Biology subjects

Qiu, P.

Publications and source records attributed to Qiu, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Indole-3-propionic acid nano-prodrugs sustain increased lymphoid exposure and alleviate experimental autoimmune arthritis

Chronic inflammatory diseases, including rheumatoid arthritis (RA), currently require sustained control of inflammation. Although long-acting therapies can reduce dosing frequency, those that rely on systemic immunosuppression may still compromise protective immunity. Microbiota-derived metabolites offer a complementary starting point for immune modulation through naturally occurring host-microbe signals. Indole-3-propionic acid (IPA), a gut microbial tryptophan metabolite naturally present in human circulation, has newly recognized immunoregulatory activity, yet its effects across disease contexts and its potential for therapeutic use remain incompletely defined. Here, we developed polymeric IPA nano-prodrugs (pIPANP) by conjugating IPA to a methacrylamide polymer through ester linkages and formulating the polymer into injectable nanoparticles. Following subcutaneous administration of fluorescently-labeled pIPANP, injection-site fluorescence remained detectable for at least 35 days, with labeled material detected in multiple lymph nodes. LC-MS/MS confirmed higher lymph-node IPA concentrations in mice treated with the pIPANPs compared to free IPA control at every sampled time point over 14 days. In collagen antibody-induced arthritis (CAIA), pIPANPs reduced clinical disease severity and paw swelling under both prophylaxis and therapeutic administration. pIPANP treatment increased the relative representation of CD11cCD103CD11b- cells with a conventional dendritic cell (cDC)1-associated phenotype and reduced costimulatory-marker frequencies in both CD11cCD103 and CD11cCD11b populations, most prominently in hock-draining lymph nodes. These changes were accompanied by increased PD-1 and IFN-{gamma} frequencies among CD4 and CD8 T cells. Fluorescent nanoparticle tracking showed preferential association with several myeloid populations, while single-cell profiling revealed divergent interferon-response signatures across splenic populations. These findings reveal arthritis-associated immunomodulatory activity of polymeric IPA and support its further development as a microbiome-inspired, long-acting approach to inflammatory arthritis.

bioengineering↗

bioRxiv2026

Uncovering key genes related to potato tuber starch concentration from the tuber bulking, maturation stage to storage

Potato is the worlds fourth major staple crop, and tuber starch is a critical dietary energy source and a food processing raw material. In this study, hybrid progenies of the high-starch cultivar Huasheng No.7 and China main commercial varieties were used as materials, high-throughput sequencing was performed on tuber samples at tuber bulking, maturation and storage stages. Combined with bioinformatics analysis, parent-progeny resequencing, SNP screening, the regulatory mechanism of tuber starch metabolism was explored. The results showed significant stage-specific transcriptional reprogramming in potato tubers: few differentially expressed genes (DEGs) were identified at tuber bulking and maturation stages, but massive transcriptional changes occurred during storage. XET family genes regulated cell wall remodeling and carbon translocation across all stages, and WRKY transcription factors specifically controlled starch homeostasis in stored tubers. The DEGs at different stage were induced by the SNPs from their parent, and the allele from the high-starch parent, Huasheng 7 may contribute the high-starch allele to the offsprings. One key gene, Soltu.DM.02G019910, which encoded {beta}-glucosidase, have a negatively relationship with tuber starch concentration. The low-starch potato breeding tubers shown higher significantly {beta}-glucosidase activity than high-starch breeding population at maturation stage. This study clarifies the molecular regulatory network of potato tuber starch metabolism and screens core regulatory genes from the tuber bulking stage to tuber storage, thereby providing theoretical support and genetic resources for molecular breeding of high-starch and storage-resistant potato varieties.

plant biology↗

bioRxiv2026

Evolutionarily informed gene sets reveal conserved and lineage-modified transcriptional programs during vertebrate forebrain evolution

