Biology subjects

Wei, Z.

Publications and source records attributed to Wei, Z..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

β-Amyloid and Glutathione Dysregulation Cooperatively Drive Lipid Peroxidation and Ferroptosis in Neuron-Like Cells

Alzheimers disease (AD) is a progressive neurodegenerative disorder characterized by {beta}-amyloid (A{beta}) accumulation and oxidative stress, with aging being its greatest risk factor. Age-related decline in antioxidant defenses, particularly glutathione (GSH), may increase neuronal vulnerability to A{beta}-mediated toxicity; however, the mechanisms linking redox dysregulation to neuronal death remain incompletely understood. In this study, we investigated how impaired GSH homeostasis influences neuronal susceptibility to A{beta}-associated injury. Human SH-SY5Y neuron-like cells were engineered to express either wild-type APP695 or the familial AD-associated APPSwe/Ind mutant, and intracellular GSH depletion was induced using both pharmacological and genetic approaches. GSH depletion markedly sensitized APPSwe/Ind-expressing cells to cell death, accompanied by increased plasma membrane lipid peroxidation, elevated malondialdehyde (MDA) and 4-hydroxynonenal (4-HNE) levels, and enhanced lactate dehydrogenase (LDH) release. This cell death was not prevented by the pan-caspase inhibitor Z-VAD-FMK but was effectively rescued by the ferroptosis inhibitors ferrostatin-1 (Fer-1) and liproxstatin-1 (Lip-1), indicating a ferroptotic mechanism. Similar ferroptotic responses were observed when A{beta} oligomers were combined with intracellular GSH depletion. Mechanistically, A{beta} and GSH depletion synergistically increased transferrin receptor-1 expression and intracellular iron levels while markedly suppressing glutathione peroxidase 4 (GPX4), a central regulator of ferroptosis. Importantly, inhibition of autophagy with bafilomycin A1 restored GPX4 expression and rescued cells from ferroptotic death, suggesting that autophagy-mediated GPX4 degradation contributes to this process. Collectively, our findings demonstrate that GSH dysregulation synergizes with A{beta} to induce lipid peroxidation and ferroptosis in neuron-like cells. These results identify impaired redox homeostasis as a critical driver of neuronal vulnerability in AD and suggest that preserving GSH levels or targeting ferroptotic pathways may offer promising therapeutic strategies for neurodegeneration.

neuroscience↗

bioRxiv2026

FXR and BET signaling orchestrate to protect β cells

In both type 1 and type 2 diabetes (T1D and T2D), insulin-producing {beta} cells undergo progressive dysfunction due to inflammation, leading to impaired glucose responsiveness, dedifferentiation, and cell loss. While bile acid (BA) dysregulation under diabetic conditions is known to influence metabolic and inflammatory pathways, its mechanistic role in {beta} cell regulation remains incompletely defined1-3. Here we show that bile acid sensor Farnesoid X receptor (FXR) and Bromodomain and Extra-Terminal motif (BET) signaling cooperatively regulates {beta} cell inflammatory response and {beta} cell identity. We identified the physiological protein-protein interaction between FXR and the bromodomain-containing protein 4 (BRD4) as a regulatory axis that protects against {beta} cell dysfunction. We show that FXR activation by Fexaramine (Fex) together with BRD4 inhibition by JQ1 synergistically suppressed IL-1{beta}-induced inflammation while also improving {beta} cell identity and insulin secretion in both db/db model and high-fat diet (HFD) plus multi low-dose streptozotocin (MLD-STZ) model of diabetes. Importantly, this cooperative effect is abolished in {beta} cell-specific FXR knockout ({beta}FXRKO) mice, establishing that FXR is required for the functional synergy between these pathways in vivo. Mechanistically, structure-guided modeling and mutational analyses identified a direct interaction between FXR and the BD2 domain of BRD4, depending on specific lysine acetylation sites. Additionally, inhibition of the BD2 domain of BET combined with FXR activation markedly improved {beta} cell survival in human T1D and T2D models established from human pluripotent stem cell (hPSC)-derived islet-like organoids (HILOs). Collectively, these findings establish a BA-bromodomain axis as a transcriptional interface linking metabolic signaling and chromatin regulation, and highlight FXR-BET targeting as a promising strategy to counter progressive {beta} cell failure in diabetes.

