. In a discovery task starting from a whole-body spatial map and the term "Alzheimers disease", OmicOS composed a non-canonical workflow that integrated spatial expression, genetic association, eQTL and colocalization evidence to nominate a colon epithelial risk axis centred on PICALM, CD2AP and CR1. Together, OmicVerse and OmicOS define an open foundation for AI-era omics, showing how a community of biological methods can be transformed into a reliable, extensible and agent-operable system for discovery. HighlightO_LIOmicVerse 2.0 consolidates 694 methods spanning 11 omics domains into agent-callable high-level APIs. C_LIO_LIRebuildR automatically reconstructs and evolves R/Bioconductor methods as Python-native implementations under output-equivalence gates. C_LIO_LIOmicOS establishes a state-of-the-art omics agent harness, ranking first on general omics benchmarks across models and substantially improving the analytical capability of local open-source models. C_LIO_LICompositional use of ecosystem modules nominates a colon epithelial axis associated with Alzheimers disease risk. C_LIO_LIExternal algorithm packages supporting automatic iterative evolution can be integrated into the OmicOS ecosystem. C_LI

bioinformatics↗

bioRxiv2026

KCNQ2/3 Regulates the Synaptic Remodeling Associated with Drug Seeking in Addiction

Drug-seeking behavior during withdrawal represents a critical obstacle to addiction treatment. In the nucleus accumbens, hyperactive dopamine D1 receptor-expressing medium spiny neurons (D1R-MSNs) promote cocaine-seeking through aberrant synaptic remodeling, including synapse formation and calcium-permeable AMPA receptor (CP-AMPAR) insertion. However, the intermediate molecular control mechanism remains unclear. We identified KCNQ2/3 potassium channels as key regulators of synaptic pathology during withdrawal. Cocaine-conditioned mice showed increased spine density, enhanced surface CP-AMPAR, and elevated neuronal activity 14 days after withdrawal. These phenotypes were reversed by repeated administration of KCNQ2/3 openers or D1R-MSN-specific expression of constitutively active KCNQ2. Functional restoration of KCNQ2/3 suppressed cocaine-seeking behavior and normalized D1R-MSN excitability. These findings suggest that sustained KCNQ2/3 deactivation drives synaptic remodeling during withdrawal, while channel activation offers a potential therapeutic strategy. Furthermore, the results position KCNQ2/3 as a master regulator of drug-seeking behavior and channel activation in D1R-MSNs as a logical target for relapse prevention in addiction.

neuroscience↗

bioRxiv2026

Modeling CISH-KO TIL therapy: T-cell persistence and endogenous competition determine response in gastrointestinal cancer

BackgroundTumor-infiltrating lymphocyte (TIL) therapies have shown promise in murine models; however, early-phase clinical trials reveal substantial heterogeneity in treatment response across patients. Elucidating the biological drivers of this variability is critical for improving patient selection and therapeutic design. MethodsWe developed a mechanistic mathematical model of Cish-inactivated TIL therapy capturing interactions among tumor cells, endogenous TILs, and infused CISH-KO (Cish gene knockout) T-cells. The model was calibrated to longitudinal tumor size data from two murine studies and also fitted to clinical data from a first-in-human phase 1 trial enrolling 22 and treating 12 patients with metastatic gastrointestinal cancers (27 independently tracked tumor lesions). We used the calibrated model to identify patient-level determinants of response and to simulate combination and fractionation treatment strategies in silico. FindingsFour mechanistically distinct parameters emerged as dominant determinants of therapeutic outcome: CISH-KO T-cell persistence (r = 0.827, p < 10-4) was the single strongest predictor of therapeutic failure; other major determinants included endogenous T-cell proliferation rate (r = 0.648, p < 10-4) , CISH-KO T-cell killing rate, and intrinsic tumor growth rate. In silico simulations predicted that the addition of anti-PD-1 checkpoint blockade to standard-dose CISH-KO TIL therapy achieved comparable or superior tumor control relative to dose doubling, while maintaining lower peak T-cell level. Additionally, fractionating the same total dose across multiple delayed infusions further improved predicted tumor control by prolonging CISH-KO T-cell persistence, again without raising peak effector burden. InterpretationThe efficacy of current CISH-KO TIL protocols is influenced not only by the cytotoxic potency of infused T-cells, but also by their persistence within the tumor microenvironment and competitive pressure from the reconstituting host immune compartment. These findings support strategies that enhance the functional efficiency and prolong the availability of infused T-cells--such as concurrent PD-1 blockade or fractionated dosing of the same total cell product--rather than dose escalation alone and identify CISH-KO T-cell persistence and endogenous immune competition as actionable targets for improving TIL-based immunotherapies.