The vertebrate forebrain exhibits striking diversity in anatomical architecture, yet is built from deeply conserved gene regulatory programs and cell types that underlie shared neural functions and behaviors. Understanding how these conserved cellular programs are maintained and modified across [~]500 million years of vertebrate evolution requires systematic cross-species single-cell comparisons, a challenge compounded by complex gene evolutionary histories that constrain joint analyses to shared one-to-one orthologs. Here we derive evolutionarily informed gene sets from a global homology graph and represent cells in a shared, interpretable gene-set feature space. Applying this framework to forebrain profiles from eleven vertebrate species, from sea lamprey to human, we construct a unified cross-vertebrate cell atlas. We find that conserved transcriptional programs define stable cell-type identities across vertebrates, with evolutionary divergence occurring predominantly within, rather than between, cell types. Gene-set conservation scales with evolutionary age, whereas lineage-modified programs reflect coordinated clade-level remodeling. Radial glia exhibit a conserved fate bifurcation into neurogenic and gliogenic trajectories with lineage-dependent modulation of transcriptional dynamics. Finally, human neuropsychiatric GWAS signals map onto conserved neural substrates across vertebrates. Together, our results demonstrate that vertebrate forebrain evolution proceeds through lineage-specific tuning of deeply conserved transcriptional programs embedded within stable cellular architectures.

evolutionary biology↗

bioRxiv2026

Spatio-temporal mapping of immune cell dynamics during human sequential lymph node metastasis

Regional lymph node (LN) metastasis critically influences distant metastatic progression, anti-tumour immunity, and patient prognosis. While tumour-induced immune modulation in tumour-draining LNs (TDLNs) has been extensively studied using murine models, the systematic reconstruction of the immune system from primary tumours through TDLNs and subsequent lymph nodes in human cancer progression remains understudied. Here, we utilised integrated multi-omics approaches, including imaging mass cytometry, single-cell RNA sequencing, Visium and Xenium spatial transcriptomics, and multi-colour immunofluorescence to systematically characterise immune cell dynamics across 147 paired primary tumours, sentinel TDLNs (S-TDLNs), and secondary axillary LNs (ALNs) obtained from 50 treatment-naive triple-negative breast cancer patients with different progression statuses. Our comprehensive profiling revealed critical immune alterations, such as decreased type-2 conventional dendritic cells (cDC2), naive T cells, and B cells, along with an increase in immunosuppressive macrophages. Developing a novel single-cell transformer model, we identified substantial alterations in various immune cell populations, notably MARCO+ macrophages, which strongly correlated with breast cancer patient survival outcomes. Spatial analysis combined with our newly integrated cell-cell interaction platform revealed diminished immune cell communication and impaired priming interactions among dendritic cells, B cells, and T cells within metastatic lymph nodes and primary tumour sites. In an independent neoadjuvant immunotherapy cohort of 36 TNBC patients with 52 lymph node samples, we found preservation of CD1c cDC2 in lymph nodes predicted pathological complete response and longer event-free survival, highlighting cDC2 as a potential biomarker and therapeutic target. Collectively, this systematic mapping of immune landscape alterations during human sequential LN metastasis provides essential insights for understanding cancer metastasis mechanisms and paves the way for innovative immunotherapeutic strategies.

immunology↗

bioRxiv2026

Programming T cells for Early Cancer Detection with Customized Protease-Activatable Receptors

Early cancer detection has the potential to reduce cancer mortality, yet endogenous tumor-shed biomarkers lack sensitivity for early-stage disease. We report OncoSCOUT, a cancer detection strategy using T cells engineered with protease-activatable receptors (PARs) that conditionally recognize tumor cells and release a synthetic biomarker for detection in urine. These PARs comprise masked synthetic Notch receptors in which antigen binding is blocked by a peptide mimotope tethered via a protease-cleavable linker. We demonstrate that requiring both extracellular protease activity and tumor antigen recognition improves spatial specificity and minimizes off-tumor activation of PAR T cells in vivo. To identify tumor-selective PARs, we adoptively transferred a HER2-targeted PAR library displaying [~]160,000 unique 4-mer amino acid linkers and discovered multiple variants significantly enriched in a HER2-positive cancer xenograft model. Using a single customized PAR, we show that OncoSCOUT can detect total tumor burdens as small as 10-30 mm3 with significantly improved sensitivity than the protein biomarker CA 15-3 or a 20-plex circulating tumor DNA (ctDNA) assay.

bioengineering↗

bioRxiv2025

OmniCell: Unified Foundation Modeling of Single-Cell and Spatial Transcriptomics for Cellular and Molecular Insights

