Biology subjects

Wang, C.

Publications and source records attributed to Wang, C..

At least 199 records · Page 11Linked to original sources

bioRxiv2026

Enhancing interpretability of cryo-EM maps with hybrid attention Transformers

Cryo-electron microscopy (cryo-EM) has become a central technique for determining the structures of macromolecular complexes. However, experimental cryo-EM density maps often exhibit limited interpretability due to noise and heterogeneous, anisotropic resolution, complicating downstream atomic model construction. Here we present DEMO-EMReF, a Swin Transformer-based framework specifically designed for cryo-EM density map refinement that integrates channel attention with hybrid spatial attention. By combining local window-based attention with grid-based sparse global attention, DEMO-EMReF captures fine-grained local density features while modeling long-range, cross-region spatial dependencies, enabling more robust density refinement in heterogeneous and anisotropic map regions. DEMO-EMReF was systematically evaluated on large benchmark sets of both primary maps and half-maps spanning resolutions of 3.0-8.0 [A] and was compared against multiple state-of-the-art map enhancement methods. DEMO-EMReF consistently improves resolution-related metrics, map-model correlations, and local atomic resolvability, with particularly robust gains in challenging heterogeneous or anisotropic cases. Importantly, the refined maps facilitate more accurate and efficient automated atomic model building. Together, DEMO-EMReF provides a robust approach for enhancing cryo-EM density maps and enabling more reliable downstream studies.

bioinformatics↗

bioRxiv2026

Wetting-mediated extracellular phase separation drives long-range cell adhesion

Cells must efficiently locate and engage for tissue formation and immune coordination, yet classical receptor-ligand binding is limited to nanometre distances and is inherently slow [1-3]. Here, we uncover a previously unrecognised physical principle, liquid-like adhesion by phase separation (LAPS). This process creates dynamic wetting layers on cell surfaces [4, 5], functioning as "liquid bridges" that enable robust, long-range cell capture across tens of micrometres. Remarkably, this wetting-mediated attraction remains effective at nanomolar concentrations--conditions where bulk phase separation would not be expected--and facilitates high-fidelity cell sorting through competitive wetting. By integrating aqueous two-phase systems, endogenous proteins (Galectin-3, CCL5), and fluid-particle-dynamics simulations [6], we demonstrate that extracellular liquid-liquid phase separation not only mediates long-range cell capture but also acts as a physical catalyst for contact-dependent signaling. These findings establish extracellular phase separation as a key physical principle complementing molecular recognition in multicellular systems, offering new opportunities for understanding immune response, tissue morphogenesis, and therapeutic strategies targeting the extracellular environment [7, 8].

biophysics↗

bioRxiv2026

Single-Cell Raman Profiling Enables Rapid Precision Phage Therapy Against Multidrug-Resistant Hypervirulent Klebsiella pneumoniae

Multidrug-resistant hypervirulent Klebsiella pneumoniae (MDR-hvKP) poses a severe global health threat. Phage therapy is a promising alternative, but requires precise matching of phage to the bacterial strain. Here, we present a proof-of-concept method that integrates single-cell Raman spectroscopy with deep learning to enable rapid and precise selection of lytic phages against MDR-hvKP. By profiling Raman signatures of strains across multiple KL-types (capsule locus types), we trained three deep learning architectures for phage-host matching. Among them, the CNN_MLP-Transformer achieved the best performance (99.7%), slightly outperforming CNN_MLP (99.2%) and CNN_MLP-Attention (99.5%). Validation using 10 hvKP clinical isolates yielded an average phage selection accuracy of 78.3%. These findings demonstrate the feasibility and clinical potential of AI- augmented Raman spectroscopy as a rapid, label-free, precise strategy for guiding phage therapy against MDR-hvKP infections. TeaserAI-guided single-cell Raman profiling enables rapid precision phage selection against multidrug-resistant K. pneumoniae.

microbiology↗

bioRxiv2026

Discovery and characterization of small molecule inhibitors of CBL-B that act as intramolecular glue to enhance T-cell anti-tumor activity