molecular biology↗

bioRxiv2026

A neuron-glia circuit anticipates hypoxia to regulate organismal oxygen use

Organisms must regulate metabolic resources such as oxygen (O2) and nutrients despite environmental variability and the energetic costs of their own actions1-3. Such regulation can occur reactively, through homeostatic corrections of recent imbalances, or predictively, through allostatic adjustments that anticipate future demand4,5. Predictive regulation is particularly important because metabolic resources often continue to be consumed for seconds to minutes after motor actions cease as tissues repay incurred costs, making it advantageous to prevent depletion before it occurs6. However, the cellular and circuit mechanisms for allostatic control remain largely unknown5,7,8. Using whole-brain neuronal and astroglial imaging and O2 measurements in behaving zebrafish, we identified a noradrenergic-astroglial circuit that detects, anticipates, and prevents internal O2 depletion. We found that swimming exacerbated internal hypoxia with a multi-second delay, but behavioral adaptations occurred before such self-generated hypoxia manifested, suggesting predictive control, confirmed using computational modeling. Noradrenergic neurons in the nucleus of the solitary tract directly detected brain hypoxia and received efference copies of swimming actions; these inputs summed at the level of membrane voltage to increase spiking and norepinephrine release when actions and resource scarcity co-occurred. Astroglia integrated noradrenergic input into prolonged Ca2+ elevation that tracked the O2 cost of recent actions and thereby predicted O2 debt relative to O2 availability, rising ~8 s before O2 fell. This astroglial prediction reorganized brain-wide activity to suppress locomotion and promote respiration, preempting O2 depletion. Silencing noradrenergic neurons or astroglial signaling abolished these hypoxia coping behaviors, whereas selective activation evoked them. This neuronal-astroglial mechanism constitutes a predictive control system that integrates physiological state with behavioral intent to avert metabolic crisis, revealing a cellular substrate for proactive energy management.

neuroscience↗

bioRxiv2026

BioClaw: Human-Bot Research Collaboration Ecosystems in Group Chats

Day-to-day research discussions in group chats often generate hypotheses, analysis requests, and interpretation decisions, yet executing those analyses still requires researchers to leave the conversation and rely on fragmented local tools, databases, visualization software, and literature search engines. In this work, we present BioClaw, a human-bot research collaboration ecosystem that converts natural-language requests in group conversations into tool-grounded analyses executed within isolated Docker containers. Deployed across 8 messaging platforms, BioClaw turns each group chat into a persistent execution workspace. Its design combines multi-channel orchestration, per-group state and workspace management, and isolated containerized execution for reliable shared use over long-lived conversations. To support practical research workflows, BioClaw combines containerized execution with preinstalled 31 biomedical tools and 95+ skills. The application of BioClaw spans various biomedical domains (e.g., genomics, clinics, structural biology) and data modalities (e.g., sequencing data, EHR data, protein structure data). These results establish the viability of embedding executable, tool-rich agent workflows within shared digital workspaces, positioning group chats as a transformative paradigm for collaborative scientific discovery and innovation.

bioinformatics↗

bioRxiv2026

Single-cell lineage tracing maps clonal and transcriptional dynamics in melanoma metastasis

Melanoma metastasis is driven by extensive intratumoral heterogeneity and phenotypic plasticity, yet how clonal identity relates to transcriptional programs during metastasis remains unclear. Here, we applied MeRLin, a single-cell lineage tracing platform, to dissect the clonal and transcriptional heterogeneity of metastatic melanoma in a patient-derived spontaneous metastasis model. Clonal analyses revealed hierarchical structures during tumor progression, with a subset of lineages from primary tumors consistently enriched across metastatic sites, supporting a model of polyclonal seeding followed by selective expansion of pre-existing highly metastatic subpopulations. Single-cell transcriptomic profiling identified two major metastatic subpopulations of distinct transcriptional programs, characterized by neural crest stem cell-like and lipid metabolism signatures. Both programs were enriched for invasion-associated genes and maintained across organs through distinct regulatory networks. Spatial mapping by barcode RNA-FISH linked these transcriptional states to their tissue context and showed that OLFML3 expression partially co-localized with a dominant subpopulation at the tumor-liver interface, marking the invasive fronts of metastatic growth. Together, these findings establish a framework in which clonal identity, transcriptional state, and spatial organization jointly shape metastatic melanoma progression.