A cells transcriptional programme is not fully defined by gene expression alone, but by the tissue context in which that programme is enacted. Single-cell RNA sequencing resolves molecular identity after dissociation, whereas spatial transcriptomics preserves tissue architecture but remains constrained by assay-specific sparsity and gene coverage. Here we present OmniCell, a tissue-contextual transcriptomic foundation model pretrained on 67 million dissociated and spatially resolved profiles. By integrating gene identity, expression magnitude and tissue context, OmniCell links transcriptional programmes to the cellular neighbourhoods and anatomical contexts in which they operate. OmniCell organised transcriptomes across molecular, cellular and tissue scales. It recovered cell-type-specific programmes and tissue-aligned gene modules, preserved robust cell-state structure across batches, species and rare populations, and improved the reconstruction of spatial cell identity, anatomical domains and cell-type composition. In human liver cancer Stereo-seq data, OmniCell resolved a tumour-margin transition zone characterised by immune infiltration, acute-phase inflammation, coagulation/complement activity and metallothionein-linked metal-ion detoxification. Contextual gene-embedding similarity analysis showed that gene relationships differed across tumour core, transition-zone and paratumour/adjacent non-malignant niches, indicating that OmniCell captures tissue-dependent gene function rather than expression similarity alone. In mouse brain development and macaque cortex, spatial virtual perturbations mapped regulatory genes onto stage- and region-specific anatomical programmes. Together, these results establish tissue context as a primary axis of transcriptomic representation and provide a framework for studying how cellular programmes acquire context-dependent biological meaning in intact tissues.

bioinformatics↗

bioRxiv2025

SOFT METROPOLIS-HASTINGS CORRECTION FOR GENERATIVE MODEL SAMPLING

AO_SCPLOWBSTRACTC_SCPLOWMolecular diffusion models suffer from systematic sampling biases that prevent optimal structure formation, resulting in chemically suboptimal molecules with metastable conformations trapped in local energy minima. We introduce Metropolis-Hastings (MH) correction to molecular diffusion models, providing a principled framework to address these systematic sampling biases. The traditional hard accept-reject Metropolis-Hastings corrector creates discontinuous trajectories incompatible with the continuous nature of molecular potential energy surfaces, disrupting proper structure assembly. To address this, we develop a soft Metropolis-Hastings correction that replaces binary acceptance with continuous interpolation weighted by acceptance probabilities, maintaining smooth navigation in the chemical space while providing principled bias correction. We design three molecular-specific variants and demonstrate through extensive experiments on small molecules, drug conformations, and therapeutic antibody CDR-H3 loops that our method consistently improves chemical validity, structural stability, and conformational quality across diverse molecular families. Our method establishes MH correction as a powerful component for molecular generation.

bioinformatics↗

bioRxiv2025

Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for AntibodyDesign via Online Optimization

Recent advances in diffusion models have shown remarkable potential for antibody design, yet existing approaches apply uniform generation strategies that cannot adapt to each antigens unique requirements. Inspired by B cell affinity maturation--where antibodies evolve through multi-objective optimization balancing affinity, stability, and self-avoidance--we propose the first biologically-motivated framework that leverages physics-based domain knowledge within an online meta-learning system. Our method employs multiple specialized experts (van der Waals, molecular recognition, energy balance, and interface geometry) whose parameters evolve during generation based on iterative feedback, mimicking natural antibody refinement cycles. Instead of fixed protocols, this adaptive guidance discovers personalized optimization strategies for each target. Our experiments demonstrate that this approach: (1) discovers optimal SE(3)-equivariant guidance strategies for different antigen classes without pre-training, preserving molecular symmetries throughout optimization; (2) significantly enhances hotspot coverage and interface quality through target-specific adaptation, achieving balanced multi-objective optimization characteristic of therapeutic antibodies; (3) establishes a paradigm for iterative refinement where each antibody-antigen system learns its unique optimization profile through online evaluation; (4) generalizes effectively across diverse design challenges, from small epitopes to large protein interfaces, enabling precision-focused campaigns for individual targets.

bioinformatics↗

bioRxiv2025

UniCell: Towards a Unified Solution for Cell Annotation, Nomenclature Harmonization, Atlas Construction in Single-Cell Transcriptomics