CBL-B is a RING-type E3 ubiquitin ligase that acts as a critical negative regulator of T-cell activation. It promotes T-cell anergy and suppresses immune responses through ubiquitin-mediated control of signaling proteins at the immunological synapse. T cells deficient in CBL-B activity lose their dependence on CD28 co-stimulation, exhibit heightened activation and increased cytokine production, and fail to re-establish anergy. In addition, mice deficient in CBL-B activity reject tumors. Together, this cellular mechanism and in vivo phenotype suggest inhibition of CBL-B may be a viable immuno-oncology therapeutic strategy. Here, we report the rational design and execution of a high-throughput screen (HTS) to identify small molecule inhibitors of CBL-B. This campaign led to the discovery of a scaffold that inhibits CBL-B E3 ligase activity with micromolar potency. Structural characterization revealed an intramolecular glue mechanism, in which the compound stabilizes the closed state of CBL-B, preventing phosphorylation of a tyrosine residue that is critical for activation and E2 binding. Iterative structure-activity optimization yielded compounds with nanomolar activity that enhanced T-cell activation and cytokine secretion in primary human T cells and suppressed tumor growth in a syngeneic colorectal mouse model. Together, these studies validate the biological rationale for pharmacological CBL-B inhibition and enabled the de novo discovery of intramolecular CBL-B glue inhibitors. This work culminated in the identification of NX-1607, a first-in-class oral CBL-B inhibitor now in clinical development for cancer immunotherapy.

immunology↗

bioRxiv2026

Extracellular vesicles facilitate the horizontal transfer of drug resistance and stem-like properties between ovarian tumor cells

Ovarian cancer stem cells (CSCs) can seed recurrent drug-resistant disease. Likewise, non-CSCs can acquire CSC phenotypic properties. How this process is orchestrated is of interest to inform how it might be prevented. We tested the hypothesis that ovarian CSC and/or drug-resistant tumor cells confer stem-like properties via extracellular vesicles (EVs). We focused our investigation on how EVs might mediate EZH2 signaling to promote a phenotypic change in drug-sensitive, non-CSCs. To accomplish this, we utilized paired PARP inhibitor-sensitive and - resistant ovarian cancer (OvCa) cell lines, EZH2 knockdown lines, and patient-derived organoids (PDOs) originating from recurrent high-grade serous OvCa. Small EVs isolated from drug-sensitive, CSC and/or drug-resistant enriched cultures, PARP inhibitor (olaparib) resistant lines, or drug-treated (olaparib or carboplatin) lines were cultured with treatment naive or sensitive lines for defined time points. The impact of small EV exposure was determined by assessing cell number, metabolic activity, viability, sphere and colony-forming capacity, ALDH activity, DNA damage, and changes in associated signaling pathways. We found that EVs from CSC or drug-resistant enriched cell fractions communicate CSC-like phenotypes to the more sensitive tumor cells via EZH2 canonical and non-canonical signaling pathways, promoting stemness. We conclude that EV-mediated activation of EZH2 signaling represents a targetable mechanism contributing to stemness-associated drug resistance in OvCa. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/699925v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@6b4ffborg.highwire.dtl.DTLVardef@1501931org.highwire.dtl.DTLVardef@1a5f31aorg.highwire.dtl.DTLVardef@1fb475f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2026

Prevalence and Actionability of MTAP Loss in Oncogene-Driven Lung Cancer

BackgroundMethylthioadenosine phosphorylase (MTAP) loss co-occurs with actionable genomic alterations in non-small cell lung cancer (NSCLC) and creates vulnerability to protein arginine methyltransferase 5 (PRMT5) inhibition. Estimates of prevalence of MTAP loss rely on next-generation sequencing which can underestimate copy losses. Moreover, the activity of PRMT5 inhibitors in oncogene-driven NSCLC is not well established. MethodsWe assessed MTAP expression by immunohistochemistry (IHC) in 243 NSCLCs (n=132 early stage, n=111 metastatic), including 33 specimens with paired lymph nodes. Antiproliferative activity of PRMT5 inhibitor monotherapy and in combination with tyrosine kinase inhibitors (TKIs) was evaluated in MTAP-deleted NSCLC cell lines harboring EGFR or KRAS mutations or ALK rearrangements. ResultsAmong 243 NSCLC specimens from 240 patients (72% with driver alterations, 90% adenocarcinoma), MTAP loss was identified in 43 (18%) specimens from 40 (17%) patients, including 18 (14%) early-stage and 22 (20%) metastatic tumors. MTAP loss occurred in 24% of stage 4 driver-positive NSCLCs versus 14% of driver-negative tumors (p=0.314). Twenty (61%) lung-nodal pairs demonstrated concordance; eight cases only exhibited decreased MTAP expression in nodes. Variable sensitivity to PRMT5 inhibitors was observed in 22 MTAP-deleted NSCLC cell lines (9 EGFR-mutant, 7 KRAS-mutant, 6 ALK-rearranged), with responses seen in TKI-sensitive and TKI-resistant lines. SDMA (symmetric dimethylarginine) expression did not predict PRMT5 inhibitor sensitivity. TKI + PRMT5 inhibitor combos had greater activity than monotherapy. ConclusionsMTAP loss occurs in 1-in-5 oncogene-driven metastatic NSCLCs. PRMT5 inhibitor activity is independent of TKI exposure, driver alteration, and SDMA expression and enhanced by addition of TKI. These findings support clinical evaluation of PRMT5 inhibitor + TKI combinations for advanced NSCLC.