cancer biology↗

bioRxiv2026

Fast and reliable association discovery in large-scale microbiome studies and meta-analyses using PALM

Identifying microbial features associated with various covariates is a long-standing goal in microbiome research. Modern association studies incorporate an ever-increasing number of microbial features, covariates, and datasets from diverse cohorts. However, the complexity of microbiome data challenges analysis, often leading to poor replication of findings. We introduce PALM, a quasi-Poisson regression framework that enables fast and reliable association discovery in large-scale studies and meta-analyses. Extensive, realistic simulations demonstrate PALMs advantages in controlling false discovery rates, boosting power, improving computational efficiency, and preserving cross-study homogeneity of association effects. Three real-world applications at different scales illustrate PALMs utility, underscoring its potential to advance microbiome research.

microbiology↗

bioRxiv2026

Cerebellar activation in human placebo analgesia: Bridging findings from mice to humans

Placebo analgesia has traditionally been explained by top-down cortical regulation of brainstem and spinal pathways. Recent circuit-level work in animal models identified a rostral anterior cingulate-pontine-cerebellar pathway that contributes to expectation-based analgesia, implicating cerebellum circuits in placebo effects1. Building on these findings, we examined pontine and cerebellar contributions within a large individual-participant meta-analysis of human neuroimaging studies of placebo analgesia2 (n = 603). We found that the effects of human pain and placebo converge in cerebellar territories embedded in higher-order cognitive3,4 and action-mode networks5. These regions exhibit placebo-induced anticipatory increases and reduced responses during painful stimulation, which correlate with the magnitude of placebo analgesia, consistent with predictive configuration of the system. Pontine responses also correlate with individual differences in placebo analgesia. In independent Human Connectome Project data (n = 820), pontine activity is functionally connected with cingulate and cerebellar regions implicated in placebo analgesia. Together, these findings support a model in which expectation effects are implemented via predictive configuration of a cortico-pontine-cerebellar system.

neuroscience↗

bioRxiv2026

OpenAc4C: A gateway to decode the landscape, regulation and pathogenesis of N4-acetylcytidine (ac4C) epitranscriptome

N4-acetylcytidine (ac4C) is an ancient and highly conserved chemical marker found in all domains of life. Recent advancements in sequencing techniques have enabled the functional analysis of ac4C occurrence by accurately capturing its locations and levels, shedding light on its significant regulatory potential and emerging role in diseases. The OpenAc4C, the first comprehensive knowledgebase exclusively designed for unraveling the ac4C epitranscriptome across diverse species, spanning vertebrates, mammals, insects, fungi, plants, bacteria, archaea, and viruses. By mining a large array of ac4C epitranscriptome datasets with deep learning-based pipelines, OpenAc4C features a collection of 536,745 ac4C sites identified from four distinct next-generation sequencing (NGS)-based techniques, alongside novel insights from Oxford Nanopore direct RNA sequencing (ONT)-based samples, encompassing a total of 33 species. Beyond the ac4C landscape, a total of 536,986 ac4C-affecting variants were identified in seven species. Among them, 4,766 pathogenic ac4C-SNPs may drive ac4C dysregulation with implications for disease pathogenesis. In addition, OpenAc4C offers a user-friendly graphical interface and a web-based analysis platform for comprehensive querying and interactive exploration of the database collections. Together, OpenAc4C will serve as a valuable integrated resource to facilitate studies of ac4C modification. It is freely accessible at: www.rnamd.org/ac4cportal.

bioinformatics↗

bioRxiv2026

When Multimodal Fusion Fails: Contrastive Alignment as a Necessary Stabilizer for TCR--Peptide Binding Prediction

