Biology subjects

Lu, Q.

Publications and source records attributed to Lu, Q..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Molecular Arms Race: Tannin Biosynthesis and Laccase-Based Detoxification in the Aphid-Gall System

O_LIPlant-insect coevolution is exemplified by Schlechtendalia chinensis inducing gallnuts with record-breaking hydrolyzable tannin (HT) concentrations of 74.49%--32-fold higher than normal leaves. How do plants achieve this extreme defensive chemistry, and how do aphids survive it? C_LIO_LIWe integrated transcriptomics, heterologous gene validation in Arabidopsis, and enzyme assays to investigate both plant HT biosynthesis and aphid detoxification mechanisms throughout gall development. C_LIO_LIThree key genes--Phosphoglucomutase (PGM), UDP-glucosyltransferase BX9 (BX9), and gallate 1-beta-D-glucosyltransferase (GDG) -- govern HT biosynthesis, with expression patterns closely tracking tannin accumulation dynamics, and GDG as the rate-limiting enzyme. Arabidopsis transformants showed threefold HT increases. Critically, S. chinensis employs specialized laccases rather than tannase for detoxification, with laccase activity exceeding tannase by 20,000-fold. The aphid genome encodes three laccase genes, with Sc-Lac1 expressed in digestive tissues, achieving 39-54% HT degradation. C_LIO_LIThese findings reveal how plants weaponize secondary metabolism while herbivores evolve enzymatic countermeasures. The identified genes enable engineering enhanced defenses or pharmaceutical tannin production, while laccase-based detoxification offers new insights into insect adaptation to chemical defenses. C_LI

evolutionary biology↗

bioRxiv2025

Excitatory neurons as the default fate in bifurcation of excitatory and inhibitory neuron lineages

In the brain, a precise excitation-inhibition balance underpins every neural operation. Strikingly, across the vertebrate CNS, excitatory and inhibitory neurons often emerge from common progenitors (termed EX-IN lineage), yet how such dichotomous fates diverge has remained enigmatic. Here, exploiting spinal v2a (excitatory)-v2b (inhibitory) and retinal bipolar (excitatory) - amacrine (inhibitory) lineages as EX-IN lineage paradigm, we report a single, conserved genetic program: excitatory neural fate is the default state when sibling-cell communication is silenced; progenitor- and excitatory neuron-expressed homeobox genes actively repress the inhibitory program, while Notch signaling releases this repression to unleash inhibitory identity. Thus, we define a unifying principle governing EX-IN lineage divergence that generate the fundamental dichotomy of neural circuits.

developmental biology↗

bioRxiv2025

RNA viruses in water shape the viromes of shrimp and fish in aquaculture systems

Aquatic environments host immense RNA viral diversity, yet the extent to which waterborne viruses influence the virome of aquatic animals remains unclear. Here, we conducted a comprehensive longitudinal metatranscriptomic analysis of water, shrimp, and fish across multiple culture stages in aquaculture systems. We identified 3,211 RNA viruses spanning 21 viral supergroups, with 97.4% representing novel species. Water exhibited the greatest viral diversity (3,093 species), followed by shrimp (1,280) and fish (398), with most viruses detected in shrimp and fish also detected in water. Diversity analyses revealed increasing richness and significant compositional shifts in 92 high-abundance viruses (>1,000 RPM) across culture stages. Viral abundance in aquatic animals was strongly correlated with that in water, and the temporal trajectories of the 16 most abundant viruses were closely synchronized between water and shrimp. Furthermore, 337 virus species were shared among shrimp and two fish species, of which 309 were present in water. These findings demonstrated that RNA viruses in water shape the composition and dynamics of aquatic animal viromes, underscoring the importance of water virome surveillance and management for disease prevention in shrimp aquaculture.