cancer biology↗

bioRxiv2026

Dual-antigen Doggybone DNA vaccine induces potent anti-tumor immunity against immunosuppressive oral cancer

Anti-PD1 blockade benefits only a subset of patients with head and neck squamous cell carcinoma (HNSCC), highlighting the need for approaches that overcome tumor immune resistance. Here, using the doggybone DNA (dbDNATM) platform, we developed CaVac OPT, an optimized dual-antigen DNA vaccine targeting MAGED4B and FJX1, which are overexpressed in most HPV-negative HNSCC and multiple solid tumors. In the MOC-2 oral cancer model, CaVac OPT significantly reduced tumor growth and when combined with anti-PD1 therapy, further delayed progression and improved survival. Immune profiling showed increased infiltration of CD4+ and CD8+ T cells, expansion of stem-like Tcf1+ populations, without increase in regulatory T cells, a reduced M2/M1 macrophage ratio and activation of interferon gamma associated pathways with suppression of tumor-promoting signals. These findings demonstrate that CaVac OPT reprograms the tumor microenvironment, converting cold HNSCC into T cell-inflamed responsive tumors. CaVac OPT represents a promising strategy for achieving durable control of aggressive, immunotherapy-resistant head and neck cancer.

cancer biology↗

bioRxiv2026

Lung-Targeting Interleukin-10 mRNA Lipid Nanoparticles Ameliorate Acute Lung Injury

Acute respiratory distress syndrome (ARDS) is the most severe manifestation of acute lung injury (ALI), characterized by diffuse pulmonary inflammation, impaired gas exchange, and high morbidity and mortality. Despite its clinical significance, no specific or effective pharmacological therapies are currently available for its treatment. In this study, we developed a lung -targeted mRNA-sulfonium lipid nanoparticle (mRNA/sLNP) delivery system for the treatment of ALI in a mouse model. We first optimized sulfonium lipid structures, and the optimized sLNP was comprehensively characterized and subsequently loaded with interleukin-10 (IL-10) mRNA. In a lipopolysaccharide (LPS)-induced ALI mousemodel, IL-10/sLNPdemonstrated both prophylactic and therapeutic efficacy, significantly attenuating pulmonary and systemic inflammation, restoring barrier integrity, and reducing tissue injury.

bioengineering↗

bioRxiv2026

Myeloid CD209 impairs T-cell activation by inducing ICAM-2-ERM-dependent cortical stiffening

Resistance to PD-1-based immunotherapy remains a major challenge in cancer treatment, especially in immunologically "cold" tumors characterized by T-cell exclusion and impaired effector function. Tumor-associated myeloid cells are important mediators of this resistance, but whether they directly suppress T-cell immunity through mechanical regulation remains unclear. Here, we identify DC-SIGN (CD209), a myeloid-expressed C-type lectin receptor, as a mechanosuppressive regulator of antitumor T-cell immunity. DC-SIGN is selectively enriched in tumor-infiltrating M2-like macrophages and monocytes, and its expression is associated with poor clinical response to PD-1 blockade and elevated TIDE scores across multiple cancer types. Mechanistically, DC-SIGN directly engages ICAM-2 on T cells and activates ROCK1-ERM signaling. This interaction increases T-cell cortical stiffness, destabilizes T cell-antigen-presenting cell interactions, and attenuates T-cell receptor signaling. Blocking the DC-SIGN-ICAM-2-ERM pathway restores T-cell activation and promotes CD8+ T-cell infiltration and effector function in mouse, humanized, and ex vivo human tumor systems. Importantly, DC-SIGN blockade overcomes resistance to PD-1-based immunotherapy in refractory and immunologically "cold" tumor models, and further enhances the response to PD-1 inhibition. Together, our findings define a myeloid-derived mechanical immune checkpoint and reveal T-cell mechanosuppression as a targetable mechanism of tumor immune evasion and immunotherapy resistance.