Multimodal learning is commonly assumed to improve predictive performance, yet in biological applications auxiliary modalities are often imperfect and can degrade learning if fused naively. We investigate this problem in TCR-peptide binding prediction, where sequence embeddings from pretrained protein language models are strong and transferable, but structure-derived residue graphs are built from predicted folds and heuristic discretization. In this setting, structural views can be noisy, inconsistent, and difficult to optimize jointly with sequence features. We introduce TRACE, a lightweight multimodal framework that encodes each entity (TCR and peptide) with parallel sequence and graph towers, then applies CLIP-style intra-entity contrastive alignment before interaction modeling. The alignment objective regularizes representation geometry by encouraging modality consistency for the same biological entity, thereby preventing unstable graph signals from dominating fusion. Across protocol-aware TCHard RN evaluations, naive sequence+graph fusion frequently underperforms a sequence-only baseline and can collapse toward near-random behavior. In contrast, TRACE consistently restores and improves performance. Controlled noise and supervision sweeps show that these gains persist under increasing graph corruption and positive-label scarcity, indicating that alignment is especially important when training conditions are hard. Our results challenge the assumption that adding modalities is inherently beneficial. Instead, they highlight a central principle for robust multimodal bioinformatics: performance depends not only on what modalities are used, but on how their interaction is constrained during optimization. TRACE provides a simple and general recipe for leveraging imperfect structural information without sacrificing stability.

bioinformatics↗

bioRxiv2026

CoLa-VAE: Cell-Cell Communication-aware Variational Autoencoder with Dynamic Graph Laplacian Constraints

Single-cell RNA sequencing provides a powerful view of cellular heterogeneity, but its sparsity and dropout noise remain major obstacles for recovering biologically meaningful gene expression programs and for downstream analyses that depend on reliable expression measurements. Ligand-receptor-based cell-cell communication inference is such analysis, missing ligand or receptor expression can cause substantial false negatives in sparse single-cell data. Here, we present CoLa-VAE, a cell-cell communication-aware variational autoencoder that jointly learns latent representations and denoised expression profiles by incorporating ligand-receptor-derived communication topology through dynamic graph Laplacian regularization. Rather than treating denoising as a secondary output of representation learning, CoLa-VAE uses denoised expression to iteratively refine communication estimates and uses the resulting communication structure to guide both latent organization and expression reconstruction. In addition to improving latent space organization and producing robust denoised expression matrices, CoLa-VAE-denoised matrices also improved downstream biological analyses, including the detection of robust differential cell-cell communication programs, mitigation of batch-associated variation and enhanced spatial transcriptomic deconvolution when spatially constrained communication structure was incorporated. Together, these results establish CoLa-VAE as a communication-guided denoising and representation learning framework that recovers biologically meaningful expression signals from sparse single-cell and spatial transcriptomic data, enabling more sensitive and reliable downstream analysis.

bioinformatics↗

bioRxiv2026

eBiota: Designing microbial communities from large seed pools with desired function using rapid optimization and deep learning

Designing microbial communities to generate target products is crucial for biotechnology, agriculture, and disease treatment. However, rationally designing such communities from large seed pools has become a major challenge, as the rapidly expanding number of complete microbial genomes greatly expands the search space and sharply increases the required screening time and computational cost. Here, we introduce eBiota, a platform for ab initio design of microbial communities from a pool of 21,514 strains to generate target products. eBiota not only identifies optimal strain combinations but also simulates community behaviors, including microbial interactions and relative abundances. eBiota integrates three modules: CoreBFS, a graph-based search algorithm that rapidly screens for bacteria with complete metabolic pathways related to the target product; ProdFBA, an extended flux balance analysis that identifies microbial consortia with maximal production efficiency; and DeepCooc, a deep learning model trained on 23,323 microbiome samples across various environments to infer co-occurrence patterns. We validated eBiotas capabilities in microbial community design and production efficiency calculation using public microbiome datasets, ranging from single strains to six-member consortia. Further in vitro experiments involving 94 strains confirmed eBiotas ability to identify species that inhibit pathogen growth and to accurately model the relative abundances within complex microbial communities. As an initial digital twin, eBiota provides a powerful platform for the rational design of functional microbial communities, offering new opportunities for metabolic engineering and synthetic biology.

bioengineering↗

bioRxiv2026

KuafuPrimer: Machine learning empowers the design of 16S amplicon sequencing primers toward minimal bias for bacterial communities

