Biology subjects

Li, y.

Publications and source records attributed to Li, y..

9 recordsLinked to original sources

bioRxiv2026

RevPert: ranking candidate drivers of transcriptomic state transitions via gallery-native reverse perturbation

Cellular state transitions underlie adaptation, ageing and disease, yet prioritizing catalogued genetic perturbations whose expression signatures match an observed transcriptomic shift remains difficult. Most models predict phenotype from a nominated intervention, whereas genetic inverse benchmarks are largely restricted to within-screen identity recovery. Here we introduce RevPert, a gallery-native reverse perturbation model that ranks a fixed genetic catalog for a query contrast {Delta}Y* = YB - YA by combining signed Pearson connectivity with a learned residual. Across Replogle Essential Perturb-seq (four lines) and LINCS-KO screens (ten lines), RevPert recovered held-out interventions at leading performance relative to matched baselines. Applied to public drug-resistance contrasts in HCC and CML, dual-arm ranking placed pre-specified disease anchors far higher on the expected arms than ranking the same signatures by differential-expression magnitude alone (Essential residual model for HCC; a transductive GWPS residual for CML). RevPert therefore couples within-screen reverse ranking to a screen-external signed-geometry check; the latter calibrates literature anchors and is not claimed as held-out recovery.

bioinformatics↗

bioRxiv2026

An electron-bifurcating hydrogenase-like activity associated with mitochondrial Complex I under hypoxia in vascular plants

Abstract Molecular hydrogen (H2) evolution by higher plants under oxygen limitation was reported more than six decades ago, yet its origin, biochemical basis and physiological significance have remained unresolved. Here, we localize this activity to a mitochondrial, Complex I-associated electron-transfer system. Mitochondrial fractions from etiolated Vigna radiata hypocotyls accumulated H2 under hypoxia and mildly acidic conditions. H2 accumulation was nearly abolished by rotenone and was sensitive to perturbations of Complex II, quinone-pool turnover, terminal oxidases and protonmotive force. NADH-generating substrates and succinate-fumarate supplementation stimulated H2 accumulation, while metabolic profiling linked H2 production to succinate and NADH accumulation. Cumulative H2 production exceeded the measured NADH pool by approximately two orders of magnitude, requiring sustained NADH regeneration. These findings support a division-of-labour model involving coexisting mature Complex I and CI*, a Complex I assembly or remodeling state. TCA-cycle dehydrogenation and succinate-driven reverse electron transfer (RET) through mature Complex I replenish the matrix NADH pool, whereas CI* consumes NADH through an H2-evolving branch, regenerating NAD. This model identifies a potential conditional redox function for CI* and an electron outlet coupled to continuous NADH cycling. Unlike mammalian RET, which can generate pronounced reactive oxygen species (ROS), diversion of reducing equivalents into H2 could attenuate the corresponding ROS signature, potentially helping explain the elusive physiological evidence for plant RET. The data establish a RET-compatible supply route, not direct reverse electron flux. Structural and computational analyses support an FMN-Fe-S branched mechanism whose catalytic steps remain unverified. More broadly, Complex I assembly may not merely build the respiratory machinery; under specific physiological conditions, its intermediate states may reshape electron fate, exposing latent hydrogenase-related chemistry associated with Complex I's evolutionary ancestry.

biochemistry↗

bioRxiv2026

Physics-Guided Deep Neural Networks: Correcting Physical Distortions in Protein Phase Separation Prediction

BackgroundIn computational biology, embedding known physical laws into deep learning models to construct "Physics-Informed Neural Networks" (PINNs) is a mainstream paradigm for enhancing model interpretability and extrapolation capability. However, in complex multi-physics coupling problems, there is a risk of competitive imbalance between the physical term and the flexible artificial intelligence (AI) residual term, causing the model to degenerate into a "black-box" fit and lose the original purpose of being physics-driven. MethodsIn this study, targeting the problem of predicting protein liquid-liquid phase separation (LLPS) behavior in response to environmental factors (temperature, salt concentration), we identified physical distortions, gradient vanishing, and numerical instability in the initial physics-AI hybrid model. Three core correction strategies were proposed: (1) Weight Allocation Logic Reconstruction: Force the physical trunk weight to 1.0 at the output layer, suppressing the AI residual term to the perturbation level of 0.05~0.1, ensuring physics dominance; (2) Robust Physics Formula Construction: Abandon the unstable power function and introduce a combination of Softplus and logarithmic functions to stably simulate the nonlinear effects of charge shielding; (3) Gain Compensation Alignment: Apply gain compensation to the weak signal branch (temperature) to ensure its effective participation in optimization. ResultsThe optimized model maintained a fitting accuracy of R2{approx}0.62 on the test set, while physical consistency was significantly enhanced. The model successfully restored the monotonic increase in solubility with temperature characteristic of UCST-type phase diagrams and correctly captured the nonlinear charge shielding features in the salt concentration response. The weights of key physical parameters (e.g., hydrophobic contribution w_h, net charge contribution w_ncpr) increased from <10-3 to the 10-2 magnitude, demonstrating the reactivation of the physical branch. ConclusionsThe weight control, formula stabilization, and signal gain alignment strategies proposed in this study effectively address the classic problem of "AI hijacking" physics in physics-AI hybrid models. This work provides a universal solution for constructing biophysical predictive models that combine high fitting accuracy with strong physical interpretability.