immunology↗

bioRxiv2026

Dopamine signatures of excessive and compulsive cocaine and fentanyl use

Excessive and compulsive drug use despite adverse consequences is a hallmark of substance use disorder, yet individuals differ markedly in their vulnerability to develop these behaviors. Drugs of abuse are long known to alter endogenous dopamine (DA) signaling, but shared principles for how DA dynamics impact compulsive use among individuals and across drug classes are lacking. Here, we monitored DA release in the medial shell of nucleus accumbens (NAc) during cocaine and fentanyl self-administration, with or without coincident punishment, in large cohorts of mice. Contingent cocaine and fentanyl self-administration evoked complex and individually distinct DA dynamics; nevertheless, a robust negative correlation held across both drugs, such that high takers exhibited lower drug-evoked DA signals. During punished drug taking, cocaine and fentanyl cases were associated with distinct DA signatures of compulsivity. For cocaine, punishment-resistant mice showed lower sustained DA responses during the post-shock, drug-associated cue period, whereas for fentanyl, punishment-resistant mice displayed larger phasic DA at the co-occurrence of footshock and drug infusion. To identify common principles underlying these observations, we developed a computational model grounded in an Actor-Critic temporal-difference (TD) learning framework that incorporates internal states, agents uncertainty, and drug-specific effects. Remarkably, this model captures the observed diversity in DA dynamics across drug classes and among mice with variable drug taking propensities, hereby providing a unified interpretation of NAc DA signals as encoding TD reward prediction errors.

neuroscience↗

bioRxiv2026

CellFluxV2: An Image Generative Foundation Model for Virtual Cell Modeling

Building a virtual cell that simulates cellular behavior in silico is a central goal of computational biology. We introduce CellFluxV2, an image-generative model that predicts how cell morphologies change in response to chemical and genetic perturbations. A core innovation of CellFluxV2 is to learn distribution-level transformations from unperturbed to perturbed cells within the same experimental batch using flow matching, enabling it to disentangle true perturbation effects from confounding batch effects. Incorporating three methodological advances, CellFluxV2 achieves up to a 77% improvement in image fidelity over diffusion- and GAN-based baselines, while maintaining biological fidelity comparable to ground-truth images. Scaling up CellFluxV2, we establish the first scaling laws in image-based virtual cell modeling, demonstrating that performance improves consistently with both dataset size and model capacity. Furthermore, the scaled-up model generalizes well to out-of-distribution perturbations and exhibits two novel capabilities: batch-effect correction and cell-state interpolation. Together, these results position CellFluxV2 as a powerful foundation model advancing the vision of a virtual cell, unlocking novel opportunities for in silico drug screening.

bioinformatics↗

bioRxiv2026

A comprehensive proteome and the first phosphoproteome reveal extensive phosphorylation of carbohydrate metabolism in Cryptosporidium parvum sporozoites

Cryptosporidium parvum is an obligate intracellular apicomplexan parasite and a major cause of diarrheal disease in humans and animals worldwide. Despite its public health importance, the molecular regulation of parasite metabolism, particularly at the post-translational level, remains poorly understood. Here, we present a comprehensive proteomic analysis and the first phosphoproteomic profile of excysted C. parvum sporozoites, the invasive stage responsible for host cell entry. Using data-independent acquisition-based mass spectrometry, we identified 2,272 proteins, representing approximately 58% of the predicted parasite proteome, and 8,994 phosphorylation sites across 833 phosphoproteins. Comparative analyses revealed weak correlations between transcript and protein abundance, underscoring extensive post- transcriptional regulation in sporozoites. Functional enrichment analyses showed that proteins involved in carbohydrate metabolism, particularly glycolysis, are highly abundant in the sporozoite proteome. In contrast, phosphoproteomic data revealed that many core glycolytic enzymes exhibit relatively low phosphorylation propensity, suggesting limited reliance on phosphorylation-based regulation for basal energy metabolism at this stage. To integrate proteomic and phosphoproteomic measurements acquired independently, we developed a relative phosphorylation index (RPI) that enables comparative assessment of phosphorylation propensity across proteins. Application of this metric highlighted selective phosphorylation of proteins associated with signaling, cytoskeletal organization, and host-parasite interaction, while key metabolic entry-point enzymes, such as hexokinase, showed high abundance but minimal detectable phosphorylation, in contrast to their mammalian counterparts. Together, these findings provide the most comprehensive molecular resource to date for C. parvum sporozoites and reveal a phosphorylation landscape that emphasizes regulatory and structural processes over core metabolic flux. This work establishes a foundation for understanding parasite-specific regulatory strategies and may inform the identification of novel therapeutic targets against cryptosporidiosis. Author SummaryCryptosporidium parvum is a microscopic parasite that causes diarrheal disease in humans and animals worldwide, yet we still know surprisingly little about how it regulates its biology at the molecular level. In this study, we set out to better understand how the parasite prepares for infection by examining the proteins present in sporozoites, the life stage that actively invades host cells. We generated the most comprehensive protein catalog to date for C. parvum sporozoites and, for the first time, mapped thousands of chemical modifications known as protein phosphorylation. These modifications often act as molecular switches that control how proteins behave. We found that proteins involved in basic energy production are extremely abundant but show relatively little phosphorylation, while proteins linked to cell structure, movement, and interaction with the host are more frequently modified. This suggests that the parasite prioritizes regulation of invasion-related processes over fine-tuning its core metabolism at this stage. By providing a detailed molecular resource and a new way to compare protein phosphorylation patterns, our work lays the groundwork for future studies of parasite biology and may help guide the search for new strategies to combat cryptosporidiosis.