Amplicon sequencing protocol targeting the 16S rRNA gene is a widely used and cost-effective method for exploring bacterial communities. However, its performance is often limited by primer bias arising from the arbitrary use of universal primers across diverse microbial communities and habitats. We propose KuafuPrimer to design the optimal 16S rRNA gene primers toward minimal bias for targeted bacterial communities, using few-shot machine learning to guide the primer design procedure based on a small number of samples. Simulations on 809 samples across 26 representative environments and habitats showed that KuafuPrimer-designed primers outperformed the universal primers in taxonomic accuracy, achieving an averaged 16.31% relative reduction in primer bias, with reductions up to 46.08% in plant samples. Notably, KuafuPrimer detected 29 rare and key taxa undetectable by the universal primers. Validation with 317 longitudinal gut microbiota samples demonstrated that KuafuPrimer-designed primers consistently outperformed the universal primers across temporal, individual, and cohort levels, with relative bias reductions of 5.03%, 3.53%, and 3.10%, respectively. Finally, in real PCR experiments on human gut samples from Clostridioides difficile-infected and healthy groups showed that polymerase chain reaction products using KuafuPrimer-designed primers correlated better with metagenomic data compared to the universal primers. More importantly, KuafuPrimer successfully detected Clostridioides difficile, the key pathogen missed by the universal primers, highlighting its potential for improving clinical diagnostics. In summary, KuafuPrimer provides a machine learning-based primer design strategy for targeted bacterial communities, with demonstrated utility in large-scale microbiome initiatives, longitudinal surveys and clinical diagnostics.

bioinformatics↗

bioRxiv2026

Imaging Intrinsic Stochastic Magnetic Fluctuations in Living Cells

SignificanceThis work establishes a probabilistic magnetometry framework for detecting weak stochastic magnetic fluctuations at the nanoscale, which provides the first quantitative access to intrinsic magnetic activity in living cells. Weak stochastic magnetic fluctuations at nanoscale are difficult to quantify. In living cells, ionic transport and molecular currents generate electromagnetic activity whose magnetic component is likewise nanoscale, weak, stochastic, and rapidly varying and has therefore remained experimentally inaccessible. Here we introduce Bio-Spin Probabilistic Inference (BISPIN), a digital statistical framework that can quantify weak, stochastic magnetic fluctuations at the nanoscale. Using threshold-resolved signals from enhanced nitrogen-vacancy quantum sensors, BISPIN converts unstable analog magnetic readouts into statistically convergent digital observables and infers fluctuation strength through probabilistic modeling, enabling robust quantification under random sensor orientations and biological heterogeneity within the experimental bandwidth. Applied to living cells, this approach distinguishes live from fixed cells, resolves agonist-induced activation, and maps subcellular variations in magnetic fluctuation strength. By providing the first quantitative access to intrinsic stochastic magnetic fluctuations in living cells, this work establishes a probabilistic magnetometry framework for cellular electrodynamics and opens a new magnetic dimension of cellular phenotyping for bio-spin omics.

biophysics↗

bioRxiv2026

The enhanced multi-tissue atlas of regulatory effects in cattle

Cattle are integral to global food security, yet the molecular architecture of their complex traits remains poorly understood. Here, we present the Cattle Genotype-Tissue Expression (CattleGTEx) Phase 1 resource (https://cattlegtex.farmgtex.org/), a substantial expansion of the pilot study. By leveraging 12,422 RNA-seq profiles across 43 tissues and 82 breeds, we characterized 433,972 primary and 161,428 non-primary regulatory effects spanning seven molecular phenotypes. This high-resolution atlas resolves 75% of GWAS signals for 44 complex traits, significantly addressing the "missing regulation" in livestock. We propose a genetic regulatory model demonstrating how variants across multiple biological layers interact with specific biological contexts to shape phenotypic variation. Furthermore, CattleGTEx elucidates mechanisms underlying adaptive evolution between Bos taurus and Bos indicus, as well as artificial selection in dairy and beef breeds. Finally, by mapping evolutionary constraints on these regulatory effects, we demonstrate the translational value of this resource for prioritizing causal variants in human complex diseases. Together, Phase 1 of CattleGTEx provides a transformative framework for functional genomics, precision breeding, and comparative genetics.