cell biology↗

bioRxiv2026

AI-Driven Reconstruction of the Research Paradigm for Phase Separation in Membraneless Organelle

Liquid-liquid phase separation (LLPS) of biomacromolecules is a key mechanism driving the formation of membraneless organelles (MLOs) within cells, playing a crucial role in fundamental biological processes such as cell proliferation and stress response. Accurately understanding and predicting the phase separation propensity of proteins is essential for unraveling the assembly mechanisms of MLOs and their functions under both physiological and pathological conditions. Traditional research methods primarily rely on biochemical experiments, which are limited by low throughput, high cost, and difficulty in systematically exploring sequence-phase transition relationships. This study proposes and implements a novel three-stage, iterative paradigm based on artificial intelligence (AI) to propel phase separation research towards systematization, predictability, and mechanistic understanding. O_LIBenchmark Model Construction: A preliminary predictive model was established based on a Multilayer Perceptron (MLP) neural network, and the driving effect of phenylalanine/tyrosine (F/Y) residue-mediated {pi}-{pi} interactions on LLPS was validated. C_LIO_LIModel Robustness Enhancement: The model was optimized through adversarial training strategies, which effectively identified and eliminated misclassifications of "highly disordered non-phase-separating" trap sequences. This significantly improved the models generalization capability and reliability when handling complex, real-world sequences. C_LIO_LIPhysical Mechanism Integration and Functional Expansion: Incorporating the Uniform Manifold Approximation and Projection (UMAP) manifold learning method and constraints from non-equilibrium thermodynamics, a "fingerprint space" capable of characterizing the thermodynamic behavior of phase separation was constructed. This space enables cluster analysis of different MLO types, and the model can output a thermodynamic stability score for protein phase separation. Based on this score, we identified 10 high-confidence candidate proteins with the potential to form novel MLOs. The paradigm established in this study upgrades phase separation prediction from the traditional "binary classification" approach to a novel research framework characterized by "physical mechanism analysis + novel MLO discovery." It provides the phase separation field with a computational tool that combines high accuracy, strong robustness, and good physical interpretability. C_LI

cell biology↗

bioRxiv2026

Beyond Acoustic Cues: Olfactory-Mediated Avoidance of Bats by Crickets

The evolutionary arms race between insectivorous bats and their insect prey is a classic paradigm of acoustic predation and evasion, with insects having evolved sophisticated auditory countermeasures. Both bats and insects also rely heavily on olfaction for key behaviors, such as social communication. Moreover, predator-derived odors are well-established as risk cues in many other predator-prey systems. However, whether olfaction plays a role in the bat-insect arms race remains unknown. Here, we unveil a previously unknown olfactory dimension to this interaction. We demonstrated that the body odor of the insectivorous bat Scotophilus kuhlii triggered robust avoidance and electrophysiological antennal responses in a common cricket prey, Loxoblemmus equestris. We identified limonene as a behaviorally active volatile in bat odor that elicited electrophysiological responses in cricket antennae and was sufficient to elicit avoidance in crickets. Field experiments confirmed that limonene exposure reduced cricket calling activity, demonstrating the ecological relevance of this cue. Our findings establish that insects can detect and initiate avoidance of phylogenetically distant vertebrate predators via olfaction, a process that could be mediated by the elemental perception of individual odor compounds. This work broadens the sensory framework of a classic predator-prey system, and highlights olfactory eavesdropping as a functional strategy in phylogenetically distant predator-prey systems.

ecology↗

bioRxiv2025

To explore the role of NABP2 in the immune microenvironment and metabolic pathway of liver cancer

ObjectiveThis study aims to explore the specific signaling pathways associated with the NABP2 gene and its potential molecular mechanisms influencing the progression of hepatocellular carcinoma (HCC), thereby identifying new therapeutic targets. MethodWe collected 50 normal and 374 tumor samples to investigate the differential expression of NABP2 in HCC. Variousgenomic, single-cell, and bioinformatic analyses, including gene microarray technology, were employed. Data processing and analysis were conducted using R language, focusing on the correlation between NABP2 expression, immune cell infiltration, drug sensitivity, and metabolic pathways. ResultsOur findings indicate that NABP2 plays a significant role in the development of HCC and the tumor immune microenvironment. Analyses such as GSVA and GSEA suggest that NABP2 may influence disease progression through involvement in key molecular signaling pathways, including DNA replication, mRNA splicing, and protein degradation. Furthermore, a notable correlation was observed between NABP2 and various metabolic pathways, as well as specific immune cell populations. Additionally, we identified potential chemotherapeutic agents for HCC based on drug sensitivity analysis. ConclusionThese findings position NABP2 as a crucial biomarker for the progression and treatment of hepatocellular carcinoma, offering new insights into the pathogenesis and prognostic strategies for HCC in future research.