microbiology↗

bioRxiv2026

Hyperactivated YAP1 Drives an Invasive EMT Subtype of Cervical Squamous Cell Carcinoma

Cervical cancer (CVC) is classically understood as an HPV-driven disease in which high-risk HPV infects cervical basal epithelial cells, induces neoplastic transformation, and drives upward epithelial expansion, pathogenic events underlying the success of cytology- and HPV-based screening. With widespread HPV vaccination and screening, the WHO has launched the CVC elimination initiative and believes that CVC will be the first cancer that would be eliminated as a public health problem. However, statistics showed that CVC remains the most common gynecologic malignancy worldwide, and in the United States, where HPV testing and Pap smears have reached near-maximal implementation, the CVC mortality rate has plateaued for more than two decades. These epidemiologic trends suggest the existence of a subset of CVC that can escape current screening strategies. Here, we demonstrate that hyperactivation of YAP1 caused by disruption of Hippo-YAP signaling is sufficient to induce a subtype of HPV-independent invasive CVC that lacks surface lesions and therefore evades HPV- and cytology-based detection. Using single-cell RNA sequencing combined with high-resolution spatial transcriptomics, we define the cellular architecture, molecular pathways, and immune microenvironment underlying this invasive subtype. We show that YAP1-driven tumors adopt an EMT-high transcriptional state and selectively recruit immunosuppressive myeloid-derived suppressor cells that functionally interact with cancer cells to promote invasion and progression. Our findings suggest that targeting the disrupted Hippo signaling may offer a new strategy to prevent the subset of invasive CVC that current HPV- and Pap-based programs cannot detect, an essential step toward achieving global CVC elimination.

cancer biology↗

bioRxiv2026

Covalent Leader Peptide Probes Enable Proteome-Level Mapping of RiPP Enzymes beyond Biosynthetic Gene Clusters

Genome-based analyses have enabled widespread discovery and biosynthetic annotation of ribosomally synthesized and post-translationally modified peptides (RiPPs) pathways, yet often fail to capture all participating enzymes, particularly those encoded outside canonical biosynthetic gene clusters. In this study, leveraging the central role of leader peptides (LPs) in RiPP enzyme-substrate recognition, we developed a substrate-guided chemical-proteomic strategy using covalent LP probes to directly identify LP-binding enzymes from native proteomes. Using the LctA-LctM and PatE-LynD systems, we demonstrate that LP probes carrying a photocrosslinker engage enzymes at their authentic LP-recognition sites while preserving full catalytic competence. Application of this strategy to a model strain Streptomyces sparsogenes uncovered lanthipeptide synthetases encoded outside canonical biosynthetic gene clusters and revealed graded cross-cluster activities among multiple RiPP pathways. This work establishes a proof-of-concept model platform for proteome-level discovery of LP-binding RiPP enzymes and highlights cross-cluster enzymatic crosstalk as a latent mechanism for structural diversification.

biochemistry↗

bioRxiv2026

Magnetically activatable insect viral vectors promote anticancer immunity through spatially confined gene disruption