microbiology↗

bioRxiv2025

A neural network model of free recall learns multiple memory strategies

Humans exhibit structured patterns of memory recall, including a tendency to recall more recent information and to recall events in the same order they were experienced. Classic computational models explain these patterns by positing that memories incorporate the ongoing "temporal context", formed by smoothly integrating the stimulus history. However, it is unclear whether a single mechanism can account for the full repertoire of human memory strategies, as the optimal approach may be task-dependent. For example, human memory experts widely apply the "memory palace" strategy, which is empirically better but not captured by temporal context models. Here we show that neural networks optimized for free recall develop diverse retrieval strategies, with only some of them resembling temporal context models. The best-performing models discovered a stimulus-invariant index code that emphasizes the studied position of each list item, instead of its temporal context. This creates a stable scaffold for forward recall akin to the memory palace technique. This index code was more likely to emerge when networks were i) encouraged to recall all studied items rather than prioritizing only the next few items, and ii) prevented from relying on recency. Our findings demonstrate that human-like recall patterns can arise from multiple distinct computational mechanisms, and that sequential retrieval using item index is an optimal strategy that explains expert-level recall performance.

neuroscience↗

bioRxiv2025

BayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell data

Understanding gene regulatory networks (GRNs) is essential for deciphering biological processes and disease mechanisms. Single-cell multiome technologies now enable joint profiling of chromatin accessibility and gene expression, offering an powerful means to infer cell type-specific GRNs. However, existing methods analyze each cell type independently or aggregate data into pseudo-bulk profiles, limiting their ability to resolve rare populations and capture cellular heterogeneity. We introduce BayesCNet, a Bayesian hierarchical model that jointly infers enhancer-gene linkages across all cell types while leveraging their hierarchical relationships for information sharing. Through extensive simulations, BayesCNet consistently outperforms state-of-the-art methods, with the largest improvements in rare cell types. When applied to real datasets, BayesCNet identifies enhancer-gene linkages with higher accuracy validated by promoter-capture Hi-C data, and reconstructs cell type-specific GRNs that highlight key regulators, demonstrating its power to resolve gene regulatory programs across diverse cell types.

bioinformatics↗

bioRxiv2025

Reference-Informed Spatial Domain Detection Using Weak Supervision for Spatial Transcriptomics

One of the key objectives in spatial transcriptomics (ST) studies is to map the complex organization and functions of tissues. We introduce GraphScrDom, a reference-informed and weakly supervised contrastive learning model that uniquely integrates expert-provided manual annotations (i.e., scribbles) on spatial grids or histology images with cell type-specific gene expression profiles derived from reference single-cell RNA-seq data to perform tissue segmentation. With only limited scribble annotations, GraphScrDom consistently outperforms existing methods across various ST platforms and at both bulk and single-cell resolutions, as evaluated by six widely used metrics, demonstrating strong generalizability and robustness. Additionally, we have developed an integrative software toolkit that includes an interactive annotation interface and a model training module for spatial domain detection, providing a unified and user-friendly framework to facilitate spatial domain analysis.

bioinformatics↗

bioRxiv2025

Challenges to case-only analysis for gene-environment interaction detection using polygenic risk scores: model assumptions and biases in large biobanks

Gene-environment interaction is important for studying complex diseases. Case-only analysis has been proposed to improve power for GxE detection. However, case-only analysis relies on key assumptions, including correct specification of the disease risk model and marginal independence between genetic and environmental variables. In this study, we systematically investigate the challenges of case-only analysis using polygenic risk scores (PRS) as genetic variables in large biobanks. Through simulations, we demonstrate that the false positive control of PRS-based case-only analysis depends on the log-linear disease risk model and weak main effects, and that it is prone to false positives under other commonly used disease risk models. We then conduct case-only analyses for breast cancer, prostate cancer, class 3 obesity, and short stature in the UK Biobank, using PRS derived from non-overlapping chromosome sets (e.g., even-numbered and odd-numbered chromosomes) that are unlikely to interact with each other. The resulting case-only regression estimates consistently show negative shifts compared to population-based estimates, suggesting false positives driven by collider bias due to model misspecification. Furthermore, correlations between chromosome set-specific PRS, likely driven by assortative mating or population stratification, suggest additional sources of confounding. Our results underscore the challenges of applying PRS-based case-only analysis in large biobank settings and highlight the need for caution when interpreting case-only results.

genetics↗

bioRxiv2025

Single-vessel transcriptome map pathological landscapes and reveal NR2F2-mediated smooth muscle cell phenotype acquisition in capillary malformations