Standardizing cell type annotations across single-cell RNA-seq datasets remains a major challenge due to inconsistencies in nomenclature, variation in annotation granularity, and the presence of rare or previously unseen populations. We present UniCell, a hierarchical annotation framework that combines Cell Ontology structure with transcriptomic data for scalable, interpretable, and ontology-aware cell identity inference. UniCell leverages a multi-task architecture that jointly optimizes local and global classifiers, yielding coherent predictions across multiple levels of the ontology-defined hierarchy. When benchmarked across 20 human and mouse datasets, UniCell consistently outperformed state-of-the-art tools, including CellTypist, scANVI, OnClass, and SingleR, in annotation performance, and sensitivity to low-abundance populations. In disease settings, UniCell effectively identified previously unseen cell types through confidence-guided novelty detection. Applied to 45 human and 23 mouse tissue atlases, UniCell enabled cross-dataset and cross-species harmonization by embedding cells into a unified latent space aligned with Cell Ontology structure. Moreover, when used to supervise single-cell foundation models, UniCell substantially improved downstream annotation accuracy, rare cell detection, and hierarchical consistency. Together, these results establish UniCell as a generalizable framework that supports high-resolution annotation, nomenclature standardization, and atlas-level integration, providing a scalable and biologically grounded solution for single-cell transcriptomic analysis across diverse biological systems.

bioinformatics↗

bioRxiv2025

Iron acquisition in the weevil-farming fungus Penicillium herquei: Implications of mineral elements in insect-fungus mutualistic association

Mutualistic interactions between insects and fungi are pivotal in ecosystem dynamics, yet the underlying molecular mechanisms remain largely unexplored. This study investigates iron acquisition strategies of the weevil-farming Penicillium herquei, revealing the involvement of mineral elements in insect-fungus symbiosis. Comparative transcriptomics of weevil-farming strain (WFS) and soil free-living strain (SFS) revealed distinct transcriptional profiles, with 4,357 up-regulated genes in WFS. Enrichment analyses highlighted a significant up-regulation of genes linked to oxidoreductase activity, iron and heme binding, with a notable prevalence of Cytochrome P450 (CYP450). qRT-PCR confirmed differential expression of CYP450 and siderophore-related genes, indicating enhanced iron absorption in WFS. Comparative analysis of iron content further demonstrated significantly higher iron levels in WFS than in SFS and weevil host plant leaves, suggesting a nutritional adaptation for symbiotic lifestyle. These findings provide novel insights into the role of iron metabolism in insect-fungus mutualism, highlighting potential evolutionary mechanisms that bolster symbiotic fitness. IMPORTANCEUnraveling the complex interplay between insects and fungi is crucial for deciphering the intricate dynamics of ecosystems. In this study a notable up-regulation of genes associated with iron and heme binding, as well as a significant increase in iron content within WFS, suggesting a specialized adaptation for enhanced iron acquisition, potentially enabling the fungus to efficiently provide essential nutrients, including bioavailable iron, to weevil host. This research not only advances our understanding of the molecular mechanisms governing insect-fungus mutualism but also highlights the potential evolutionary mechanisms that bolster symbiotic fitness and contribute to the co-evolution of these interacting species.

microbiology↗

bioRxiv2025

RegFormer: A Single-Cell Foundation Model Powered by Gene Regulatory Hierarchies

Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of cellular diversity, but current computational models often fail to incorporate regulatory priors, handle data sparsity, or efficiently process long gene sequences. Here, we present RegFormer, a foundation model that integrates gene regulatory networks (GRNs) with Mamba-based state-space modeling, overcoming the scalability and context-length limitations of Transformer architectures. RegFormer encodes each gene through dual embeddings, a value embedding for quantitative expression and a token embedding for regulatory identity, organized within a GRN-guided gene order to capture both expression dynamics and hierarchical regulation. Pretrained on 26 million human single cells spanning 45 tissues and diverse biological contexts, RegFormer achieves superior scalability and biological fidelity. Across comprehensive benchmarks, it consistently outperforms state-of-the-art single-cell foundation models (scGPT, Geneformer, scFoundation, and scBERT), delivering higher clustering accuracy, improved batch integration, and more precise cell type annotation. RegFormer also reconstructs biologically coherent GRNs, accurately models transcriptional responses to genetic perturbations, and enhances drug response prediction across cancer cell lines. By combining regulatory priors with efficient long-sequence Mamba modeling, RegFormer establishes a biologically grounded and scalable framework for single-cell representation learning, enabling deeper mechanistic insight into gene regulation and cellular state transitions.

bioinformatics↗

bioRxiv2024

Multimodal normative modeling in Alzheimer Disease with introspective variational autoencoders