Precise control of genome editing activity in vivo remains a major barrier to the clinical translation of CRISPR-based therapeutics, as current approaches cannot reliably restrict editing activity to target tissues 1,2,3. Here we show that a magnetically gated baculoviral CRISPR platform enables spatial control of genome editing activity 4. By coupling magnetic activation of viral transduction at target sites with complement-mediated inactivation of circulating vectors, this system functionally confines genome editing activity to defined spatial contexts, effectively decoupling editing activity from biodistribution. We further show that spatially confined genome editing can be coupled with endogenous antiviral sensing to drive coordinated immune modulation within tumours. In a syngeneic murine tumour model, local administration combined with magnetic activation restricts genome editing to tumours without detectable editing in major organs, remodels dendritic cell and CD8{square} T cell states, suppresses tumour growth, and extends survival. Together, these findings establish a generalizable strategy for spatial control of genome editing and localized immunomodulation in cancer.

bioengineering↗

bioRxiv2026

Hypatia: a set of quantitative methods for profiling isoform across cell populations

High-throughput long-read single-cell RNA-sequencing enables isoform-level study across single cells, yet methods for systematically assessing cell-to-cell variations remain limited. Here, we develop Hypatia, a comprehensive platform for dissecting isoform complexities across cell populations, devising Tsallis entropy and Cramers V to facilitate robust comparative profiling. Hypatia revealed prominent isoform species variations and usage shifts across cell-types in glioblastoma, renal cell carcinoma, and heart, highlighting clinically relevant applications for studying isoform-derived, cell-specific functions.

bioinformatics↗

bioRxiv2026

Analyzing an organisms sensors using Maximum Entropy models with bias, variance, and confusion matrices

Biological organisms have sensors that communicate information about the environment. Analyzing how well these biological sensors function has usually been done with mutual information between the sensor signal and the environment, but that can be computationally intractable and summarize something quite complex with just a single number. We suggest that alternatively, one may profitably analyze these biosensors using bias and variance or confusion matrices, depending on the kind of environment. Stimulus-dependent Maximum Entropy models are used to develop estimators of the environmental state given the sensor state, and these estimators in turn are then used to calculate either the bias and variance of the estimator or confusion matrices. We focus on several examples to understand the utility of non-information-based analyses: ligand-receptor binding models spanning genetic regulation to neuronal communication to bacterial chemotaxis, and spin-glass Ising models for neural activity in cultured neurons. These new computationally-efficient analyses add insight to existing analyses based on mutual information; in particular, mutual information estimates give one number to characterize responses to all environmental inputs, and this analysis method characterizes how sensors respond to each environmental input. Categorical analyses, meanwhile, indicate the presence of memory without much prediction in confusion matrix elements in cultured neural networks, adding to previous understanding from mutual information estimates. Author summaryAll living organisms use external stimuli to navigate their environment via their sensors. Because encoding information costs energy, organisms retain only a fraction of the information received from their sensors, ideally information that maximizes their ability to remember past environmental states or predict future ones, key functions that support survival. To better understand how well sensor systems absorb stimulus information, we used stimulus-dependent Maximum Entropy (MaxEnt) models with maximum likelihood estimation and typical statistical metrics, such as confusion matrices or bias and variance. This approach provides two primary benefits over previous approaches: it is more computationally efficient, and it provides a more information-rich picture on how sensors interact with stimulus.

biophysics↗

bioRxiv2026

LOTUS-domain proteins activate Vasa to drive germ granule assembly

Germline development relies on perinuclear membraneless germ granules, yet the mechanisms underlying their assembly remain incompletely understood. Here we uncover a conserved and central role for LOTUS-domain proteins in driving germ granule assembly. In C. elegans, the LOTUS-domain protein EGGD-1/MIP-1 at sub-stoichiometric levels recruits Vasa protein GLH-1 to the nuclear periphery. Acting as a catalyst, EGGD-1 impacts the ATPase cycles of GLH-1 by preferentially binding to its open conformation, enhancing its RNA binding activities, and facilitating its transition to the closed conformation. GLH-1 in the closed state enriches mRNAs at the nuclear periphery, which enables the accumulation of RNA-binding proteins including PGL-1 and PGL-3. Using human cells, we demonstrate the LOTUS-domain protein TDRD5 similarly recruits DDX4, the human Vasa homolog, and stimulates the formation of intermitochondrial cement. Collectively, these findings reveal evolutionarily conserved stimulatory effect of LOTUS-domain protein in Vasa activity and provide a unified model for germ granule assembly across species.

cell biology↗