BackgroundCapillary malformation (CM) is a congenital vascular anomaly affecting the skin, mucosa, and brain, yet the understanding of its vascular pathogenesis remains limited. MethodsWe applied spatial whole-transcriptome profiling (GeoMx) and gene set enrichment analysis within CM lesions at single vasculature level. Differentially expressed genes were validated by immunofluorescence staining. Phosphoproteomics was profiled to uncover lesion- wide phosphorylation sites on proteins. Single-cell RNA sequencing was performed on CM- derived induced pluripotent stem cells (iPSCs) to determine differentiation trajectories of lesional vascular lineages. In silico gene perturbation was used to predict candidate genes for modulating vascular pathological progression, followed by functional validation in CM iPSC-derived endothelial cells (ECs) using a Tet-on system. ResultsA spatial transcriptomic atlas was constructed, and pathological landscape of individual CM vasculature was delineated. CM vessels exhibited hallmarks of endothelial-to-mesenchymal transition (EndMT), including disruption of adherens junctions (AJs), vascular identity transitions, and metabolic remodeling. Phosphoproteomics confirmed that differentially phosphorylated proteins were enriched in EndMT- and AJ-related pathways. Aberrant expression of venous transcriptional factor NR2F2 was observed in lesional ECs and correlated with progressive enlargement from capillaries to larger-caliber vessels containing multiple layers of smooth muscle cells (SMCs). In CM iPSCs, differentiation course yielded reduced ECs but increased SMCs. In silico knockout simulation predicted NR2F2 as a crucial regulator of facilitating SMC phenotype in CM. Consistently, enforced NR2F2 expression during iPSC differentiation suppressed endothelial markers while inducing SMC-associated genes. ConclusionsSingle CM vasculature displays pathological hallmarks characterized by EndMT and AJ disruption, leading to progressive vascular remodeling. NR2F2 functions as a central regulatory factor orchestrating the acquisition of the SMC phenotype, thereby representing a potential therapeutic target in CM.

pathology↗

bioRxiv2025

A neural network with episodic memory learns causal relationships between narrative events

Humans reflect on memories to make sense of ongoing events. Past work has shown that people retrieve causally related memories during comprehension, but the mechanisms underlying this process remain unclear. Here, we used a recurrent neural network augmented with a key-value episodic memory buffer and trained it to predict upcoming scenes while watching a television episode. At each time step, the model transformed the current scene into a value representing memory content and a key representing memory address, both stored as episodic memory. The model retrieved selective past values by applying self-attention over stored keys and integrated these memories with the current scene representation to generate predictions. The model retrieved memories similar to those retrieved by human participants watching the same episode during fMRI. Importantly, this similarity disappeared when causal relationships between events were controlled for. The model also represented causally related events with similar patterns, similar to how the human brain represents these events. These findings suggest that using two distinct memory representations allows the model to retrieve memories and organize events based on causal relationships, beyond semantic or perceptual similarities. Together, this work proposes a key-value episodic memory system as a candidate computational mechanism for how humans retrieve causally related memories to comprehend naturalistic events.

neuroscience↗

bioRxiv2025

Multi-omics reveals lipid metabolism, mitochondrial and extracellular matrix dysregulation of diabetic cardiomyopathy in the human heart

Diabetic cardiomyopathy (DbCM) is a condition characterized by myocardial dysfunction in diabetes. Although the clinical recognition of DbCM is increasing, its underlying molecular mechanisms remain poorly understood. We aimed to conduct a comprehensive multi-omics analysis of heart tissue from cardiomyopathy patients (both with and without diabetes),alongside tissue from healthy donors, by integrating transcriptomic, 4D-DIA proteomic, and full-spectrum widely targeted metabolomic data. Differential analyses revealed a significant metabolic pattern in patients with DbCM, characterized by increasing utilization of triglyceride-derived fatty acids. Correlation analyses highlighted impaired BNIP3L-mediated mitophagy as a potential contributor to the disruption of fatty acid metabolism and mitochondrial dysfunction in DbCM. Protein interaction and fibrotic trichrome analyses showed that patients with DbCM had impaired cardiac matrix repair compared to patients with cardiomyopathy without diabetes. These findings provide a system-level understanding of DbCM and reveal molecular signatures driving its progression, offering insights for targeted therapeutic strategies against diabetic heart disease.