Cancer Biology↗

bioRxiv2026

DigiMus: a connectome-informed spiking framework for multi-region mouse neural-behavior modeling

Computational models are increasingly used to relate mouse brain structure, neural activity and behavior, but most models still learn from task data with limited constraints from biological circuit organization. Here we present DigiMus, a connectome-informed spiking framework for multi-region-capable mouse neural-behavior modeling. DigiMus combines leaky integrate-and-fire spiking dynamics with brain-region-specific motif regularization in a trainable sequence-modeling architecture, allowing directed three-node circuit motifs derived from 38,481 reconstructed neuronal morphologies across approximately 50 brain regions to guide recurrent coupling during learning. We evaluate DigiMus on 18 rule-based cognitive tasks spanning sensorimotor mapping and perceptual decision-making, and on three mouse neural decoding datasets involving auditory discrimination, fixed-interval licking and visual decoding. Across synthetic tasks, DigiMus showed stable performance relative to TCN, LSTM and Transformer baselines, with stronger advantages in more complex decision-making settings. In real neural datasets, single-region instantiations of DigiMus produced small, consistent and dataset-dependent improvements over a structure-free sequence baseline, while retaining motif-prior signatures in trained connectivity. Internal state analyses further linked task-dependent state dynamics to behavioral error patterns. These results suggest that connectome-derived structural priors can shape neural sequence models, and establish DigiMus as a modular, connectome-informed workflow for mouse neural-behavior modeling and hypothesis generation, rather than a complete digital reconstruction.

neuroscience↗

bioRxiv2026

Chromosome gigantism and auxin deconjugation underpin gall induction in a horned gall aphid

Insect galls are extended phenotypes built through sustained reprogramming of host development, yet the genomic innovations and effector mechanisms that enable this process remain poorly resolved. Here we generated chromosome-level genomes for the horned gall aphid Schlechtendalia chinensis and its host tree Rhus chinensis, and integrated these resources with morph-resolved salivary proteomics, metabolomics and functional assays. The aphid genome contains an anomalously expanded chromosome 1 spanning 93.61 Mb that carries 33.08% of anchored genes and concentrates 70.47% of genome-wide segmental duplications, together with elevated transposable-element load and multiple tandem gene clusters. We identified a salivary M20-family enzyme, ACY1, that consistent with an auxin-conjugate hydrolase. Loss- and gain-of-function assays show that ACY1 hydrolyzes stored, inactive auxin-amino acid conjugates in host tissue, increasing local active auxin levels. This hormone manipulation reprograms plant cell development to form galls. Our findings demonstrate how concentrated genomic duplication can drive the evolution of an effector that controls host physiology, suggesting a potential principle in the evolution of parasitic traits.

molecular biology↗

bioRxiv2026

Low-Energy Polishing Facilitates Breaking the Resolution Barrier of In Situ Cryo-EM

In situ structural analysis allows direct visualization of protein structures in their native cellular environments, but near-atomic resolution in cellular lamellae has been significantly limited to exceptionally large complexes such as ribosomes. A key factor underlying this limitation is the degradation of data quality of thin lamellae caused by substantial subsurface damage from cryo-focused ion beam (cryo-FIB). Here, we developed a cryogenic low-energy polishing in FIB approach, which reliably produces thin lamellae with low damage across different cell types. This advance, combined with in situ single particles analysis, has pushed down the molecular weight lower limit for in situ reconstruction at near-atomic resolution to [~]400 kDa. We demonstrate this by resolving photosynthetic complexes (3.4 [A] and 3.3 [A]), metabolic enzymes (3.3 [A]), chloroplast ribosome (4.0 [A]) and respiratory chain complexes (3.7 [A]) from Chlamydomonas reinhardtii. Furthermore, the efficient workflow enables rapid structural feedback upon changes in cellular states, offering a practical way to perform multi-condition in situ structural analysis.

biophysics↗

bioRxiv2026

Ca2+-activated CKL3 phosphorylates nucleoporin 58 to reprogram nuclear transport and execute effector-triggered immunity