bioinformatics↗

bioRxiv2022

Kindlin-2 inhibits TNF/NF-kB-caspase 8 pathway in hepatocytes to maintain liver development and function

Inflammatory liver diseases are a major cause of morbidity and mortality worldwide; however, underlying mechanisms are incompletely understood. Here we show that deleting the focal adhesion protein Kindlin-2 in hepatocytes using the Alb-Cre transgenic mice causes a severe inflammation, resulting in premature death. Kindlin-2 loss accelerates hepatocyte apoptosis with subsequent compensatory cell proliferation and accumulation of the collagenous extracellular matrix, leading to massive liver fibrosis and dysfunction. Mechanistically, Kindlin-2 loss abnormally activates the tumor necrosis factor (TNF) pathway. Blocking activation of the TNF signaling pathway by deleting TNF receptor or deletion of caspase 8 expression in hepatocytes essentially restores liver function and prevents premature death caused by Kindlin-2 loss.Finally, of translational significance, adeno-associated virus mediated overexpression of Kindlin-2 in hepatocytes attenuates the D-galactosamine and lipopolysaccharide-induced liver injury and death in mice. Collectively, we establish that Kindlin-2 acts as a novel intrinsic inhibitor of the TNF pathway to maintain liver homeostasis and may define a useful therapeutic target for liver diseases.

cell biology↗

bioRxiv2022

The largest haplotype-phased and chromosome-scale genome assembly of the fungal pathogen, Puccinia polysora, by HiFi reads and Hi-C data

Rust fungi are characterized by large genomes with high repeat content and have two haploid nuclei in most life stages, which makes achieving high-quality genome assemblies challenging. Here, we described a pipeline using HiFi reads and Hi-C data to assemble a gigabase-sized fungal pathogen, Puccinia polysora f.sp. zeae, to haplotype-phased and chromosome-scale. The final assembled genome is 1.71 Gbp, with ~850 Mbp and 18 chromosomes in each haplotype, being currently one of the two giga-scale fungi assembled to chromosome level. Transcript-based annotation identified 47,512 genes for dikaryotic genome with a similar number for each haplotype. A high level of interhaplotype variation was found with 10% haplotype-specific BUSCO genes, 5.8 SNPs/kbp and structural variation accounting for 3% of the genome size. The P. polysora genome displayed over 85% repeat contents, with genome-size expansion and copy number increasing of species-specific orthogroups. Interestingly, these features did not affect overall synteny with other Puccinia species having smaller genomes. Fine-time-point transcriptomics revealed seven clusters of co-expressed secreted proteins that are conserved between two haplotypes. The fact that candidate effectors interspersed with all genes indicated the absence of a "two-speed genome" evolution in P. polysora. Genome resequencing of 79 additional isolates revealed a clonal population structure of P. polysora in China with low geographic differentiation. Nevertheless, a minor population differentiated from the major population by having mutations on secreted proteins including AvrRppC, indicating the ongoing virulence to evade recognition by RppC, a major resistance gene in Chinese corn cultivars. The high-quality assembly provides valuable genomic resources for future studies on disease management and the evolution of P. polysora.

genomics↗

bioRxiv2020

RANKL from Bone Marrow Adipocytes Regulates Osteoclast Formation and Bone Loss in Mice

Receptor activator of NF-{kappa}B ligand (RANKL) is essential for osteoclast formation and bone remodeling. Nevertheless, the cellular source of RANKL for osteoclastogenesis has not been fully uncovered. Different from peripheral adipose tissue, bone marrow (BM) adipose lineage cells originate from bone marrow mesenchymal stromal cells(BMSCs). Here we demonstrate that adiponectin promoter-driven Cre expression (AdipoqCre) can target bone marrow adipose lineage cells. We cross the AdipoqCre mice with ranklfl/fl mice to conditionally delete RANKL from BM adipose lineage cells. Conditional deletion of RANKL increases cancellous bone mass of long bones in mice by reducing the formation of trabecular osteoclasts and inhibiting bone resorption but does not affect cortical bone thickness or resorption of calcified cartilage. AdipoqCre; ranklfl/fl mice exhibit resistance to estrogen deficiency and rosiglitazone (ROS) induced trabecular bone loss but show bone loss induced by unloading. BM adipose lineage cells therefore represent an essential source of RANKL for the formation of trabecula osteoclasts and resorption of cancellous bone during remodeling under physiological and pathological conditions. Targeting bone marrow adiposity is a promising way of preventing pathological bone loss.

cell biology↗