bioinformatics↗

bioRxiv2025

Characterization of membrane structures regulating primary ciliogenesis by quantitative isotropic ultrastructure imaging

The trafficking, docking, and fusion of membrane vesicles at the mother centriole (MC) are required to construct the primary cilium. Here, we determined the three-dimensional (3D) membrane ultrastructures, and associated proteins, involved in primary cilium assembly upstream of axoneme growth. Our work reveals that the enlargement of small vesicles docked to the MC is a key trigger for ciliogenesis progression, a process requiring the MC distal appendage protein CEP164. We show these vesicles subsequently fuse to form tubular C-shaped and an unprecedented toroidal membrane intermediates, which ultimately organize into the ciliary vesicle covering the MC distal end. The formation of these previously uncharacterized tubular membrane ciliogenesis intermediates is orchestrated by the membrane trafficking regulators EHD1 and RAB8, and requires the IFT-B complex protein IFT88. Remarkably, we show that EHD1, through its membrane tubulation function, regulates ciliogenesis progression by directly promoting CP110/CEP97 removal from the MC cap. The establishment of these tubular membrane structures is also associated with the recruitment of the ciliary gate transition zone proteins. This study changes the architectural framework for understanding ciliogenesis mechanisms and highlights the application of isotropic ultrastructure imaging and three-dimensional quantitative analysis in understanding membrane trafficking and organelle biogenesis mechanisms.

cell biology↗

bioRxiv2025

Post-Domestication selection of MKK3 Shaped Seed Dormancy and End-Use Traits in Barley

Anthropogenic selection of grain traits such as dormancy has shaped the developmental trajectories of crop plants (1). In cereals, shortening dormancy provides rapid and even post-harvest germination, but increases the risk of weather-induced pre-harvest sprouting (PHS) with harvest losses estimated at beyond 1 billion USD a year (2, 3, 4). Our understanding of how, why, when and where diversification of cereal dormancy arose is fragmentary. Here, we show in the founder cereal crop barley (Hordeum vulgare) that the Mitogen-Activated Protein Kinase (MAPK) pathway regulates dormancy primarily through a mosaic of locus haplotypes comprising copy-number variation and inherent kinase activity of Mitogen-activated protein kinase kinase 3 (MKK3). We provide evidence supporting the historical selection of specific MKK3 haplotypes that shape dormancy levels according to changing climatic pressures. Understanding the regulatory landscape of MKK3 provides a genetic framework to balance short grain dormancy and PHS-avoidance during a period of rapid climate change.

plant biology↗

bioRxiv2025

Sludge liquefaction and stratification enable ultra-high digestion rate and resource recovery for waste activated sludge treatment

Most of the energy, nitrogen/N and phosphorus/P entering wastewater treatment plants (WWTPs) accumulates in waste activated sludge (WAS). While these resources are theoretically recoverable through anaerobic digestion (AD), conventional sludge AD faces long-standing challenges of low organic loading rate and limited N/P recovery. Here, building on an innovative sludge liquefaction and stratification technique, we present a novel process entitled SPREAD that boasts 10 times higher organic loading rates and recovers 5 folds of N/P resources, relative to conventional sludge AD. We develop an anaerobic digestion database (ADDB) for multi-omics analyses of digestion microbiomes, and pinpoint the mechanism underlying the exceptionally high organic loading rate and remarkable N/P recovery in SPREAD. Importantly, we identify a negative correlation between the Gibbs free energy ({Delta}G) and the relative abundance of methanogens, enabling identification of bottleneck steps for stimulation and augmentation of methanogenic digestion. Our study provides a game-changing technology for the treatment and resource recovery from WAS and opens a new avenue for sustainable management and carbon/energy-neutrality of WWTPs.

bioengineering↗

bioRxiv2025

Palmdelphin facilitates R-spondin2 secretion to activate Wnt signaling and promote colorectal cancer stemness and tumorigenesis