genetics↗

bioRxiv2026

Bacterial endosymbionts enhance fungal virulence by disrupting the disease-suppressive rhizobiome

The mechanisms by which bacterial endosymbionts (bacteria living within fungal cells) enhance the fitness and virulence of fungal pathogens remain poorly understood. Here, we report that the tomato Fusarium wilt pathogen Fusarium oxysporum f. sp. lycopersici (FOL) hosts Achromobacter sp. endosymbionts that enhance fungal virulence. This virulence potentiation is partially dependent on interactions with the native rhizosphere microbiota. We show that bacterial endosymbiont-harboring FOL reshapes the rhizosphere bacterial community during pathogen infection and decreased the abundance of disease{square}suppressive bacteria, including Streptomyces sp. taxa. This inhibitory effect is mediated by bacterial endosymbiont{square}stimulated production of beauvericin (an antibacterial cyclic hexadepsipeptide) by FOL. Together, our findings reveal a tripartite interaction in which a fungal pathogen leverages its bacterial endosymbiont to weaken rhizosphere microbiome{square}based disease suppression by inhibiting plant-protective bacterial taxa. This work highlights how cross{square}kingdom symbioses can modulate pathogen ecology and virulence in soil environments.

microbiology↗

bioRxiv2026

CellPheno: A High-throughput Computational Platform for Quantifying Cellular Resolution Whole Brain Microscopy Images

Advances in tissue clearing and light-sheet microscopy enable cellular resolution whole-brain 3D imaging. However, whole-brain quantification tools do not yet meet demands for efficiency or assess morphometry. Here we present CellPheno, a 3D nuclei instance segmentation framework for high-throughput cellular phenotyping. CellPheno quantifies an entire P4 mouse brain within 15 hours. We showcase whole-brain morphometry, enhanced stitching, and co-localization across multiple cell types in 53 brains.

neuroscience↗

bioRxiv2026

Chd8 haploinsufficiency leads to molecular layer heterotopias and age-dependent cortical expansion

Mutations in the chromatin remodeler CHD8 are associated with autism and macrocephaly. While mouse models of Chd8 haploinsufficiency recapitulate brain overgrowth, the specific cellular mechanisms and developmental timing that lead to these anatomical abnormalities remain poorly understood. Here, we conducted 3D imaging of Chd8V986*/+ mouse brains using magnetic resonance imaging followed by tissue clearing and cellular resolution light-sheet microscopy across embryonic and postnatal developmental stages. We found that brain overgrowth occurs postnatally, driven by an increase in non-neuronal cells prior to volumetric expansion. Unexpectedly, we identified prevalent molecular layer heterotopias (MLH) within the frontal cortex of Chd8V986*/+ mice composed of neurons breaking through the pial surface during embryonic development and persisting throughout life. Increased incidence of MLH, previously identified in individuals with idiopathic autism, was replicated across three independent Chd8+/- mouse models and present across multiple genomic backgrounds, establishing aberrant neuronal migration as a core feature of Chd8 haploinsufficiency.

neuroscience↗

bioRxiv2026

Benchmarking Large Language Models for Predicting Therapeutic Antisense Oligonucleotide Efficacy

Antisense oligonucleotides (ASOs) are a promising class of therapeutic drugs that can target and modulate genes associated with various diseases. This study benchmarks Large Language Models (LLMs) for predicting ASO therapeutic efficacy through a two-stage approach: (1) molecular embedding-based fine-tuning using SMILES representations, and (2) prompt engineering with zero-shot and few-shot learning using DNA sequences with target gene information. We evaluated general-purpose models (GPT-3.5-Turbo, LLaMA2-7B, Galactica-6.7B) and chemistry-specific models (ChemBERTa, Molformer, BERT) across three datasets: PFRED (522 sequences), openASO (1708 sequences), and ASOptimizer (1267 sequences). DNA sequence inputs with target gene information outperformed SMILES representations. GPT-3.5-Turbo achieved R2 values of 0.6381 (PFRED) and 0.6340 (ASOptimizer) for few-shot prompting with k=3 examples. Code and datasets available at: https://github.com/asundar0128/IndependentStudy

bioinformatics↗