Normative models in neuroimaging learn patterns of healthy brain distributions to identify deviations in disease subjects, such as those with Alzheimers Disease (AD). This study addresses two key limitations of variational autoencoder (VAE)-based normative models: (1) VAEs often struggle to accurately model healthy control distributions, resulting in high reconstruction errors and false positives, and (2) traditional multimodal aggregation methods, like Product-of-Experts (PoE) and Mixture-of-Experts (MoE), can produce uninformative latent representations. To overcome these challenges, we developed a multimodal introspective VAE that enhances normative modeling by achieving more precise representations of healthy anatomy in both the latent space and reconstructions. Additionally, we implemented a Mixture-of-Product-of-Experts (MOPOE) approach, leveraging the strengths of PoE and MoE to efficiently aggregate multimodal information and improve abnormality detection in the latent space. Using multimodal neuroimaging biomarkers from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset, our proposed multimodal introspective VAE demonstrated superior reconstruction of healthy controls and outperformed baseline methods in detecting outliers. Deviations calculated in the aggregated latent space effectively integrated complementary information from multiple modalities, leading to higher likelihood ratios. The model exhibited strong performance in Out-of-Distribution (OOD) detection, achieving clear separation between control and disease cohorts. Additionally, Z-score deviations in specific latent dimensions were mapped to feature-space abnormalities, enabling interpretable identification of brain regions associated with AD pathology.

neuroscience↗

bioRxiv2024

BioLLM: A Standardized Framework for Integrating and Benchmarking Single-Cell Foundation Models

The application and evaluation of single cell foundational models (scFMs) present significant challenges stemming from the heterogeneity of architectural frameworks and coding standards. To address these issues, we introduce BioLLM, a framework facilitating the integration and application of scFMs in single-cell RNA sequencing data analysis. BioLLM provides a universal interface, bridging diverse scFMs into a seamless ecosystem. By mitigating architectural disparities and coding conventions, it empowers researchers with streamlined access to scFMs. With standardized APIs and comprehensive documentation, BioLLM streamlines model switching and comparative analyses, while incorporating best practices for consistent model evaluation. Our comprehensive evaluation of scFMs revealed distinct strengths and limitations, highlighting scGPTs robust performance across all tasks, both in zero-shot and fine-tuning scenarios. Geneformer and scFoundation also demonstrated strong capabilities in gene-level tasks, benefiting from effective pretraining strategies. In contrast, scBERT underperformed relative to other models, likely attributable to its considerably smaller parameter count and the limited size of the training dataset. Ultimately, BioLLM aims to empower the scientific community to leverage the full potential of foundational models, advancing our understanding of complex biological systems through enhanced single-cell analysis.

bioinformatics↗

bioRxiv2024

Improving Functional Muscle Regeneration in Volumetric Muscle Loss Injuries by Shifting the Balance of Inflammatory and Pro-Resolving Lipid Mediators

Severe tissue loss resulting from extremity trauma, such as volumetric muscle loss (VML), poses significant clinical challenges for both general and military populations. VML disrupts the endogenous tissue repair mechanisms, resulting in acute and unresolved chronic inflammation and immune cell presence, impaired muscle healing, scar tissue formation, persistent pain, and permanent functional deficits. The aberrant healing response is preceded by acute inflammation and immune cell infiltration which does not resolve. We analyzed the biosynthesis of inflammatory and specialized pro-resolving lipid mediators (SPMs) after VML injury in two different models; muscle with critical-sized defects had a decreased capacity to biosynthesize SPMs, leading to dysregulated and persistent inflammation. We developed a modular poly(ethylene glycol)-maleimide hydrogel platform to locally release a stable isomer of Resolvin D1 (AT-RvD1) and promote endogenous pathways of inflammation resolution in the two muscle models. The local delivery of AT-RvD1 enhanced muscle regeneration, improved muscle function, and reduced pain sensitivity after VML by promoting molecular and cellular resolution of inflammation. These findings provide new insights into the pathogenesis of VML and establish a pro-resolving hydrogel therapeutic as a promising strategy for promoting functional muscle regeneration after traumatic injury.

bioengineering↗

bioRxiv2024

RGS10 deficiency facilitates distant metastasis by inducing epithelial-mesenchymal transition in breast cancer