The Wnt signaling pathway is a key driver of stemness and progression which contributes to mortality in colorectal cancer (CRC). R-spondins bind to LGR receptors to inhibit ubiquitin E3 ligases, thus protecting Frizzled from degradation and activating downstream Wnt signaling. Herein, we identify Palmdelphin (PALMD) as a functional marker of CRC stem cells, interspersed between intestinal and colonic crypt base epithelial cells, and predictive of aggressive CMS4 CRC and poor survival. Gene knockdown and overexpression studies revealed that PALMD initiates paracrine activation of Wnt/{beta}-catenin signaling via interacting with and facilitating the secretion of R-spondin2 (RSPO2), resulting in enhanced stemness and tumor growth in vitro and in vivo. Physiologic or pharmacologic inhibition of the PALMD-RSPO2 axis using R-spondin2-specific antibody or 6-methyl-1,3,8-trihydroxyanthraquinone (emodin), respectively, attenuates PALMD- mediated Wnt reporter activation, self-renewal, and tumorigenesis in cell and patient-derived organoid models. Together, these findings identify PALMD as a previously unknown player in Wnt signaling in CRC, and underscore the pro-tumorigenic role of R-spondin2 in this context.

cancer biology↗

bioRxiv2025

Nanosecond pulsed electric field precisely manipulates cytoplasmic Ca2+ oscillations during oocyte activation

Oocyte activation deficiency (OAD) is a primary cause of fertilization failure following intracytoplasmic sperm injection (ICSI), a problem that can potentially be overcome through artificial oocyte activation (AOA). However, concerns persist regarding the safety and efficacy of AOA techniques in clinical practice. In this study, nanosecond pulsed electric field (nsPEF) is proposed as a safe and controllable method for oocyte activation in vitro by precisely manipulating Ca2+ signaling in the ooplasm. Mouse oocytes collected from oviducts were exposed to nsPEF stimulation at varying intensities to induce distinct Ca2+ signaling patterns. Subsequently, these oocytes underwent parthenogenetic activation and were cultured to assess developmental potential up to the blastocyst stage. The sperm-initiated physiological Ca2+ oscillations are successfully mimicked by one series of Ca2+ signals induced by nsPEF at low or medium intensities, and improve activation efficiency and developmental potential compared to calcium ionophore A23187. Low-intensity nsPEF pulses mediated repetitive extracellular Ca2+ influx in a non-invasive, electro-permeable manner. Medium-intensity nsPEF stimulation triggered periodic Ca2+ release from the endoplasmic reticulum (ER) via the PIP2-IP3-IP3R pathway, producing physiological-like Ca2+ oscillations. The non-invasive nsPEF method ensures safe oocyte activation by preserving cellular integrity and minimizing stress responses. The efficacy of nsPEF in precisely manipulating Ca2+ signaling patterns was further validated in human unfertilized oocytes. Our findings present a novel AOA approach with enhanced safety and efficacy, especially for patients experiencing repeated fertilization failures. It is anticipated that nsPEF is emerging as a promising technology for addressing male infertility issues, and may offer a potential alternative fertility treatment, bypassing sperm for initiation of oocyte activation.

developmental biology↗

bioRxiv2025

Assessing climate change impacts for small-scale fisheries in the Gulf of California using Deep Learning

Small-scale, multispecific fisheries in the Gulf of California face significant challenges including limited species-specific catch data, uncertainty about climate change impacts, and insufficient biological information needed for traditional deterministic models. These knowledge gaps hamper efforts to forecast future conditions and develop appropriate management strategies accurately. The complexity of these multi-species fisheries, combined with data scarcity for many target species, creates substantial barriers to quantifying and addressing climate vulnerability. Deep learning approaches offer a promising alternative by leveraging available data to identify patterns and project trends despite these limitations, providing valuable insights for fisheries management in data-poor contexts. Here, we apply a Mixture of Expert, a deep learning forecasting models for small-scale, multi-specific fisheries in the Gulf of California under future climate change scenarios. Results show varied responses across marine habitats, with reef and benthic fish projected to experience substantial declines (-12.46% and -9.37%) during the 2050s-2060s, followed by recovery in the 2070s-2080s. Economic implications are significant, with reef fish facing projected losses of $1.2 million by the 2050s before recovering by the 2080s. Shapley Additive Explanations (SHAP) analysis was applied to evaluate the importance of features for each predictive model, the analysis revealed the effects of the temperature in different depths for each fishery, and the sensitive analysis pointed to the magnitude of the effect. Our findings suggest that climate impacts will not be uniform across the Gulf, necessitating region-specific management approaches and highlighting the value of maintaining diverse fishing portfolios to enhance resilience against climate-driven changes.