Recognition of pathogen effectors by nucleotide-binding leucine-rich repeat receptors (NLRs) activates effector-triggered immunity (ETI) in plants through resistosome formation, which generates a sustained cytosolic Ca2+ influx and ultimately leads to programmed cell death (PCD) at infection sites and resistance. However, the mechanisms linking Ca2+ influx to ETI execution remains a major knowledge gap. Using TurboID proximity labeling with the central transport channel nucleoporin 58 (Nup58) as a probe, we show that, instead of deregulation, ETI induction restricts general nuclear trafficking while selectively enhancing nuclear import of defense-related proteins. This switch is driven by elevated cytosolic Ca2+, which binds to the conserved Asp-149 in CASEIN KINASE 1-LIKE 3 (CKL3), promoting its interaction with and phosphorylation of Nup58 at Ser-149, thereby altering nuclear pore selectivity. This study identifies CKL3 and Nup58 as key regulators of ETI by establishing a mechanistic link from resistosome-mediated Ca2+ influx to nuclear transport reprogramming and immune execution.

plant biology↗

bioRxiv2026

X-Gutor: a high-fidelity in vitro platform for long-term gut microbiome recovery

In vitro gut microbiome-culturing models are essential for studying host-microbe interactions, yet achieving high microbial recovery and long-term stability remains a long-standing challenge. Here we devise X-Gutor, an ecologically designed in vitro platform that recapitulates the monogastric digestive system with precise control of pH, redox potential, and a mucin-based biofilm matrix. Using an integrated assessment framework, microbial source tracking, a curated gut metabolism database (GutDB), and short-chain fatty acid profiling, we benchmarked gut community recovery. Building on this platform, HanGutor and PigGutor were developed to recover 96.27{+/-}4.92% and 90.48{+/-}5.95% of human and swine gut microbiota, respectively. Ecological fine-tuning revealed that redox potential drives the Prevotella-Bacteroides trade-off and that spatial biofilm structure suppresses cheater (Succinivibrio) growth. The X-Gutor provides a scalable, modular platform for gut microbiome engineering, dietary intervention, and microbiota-targeted therapy development.

microbiology↗

bioRxiv2026

Procedure-Matched Comparison of TNBS/Ethanol Enema and DSS Free-Drinking Models of Ulcerative Colitis in Rats: Immune Endotype Profiling and a Translational Model-Selection Framework

ObjectiveChoosing between TNBS/ethanol enema and DSS free-drinking water protocols for establishing rat ulcerative colitis(UC) models remains a practical challenge in preclinical research. Beyond confirming that both methods induce colitis, no prior study has quantified the independent contribution of enema procedural trauma to inflammatory readouts, reported sex-stratified cytokine responses, or formally linked model-specific immune profiles to human UC endotypes. MethodsTen SPF-grade SD rats(half male and half female) were allocated to a normal control, a procedure-matched saline enema control(anesthesia and enema without chemical agent), a TNBS group(100 mg/kg TNBS + 50% ethanol enema), and a DSS group(3% DSS free-drinking, 7 days). Endpoint assessments included body weight, colon length, macroscopic and histological scoring(modified Macpherson criteria, 0-10), and serum TNF- and IL-6 by ELISA. Post-hoc exploratory analyses included sex-stratified cytokine sub-analysis and coefficient of variation(CV%) for each readout. ResultsBoth model groups successfully induced UC(P<0.05 vs. controls for all endpoints). Critically, the procedure-matched saline enema control confirmed that colonic inflammation in the TNBS group was attributable to the chemical agent rather than mechanical trauma. TNBS induced significantly higher TNF-(90.2{+/-}10.5 vs. 73.5{+/-}9.8 pg/mL, P<0.05) and IL-6(66.5{+/-}8.3 vs. 53.2{+/-}7.6 pg/mL, P<0.05) than DSS,consistent with Th1-polarized vs. innate barrier-disruption immune endotypes, respectively. Coefficient of variation analysis indicated greater intra-group variability in TNBS versus DSS, reflecting the procedural dependency of enema-based models. ConclusionA procedure-matched control design is essential for valid TNBS-vs.-DSS comparisons and has been systematically underutilized in prior studies. Based on immune endotype alignment, TNBS is recommended for screening T-cell-targeted therapies and acute anti-inflammatory agents, while DSS is recommended for epithelial barrier repair and microbiome-modulating interventions. A model-selection decision framework derived from these data is provided to guide preclinical study design.

animal behavior and cognition↗