AbstractDistant metastasis is the major cause of death in patients with breast cancer. Epithelial-mesenchymal transition (EMT) contributes to breast cancer metastasis. Regulator of G protein-signaling (RGS) proteins modulate metastasis in various cancers. This study identified a novel role for RGS10 in EMT and metastasis in breast cancer. RGS10 protein levels were significantly lower in breast cancer tissues compared to normal breast tissues, and deficiency in RGS10 protein predicted a worse prognosis in patients with breast cancer. RGS10 protein levels were lower in the highly aggressive cell line MDA-MB-231 than in the poorly aggressive, less invasive cell lines MCF7 and SKBR3. Silencing RGS10 in SKBR3 cells enhanced EMT and caused SKBR3 cell migration and invasion. The ability of RGS10 to suppress EMT and metastasis in breast cancer was dependent on lipocalin-2 and miR-539-5p. These findings identify RGS10 as a tumor suppressor, prognostic biomarker, and potential therapeutic target for breast cancer.

cancer biology↗

bioRxiv2023

Hypoxia impacts small intestinal organoid stemness and differentiation

Comprised of several cell types functioning differently, the small intestinal epithelial cells perform their functions and synergistically maintain homeostasis in the first part of the mammalian intestine. Due to the uneven distribution of the vessel, the oxygen level exhibits a gradient decreasing pattern in the normal intestine and becomes aberrant in some intestinal diseases. In this work, we find certain levels of hypoxia simulated by cobaltous chloride (CoCl2) caused an increase in the secretive cell types and a decrease in the absorptive cell types in vitro cultured mouse small intestinal organoids. Importantly, the intestinal stem cell amount is impacted which leads to attenuated epithelial regeneration. Our study highlights the cell-type-specific alterations under the hypoxia insult, which gives possible therapeutic hints for hypoxia-relevant gastrointestinal diseases.

cancer biology↗

bioRxiv2023

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Tracing cellular dynamic changes across conditions, time, and space is crucial for understanding the molecular mechanisms underlying complex biological systems. However, integrating multi-sample data in a unified and flexible way to explore cellular heterogeneity remains a major challenge. Here, we present Stereopy, a flexible and versatile framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this flexible framework, we have developed three key components: a multi-sample tailored data container, a scope controller, and an analysis transformer. Furthermore, Stereopy showcases three transformative applications supported by pivotal algorithms. Firstly, the multi-sample cell community detection (CCD) algorithm introduces an innovative capability to detect specific cell communities and identify genes responsible for pathological changes in comparable datasets. Secondly, the spatially resolved temporal gene pattern inference (TGPI) algorithm represents a notable advancement in detecting important spatiotemporal gene patterns while concurrently considering spatial and temporal features, which enhances the identification of important genes, domains and regulatory factors closely associated with temporal datasets. Finally, the 3D niche-based regulation inference tool, named NicheReg3D, reconstructs the 3D cell niches to enable the inference of cell-gene interaction network within the spatial texture, thus bridging intercellular communications and intracellular regulations to unravel the intricate regulatory mechanisms that govern cellular behavior. Overall, Stereopy serves as both a bioinformatics toolbox and an extensible framework that provides researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data.

bioinformatics↗

bioRxiv2023

High-efficiency gene editing in Anopheles sinensis using ReMOT control

CRISPR/Cas9-mediated gene editing provides an effective method for deciphering the molecular mechanisms underlying mosquito development and mosquito-borne disease transmission, as well as for exploring genetic control strategies. However, delivering the Cas9 ribonucleoprotein complex by embryo injection to produce genetic modifications is challenging, is mostly confined to model mosquitoes and specialized laboratories, and has low editing efficiency. Here, we established an effective Receptor-Mediated Ovary Transduction of Cargo (ReMOT) control method, enabling the introduction of heritable mutations into Anopheles sinensis, the major malaria vector in China and Southeast Asia, via the injection of female adult mosquitoes. Injection of a mixture of P2C-DsRed and saponin resulted in red fluorescence in the ovaries, with a 100% success rate. Using this system, we knocked-out the pigment synthesis genes, Aswhite and Asyellow, using injected wild-type (WT) females mated with WT males, resulting in the highest efficiency of gene editing among mosquitoes under the same mating conditions. Furthermore, the gene-editing efficiency was increased by at least 2.1-fold using injected WT females mated with mutant males. This improved ReMOT control method exhibits high editing efficiency, with important benefits in terms of functional genomics research and genetic control strategies in An. sinensis. Moreover, this represents a convenient method for gene manipulation in laboratories that are unable to perform embryo injection or that lack embryo-injection equipment.

genetics↗