ecology↗

bioRxiv2025

Homoharringtonine Promotes FTO Degradation to Suppress LILRB4-Mediated Immune Evasion in Acute Monocytic Leukemia

Acute monocytic leukemia (AML-M5), a subtype of acute myeloid leukemia, is a highly aggressive malignancy characterized by a poor prognosis, primarily due to the ability of leukemic cells to evade immune surveillance. In this study, we demonstrate that homoharringtonine (HHT), an FDA-approved therapeutic agent for chronic myeloid leukemia (CML), inhibits this immune evasion by targeting the FTO/m6A/LILRB4 signaling pathway in monocytic AML. Utilizing RNA sequencing (RNA-seq) and various functional assays, we reveal that HHT treatment significantly reduces LILRB4 expression at both the RNA and protein levels, suggesting that the effects of HHT on LILRB4 are distinct from its well-established role as a protein synthesis inhibitor. Mechanistically, HHT treatment markedly increases global levels of RNA m6A in THP-1 cells by promoting the degradation of FTO, which subsequently diminishes the expression of its downstream targets, MLL1 and LILRB4. Furthermore, in vitro and in vivo analyses employing monocytic AML cell lines, mouse-derived AML xenograft models, and patient samples collectively support the conclusion that HHT suppresses immune evasion in monocytic AML by reducing LILRB4 expression. Importantly, the downregulation of LILRB4 resulting from HHT treatment enhances the susceptibility of THP-1 cells to CD8+ T cell cytotoxicity, accompanied by increased markers of immune activation. Overall, our findings position HHT as a promising clinical agent for enhancing CD8+ T cell-based cancer immunotherapy by mitigating immune evasion in monocytic AML. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=147 HEIGHT=200 SRC="FIGDIR/small/643466v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@e72e5dorg.highwire.dtl.DTLVardef@19519aeorg.highwire.dtl.DTLVardef@1c8f786org.highwire.dtl.DTLVardef@f2a963_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Sky islands of Southwest China. II: Unraveling hidden species diversity of talpid moles using phylogenomics and skull-based deep learning

The sky islands of Southwest China, characterized by dramatic topographical and climatic variations, are prominent hotspots of biodiversity and endemism. Organisms inhabiting middle-to-high elevation habitats in this region are geographically isolated within distinct mountain chains, which over geological time have been subjected to isolation-by-distance and isolation-by-environment. These processes have led to profound allopatric diversification and strong phylogeographic structuring, resulting in a plethora of genetically distinct cryptic species, as is becoming increasingly evident for many small mammal families. However, morphological conservatism can pose significant challenges in delineating these clades within species complexes. In this study, we leverage artificial intelligence technologies to unravel the hidden species diversity of moles (family Talpidae) in Southwest Chinas sky islands. We first employed ultraconserved elements (UCEs) to investigate the evolutionary history of talpid moles, conducted molecular species delimitation using mitochondrial and multi-locus genes, and utilized both traditional and geometric morphometrics to examine their morphological disparity. To address the challenges of morphology based cryptic species identification, we developed a deep learning Hierarchical Identification of Species NETwork (HIS-NET) to create an image-based model that analyzes four different views of the skull/mandible to distinguish genera and species hierarchically. HIS-NET not only achieved expert-level accuracy in species identification but also effectively distinguished between cryptic and known species, aiding in the identification of key morphological variation intervals. Our results support the recognition of allopatrically distributed taxa in Euroscaptor and Parascaptor as full species, thereby confirming that species diversity in this region remains underestimated. Beyond advancing our understanding of speciation in this unique and fragile ecosystem, our study serves as a proof-of-concept, demonstrating the power of deep learning in unraveling hidden biodiversity within this and other species complexes.

zoology↗